diff --git a/.DS_Store b/.DS_Store
index e98b18b1..ca39b6e3 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/Dockerfile.langflow b/Dockerfile.langflow
index 2acb4877..86ee0ea5 100644
--- a/Dockerfile.langflow
+++ b/Dockerfile.langflow
@@ -7,7 +7,7 @@ ENV RUSTFLAGS="--cfg reqwest_unstable"
 
 # Accept build arguments for git repository and branch
 ARG GIT_REPO=https://github.com/langflow-ai/langflow.git
-ARG GIT_BRANCH=load_flows_autologin_false
+ARG GIT_BRANCH=test-openai-responses
 
 WORKDIR /app
 
diff --git a/README.md b/README.md
index df1d6451..a0178f28 100644
--- a/README.md
+++ b/README.md
@@ -62,7 +62,7 @@ LANGFLOW_CHAT_FLOW_ID=your_chat_flow_id
 LANGFLOW_INGEST_FLOW_ID=your_ingest_flow_id
 NUDGES_FLOW_ID=your_nudges_flow_id
 ```
-See extended configuration, including ingestion and optional variables: [docs/configure/configuration.md](docs/docs/configure/configuration.md)
+See extended configuration, including ingestion and optional variables: [docs/reference/configuration.md](docs/docs/reference/configuration.md)
 ### 3. Start OpenRAG
 
 ```bash
diff --git a/docker-compose.yml b/docker-compose.yml
index daa921ae..be31fb71 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -40,9 +40,9 @@ services:
 
   openrag-backend:
     image: phact/openrag-backend:${OPENRAG_VERSION:-latest}
-    #build:
-    #context: .
-    #dockerfile: Dockerfile.backend
+    # build:
+    #   context: .
+    #   dockerfile: Dockerfile.backend
     container_name: openrag-backend
     depends_on:
       - langflow
@@ -77,9 +77,10 @@ services:
 
   openrag-frontend:
     image: phact/openrag-frontend:${OPENRAG_VERSION:-latest}
-    #build:
-    #context: .
-    #dockerfile: Dockerfile.frontend
+    # build:
+    #   context: .
+    #   dockerfile: Dockerfile.frontend
+    #   #dockerfile: Dockerfile.frontend
     container_name: openrag-frontend
     depends_on:
       - openrag-backend
@@ -92,6 +93,9 @@ services:
     volumes:
       - ./flows:/app/flows:z
     image: phact/openrag-langflow:${LANGFLOW_VERSION:-latest}
+    # build:
+    #   context: .
+    #   dockerfile: Dockerfile.langflow
     container_name: langflow
     ports:
       - "7860:7860"
@@ -99,7 +103,7 @@ services:
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - LANGFLOW_LOAD_FLOWS_PATH=/app/flows
       - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY}
-      - JWT="dummy"
+      - JWT=None  
       - OWNER=None
       - OWNER_NAME=None
       - OWNER_EMAIL=None
diff --git a/docs/docs/_partial-onboarding.mdx b/docs/docs/_partial-onboarding.mdx
index 5efbf2eb..44222371 100644
--- a/docs/docs/_partial-onboarding.mdx
+++ b/docs/docs/_partial-onboarding.mdx
@@ -5,10 +5,12 @@ import TabItem from '@theme/TabItem';
 
 The first time you start OpenRAG, whether using the TUI or a `.env` file, you must complete application onboarding.
 
-Values input during onboarding can be changed later in the OpenRAG **Settings** page, except for the language model and embedding model _provider_. 
-**Your provider can only be selected once, and you must use the same provider for your language model and embedding model.**
-The language model can be changed, but the embeddings model cannot be changed.
-To change your provider selection, you must completely reinstall OpenRAG.
+Most values from onboarding can be changed later in the OpenRAG **Settings** page, but there are important restrictions.
+
+The **language model provider** and **embeddings model provider** can only be selected at onboarding, and you must use the same provider for your language model and embedding model.
+To change your provider selection later, you must completely reinstall OpenRAG.
+
+The **language model** can be changed later in **Settings**, but the **embeddings model** cannot be changed later.
 
     <Tabs groupId="Provider">
     <TabItem value="OpenAI" label="OpenAI" default>
@@ -36,14 +38,12 @@ To change your provider selection, you must completely reinstall OpenRAG.
     :::   
     1. Enter your Ollama server's base URL address.
     The default Ollama server address is `http://localhost:11434`.
-    Since OpenRAG is running in a container, you may need to change `localhost` to access services outside of the container. For example, change `http://localhost:11434` to `http://host.docker.internal:11434` to connect to Ollama.
-    OpenRAG automatically sends a test connection to your Ollama server to confirm connectivity.
+    OpenRAG automatically transforms `localhost` to access services outside of the container, and sends a test connection to your Ollama server to confirm connectivity.
     2. Select the **Embedding Model** and **Language Model** your Ollama server is running.
-    OpenRAG automatically lists the available models from your Ollama server.
+    OpenRAG retrieves the available models from your Ollama server.
     3. To load 2 sample PDFs, enable **Sample dataset**.
     This is recommended, but not required.
     4. Click **Complete**.
     5. Continue with the [Quickstart](/quickstart).        
     </TabItem>
-    </Tabs>
-
+    </Tabs>
\ No newline at end of file
diff --git a/docs/docs/configure/configuration.mdx b/docs/docs/configure/configuration.mdx
deleted file mode 100644
index d8058254..00000000
--- a/docs/docs/configure/configuration.mdx
+++ /dev/null
@@ -1,110 +0,0 @@
----
-title: Configuration
-slug: /configure/configuration
----
-
-import PartialExternalPreview from '@site/docs/_partial-external-preview.mdx';
-
-<PartialExternalPreview />
-
-OpenRAG supports multiple configuration methods with the following priority:
-
-1. **Environment Variables** (highest priority)
-2. **Configuration File** (`config.yaml`)
-3. **Langflow Flow Settings** (runtime override)
-4. **Default Values** (fallback)
-
-## Configuration File
-
-Create a `config.yaml` file in the project root to configure OpenRAG:
-
-```yaml
-# OpenRAG Configuration File
-provider:
-  model_provider: "openai" # openai, anthropic, azure, etc.
-  api_key: "your-api-key" # or use OPENAI_API_KEY env var
-
-knowledge:
-  embedding_model: "text-embedding-3-small"
-  chunk_size: 1000
-  chunk_overlap: 200
-  ocr: true
-  picture_descriptions: false
-
-agent:
-  llm_model: "gpt-4o-mini"
-  system_prompt: "You are a helpful AI assistant..."
-```
-
-## Environment Variables
-
-Environment variables will override configuration file settings. You can still use `.env` files:
-
-```bash
-cp .env.example .env
-```
-
-## Required Variables
-
-| Variable                      | Description                                 |
-| ----------------------------- | ------------------------------------------- |
-| `OPENAI_API_KEY`              | Your OpenAI API key                         |
-| `OPENSEARCH_PASSWORD`         | Password for OpenSearch admin user          |
-| `LANGFLOW_SUPERUSER`          | Langflow admin username                     |
-| `LANGFLOW_SUPERUSER_PASSWORD` | Langflow admin password                     |
-| `LANGFLOW_CHAT_FLOW_ID`       | ID of your Langflow chat flow               |
-| `LANGFLOW_INGEST_FLOW_ID`     | ID of your Langflow ingestion flow          |
-| `NUDGES_FLOW_ID`              | ID of your Langflow nudges/suggestions flow |
-
-## Ingestion Configuration
-
-| Variable                       | Description                                            |
-| ------------------------------ | ------------------------------------------------------ |
-| `DISABLE_INGEST_WITH_LANGFLOW` | Disable Langflow ingestion pipeline (default: `false`) |
-
-- `false` or unset: Uses Langflow pipeline (upload → ingest → delete)
-- `true`: Uses traditional OpenRAG processor for document ingestion
-
-## Optional Variables
-
-| Variable                                                                  | Description                                                        |
-| ------------------------------------------------------------------------- | ------------------------------------------------------------------ |
-| `LANGFLOW_PUBLIC_URL`                                                     | Public URL for Langflow (default: `http://localhost:7860`)         |
-| `GOOGLE_OAUTH_CLIENT_ID` / `GOOGLE_OAUTH_CLIENT_SECRET`                   | Google OAuth authentication                                        |
-| `MICROSOFT_GRAPH_OAUTH_CLIENT_ID` / `MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET` | Microsoft OAuth                                                    |
-| `WEBHOOK_BASE_URL`                                                        | Base URL for webhook endpoints                                     |
-| `AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY`                             | AWS integrations                                                   |
-| `SESSION_SECRET`                                                          | Session management (default: auto-generated, change in production) |
-| `LANGFLOW_KEY`                                                            | Explicit Langflow API key (auto-generated if not provided)         |
-| `LANGFLOW_SECRET_KEY`                                                     | Secret key for Langflow internal operations                        |
-
-## OpenRAG Configuration Variables
-
-These environment variables override settings in `config.yaml`:
-
-### Provider Settings
-
-| Variable           | Description                              | Default  |
-| ------------------ | ---------------------------------------- | -------- |
-| `MODEL_PROVIDER`   | Model provider (openai, anthropic, etc.) | `openai` |
-| `PROVIDER_API_KEY` | API key for the model provider           |          |
-| `OPENAI_API_KEY`   | OpenAI API key (backward compatibility)  |          |
-
-### Knowledge Settings
-
-| Variable                       | Description                             | Default                  |
-| ------------------------------ | --------------------------------------- | ------------------------ |
-| `EMBEDDING_MODEL`              | Embedding model for vector search       | `text-embedding-3-small` |
-| `CHUNK_SIZE`                   | Text chunk size for document processing | `1000`                   |
-| `CHUNK_OVERLAP`                | Overlap between chunks                  | `200`                    |
-| `OCR_ENABLED`                  | Enable OCR for image processing         | `true`                   |
-| `PICTURE_DESCRIPTIONS_ENABLED` | Enable picture descriptions             | `false`                  |
-
-### Agent Settings
-
-| Variable        | Description                       | Default                  |
-| --------------- | --------------------------------- | ------------------------ |
-| `LLM_MODEL`     | Language model for the chat agent | `gpt-4o-mini`            |
-| `SYSTEM_PROMPT` | System prompt for the agent       | Default assistant prompt |
-
-See `.env.example` for a complete list with descriptions, and `docker-compose*.yml` for runtime usage.
diff --git a/docs/docs/core-components/agents.mdx b/docs/docs/core-components/agents.mdx
index 9b4adb4b..3ee4617b 100644
--- a/docs/docs/core-components/agents.mdx
+++ b/docs/docs/core-components/agents.mdx
@@ -52,7 +52,7 @@ This filter is the [Knowledge filter](/knowledge#create-knowledge-filters), and
 
 <PartialModifyFlows />
 
-For an example of changing out the agent's LLM in OpenRAG, see the [Quickstart](/quickstart#change-components).
+For an example of changing out the agent's language model in OpenRAG, see the [Quickstart](/quickstart#change-components).
 
 To restore the flow to its initial state, in OpenRAG, click <Icon name="Settings" aria-hidden="true"/> **Settings**, and then click **Restore Flow**.
 OpenRAG warns you that this discards all custom settings. Click **Restore** to restore the flow.
diff --git a/docs/docs/core-components/ingestion.mdx b/docs/docs/core-components/ingestion.mdx
index 08071158..d3ce81b0 100644
--- a/docs/docs/core-components/ingestion.mdx
+++ b/docs/docs/core-components/ingestion.mdx
@@ -46,7 +46,7 @@ If OpenRAG detects that the local machine is running on macOS, OpenRAG uses the
 
 ## Use OpenRAG default ingestion instead of Docling serve
 
-If you want to use OpenRAG's built-in pipeline instead of Docling serve, set `DISABLE_INGEST_WITH_LANGFLOW=true` in [Environment variables](/configure/configuration#ingestion-configuration).
+If you want to use OpenRAG's built-in pipeline instead of Docling serve, set `DISABLE_INGEST_WITH_LANGFLOW=true` in [Environment variables](/reference/configuration#document-processing).
 
 The built-in pipeline still uses the Docling processor, but uses it directly without the Docling Serve API.
 
diff --git a/docs/docs/get-started/docker.mdx b/docs/docs/get-started/docker.mdx
index 8542169a..f7ec730b 100644
--- a/docs/docs/get-started/docker.mdx
+++ b/docs/docs/get-started/docker.mdx
@@ -51,12 +51,12 @@ The following values are **required** to be set:
    ```bash
    OPENSEARCH_PASSWORD=your_secure_password
    OPENAI_API_KEY=your_openai_api_key
-   
    LANGFLOW_SUPERUSER=admin
    LANGFLOW_SUPERUSER_PASSWORD=your_langflow_password
    LANGFLOW_SECRET_KEY=your_secret_key
    ```
-   For more information on configuring OpenRAG with environment variables, see [Environment variables](/configure/configuration).
+   
+   For more information on configuring OpenRAG with environment variables, see [Environment variables](/reference/configuration).
 
 4. Deploy OpenRAG with Docker Compose based on your deployment type.
 
@@ -95,12 +95,35 @@ The following values are **required** to be set:
 
 <PartialOnboarding />
 
-## Rebuild all Docker containers
+## Container management commands
 
-If you need to reset state and rebuild all of your containers, run the following command.
+Manage your OpenRAG containers with the following commands.
+These commands are also available in the TUI's [Status menu](/get-started/tui#status).
+
+### Upgrade containers
+
+Upgrade your containers to the latest version while preserving your data.
+
+```bash
+docker compose pull
+docker compose up -d --force-recreate
+```
+
+### Rebuild containers (destructive)
+
+Reset state by rebuilding all of your containers.
 Your OpenSearch and Langflow databases will be lost.
 Documents stored in the `./documents` directory will persist, since the directory is mounted as a volume in the OpenRAG backend container.
 
 ```bash
 docker compose up --build --force-recreate --remove-orphans
 ```
+
+### Remove all containers and data (destructive)
+
+Completely remove your OpenRAG installation and delete all data.
+This deletes all of your data, including OpenSearch data, uploaded documents, and authentication. 
+```bash
+docker compose down --volumes --remove-orphans --rmi local
+docker system prune -f
+```
\ No newline at end of file
diff --git a/docs/docs/get-started/install.mdx b/docs/docs/get-started/install.mdx
index 830a8f5e..1759e813 100644
--- a/docs/docs/get-started/install.mdx
+++ b/docs/docs/get-started/install.mdx
@@ -5,12 +5,12 @@ slug: /install
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
-import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
+import PartialOnboarding from '@site/docs/_partial-onboarding.mdx'; 
 import PartialExternalPreview from '@site/docs/_partial-external-preview.mdx';
 
 <PartialExternalPreview />
 
-[Install the OpenRAG Python wheel](#install-python-wheel) and then use the [OpenRAG Terminal User Interface(TUI)](#setup) to run and configure your OpenRAG deployment with a guided setup process.
+[Install the OpenRAG Python wheel](#install-python-wheel), and then run the [OpenRAG Terminal User Interface(TUI)](#setup) to start your OpenRAG deployment with a guided setup process.
 
 If you prefer running Docker commands and manually editing `.env` files, see [Deploy with Docker](/get-started/docker).
 
@@ -29,17 +29,16 @@ If you prefer running Docker commands and manually editing `.env` files, see [De
 The `.whl` file is currently available as an internal download during public preview, and will be published to PyPI in a future release.
 :::
 
-The OpenRAG wheel installs the Terminal User Interface (TUI) for running and managing OpenRAG.
+The OpenRAG wheel installs the Terminal User Interface (TUI) for configuring and running OpenRAG.
 
-1. Create a new project with a virtual environment using `uv`.
-   This creates and activates a virtual environment for your project.
+1. Create a new project with a virtual environment using `uv init`.
 
    ```bash
    uv init YOUR_PROJECT_NAME
    cd YOUR_PROJECT_NAME
    ```
 
-   The terminal prompt won't change like it would when using `venv`, but the `uv` commands will use the project's virtual environment.
+   The `(venv)` prompt doesn't change, but `uv` commands will automatically use the project's virtual environment.
    For more information on virtual environments, see the [uv documentation](https://docs.astral.sh/uv/pip/environments).
 
 2. Add the local OpenRAG wheel to your project's virtual environment.
@@ -65,7 +64,9 @@ The OpenRAG wheel installs the Terminal User Interface (TUI) for running and man
 
 ## Set up OpenRAG with the TUI {#setup}
 
-**Basic Setup** completes or auto-generates most of the required values to start OpenRAG.
+The TUI creates a `.env` file in your OpenRAG directory root and starts OpenRAG.
+
+**Basic Setup** generates all of the required values except the OpenAI API key.
 **Basic Setup** does not set up OAuth connections for ingestion from Google Drive, OneDrive, or AWS.
 For OAuth setup, use **Advanced Setup**.
 
diff --git a/docs/docs/reference/configuration.mdx b/docs/docs/reference/configuration.mdx
new file mode 100644
index 00000000..c8ca7cfa
--- /dev/null
+++ b/docs/docs/reference/configuration.mdx
@@ -0,0 +1,162 @@
+---
+title: Environment variables
+slug: /reference/configuration
+---
+
+import Icon from "@site/src/components/icon/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+OpenRAG recognizes [supported environment variables](#supported-environment-variables) from the following sources:
+
+* [Environment variables](#supported-environment-variables) - Values set in the `.env` file.
+* [Langflow runtime overrides](#langflow-runtime-overrides) - Langflow components may tweak environment variables at runtime.
+* [Default or fallback values](#default-values-and-fallbacks) - These values are default or fallback values if OpenRAG doesn't find a value.
+
+## Configure environment variables
+
+Environment variables are set in a `.env` file in the root of your OpenRAG project directory.
+
+For an example `.env` file, see [`.env.example` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/.env.example).
+
+The Docker Compose files are populated with values from your `.env`, so you don't need to edit the Docker Compose files manually.
+
+Environment variables always take precedence over other variables.
+
+### Set environment variables
+
+To set environment variables, do the following.
+
+1. Stop OpenRAG.
+2. Set the values in the `.env` file:
+   ```bash
+   LOG_LEVEL=DEBUG
+   LOG_FORMAT=json
+   SERVICE_NAME=openrag-dev
+   ```
+3. Start OpenRAG.
+
+Updating provider API keys or provider endpoints in the `.env` file will not take effect after [Application onboarding](/install#application-onboarding). To change these values, you must:
+
+1. Stop OpenRAG.
+2. Remove the containers:
+   ```
+   docker-compose down
+   ```
+3. Update the values in your `.env` file.
+4. Start OpenRAG containers.
+   ```
+   docker-compose up -d
+   ```
+5. Complete [Application onboarding](/install#application-onboarding) again.
+
+## Supported environment variables
+
+All OpenRAG configuration can be controlled through environment variables.
+
+### AI provider settings
+
+Configure which AI models and providers OpenRAG uses for language processing and embeddings.
+For more information, see [Application onboarding](/install#application-onboarding).
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `EMBEDDING_MODEL` | `text-embedding-3-small` | Embedding model for vector search. |
+| `LLM_MODEL` | `gpt-4o-mini` | Language model for the chat agent. |
+| `MODEL_PROVIDER` | `openai` | Model provider, such as OpenAI or IBM watsonx.ai. |
+| `OPENAI_API_KEY` | - | Your OpenAI API key. Required. |
+| `PROVIDER_API_KEY` | - | API key for the model provider. |
+| `PROVIDER_ENDPOINT` | - | Custom provider endpoint. Only used for IBM or Ollama providers. |
+| `PROVIDER_PROJECT_ID` | - | Project ID for providers. Only required for the IBM watsonx.ai provider. |
+
+### Document processing
+
+Control how OpenRAG processes and ingests documents into your knowledge base.
+For more information, see [Ingestion](/ingestion).
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `CHUNK_OVERLAP` | `200` | Overlap between chunks. |
+| `CHUNK_SIZE` | `1000` | Text chunk size for document processing. |
+| `DISABLE_INGEST_WITH_LANGFLOW` | `false` | Disable Langflow ingestion pipeline. |
+| `DOCLING_OCR_ENGINE` | - | OCR engine for document processing. |
+| `OCR_ENABLED` | `false` | Enable OCR for image processing. |
+| `OPENRAG_DOCUMENTS_PATHS` | `./documents` | Document paths for ingestion. |
+| `PICTURE_DESCRIPTIONS_ENABLED` | `false` | Enable picture descriptions. |
+
+### Langflow settings
+
+Configure Langflow authentication.
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `LANGFLOW_AUTO_LOGIN` | `False` | Enable auto-login for Langflow. |
+| `LANGFLOW_CHAT_FLOW_ID` | pre-filled | This value is pre-filled. The default value is found in [.env.example](https://github.com/langflow-ai/openrag/blob/main/.env.example). |
+| `LANGFLOW_ENABLE_SUPERUSER_CLI` | `False` | Enable superuser CLI. |
+| `LANGFLOW_INGEST_FLOW_ID` | pre-filled | This value is pre-filled. The default value is found in [.env.example](https://github.com/langflow-ai/openrag/blob/main/.env.example). |
+| `LANGFLOW_KEY` | auto-generated | Explicit Langflow API key. |
+| `LANGFLOW_NEW_USER_IS_ACTIVE` | `False` | New users are active by default. |
+| `LANGFLOW_PUBLIC_URL` | `http://localhost:7860` | Public URL for Langflow. |
+| `LANGFLOW_SECRET_KEY` | - | Secret key for Langflow internal operations. |
+| `LANGFLOW_SUPERUSER` | - | Langflow admin username. Required. |
+| `LANGFLOW_SUPERUSER_PASSWORD` | - | Langflow admin password. Required. |
+| `LANGFLOW_URL` | `http://localhost:7860` | Langflow URL. |
+| `NUDGES_FLOW_ID` | pre-filled | This value is pre-filled. The default value is found in [.env.example](https://github.com/langflow-ai/openrag/blob/main/.env.example). |
+| `SYSTEM_PROMPT` | "You are a helpful AI assistant with access to a knowledge base. Answer questions based on the provided context." | System prompt for the Langflow agent. |
+
+### OAuth provider settings
+
+Configure OAuth providers and external service integrations.
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY` | - | AWS integrations. |
+| `GOOGLE_OAUTH_CLIENT_ID` / `GOOGLE_OAUTH_CLIENT_SECRET` | - | Google OAuth authentication. |
+| `MICROSOFT_GRAPH_OAUTH_CLIENT_ID` / `MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET` | - | Microsoft OAuth. |
+| `WEBHOOK_BASE_URL` | - | Base URL for webhook endpoints. |
+
+### OpenSearch settings
+
+Configure OpenSearch database authentication.
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `OPENSEARCH_HOST` | `localhost` | OpenSearch host. |
+| `OPENSEARCH_PASSWORD` | - | Password for OpenSearch admin user. Required. |
+| `OPENSEARCH_PORT` | `9200` | OpenSearch port. |
+| `OPENSEARCH_USERNAME` | `admin` | OpenSearch username. |
+
+### System settings
+
+Configure general system components, session management, and logging.
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `LANGFLOW_KEY_RETRIES` | `15` | Number of retries for Langflow key generation. |
+| `LANGFLOW_KEY_RETRY_DELAY` | `2.0` | Delay between retries in seconds. |
+| `LOG_FORMAT` | - | Log format (set to "json" for JSON output). |
+| `LOG_LEVEL` | `INFO` | Logging level (DEBUG, INFO, WARNING, ERROR). |
+| `MAX_WORKERS` | - | Maximum number of workers for document processing. |
+| `SERVICE_NAME` | `openrag` | Service name for logging. |
+| `SESSION_SECRET` | auto-generated | Session management. |
+
+## Langflow runtime overrides
+
+Langflow runtime overrides allow you to modify component settings at runtime without changing the base configuration.
+
+Runtime overrides are implemented through **tweaks** - parameter modifications that are passed to specific Langflow components during flow execution.
+
+For more information on tweaks, see [Input schema (tweaks)](https://docs.langflow.org/concepts-publish#input-schema).
+
+## Default values and fallbacks
+
+When no environment variables or configuration file values are provided, OpenRAG uses default values.
+These values can be found in the code base at the following locations.
+
+### OpenRAG configuration defaults
+
+These values are defined in [`config_manager.py` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/src/config/config_manager.py).
+
+### System configuration defaults
+
+These fallback values are defined in [`settings.py` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py).
\ No newline at end of file
diff --git a/docs/docs/support/troubleshoot.mdx b/docs/docs/support/troubleshoot.mdx
index dc62cf43..9946db38 100644
--- a/docs/docs/support/troubleshoot.mdx
+++ b/docs/docs/support/troubleshoot.mdx
@@ -13,12 +13,12 @@ This page provides troubleshooting advice for issues you might encounter when us
 
 ## OpenSearch fails to start
 
-Check that `OPENSEARCH_PASSWORD` set in [Environment variables](/configure/configuration) meets requirements.
+Check that `OPENSEARCH_PASSWORD` set in [Environment variables](/reference/configuration) meets requirements.
 The password must contain at least 8 characters, and must contain at least one uppercase letter, one lowercase letter, one digit, and one special character that is strong.
 
 ## Langflow connection issues
 
-Verify the `LANGFLOW_SUPERUSER` credentials set in [Environment variables](/configure/configuration) are correct.
+Verify the `LANGFLOW_SUPERUSER` credentials set in [Environment variables](/reference/configuration) are correct.
 
 ## Memory errors
 
@@ -108,4 +108,4 @@ To reset your local containers and pull new images, do the following:
 
 3. In the OpenRAG TUI, click **Status**, and then click **Upgrade**.
 When the **Close** button is active, the upgrade is complete.
-Close the window and open the OpenRAG appplication. 
+Close the window and open the OpenRAG appplication.
\ No newline at end of file
diff --git a/docs/sidebars.js b/docs/sidebars.js
index e95a6f19..9d0c49c8 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -70,11 +70,11 @@ const sidebars = {
     },
     {
       type: "category",
-      label: "Configuration",
+      label: "Reference",
       items: [
         {
           type: "doc",
-          id: "configure/configuration",
+          id: "reference/configuration",
           label: "Environment variables"
         },
       ],
@@ -93,4 +93,4 @@ const sidebars = {
   ],
 };
 
-export default sidebars;
+export default sidebars;
\ No newline at end of file
diff --git a/flows/components/ollama_embedding.json b/flows/components/ollama_embedding.json
index 01b83c44..24974b46 100644
--- a/flows/components/ollama_embedding.json
+++ b/flows/components/ollama_embedding.json
@@ -1,28 +1,114 @@
 {
   "data": {
-    "id": "OllamaEmbeddings-4ah5Q",
     "node": {
-      "base_classes": [
-        "Embeddings"
-      ],
-      "beta": false,
-      "conditional_paths": [],
-      "custom_fields": {},
+      "template": {
+        "_type": "Component",
+        "base_url": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "trace_as_metadata": true,
+          "load_from_db": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": true,
+          "placeholder": "",
+          "show": true,
+          "name": "base_url",
+          "value": "OLLAMA_BASE_URL",
+          "display_name": "Ollama Base URL",
+          "advanced": false,
+          "input_types": ["Message"],
+          "dynamic": false,
+          "info": "",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageTextInput"
+        },
+        "code": {
+          "type": "code",
+          "required": true,
+          "placeholder": "",
+          "list": false,
+          "show": true,
+          "multiline": true,
+          "value": "from typing import Any\nfrom urllib.parse import urljoin\n\nimport httpx\nfrom langchain_ollama import OllamaEmbeddings\n\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.ollama_constants import OLLAMA_EMBEDDING_MODELS, URL_LIST\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import DropdownInput, MessageTextInput, Output\n\nHTTP_STATUS_OK = 200\n\n\nclass OllamaEmbeddingsComponent(LCModelComponent):\n    display_name: str = \"Ollama Embeddings\"\n    description: str = \"Generate embeddings using Ollama models.\"\n    documentation = \"https://python.langchain.com/docs/integrations/text_embedding/ollama\"\n    icon = \"Ollama\"\n    name = \"OllamaEmbeddings\"\n\n    inputs = [\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Ollama Model\",\n            value=\"\",\n            options=[],\n            real_time_refresh=True,\n            refresh_button=True,\n            combobox=True,\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"base_url\",\n            display_name=\"Ollama Base URL\",\n            value=\"\",\n            required=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        try:\n            output = OllamaEmbeddings(model=self.model_name, base_url=self.base_url)\n        except Exception as e:\n            msg = (\n                \"Unable to connect to the Ollama API. \",\n                \"Please verify the base URL, ensure the relevant Ollama model is pulled, and try again.\",\n            )\n            raise ValueError(msg) from e\n        return output\n\n    async def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None):\n        if field_name in {\"base_url\", \"model_name\"} and not await self.is_valid_ollama_url(field_value):\n            # Check if any URL in the list is valid\n            valid_url = \"\"\n            for url in URL_LIST:\n                if await self.is_valid_ollama_url(url):\n                    valid_url = url\n                    break\n            build_config[\"base_url\"][\"value\"] = valid_url\n        if field_name in {\"model_name\", \"base_url\", \"tool_model_enabled\"}:\n            if await self.is_valid_ollama_url(self.base_url):\n                build_config[\"model_name\"][\"options\"] = await self.get_model(self.base_url)\n            elif await self.is_valid_ollama_url(build_config[\"base_url\"].get(\"value\", \"\")):\n                build_config[\"model_name\"][\"options\"] = await self.get_model(build_config[\"base_url\"].get(\"value\", \"\"))\n            else:\n                build_config[\"model_name\"][\"options\"] = []\n\n        return build_config\n\n    async def get_model(self, base_url_value: str) -> list[str]:\n        \"\"\"Get the model names from Ollama.\"\"\"\n        model_ids = []\n        try:\n            url = urljoin(base_url_value, \"/api/tags\")\n            async with httpx.AsyncClient() as client:\n                response = await client.get(url)\n                response.raise_for_status()\n                data = response.json()\n\n            model_ids = [model[\"name\"] for model in data.get(\"models\", [])]\n            # this to ensure that not embedding models are included.\n            # not even the base models since models can have 1b 2b etc\n            # handles cases when embeddings models have tags like :latest - etc.\n            model_ids = [\n                model\n                for model in model_ids\n                if any(model.startswith(f\"{embedding_model}\") for embedding_model in OLLAMA_EMBEDDING_MODELS)\n            ]\n\n        except (ImportError, ValueError, httpx.RequestError) as e:\n            msg = \"Could not get model names from Ollama.\"\n            raise ValueError(msg) from e\n\n        return model_ids\n\n    async def is_valid_ollama_url(self, url: str) -> bool:\n        try:\n            async with httpx.AsyncClient() as client:\n                return (await client.get(f\"{url}/api/tags\")).status_code == HTTP_STATUS_OK\n        except httpx.RequestError:\n            return False\n",
+          "fileTypes": [],
+          "file_path": "",
+          "password": false,
+          "name": "code",
+          "advanced": true,
+          "dynamic": true,
+          "info": "",
+          "load_from_db": false,
+          "title_case": false
+        },
+        "model_name": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "options": ["nomic-embed-text:latest", "all-minilm:latest"],
+          "options_metadata": [],
+          "combobox": true,
+          "dialog_inputs": {},
+          "toggle": false,
+          "required": true,
+          "placeholder": "",
+          "show": true,
+          "name": "model_name",
+          "value": "",
+          "display_name": "Ollama Model",
+          "advanced": false,
+          "dynamic": false,
+          "info": "",
+          "real_time_refresh": true,
+          "refresh_button": true,
+          "title_case": false,
+          "external_options": {},
+          "type": "str",
+          "_input_type": "DropdownInput"
+        }
+      },
       "description": "Generate embeddings using Ollama models.",
+      "icon": "Ollama",
+      "base_classes": ["Embeddings"],
       "display_name": "Ollama Embeddings",
       "documentation": "https://python.langchain.com/docs/integrations/text_embedding/ollama",
-      "edited": false,
-      "field_order": [
-        "model_name",
-        "base_url"
-      ],
+      "minimized": false,
+      "custom_fields": {},
+      "output_types": [],
+      "pinned": false,
+      "conditional_paths": [],
       "frozen": false,
-      "icon": "Ollama",
-      "last_updated": "2025-09-22T20:18:27.128Z",
+      "outputs": [
+        {
+          "types": ["Embeddings"],
+          "selected": "Embeddings",
+          "name": "embeddings",
+          "display_name": "Embeddings",
+          "method": "build_embeddings",
+          "value": "__UNDEFINED__",
+          "cache": true,
+          "required_inputs": null,
+          "allows_loop": false,
+          "group_outputs": false,
+          "options": null,
+          "tool_mode": true
+        }
+      ],
+      "field_order": ["model_name", "base_url"],
+      "beta": false,
       "legacy": false,
+      "edited": false,
       "metadata": {
-        "code_hash": "0db0f99e91e9",
+        "keywords": [
+          "model",
+          "llm",
+          "language model",
+          "large language model"
+        ],
+        "module": "lfx.components.ollama.ollama_embeddings.OllamaEmbeddingsComponent",
+        "code_hash": "c41821735548",
         "dependencies": {
+          "total_dependencies": 3,
           "dependencies": [
             {
               "name": "httpx",
@@ -33,125 +119,24 @@
               "version": "0.2.1"
             },
             {
-              "name": "langflow",
+              "name": "lfx",
               "version": null
             }
-          ],
-          "total_dependencies": 3
-        },
-        "keywords": [
-          "model",
-          "llm",
-          "language model",
-          "large language model"
-        ],
-        "module": "langflow.components.ollama.ollama_embeddings.OllamaEmbeddingsComponent"
-      },
-      "minimized": false,
-      "output_types": [],
-      "outputs": [
-        {
-          "allows_loop": false,
-          "cache": true,
-          "display_name": "Embeddings",
-          "group_outputs": false,
-          "method": "build_embeddings",
-          "name": "embeddings",
-          "options": null,
-          "required_inputs": null,
-          "selected": "Embeddings",
-          "tool_mode": true,
-          "types": [
-            "Embeddings"
-          ],
-          "value": "__UNDEFINED__"
-        }
-      ],
-      "pinned": false,
-      "template": {
-        "_type": "Component",
-        "base_url": {
-          "_input_type": "MessageTextInput",
-          "advanced": false,
-          "display_name": "Ollama Base URL",
-          "dynamic": false,
-          "info": "",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": true,
-          "name": "base_url",
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "OLLAMA_BASE_URL"
-        },
-        "code": {
-          "advanced": true,
-          "dynamic": true,
-          "fileTypes": [],
-          "file_path": "",
-          "info": "",
-          "list": false,
-          "load_from_db": false,
-          "multiline": true,
-          "name": "code",
-          "password": false,
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "type": "code",
-          "value": "from typing import Any\nfrom urllib.parse import urljoin\n\nimport httpx\nfrom langchain_ollama import OllamaEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.ollama_constants import OLLAMA_EMBEDDING_MODELS, URL_LIST\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import DropdownInput, MessageTextInput, Output\n\nHTTP_STATUS_OK = 200\n\n\nclass OllamaEmbeddingsComponent(LCModelComponent):\n    display_name: str = \"Ollama Embeddings\"\n    description: str = \"Generate embeddings using Ollama models.\"\n    documentation = \"https://python.langchain.com/docs/integrations/text_embedding/ollama\"\n    icon = \"Ollama\"\n    name = \"OllamaEmbeddings\"\n\n    inputs = [\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Ollama Model\",\n            value=\"\",\n            options=[],\n            real_time_refresh=True,\n            refresh_button=True,\n            combobox=True,\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"base_url\",\n            display_name=\"Ollama Base URL\",\n            value=\"\",\n            required=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        try:\n            output = OllamaEmbeddings(model=self.model_name, base_url=self.base_url)\n        except Exception as e:\n            msg = (\n                \"Unable to connect to the Ollama API. \",\n                \"Please verify the base URL, ensure the relevant Ollama model is pulled, and try again.\",\n            )\n            raise ValueError(msg) from e\n        return output\n\n    async def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None):\n        if field_name in {\"base_url\", \"model_name\"} and not await self.is_valid_ollama_url(field_value):\n            # Check if any URL in the list is valid\n            valid_url = \"\"\n            for url in URL_LIST:\n                if await self.is_valid_ollama_url(url):\n                    valid_url = url\n                    break\n            build_config[\"base_url\"][\"value\"] = valid_url\n        if field_name in {\"model_name\", \"base_url\", \"tool_model_enabled\"}:\n            if await self.is_valid_ollama_url(self.base_url):\n                build_config[\"model_name\"][\"options\"] = await self.get_model(self.base_url)\n            elif await self.is_valid_ollama_url(build_config[\"base_url\"].get(\"value\", \"\")):\n                build_config[\"model_name\"][\"options\"] = await self.get_model(build_config[\"base_url\"].get(\"value\", \"\"))\n            else:\n                build_config[\"model_name\"][\"options\"] = []\n\n        return build_config\n\n    async def get_model(self, base_url_value: str) -> list[str]:\n        \"\"\"Get the model names from Ollama.\"\"\"\n        model_ids = []\n        try:\n            url = urljoin(base_url_value, \"/api/tags\")\n            async with httpx.AsyncClient() as client:\n                response = await client.get(url)\n                response.raise_for_status()\n                data = response.json()\n\n            model_ids = [model[\"name\"] for model in data.get(\"models\", [])]\n            # this to ensure that not embedding models are included.\n            # not even the base models since models can have 1b 2b etc\n            # handles cases when embeddings models have tags like :latest - etc.\n            model_ids = [\n                model\n                for model in model_ids\n                if any(model.startswith(f\"{embedding_model}\") for embedding_model in OLLAMA_EMBEDDING_MODELS)\n            ]\n\n        except (ImportError, ValueError, httpx.RequestError) as e:\n            msg = \"Could not get model names from Ollama.\"\n            raise ValueError(msg) from e\n\n        return model_ids\n\n    async def is_valid_ollama_url(self, url: str) -> bool:\n        try:\n            async with httpx.AsyncClient() as client:\n                return (await client.get(f\"{url}/api/tags\")).status_code == HTTP_STATUS_OK\n        except httpx.RequestError:\n            return False\n"
-        },
-        "model_name": {
-          "_input_type": "DropdownInput",
-          "advanced": false,
-          "combobox": true,
-          "dialog_inputs": {},
-          "display_name": "Ollama Model",
-          "dynamic": false,
-          "info": "",
-          "name": "model_name",
-          "options": [
-            "all-minilm:latest"
-          ],
-          "options_metadata": [],
-          "placeholder": "",
-          "real_time_refresh": true,
-          "refresh_button": true,
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "toggle": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "all-minilm:latest"
+          ]
         }
       },
-      "tool_mode": false
+      "tool_mode": false,
+      "last_updated": "2025-09-29T18:40:10.242Z",
+      "official": false
     },
     "showNode": true,
-    "type": "OllamaEmbeddings"
-  },
-  "dragging": false,
-  "id": "OllamaEmbeddings-4ah5Q",
-  "measured": {
-    "height": 286,
-    "width": 320
+    "type": "OllamaEmbeddings",
+    "id": "OllamaEmbeddings-vnNn8"
   },
+  "id": "OllamaEmbeddings-vnNn8",
   "position": {
-    "x": 282.29416840859585,
-    "y": 279.4218065717267
+    "x": 0,
+    "y": 0
   },
-  "selected": false,
   "type": "genericNode"
 }
\ No newline at end of file
diff --git a/flows/components/ollama_llm.json b/flows/components/ollama_llm.json
index 0edf7f13..4aa6ee62 100644
--- a/flows/components/ollama_llm.json
+++ b/flows/components/ollama_llm.json
@@ -1,18 +1,611 @@
 {
-  "data": {
-    "id": "OllamaModel-eCsJx",
-    "node": {
-      "base_classes": [
-        "LanguageModel",
-        "Message"
+        "data": {
+          "node": {
+            "template": {
+              "_type": "Component",
+              "base_url": {
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "load_from_db": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "base_url",
+                "value": "OLLAMA_BASE_URL",
+                "display_name": "Base URL",
+                "advanced": false,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "Endpoint of the Ollama API.",
+                "real_time_refresh": true,
+                "title_case": false,
+                "type": "str",
+                "_input_type": "MessageTextInput"
+              },
+              "code": {
+                "type": "code",
+                "required": true,
+                "placeholder": "",
+                "list": false,
+                "show": true,
+                "multiline": true,
+                "value": "import asyncio\nfrom typing import Any\nfrom urllib.parse import urljoin\n\nimport httpx\nfrom langchain_ollama import ChatOllama\n\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.ollama_constants import URL_LIST\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageTextInput, SliderInput\nfrom lfx.log.logger import logger\n\nHTTP_STATUS_OK = 200\n\n\nclass ChatOllamaComponent(LCModelComponent):\n    display_name = \"Ollama\"\n    description = \"Generate text using Ollama Local LLMs.\"\n    icon = \"Ollama\"\n    name = \"OllamaModel\"\n\n    # Define constants for JSON keys\n    JSON_MODELS_KEY = \"models\"\n    JSON_NAME_KEY = \"name\"\n    JSON_CAPABILITIES_KEY = \"capabilities\"\n    DESIRED_CAPABILITY = \"completion\"\n    TOOL_CALLING_CAPABILITY = \"tools\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"base_url\",\n            display_name=\"Base URL\",\n            info=\"Endpoint of the Ollama API.\",\n            value=\"\",\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            info=\"Refer to https://ollama.com/library for more models.\",\n            refresh_button=True,\n            real_time_refresh=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"format\", display_name=\"Format\", info=\"Specify the format of the output (e.g., json).\", advanced=True\n        ),\n        DictInput(name=\"metadata\", display_name=\"Metadata\", info=\"Metadata to add to the run trace.\", advanced=True),\n        DropdownInput(\n            name=\"mirostat\",\n            display_name=\"Mirostat\",\n            options=[\"Disabled\", \"Mirostat\", \"Mirostat 2.0\"],\n            info=\"Enable/disable Mirostat sampling for controlling perplexity.\",\n            value=\"Disabled\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        FloatInput(\n            name=\"mirostat_eta\",\n            display_name=\"Mirostat Eta\",\n            info=\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"mirostat_tau\",\n            display_name=\"Mirostat Tau\",\n            info=\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_ctx\",\n            display_name=\"Context Window Size\",\n            info=\"Size of the context window for generating tokens. (Default: 2048)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_gpu\",\n            display_name=\"Number of GPUs\",\n            info=\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_thread\",\n            display_name=\"Number of Threads\",\n            info=\"Number of threads to use during computation. (Default: detected for optimal performance)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"repeat_last_n\",\n            display_name=\"Repeat Last N\",\n            info=\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"repeat_penalty\",\n            display_name=\"Repeat Penalty\",\n            info=\"Penalty for repetitions in generated text. (Default: 1.1)\",\n            advanced=True,\n        ),\n        FloatInput(name=\"tfs_z\", display_name=\"TFS Z\", info=\"Tail free sampling value. (Default: 1)\", advanced=True),\n        IntInput(name=\"timeout\", display_name=\"Timeout\", info=\"Timeout for the request stream.\", advanced=True),\n        IntInput(\n            name=\"top_k\", display_name=\"Top K\", info=\"Limits token selection to top K. (Default: 40)\", advanced=True\n        ),\n        FloatInput(name=\"top_p\", display_name=\"Top P\", info=\"Works together with top-k. (Default: 0.9)\", advanced=True),\n        BoolInput(name=\"verbose\", display_name=\"Verbose\", info=\"Whether to print out response text.\", advanced=True),\n        MessageTextInput(\n            name=\"tags\",\n            display_name=\"Tags\",\n            info=\"Comma-separated list of tags to add to the run trace.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"stop_tokens\",\n            display_name=\"Stop Tokens\",\n            info=\"Comma-separated list of tokens to signal the model to stop generating text.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"system\", display_name=\"System\", info=\"System to use for generating text.\", advanced=True\n        ),\n        BoolInput(\n            name=\"tool_model_enabled\",\n            display_name=\"Tool Model Enabled\",\n            info=\"Whether to enable tool calling in the model.\",\n            value=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"template\", display_name=\"Template\", info=\"Template to use for generating text.\", advanced=True\n        ),\n        *LCModelComponent.get_base_inputs(),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # Mapping mirostat settings to their corresponding values\n        mirostat_options = {\"Mirostat\": 1, \"Mirostat 2.0\": 2}\n\n        # Default to 0 for 'Disabled'\n        mirostat_value = mirostat_options.get(self.mirostat, 0)\n\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\n        if mirostat_value == 0:\n            mirostat_eta = None\n            mirostat_tau = None\n        else:\n            mirostat_eta = self.mirostat_eta\n            mirostat_tau = self.mirostat_tau\n\n        # Mapping system settings to their corresponding values\n        llm_params = {\n            \"base_url\": self.base_url,\n            \"model\": self.model_name,\n            \"mirostat\": mirostat_value,\n            \"format\": self.format,\n            \"metadata\": self.metadata,\n            \"tags\": self.tags.split(\",\") if self.tags else None,\n            \"mirostat_eta\": mirostat_eta,\n            \"mirostat_tau\": mirostat_tau,\n            \"num_ctx\": self.num_ctx or None,\n            \"num_gpu\": self.num_gpu or None,\n            \"num_thread\": self.num_thread or None,\n            \"repeat_last_n\": self.repeat_last_n or None,\n            \"repeat_penalty\": self.repeat_penalty or None,\n            \"temperature\": self.temperature or None,\n            \"stop\": self.stop_tokens.split(\",\") if self.stop_tokens else None,\n            \"system\": self.system,\n            \"tfs_z\": self.tfs_z or None,\n            \"timeout\": self.timeout or None,\n            \"top_k\": self.top_k or None,\n            \"top_p\": self.top_p or None,\n            \"verbose\": self.verbose,\n            \"template\": self.template,\n        }\n\n        # Remove parameters with None values\n        llm_params = {k: v for k, v in llm_params.items() if v is not None}\n\n        try:\n            output = ChatOllama(**llm_params)\n        except Exception as e:\n            msg = (\n                \"Unable to connect to the Ollama API. \",\n                \"Please verify the base URL, ensure the relevant Ollama model is pulled, and try again.\",\n            )\n            raise ValueError(msg) from e\n\n        return output\n\n    async def is_valid_ollama_url(self, url: str) -> bool:\n        try:\n            async with httpx.AsyncClient() as client:\n                return (await client.get(urljoin(url, \"api/tags\"))).status_code == HTTP_STATUS_OK\n        except httpx.RequestError:\n            return False\n\n    async def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None):\n        if field_name == \"mirostat\":\n            if field_value == \"Disabled\":\n                build_config[\"mirostat_eta\"][\"advanced\"] = True\n                build_config[\"mirostat_tau\"][\"advanced\"] = True\n                build_config[\"mirostat_eta\"][\"value\"] = None\n                build_config[\"mirostat_tau\"][\"value\"] = None\n\n            else:\n                build_config[\"mirostat_eta\"][\"advanced\"] = False\n                build_config[\"mirostat_tau\"][\"advanced\"] = False\n\n                if field_value == \"Mirostat 2.0\":\n                    build_config[\"mirostat_eta\"][\"value\"] = 0.2\n                    build_config[\"mirostat_tau\"][\"value\"] = 10\n                else:\n                    build_config[\"mirostat_eta\"][\"value\"] = 0.1\n                    build_config[\"mirostat_tau\"][\"value\"] = 5\n\n        if field_name in {\"base_url\", \"model_name\"}:\n            if build_config[\"base_url\"].get(\"load_from_db\", False):\n                base_url_value = await self.get_variables(build_config[\"base_url\"].get(\"value\", \"\"), \"base_url\")\n            else:\n                base_url_value = build_config[\"base_url\"].get(\"value\", \"\")\n\n            if not await self.is_valid_ollama_url(base_url_value):\n                # Check if any URL in the list is valid\n                valid_url = \"\"\n                check_urls = URL_LIST\n                if self.base_url:\n                    check_urls = [self.base_url, *URL_LIST]\n                for url in check_urls:\n                    if await self.is_valid_ollama_url(url):\n                        valid_url = url\n                        break\n                if valid_url != \"\":\n                    build_config[\"base_url\"][\"value\"] = valid_url\n                else:\n                    msg = \"No valid Ollama URL found.\"\n                    raise ValueError(msg)\n        if field_name in {\"model_name\", \"base_url\", \"tool_model_enabled\"}:\n            if await self.is_valid_ollama_url(self.base_url):\n                tool_model_enabled = build_config[\"tool_model_enabled\"].get(\"value\", False) or self.tool_model_enabled\n                build_config[\"model_name\"][\"options\"] = await self.get_models(\n                    self.base_url, tool_model_enabled=tool_model_enabled\n                )\n            elif await self.is_valid_ollama_url(build_config[\"base_url\"].get(\"value\", \"\")):\n                tool_model_enabled = build_config[\"tool_model_enabled\"].get(\"value\", False) or self.tool_model_enabled\n                build_config[\"model_name\"][\"options\"] = await self.get_models(\n                    build_config[\"base_url\"].get(\"value\", \"\"), tool_model_enabled=tool_model_enabled\n                )\n            else:\n                build_config[\"model_name\"][\"options\"] = []\n        if field_name == \"keep_alive_flag\":\n            if field_value == \"Keep\":\n                build_config[\"keep_alive\"][\"value\"] = \"-1\"\n                build_config[\"keep_alive\"][\"advanced\"] = True\n            elif field_value == \"Immediately\":\n                build_config[\"keep_alive\"][\"value\"] = \"0\"\n                build_config[\"keep_alive\"][\"advanced\"] = True\n            else:\n                build_config[\"keep_alive\"][\"advanced\"] = False\n\n        return build_config\n\n    async def get_models(self, base_url_value: str, *, tool_model_enabled: bool | None = None) -> list[str]:\n        \"\"\"Fetches a list of models from the Ollama API that do not have the \"embedding\" capability.\n\n        Args:\n            base_url_value (str): The base URL of the Ollama API.\n            tool_model_enabled (bool | None, optional): If True, filters the models further to include\n                only those that support tool calling. Defaults to None.\n\n        Returns:\n            list[str]: A list of model names that do not have the \"embedding\" capability. If\n                `tool_model_enabled` is True, only models supporting tool calling are included.\n\n        Raises:\n            ValueError: If there is an issue with the API request or response, or if the model\n                names cannot be retrieved.\n        \"\"\"\n        try:\n            # Normalize the base URL to avoid the repeated \"/\" at the end\n            base_url = base_url_value.rstrip(\"/\") + \"/\"\n\n            # Ollama REST API to return models\n            tags_url = urljoin(base_url, \"api/tags\")\n\n            # Ollama REST API to return model capabilities\n            show_url = urljoin(base_url, \"api/show\")\n\n            async with httpx.AsyncClient() as client:\n                # Fetch available models\n                tags_response = await client.get(tags_url)\n                tags_response.raise_for_status()\n                models = tags_response.json()\n                if asyncio.iscoroutine(models):\n                    models = await models\n                await logger.adebug(f\"Available models: {models}\")\n\n                # Filter models that are NOT embedding models\n                model_ids = []\n                for model in models[self.JSON_MODELS_KEY]:\n                    model_name = model[self.JSON_NAME_KEY]\n                    await logger.adebug(f\"Checking model: {model_name}\")\n\n                    payload = {\"model\": model_name}\n                    show_response = await client.post(show_url, json=payload)\n                    show_response.raise_for_status()\n                    json_data = show_response.json()\n                    if asyncio.iscoroutine(json_data):\n                        json_data = await json_data\n                    capabilities = json_data.get(self.JSON_CAPABILITIES_KEY, [])\n                    await logger.adebug(f\"Model: {model_name}, Capabilities: {capabilities}\")\n\n                    if self.DESIRED_CAPABILITY in capabilities and (\n                        not tool_model_enabled or self.TOOL_CALLING_CAPABILITY in capabilities\n                    ):\n                        model_ids.append(model_name)\n\n        except (httpx.RequestError, ValueError) as e:\n            msg = \"Could not get model names from Ollama.\"\n            raise ValueError(msg) from e\n\n        return model_ids\n",
+                "fileTypes": [],
+                "file_path": "",
+                "password": false,
+                "name": "code",
+                "advanced": true,
+                "dynamic": true,
+                "info": "",
+                "load_from_db": false,
+                "title_case": false
+              },
+              "format": {
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "load_from_db": false,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "format",
+                "value": "",
+                "display_name": "Format",
+                "advanced": true,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "Specify the format of the output (e.g., json).",
+                "title_case": false,
+                "type": "str",
+                "_input_type": "MessageTextInput"
+              },
+              "input_value": {
+                "trace_as_input": true,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "load_from_db": false,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "input_value",
+                "value": "",
+                "display_name": "Input",
+                "advanced": false,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "",
+                "title_case": false,
+                "type": "str",
+                "_input_type": "MessageInput"
+              },
+              "metadata": {
+                "tool_mode": false,
+                "trace_as_input": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "metadata",
+                "value": {},
+                "display_name": "Metadata",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Metadata to add to the run trace.",
+                "title_case": false,
+                "type": "dict",
+                "_input_type": "DictInput"
+              },
+              "mirostat": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "options": [
+                  "Disabled",
+                  "Mirostat",
+                  "Mirostat 2.0"
+                ],
+                "options_metadata": [],
+                "combobox": false,
+                "dialog_inputs": {},
+                "toggle": false,
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "mirostat",
+                "value": "Disabled",
+                "display_name": "Mirostat",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Enable/disable Mirostat sampling for controlling perplexity.",
+                "real_time_refresh": true,
+                "title_case": false,
+                "external_options": {},
+                "type": "str",
+                "_input_type": "DropdownInput"
+              },
+              "mirostat_eta": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "mirostat_eta",
+                "value": "",
+                "display_name": "Mirostat Eta",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Learning rate for Mirostat algorithm. (Default: 0.1)",
+                "title_case": false,
+                "type": "float",
+                "_input_type": "FloatInput"
+              },
+              "mirostat_tau": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "mirostat_tau",
+                "value": "",
+                "display_name": "Mirostat Tau",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Controls the balance between coherence and diversity of the output. (Default: 5.0)",
+                "title_case": false,
+                "type": "float",
+                "_input_type": "FloatInput"
+              },
+              "model_name": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "options": [],
+                "options_metadata": [],
+                "combobox": false,
+                "dialog_inputs": {},
+                "toggle": false,
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "model_name",
+                "value": "",
+                "display_name": "Model Name",
+                "advanced": false,
+                "dynamic": false,
+                "info": "Refer to https://ollama.com/library for more models.",
+                "real_time_refresh": true,
+                "refresh_button": true,
+                "title_case": false,
+                "external_options": {},
+                "type": "str",
+                "_input_type": "DropdownInput"
+              },
+              "num_ctx": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "num_ctx",
+                "value": "",
+                "display_name": "Context Window Size",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Size of the context window for generating tokens. (Default: 2048)",
+                "title_case": false,
+                "type": "int",
+                "_input_type": "IntInput"
+              },
+              "num_gpu": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "num_gpu",
+                "value": "",
+                "display_name": "Number of GPUs",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
+                "title_case": false,
+                "type": "int",
+                "_input_type": "IntInput"
+              },
+              "num_thread": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "num_thread",
+                "value": "",
+                "display_name": "Number of Threads",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Number of threads to use during computation. (Default: detected for optimal performance)",
+                "title_case": false,
+                "type": "int",
+                "_input_type": "IntInput"
+              },
+              "repeat_last_n": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "repeat_last_n",
+                "value": "",
+                "display_name": "Repeat Last N",
+                "advanced": true,
+                "dynamic": false,
+                "info": "How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
+                "title_case": false,
+                "type": "int",
+                "_input_type": "IntInput"
+              },
+              "repeat_penalty": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "repeat_penalty",
+                "value": "",
+                "display_name": "Repeat Penalty",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Penalty for repetitions in generated text. (Default: 1.1)",
+                "title_case": false,
+                "type": "float",
+                "_input_type": "FloatInput"
+              },
+              "stop_tokens": {
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "load_from_db": false,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "stop_tokens",
+                "value": "",
+                "display_name": "Stop Tokens",
+                "advanced": true,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "Comma-separated list of tokens to signal the model to stop generating text.",
+                "title_case": false,
+                "type": "str",
+                "_input_type": "MessageTextInput"
+              },
+              "stream": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "stream",
+                "value": false,
+                "display_name": "Stream",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Stream the response from the model. Streaming works only in Chat.",
+                "title_case": false,
+                "type": "bool",
+                "_input_type": "BoolInput"
+              },
+              "system": {
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "load_from_db": false,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "system",
+                "value": "",
+                "display_name": "System",
+                "advanced": true,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "System to use for generating text.",
+                "title_case": false,
+                "type": "str",
+                "_input_type": "MessageTextInput"
+              },
+              "system_message": {
+                "tool_mode": false,
+                "trace_as_input": true,
+                "multiline": true,
+                "trace_as_metadata": true,
+                "load_from_db": false,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "system_message",
+                "value": "",
+                "display_name": "System Message",
+                "advanced": false,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "System message to pass to the model.",
+                "title_case": false,
+                "copy_field": false,
+                "type": "str",
+                "_input_type": "MultilineInput"
+              },
+              "tags": {
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "load_from_db": false,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "tags",
+                "value": "",
+                "display_name": "Tags",
+                "advanced": true,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "Comma-separated list of tags to add to the run trace.",
+                "title_case": false,
+                "type": "str",
+                "_input_type": "MessageTextInput"
+              },
+              "temperature": {
+                "tool_mode": false,
+                "min_label": "",
+                "max_label": "",
+                "min_label_icon": "",
+                "max_label_icon": "",
+                "slider_buttons": false,
+                "slider_buttons_options": [],
+                "slider_input": false,
+                "range_spec": {
+                  "step_type": "float",
+                  "min": 0,
+                  "max": 1,
+                  "step": 0.01
+                },
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "temperature",
+                "value": 0.1,
+                "display_name": "Temperature",
+                "advanced": true,
+                "dynamic": false,
+                "info": "",
+                "title_case": false,
+                "type": "slider",
+                "_input_type": "SliderInput"
+              },
+              "template": {
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "load_from_db": false,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "template",
+                "value": "",
+                "display_name": "Template",
+                "advanced": true,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "Template to use for generating text.",
+                "title_case": false,
+                "type": "str",
+                "_input_type": "MessageTextInput"
+              },
+              "tfs_z": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "tfs_z",
+                "value": "",
+                "display_name": "TFS Z",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Tail free sampling value. (Default: 1)",
+                "title_case": false,
+                "type": "float",
+                "_input_type": "FloatInput"
+              },
+              "timeout": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "timeout",
+                "value": "",
+                "display_name": "Timeout",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Timeout for the request stream.",
+                "title_case": false,
+                "type": "int",
+                "_input_type": "IntInput"
+              },
+              "tool_model_enabled": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "tool_model_enabled",
+                "value": true,
+                "display_name": "Tool Model Enabled",
+                "advanced": false,
+                "dynamic": false,
+                "info": "Whether to enable tool calling in the model.",
+                "real_time_refresh": true,
+                "title_case": false,
+                "type": "bool",
+                "_input_type": "BoolInput"
+              },
+              "top_k": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "top_k",
+                "value": "",
+                "display_name": "Top K",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Limits token selection to top K. (Default: 40)",
+                "title_case": false,
+                "type": "int",
+                "_input_type": "IntInput"
+              },
+              "top_p": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "top_p",
+                "value": "",
+                "display_name": "Top P",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Works together with top-k. (Default: 0.9)",
+                "title_case": false,
+                "type": "float",
+                "_input_type": "FloatInput"
+              },
+              "verbose": {
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "list": false,
+                "list_add_label": "Add More",
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "name": "verbose",
+                "value": false,
+                "display_name": "Verbose",
+                "advanced": true,
+                "dynamic": false,
+                "info": "Whether to print out response text.",
+                "title_case": false,
+                "type": "bool",
+                "_input_type": "BoolInput"
+              }
+            },
+            "description": "Generate text using Ollama Local LLMs.",
+            "icon": "Ollama",
+            "base_classes": [
+              "LanguageModel",
+              "Message"
+            ],
+            "display_name": "Ollama",
+            "documentation": "",
+            "minimized": false,
+            "custom_fields": {},
+            "output_types": [],
+            "pinned": false,
+            "conditional_paths": [],
+            "frozen": false,
+            "outputs": [
+              {
+                "types": [
+                  "Message"
+                ],
+                "name": "text_output",
+                "display_name": "Model Response",
+                "method": "text_response",
+                "value": "__UNDEFINED__",
+                "cache": true,
+                "required_inputs": null,
+                "allows_loop": false,
+                "group_outputs": false,
+                "options": null,
+                "tool_mode": true
+              },
+              {
+                "types": [
+                  "LanguageModel"
+                ],
+                "selected": "LanguageModel",
+                "name": "model_output",
+                "display_name": "Language Model",
+                "method": "build_model",
+                "value": "__UNDEFINED__",
+                "cache": true,
+                "required_inputs": null,
+                "allows_loop": false,
+          "group_outputs": false,
+          "options": null,
+          "tool_mode": true
+        }
       ],
-      "beta": false,
-      "conditional_paths": [],
-      "custom_fields": {},
-      "description": "Generate text using Ollama Local LLMs.",
-      "display_name": "Ollama",
-      "documentation": "",
-      "edited": false,
       "field_order": [
         "base_url",
         "model_name",
@@ -41,13 +634,20 @@
         "system_message",
         "stream"
       ],
-      "frozen": false,
-      "icon": "Ollama",
-      "last_updated": "2025-09-22T20:14:45.057Z",
+      "beta": false,
       "legacy": false,
+      "edited": false,
       "metadata": {
-        "code_hash": "af399d429d23",
+        "keywords": [
+          "model",
+          "llm",
+          "language model",
+          "large language model"
+        ],
+        "module": "lfx.components.ollama.ollama.ChatOllamaComponent",
+        "code_hash": "54de3b5da388",
         "dependencies": {
+          "total_dependencies": 3,
           "dependencies": [
             {
               "name": "httpx",
@@ -58,630 +658,25 @@
               "version": "0.2.1"
             },
             {
-              "name": "langflow",
+              "name": "lfx",
               "version": null
             }
-          ],
-          "total_dependencies": 3
-        },
-        "keywords": [
-          "model",
-          "llm",
-          "language model",
-          "large language model"
-        ],
-        "module": "langflow.components.ollama.ollama.ChatOllamaComponent"
-      },
-      "minimized": false,
-      "output_types": [],
-      "outputs": [
-        {
-          "allows_loop": false,
-          "cache": true,
-          "display_name": "Model Response",
-          "group_outputs": false,
-          "method": "text_response",
-          "name": "text_output",
-          "options": null,
-          "required_inputs": null,
-          "tool_mode": true,
-          "types": [
-            "Message"
-          ],
-          "value": "__UNDEFINED__"
-        },
-        {
-          "allows_loop": false,
-          "cache": true,
-          "display_name": "Language Model",
-          "group_outputs": false,
-          "method": "build_model",
-          "name": "model_output",
-          "options": null,
-          "required_inputs": null,
-          "selected": "LanguageModel",
-          "tool_mode": true,
-          "types": [
-            "LanguageModel"
-          ],
-          "value": "__UNDEFINED__"
-        }
-      ],
-      "pinned": false,
-      "template": {
-        "_type": "Component",
-        "base_url": {
-          "_input_type": "MessageTextInput",
-          "advanced": false,
-          "display_name": "Base URL",
-          "dynamic": false,
-          "info": "Endpoint of the Ollama API.",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": true,
-          "name": "base_url",
-          "placeholder": "",
-          "real_time_refresh": true,
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "OLLAMA_BASE_URL"
-        },
-        "code": {
-          "advanced": true,
-          "dynamic": true,
-          "fileTypes": [],
-          "file_path": "",
-          "info": "",
-          "list": false,
-          "load_from_db": false,
-          "multiline": true,
-          "name": "code",
-          "password": false,
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "type": "code",
-          "value": "import asyncio\nfrom typing import Any\nfrom urllib.parse import urljoin\n\nimport httpx\nfrom langchain_ollama import ChatOllama\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.ollama_constants import URL_LIST\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageTextInput, SliderInput\nfrom langflow.logging import logger\n\nHTTP_STATUS_OK = 200\n\n\nclass ChatOllamaComponent(LCModelComponent):\n    display_name = \"Ollama\"\n    description = \"Generate text using Ollama Local LLMs.\"\n    icon = \"Ollama\"\n    name = \"OllamaModel\"\n\n    # Define constants for JSON keys\n    JSON_MODELS_KEY = \"models\"\n    JSON_NAME_KEY = \"name\"\n    JSON_CAPABILITIES_KEY = \"capabilities\"\n    DESIRED_CAPABILITY = \"completion\"\n    TOOL_CALLING_CAPABILITY = \"tools\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"base_url\",\n            display_name=\"Base URL\",\n            info=\"Endpoint of the Ollama API.\",\n            value=\"\",\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            info=\"Refer to https://ollama.com/library for more models.\",\n            refresh_button=True,\n            real_time_refresh=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"format\", display_name=\"Format\", info=\"Specify the format of the output (e.g., json).\", advanced=True\n        ),\n        DictInput(name=\"metadata\", display_name=\"Metadata\", info=\"Metadata to add to the run trace.\", advanced=True),\n        DropdownInput(\n            name=\"mirostat\",\n            display_name=\"Mirostat\",\n            options=[\"Disabled\", \"Mirostat\", \"Mirostat 2.0\"],\n            info=\"Enable/disable Mirostat sampling for controlling perplexity.\",\n            value=\"Disabled\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        FloatInput(\n            name=\"mirostat_eta\",\n            display_name=\"Mirostat Eta\",\n            info=\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"mirostat_tau\",\n            display_name=\"Mirostat Tau\",\n            info=\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_ctx\",\n            display_name=\"Context Window Size\",\n            info=\"Size of the context window for generating tokens. (Default: 2048)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_gpu\",\n            display_name=\"Number of GPUs\",\n            info=\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_thread\",\n            display_name=\"Number of Threads\",\n            info=\"Number of threads to use during computation. (Default: detected for optimal performance)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"repeat_last_n\",\n            display_name=\"Repeat Last N\",\n            info=\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"repeat_penalty\",\n            display_name=\"Repeat Penalty\",\n            info=\"Penalty for repetitions in generated text. (Default: 1.1)\",\n            advanced=True,\n        ),\n        FloatInput(name=\"tfs_z\", display_name=\"TFS Z\", info=\"Tail free sampling value. (Default: 1)\", advanced=True),\n        IntInput(name=\"timeout\", display_name=\"Timeout\", info=\"Timeout for the request stream.\", advanced=True),\n        IntInput(\n            name=\"top_k\", display_name=\"Top K\", info=\"Limits token selection to top K. (Default: 40)\", advanced=True\n        ),\n        FloatInput(name=\"top_p\", display_name=\"Top P\", info=\"Works together with top-k. (Default: 0.9)\", advanced=True),\n        BoolInput(name=\"verbose\", display_name=\"Verbose\", info=\"Whether to print out response text.\", advanced=True),\n        MessageTextInput(\n            name=\"tags\",\n            display_name=\"Tags\",\n            info=\"Comma-separated list of tags to add to the run trace.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"stop_tokens\",\n            display_name=\"Stop Tokens\",\n            info=\"Comma-separated list of tokens to signal the model to stop generating text.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"system\", display_name=\"System\", info=\"System to use for generating text.\", advanced=True\n        ),\n        BoolInput(\n            name=\"tool_model_enabled\",\n            display_name=\"Tool Model Enabled\",\n            info=\"Whether to enable tool calling in the model.\",\n            value=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"template\", display_name=\"Template\", info=\"Template to use for generating text.\", advanced=True\n        ),\n        *LCModelComponent._base_inputs,\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # Mapping mirostat settings to their corresponding values\n        mirostat_options = {\"Mirostat\": 1, \"Mirostat 2.0\": 2}\n\n        # Default to 0 for 'Disabled'\n        mirostat_value = mirostat_options.get(self.mirostat, 0)\n\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\n        if mirostat_value == 0:\n            mirostat_eta = None\n            mirostat_tau = None\n        else:\n            mirostat_eta = self.mirostat_eta\n            mirostat_tau = self.mirostat_tau\n\n        # Mapping system settings to their corresponding values\n        llm_params = {\n            \"base_url\": self.base_url,\n            \"model\": self.model_name,\n            \"mirostat\": mirostat_value,\n            \"format\": self.format,\n            \"metadata\": self.metadata,\n            \"tags\": self.tags.split(\",\") if self.tags else None,\n            \"mirostat_eta\": mirostat_eta,\n            \"mirostat_tau\": mirostat_tau,\n            \"num_ctx\": self.num_ctx or None,\n            \"num_gpu\": self.num_gpu or None,\n            \"num_thread\": self.num_thread or None,\n            \"repeat_last_n\": self.repeat_last_n or None,\n            \"repeat_penalty\": self.repeat_penalty or None,\n            \"temperature\": self.temperature or None,\n            \"stop\": self.stop_tokens.split(\",\") if self.stop_tokens else None,\n            \"system\": self.system,\n            \"tfs_z\": self.tfs_z or None,\n            \"timeout\": self.timeout or None,\n            \"top_k\": self.top_k or None,\n            \"top_p\": self.top_p or None,\n            \"verbose\": self.verbose,\n            \"template\": self.template,\n        }\n\n        # Remove parameters with None values\n        llm_params = {k: v for k, v in llm_params.items() if v is not None}\n\n        try:\n            output = ChatOllama(**llm_params)\n        except Exception as e:\n            msg = (\n                \"Unable to connect to the Ollama API. \",\n                \"Please verify the base URL, ensure the relevant Ollama model is pulled, and try again.\",\n            )\n            raise ValueError(msg) from e\n\n        return output\n\n    async def is_valid_ollama_url(self, url: str) -> bool:\n        try:\n            async with httpx.AsyncClient() as client:\n                return (await client.get(urljoin(url, \"api/tags\"))).status_code == HTTP_STATUS_OK\n        except httpx.RequestError:\n            return False\n\n    async def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None):\n        if field_name == \"mirostat\":\n            if field_value == \"Disabled\":\n                build_config[\"mirostat_eta\"][\"advanced\"] = True\n                build_config[\"mirostat_tau\"][\"advanced\"] = True\n                build_config[\"mirostat_eta\"][\"value\"] = None\n                build_config[\"mirostat_tau\"][\"value\"] = None\n\n            else:\n                build_config[\"mirostat_eta\"][\"advanced\"] = False\n                build_config[\"mirostat_tau\"][\"advanced\"] = False\n\n                if field_value == \"Mirostat 2.0\":\n                    build_config[\"mirostat_eta\"][\"value\"] = 0.2\n                    build_config[\"mirostat_tau\"][\"value\"] = 10\n                else:\n                    build_config[\"mirostat_eta\"][\"value\"] = 0.1\n                    build_config[\"mirostat_tau\"][\"value\"] = 5\n\n        if field_name in {\"base_url\", \"model_name\"}:\n            if build_config[\"base_url\"].get(\"load_from_db\", False):\n                base_url_value = await self.get_variables(build_config[\"base_url\"].get(\"value\", \"\"), \"base_url\")\n            else:\n                base_url_value = build_config[\"base_url\"].get(\"value\", \"\")\n\n            if not await self.is_valid_ollama_url(base_url_value):\n                # Check if any URL in the list is valid\n                valid_url = \"\"\n                check_urls = URL_LIST\n                if self.base_url:\n                    check_urls = [self.base_url, *URL_LIST]\n                for url in check_urls:\n                    if await self.is_valid_ollama_url(url):\n                        valid_url = url\n                        break\n                if valid_url != \"\":\n                    build_config[\"base_url\"][\"value\"] = valid_url\n                else:\n                    msg = \"No valid Ollama URL found.\"\n                    raise ValueError(msg)\n        if field_name in {\"model_name\", \"base_url\", \"tool_model_enabled\"}:\n            if await self.is_valid_ollama_url(self.base_url):\n                tool_model_enabled = build_config[\"tool_model_enabled\"].get(\"value\", False) or self.tool_model_enabled\n                build_config[\"model_name\"][\"options\"] = await self.get_models(\n                    self.base_url, tool_model_enabled=tool_model_enabled\n                )\n            elif await self.is_valid_ollama_url(build_config[\"base_url\"].get(\"value\", \"\")):\n                tool_model_enabled = build_config[\"tool_model_enabled\"].get(\"value\", False) or self.tool_model_enabled\n                build_config[\"model_name\"][\"options\"] = await self.get_models(\n                    build_config[\"base_url\"].get(\"value\", \"\"), tool_model_enabled=tool_model_enabled\n                )\n            else:\n                build_config[\"model_name\"][\"options\"] = []\n        if field_name == \"keep_alive_flag\":\n            if field_value == \"Keep\":\n                build_config[\"keep_alive\"][\"value\"] = \"-1\"\n                build_config[\"keep_alive\"][\"advanced\"] = True\n            elif field_value == \"Immediately\":\n                build_config[\"keep_alive\"][\"value\"] = \"0\"\n                build_config[\"keep_alive\"][\"advanced\"] = True\n            else:\n                build_config[\"keep_alive\"][\"advanced\"] = False\n\n        return build_config\n\n    async def get_models(self, base_url_value: str, *, tool_model_enabled: bool | None = None) -> list[str]:\n        \"\"\"Fetches a list of models from the Ollama API that do not have the \"embedding\" capability.\n\n        Args:\n            base_url_value (str): The base URL of the Ollama API.\n            tool_model_enabled (bool | None, optional): If True, filters the models further to include\n                only those that support tool calling. Defaults to None.\n\n        Returns:\n            list[str]: A list of model names that do not have the \"embedding\" capability. If\n                `tool_model_enabled` is True, only models supporting tool calling are included.\n\n        Raises:\n            ValueError: If there is an issue with the API request or response, or if the model\n                names cannot be retrieved.\n        \"\"\"\n        try:\n            # Normalize the base URL to avoid the repeated \"/\" at the end\n            base_url = base_url_value.rstrip(\"/\") + \"/\"\n\n            # Ollama REST API to return models\n            tags_url = urljoin(base_url, \"api/tags\")\n\n            # Ollama REST API to return model capabilities\n            show_url = urljoin(base_url, \"api/show\")\n\n            async with httpx.AsyncClient() as client:\n                # Fetch available models\n                tags_response = await client.get(tags_url)\n                tags_response.raise_for_status()\n                models = tags_response.json()\n                if asyncio.iscoroutine(models):\n                    models = await models\n                await logger.adebug(f\"Available models: {models}\")\n\n                # Filter models that are NOT embedding models\n                model_ids = []\n                for model in models[self.JSON_MODELS_KEY]:\n                    model_name = model[self.JSON_NAME_KEY]\n                    await logger.adebug(f\"Checking model: {model_name}\")\n\n                    payload = {\"model\": model_name}\n                    show_response = await client.post(show_url, json=payload)\n                    show_response.raise_for_status()\n                    json_data = show_response.json()\n                    if asyncio.iscoroutine(json_data):\n                        json_data = await json_data\n                    capabilities = json_data.get(self.JSON_CAPABILITIES_KEY, [])\n                    await logger.adebug(f\"Model: {model_name}, Capabilities: {capabilities}\")\n\n                    if self.DESIRED_CAPABILITY in capabilities and (\n                        not tool_model_enabled or self.TOOL_CALLING_CAPABILITY in capabilities\n                    ):\n                        model_ids.append(model_name)\n\n        except (httpx.RequestError, ValueError) as e:\n            msg = \"Could not get model names from Ollama.\"\n            raise ValueError(msg) from e\n\n        return model_ids\n"
-        },
-        "format": {
-          "_input_type": "MessageTextInput",
-          "advanced": true,
-          "display_name": "Format",
-          "dynamic": false,
-          "info": "Specify the format of the output (e.g., json).",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "format",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "input_value": {
-          "_input_type": "MessageInput",
-          "advanced": false,
-          "display_name": "Input",
-          "dynamic": false,
-          "info": "",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "input_value",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "metadata": {
-          "_input_type": "DictInput",
-          "advanced": true,
-          "display_name": "Metadata",
-          "dynamic": false,
-          "info": "Metadata to add to the run trace.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "metadata",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "type": "dict",
-          "value": {}
-        },
-        "mirostat": {
-          "_input_type": "DropdownInput",
-          "advanced": true,
-          "combobox": false,
-          "dialog_inputs": {},
-          "display_name": "Mirostat",
-          "dynamic": false,
-          "info": "Enable/disable Mirostat sampling for controlling perplexity.",
-          "name": "mirostat",
-          "options": [
-            "Disabled",
-            "Mirostat",
-            "Mirostat 2.0"
-          ],
-          "options_metadata": [],
-          "placeholder": "",
-          "real_time_refresh": true,
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "toggle": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "Disabled"
-        },
-        "mirostat_eta": {
-          "_input_type": "FloatInput",
-          "advanced": true,
-          "display_name": "Mirostat Eta",
-          "dynamic": false,
-          "info": "Learning rate for Mirostat algorithm. (Default: 0.1)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "mirostat_eta",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "float",
-          "value": ""
-        },
-        "mirostat_tau": {
-          "_input_type": "FloatInput",
-          "advanced": true,
-          "display_name": "Mirostat Tau",
-          "dynamic": false,
-          "info": "Controls the balance between coherence and diversity of the output. (Default: 5.0)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "mirostat_tau",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "float",
-          "value": ""
-        },
-        "model_name": {
-          "_input_type": "DropdownInput",
-          "advanced": false,
-          "combobox": false,
-          "dialog_inputs": {},
-          "display_name": "Model Name",
-          "dynamic": false,
-          "info": "Refer to https://ollama.com/library for more models.",
-          "name": "model_name",
-          "options": [
-            "qwen3:4b"
-          ],
-          "options_metadata": [],
-          "placeholder": "",
-          "real_time_refresh": true,
-          "refresh_button": true,
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "toggle": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "qwen3:4b"
-        },
-        "num_ctx": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Context Window Size",
-          "dynamic": false,
-          "info": "Size of the context window for generating tokens. (Default: 2048)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "num_ctx",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": ""
-        },
-        "num_gpu": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Number of GPUs",
-          "dynamic": false,
-          "info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "num_gpu",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": ""
-        },
-        "num_thread": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Number of Threads",
-          "dynamic": false,
-          "info": "Number of threads to use during computation. (Default: detected for optimal performance)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "num_thread",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": ""
-        },
-        "repeat_last_n": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Repeat Last N",
-          "dynamic": false,
-          "info": "How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "repeat_last_n",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": ""
-        },
-        "repeat_penalty": {
-          "_input_type": "FloatInput",
-          "advanced": true,
-          "display_name": "Repeat Penalty",
-          "dynamic": false,
-          "info": "Penalty for repetitions in generated text. (Default: 1.1)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "repeat_penalty",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "float",
-          "value": ""
-        },
-        "stop_tokens": {
-          "_input_type": "MessageTextInput",
-          "advanced": true,
-          "display_name": "Stop Tokens",
-          "dynamic": false,
-          "info": "Comma-separated list of tokens to signal the model to stop generating text.",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "stop_tokens",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "stream": {
-          "_input_type": "BoolInput",
-          "advanced": true,
-          "display_name": "Stream",
-          "dynamic": false,
-          "info": "Stream the response from the model. Streaming works only in Chat.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "stream",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "bool",
-          "value": false
-        },
-        "system": {
-          "_input_type": "MessageTextInput",
-          "advanced": true,
-          "display_name": "System",
-          "dynamic": false,
-          "info": "System to use for generating text.",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "system",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "system_message": {
-          "_input_type": "MultilineInput",
-          "advanced": false,
-          "copy_field": false,
-          "display_name": "System Message",
-          "dynamic": false,
-          "info": "System message to pass to the model.",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "multiline": true,
-          "name": "system_message",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "tags": {
-          "_input_type": "MessageTextInput",
-          "advanced": true,
-          "display_name": "Tags",
-          "dynamic": false,
-          "info": "Comma-separated list of tags to add to the run trace.",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "tags",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "temperature": {
-          "_input_type": "SliderInput",
-          "advanced": true,
-          "display_name": "Temperature",
-          "dynamic": false,
-          "info": "",
-          "max_label": "",
-          "max_label_icon": "",
-          "min_label": "",
-          "min_label_icon": "",
-          "name": "temperature",
-          "placeholder": "",
-          "range_spec": {
-            "max": 1,
-            "min": 0,
-            "step": 0.01,
-            "step_type": "float"
-          },
-          "required": false,
-          "show": true,
-          "slider_buttons": false,
-          "slider_buttons_options": [],
-          "slider_input": false,
-          "title_case": false,
-          "tool_mode": false,
-          "type": "slider",
-          "value": 0.1
-        },
-        "template": {
-          "_input_type": "MessageTextInput",
-          "advanced": true,
-          "display_name": "Template",
-          "dynamic": false,
-          "info": "Template to use for generating text.",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "template",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "tfs_z": {
-          "_input_type": "FloatInput",
-          "advanced": true,
-          "display_name": "TFS Z",
-          "dynamic": false,
-          "info": "Tail free sampling value. (Default: 1)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "tfs_z",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "float",
-          "value": ""
-        },
-        "timeout": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Timeout",
-          "dynamic": false,
-          "info": "Timeout for the request stream.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "timeout",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": ""
-        },
-        "tool_model_enabled": {
-          "_input_type": "BoolInput",
-          "advanced": false,
-          "display_name": "Tool Model Enabled",
-          "dynamic": false,
-          "info": "Whether to enable tool calling in the model.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "tool_model_enabled",
-          "placeholder": "",
-          "real_time_refresh": true,
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "bool",
-          "value": true
-        },
-        "top_k": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Top K",
-          "dynamic": false,
-          "info": "Limits token selection to top K. (Default: 40)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "top_k",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": ""
-        },
-        "top_p": {
-          "_input_type": "FloatInput",
-          "advanced": true,
-          "display_name": "Top P",
-          "dynamic": false,
-          "info": "Works together with top-k. (Default: 0.9)",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "top_p",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "float",
-          "value": ""
-        },
-        "verbose": {
-          "_input_type": "BoolInput",
-          "advanced": true,
-          "display_name": "Verbose",
-          "dynamic": false,
-          "info": "Whether to print out response text.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "verbose",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "bool",
-          "value": false
+          ]
         }
       },
-      "tool_mode": false
+      "tool_mode": false,
+      "last_updated": "2025-09-29T18:39:30.798Z",
+      "official": false
     },
-    "selected_output": "model_output",
     "showNode": true,
-    "type": "OllamaModel"
-  },
-  "dragging": false,
-  "id": "OllamaModel-eCsJx",
-  "measured": {
-    "height": 494,
-    "width": 320
+    "type": "OllamaModel",
+    "id": "OllamaModel-8Re0J",
+    "selected_output": "model_output"
   },
+  "id": "OllamaModel-8Re0J",
   "position": {
-    "x": 248.08287272472313,
-    "y": 216.98088326271431
+    "x": 0,
+    "y": 0
   },
-  "selected": false,
   "type": "genericNode"
 }
\ No newline at end of file
diff --git a/flows/components/ollama_llm_text.json b/flows/components/ollama_llm_text.json
index 846e8313..9b2b5482 100644
--- a/flows/components/ollama_llm_text.json
+++ b/flows/components/ollama_llm_text.json
@@ -1,687 +1,683 @@
 {
-    "data": {
-      "id": "OllamaModel-XDGqZ",
-      "node": {
-        "base_classes": [
-          "LanguageModel",
-          "Message"
-        ],
-        "beta": false,
-        "conditional_paths": [],
-        "custom_fields": {},
-        "description": "Generate text using Ollama Local LLMs.",
-        "display_name": "Ollama",
-        "documentation": "",
-        "edited": false,
-        "field_order": [
-          "base_url",
-          "model_name",
-          "temperature",
-          "format",
-          "metadata",
-          "mirostat",
-          "mirostat_eta",
-          "mirostat_tau",
-          "num_ctx",
-          "num_gpu",
-          "num_thread",
-          "repeat_last_n",
-          "repeat_penalty",
-          "tfs_z",
-          "timeout",
-          "top_k",
-          "top_p",
-          "verbose",
-          "tags",
-          "stop_tokens",
-          "system",
-          "tool_model_enabled",
-          "template",
-          "input_value",
-          "system_message",
-          "stream"
-        ],
-        "frozen": false,
-        "icon": "Ollama",
-        "last_updated": "2025-09-22T20:14:45.057Z",
-        "legacy": false,
-        "metadata": {
-          "code_hash": "af399d429d23",
-          "dependencies": {
-            "dependencies": [
-              {
-                "name": "httpx",
-                "version": "0.28.1"
-              },
-              {
-                "name": "langchain_ollama",
-                "version": "0.2.1"
-              },
-              {
-                "name": "langflow",
-                "version": null
-              }
-            ],
-            "total_dependencies": 3
-          },
-          "keywords": [
-            "model",
-            "llm",
-            "language model",
-            "large language model"
+  "data": {
+    "node": {
+      "template": {
+        "_type": "Component",
+        "base_url": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "trace_as_metadata": true,
+          "load_from_db": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "base_url",
+          "value": "OLLAMA_BASE_URL",
+          "display_name": "Base URL",
+          "advanced": false,
+          "input_types": [
+            "Message"
           ],
-          "module": "langflow.components.ollama.ollama.ChatOllamaComponent"
+          "dynamic": false,
+          "info": "Endpoint of the Ollama API.",
+          "real_time_refresh": true,
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageTextInput"
         },
-        "minimized": false,
-        "output_types": [],
-        "outputs": [
-          {
-            "allows_loop": false,
-            "cache": true,
-            "display_name": "Model Response",
-            "group_outputs": false,
-            "method": "text_response",
-            "name": "text_output",
-            "options": null,
-            "required_inputs": null,
-            "selected": "Message",
-            "tool_mode": true,
-            "types": [
-              "Message"
-            ],
-            "value": "__UNDEFINED__"
+        "code": {
+          "type": "code",
+          "required": true,
+          "placeholder": "",
+          "list": false,
+          "show": true,
+          "multiline": true,
+          "value": "import asyncio\nfrom typing import Any\nfrom urllib.parse import urljoin\n\nimport httpx\nfrom langchain_ollama import ChatOllama\n\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.ollama_constants import URL_LIST\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageTextInput, SliderInput\nfrom lfx.log.logger import logger\n\nHTTP_STATUS_OK = 200\n\n\nclass ChatOllamaComponent(LCModelComponent):\n    display_name = \"Ollama\"\n    description = \"Generate text using Ollama Local LLMs.\"\n    icon = \"Ollama\"\n    name = \"OllamaModel\"\n\n    # Define constants for JSON keys\n    JSON_MODELS_KEY = \"models\"\n    JSON_NAME_KEY = \"name\"\n    JSON_CAPABILITIES_KEY = \"capabilities\"\n    DESIRED_CAPABILITY = \"completion\"\n    TOOL_CALLING_CAPABILITY = \"tools\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"base_url\",\n            display_name=\"Base URL\",\n            info=\"Endpoint of the Ollama API.\",\n            value=\"\",\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            info=\"Refer to https://ollama.com/library for more models.\",\n            refresh_button=True,\n            real_time_refresh=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"format\", display_name=\"Format\", info=\"Specify the format of the output (e.g., json).\", advanced=True\n        ),\n        DictInput(name=\"metadata\", display_name=\"Metadata\", info=\"Metadata to add to the run trace.\", advanced=True),\n        DropdownInput(\n            name=\"mirostat\",\n            display_name=\"Mirostat\",\n            options=[\"Disabled\", \"Mirostat\", \"Mirostat 2.0\"],\n            info=\"Enable/disable Mirostat sampling for controlling perplexity.\",\n            value=\"Disabled\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        FloatInput(\n            name=\"mirostat_eta\",\n            display_name=\"Mirostat Eta\",\n            info=\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"mirostat_tau\",\n            display_name=\"Mirostat Tau\",\n            info=\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_ctx\",\n            display_name=\"Context Window Size\",\n            info=\"Size of the context window for generating tokens. (Default: 2048)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_gpu\",\n            display_name=\"Number of GPUs\",\n            info=\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_thread\",\n            display_name=\"Number of Threads\",\n            info=\"Number of threads to use during computation. (Default: detected for optimal performance)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"repeat_last_n\",\n            display_name=\"Repeat Last N\",\n            info=\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"repeat_penalty\",\n            display_name=\"Repeat Penalty\",\n            info=\"Penalty for repetitions in generated text. (Default: 1.1)\",\n            advanced=True,\n        ),\n        FloatInput(name=\"tfs_z\", display_name=\"TFS Z\", info=\"Tail free sampling value. (Default: 1)\", advanced=True),\n        IntInput(name=\"timeout\", display_name=\"Timeout\", info=\"Timeout for the request stream.\", advanced=True),\n        IntInput(\n            name=\"top_k\", display_name=\"Top K\", info=\"Limits token selection to top K. (Default: 40)\", advanced=True\n        ),\n        FloatInput(name=\"top_p\", display_name=\"Top P\", info=\"Works together with top-k. (Default: 0.9)\", advanced=True),\n        BoolInput(name=\"verbose\", display_name=\"Verbose\", info=\"Whether to print out response text.\", advanced=True),\n        MessageTextInput(\n            name=\"tags\",\n            display_name=\"Tags\",\n            info=\"Comma-separated list of tags to add to the run trace.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"stop_tokens\",\n            display_name=\"Stop Tokens\",\n            info=\"Comma-separated list of tokens to signal the model to stop generating text.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"system\", display_name=\"System\", info=\"System to use for generating text.\", advanced=True\n        ),\n        BoolInput(\n            name=\"tool_model_enabled\",\n            display_name=\"Tool Model Enabled\",\n            info=\"Whether to enable tool calling in the model.\",\n            value=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"template\", display_name=\"Template\", info=\"Template to use for generating text.\", advanced=True\n        ),\n        *LCModelComponent.get_base_inputs(),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # Mapping mirostat settings to their corresponding values\n        mirostat_options = {\"Mirostat\": 1, \"Mirostat 2.0\": 2}\n\n        # Default to 0 for 'Disabled'\n        mirostat_value = mirostat_options.get(self.mirostat, 0)\n\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\n        if mirostat_value == 0:\n            mirostat_eta = None\n            mirostat_tau = None\n        else:\n            mirostat_eta = self.mirostat_eta\n            mirostat_tau = self.mirostat_tau\n\n        # Mapping system settings to their corresponding values\n        llm_params = {\n            \"base_url\": self.base_url,\n            \"model\": self.model_name,\n            \"mirostat\": mirostat_value,\n            \"format\": self.format,\n            \"metadata\": self.metadata,\n            \"tags\": self.tags.split(\",\") if self.tags else None,\n            \"mirostat_eta\": mirostat_eta,\n            \"mirostat_tau\": mirostat_tau,\n            \"num_ctx\": self.num_ctx or None,\n            \"num_gpu\": self.num_gpu or None,\n            \"num_thread\": self.num_thread or None,\n            \"repeat_last_n\": self.repeat_last_n or None,\n            \"repeat_penalty\": self.repeat_penalty or None,\n            \"temperature\": self.temperature or None,\n            \"stop\": self.stop_tokens.split(\",\") if self.stop_tokens else None,\n            \"system\": self.system,\n            \"tfs_z\": self.tfs_z or None,\n            \"timeout\": self.timeout or None,\n            \"top_k\": self.top_k or None,\n            \"top_p\": self.top_p or None,\n            \"verbose\": self.verbose,\n            \"template\": self.template,\n        }\n\n        # Remove parameters with None values\n        llm_params = {k: v for k, v in llm_params.items() if v is not None}\n\n        try:\n            output = ChatOllama(**llm_params)\n        except Exception as e:\n            msg = (\n                \"Unable to connect to the Ollama API. \",\n                \"Please verify the base URL, ensure the relevant Ollama model is pulled, and try again.\",\n            )\n            raise ValueError(msg) from e\n\n        return output\n\n    async def is_valid_ollama_url(self, url: str) -> bool:\n        try:\n            async with httpx.AsyncClient() as client:\n                return (await client.get(urljoin(url, \"api/tags\"))).status_code == HTTP_STATUS_OK\n        except httpx.RequestError:\n            return False\n\n    async def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None):\n        if field_name == \"mirostat\":\n            if field_value == \"Disabled\":\n                build_config[\"mirostat_eta\"][\"advanced\"] = True\n                build_config[\"mirostat_tau\"][\"advanced\"] = True\n                build_config[\"mirostat_eta\"][\"value\"] = None\n                build_config[\"mirostat_tau\"][\"value\"] = None\n\n            else:\n                build_config[\"mirostat_eta\"][\"advanced\"] = False\n                build_config[\"mirostat_tau\"][\"advanced\"] = False\n\n                if field_value == \"Mirostat 2.0\":\n                    build_config[\"mirostat_eta\"][\"value\"] = 0.2\n                    build_config[\"mirostat_tau\"][\"value\"] = 10\n                else:\n                    build_config[\"mirostat_eta\"][\"value\"] = 0.1\n                    build_config[\"mirostat_tau\"][\"value\"] = 5\n\n        if field_name in {\"base_url\", \"model_name\"}:\n            if build_config[\"base_url\"].get(\"load_from_db\", False):\n                base_url_value = await self.get_variables(build_config[\"base_url\"].get(\"value\", \"\"), \"base_url\")\n            else:\n                base_url_value = build_config[\"base_url\"].get(\"value\", \"\")\n\n            if not await self.is_valid_ollama_url(base_url_value):\n                # Check if any URL in the list is valid\n                valid_url = \"\"\n                check_urls = URL_LIST\n                if self.base_url:\n                    check_urls = [self.base_url, *URL_LIST]\n                for url in check_urls:\n                    if await self.is_valid_ollama_url(url):\n                        valid_url = url\n                        break\n                if valid_url != \"\":\n                    build_config[\"base_url\"][\"value\"] = valid_url\n                else:\n                    msg = \"No valid Ollama URL found.\"\n                    raise ValueError(msg)\n        if field_name in {\"model_name\", \"base_url\", \"tool_model_enabled\"}:\n            if await self.is_valid_ollama_url(self.base_url):\n                tool_model_enabled = build_config[\"tool_model_enabled\"].get(\"value\", False) or self.tool_model_enabled\n                build_config[\"model_name\"][\"options\"] = await self.get_models(\n                    self.base_url, tool_model_enabled=tool_model_enabled\n                )\n            elif await self.is_valid_ollama_url(build_config[\"base_url\"].get(\"value\", \"\")):\n                tool_model_enabled = build_config[\"tool_model_enabled\"].get(\"value\", False) or self.tool_model_enabled\n                build_config[\"model_name\"][\"options\"] = await self.get_models(\n                    build_config[\"base_url\"].get(\"value\", \"\"), tool_model_enabled=tool_model_enabled\n                )\n            else:\n                build_config[\"model_name\"][\"options\"] = []\n        if field_name == \"keep_alive_flag\":\n            if field_value == \"Keep\":\n                build_config[\"keep_alive\"][\"value\"] = \"-1\"\n                build_config[\"keep_alive\"][\"advanced\"] = True\n            elif field_value == \"Immediately\":\n                build_config[\"keep_alive\"][\"value\"] = \"0\"\n                build_config[\"keep_alive\"][\"advanced\"] = True\n            else:\n                build_config[\"keep_alive\"][\"advanced\"] = False\n\n        return build_config\n\n    async def get_models(self, base_url_value: str, *, tool_model_enabled: bool | None = None) -> list[str]:\n        \"\"\"Fetches a list of models from the Ollama API that do not have the \"embedding\" capability.\n\n        Args:\n            base_url_value (str): The base URL of the Ollama API.\n            tool_model_enabled (bool | None, optional): If True, filters the models further to include\n                only those that support tool calling. Defaults to None.\n\n        Returns:\n            list[str]: A list of model names that do not have the \"embedding\" capability. If\n                `tool_model_enabled` is True, only models supporting tool calling are included.\n\n        Raises:\n            ValueError: If there is an issue with the API request or response, or if the model\n                names cannot be retrieved.\n        \"\"\"\n        try:\n            # Normalize the base URL to avoid the repeated \"/\" at the end\n            base_url = base_url_value.rstrip(\"/\") + \"/\"\n\n            # Ollama REST API to return models\n            tags_url = urljoin(base_url, \"api/tags\")\n\n            # Ollama REST API to return model capabilities\n            show_url = urljoin(base_url, \"api/show\")\n\n            async with httpx.AsyncClient() as client:\n                # Fetch available models\n                tags_response = await client.get(tags_url)\n                tags_response.raise_for_status()\n                models = tags_response.json()\n                if asyncio.iscoroutine(models):\n                    models = await models\n                await logger.adebug(f\"Available models: {models}\")\n\n                # Filter models that are NOT embedding models\n                model_ids = []\n                for model in models[self.JSON_MODELS_KEY]:\n                    model_name = model[self.JSON_NAME_KEY]\n                    await logger.adebug(f\"Checking model: {model_name}\")\n\n                    payload = {\"model\": model_name}\n                    show_response = await client.post(show_url, json=payload)\n                    show_response.raise_for_status()\n                    json_data = show_response.json()\n                    if asyncio.iscoroutine(json_data):\n                        json_data = await json_data\n                    capabilities = json_data.get(self.JSON_CAPABILITIES_KEY, [])\n                    await logger.adebug(f\"Model: {model_name}, Capabilities: {capabilities}\")\n\n                    if self.DESIRED_CAPABILITY in capabilities and (\n                        not tool_model_enabled or self.TOOL_CALLING_CAPABILITY in capabilities\n                    ):\n                        model_ids.append(model_name)\n\n        except (httpx.RequestError, ValueError) as e:\n            msg = \"Could not get model names from Ollama.\"\n            raise ValueError(msg) from e\n\n        return model_ids\n",
+          "fileTypes": [],
+          "file_path": "",
+          "password": false,
+          "name": "code",
+          "advanced": true,
+          "dynamic": true,
+          "info": "",
+          "load_from_db": false,
+          "title_case": false
+        },
+        "format": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "format",
+          "value": "",
+          "display_name": "Format",
+          "advanced": true,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "Specify the format of the output (e.g., json).",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageTextInput"
+        },
+        "input_value": {
+          "trace_as_input": true,
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "input_value",
+          "value": "",
+          "display_name": "Input",
+          "advanced": false,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageInput"
+        },
+        "metadata": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "metadata",
+          "value": {},
+          "display_name": "Metadata",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Metadata to add to the run trace.",
+          "title_case": false,
+          "type": "dict",
+          "_input_type": "DictInput"
+        },
+        "mirostat": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "options": [
+            "Disabled",
+            "Mirostat",
+            "Mirostat 2.0"
+          ],
+          "options_metadata": [],
+          "combobox": false,
+          "dialog_inputs": {},
+          "toggle": false,
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "mirostat",
+          "value": "Disabled",
+          "display_name": "Mirostat",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Enable/disable Mirostat sampling for controlling perplexity.",
+          "real_time_refresh": true,
+          "title_case": false,
+          "external_options": {},
+          "type": "str",
+          "_input_type": "DropdownInput"
+        },
+        "mirostat_eta": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "mirostat_eta",
+          "value": "",
+          "display_name": "Mirostat Eta",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Learning rate for Mirostat algorithm. (Default: 0.1)",
+          "title_case": false,
+          "type": "float",
+          "_input_type": "FloatInput"
+        },
+        "mirostat_tau": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "mirostat_tau",
+          "value": "",
+          "display_name": "Mirostat Tau",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Controls the balance between coherence and diversity of the output. (Default: 5.0)",
+          "title_case": false,
+          "type": "float",
+          "_input_type": "FloatInput"
+        },
+        "model_name": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "options": [],
+          "options_metadata": [],
+          "combobox": false,
+          "dialog_inputs": {},
+          "toggle": false,
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "model_name",
+          "value": "",
+          "display_name": "Model Name",
+          "advanced": false,
+          "dynamic": false,
+          "info": "Refer to https://ollama.com/library for more models.",
+          "real_time_refresh": true,
+          "refresh_button": true,
+          "title_case": false,
+          "external_options": {},
+          "type": "str",
+          "_input_type": "DropdownInput"
+        },
+        "num_ctx": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "num_ctx",
+          "value": "",
+          "display_name": "Context Window Size",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Size of the context window for generating tokens. (Default: 2048)",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "num_gpu": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "num_gpu",
+          "value": "",
+          "display_name": "Number of GPUs",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "num_thread": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "num_thread",
+          "value": "",
+          "display_name": "Number of Threads",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Number of threads to use during computation. (Default: detected for optimal performance)",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "repeat_last_n": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "repeat_last_n",
+          "value": "",
+          "display_name": "Repeat Last N",
+          "advanced": true,
+          "dynamic": false,
+          "info": "How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "repeat_penalty": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "repeat_penalty",
+          "value": "",
+          "display_name": "Repeat Penalty",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Penalty for repetitions in generated text. (Default: 1.1)",
+          "title_case": false,
+          "type": "float",
+          "_input_type": "FloatInput"
+        },
+        "stop_tokens": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "stop_tokens",
+          "value": "",
+          "display_name": "Stop Tokens",
+          "advanced": true,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "Comma-separated list of tokens to signal the model to stop generating text.",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageTextInput"
+        },
+        "stream": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "stream",
+          "value": false,
+          "display_name": "Stream",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Stream the response from the model. Streaming works only in Chat.",
+          "title_case": false,
+          "type": "bool",
+          "_input_type": "BoolInput"
+        },
+        "system": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "system",
+          "value": "",
+          "display_name": "System",
+          "advanced": true,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "System to use for generating text.",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageTextInput"
+        },
+        "system_message": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "multiline": true,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "system_message",
+          "value": "",
+          "display_name": "System Message",
+          "advanced": false,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "System message to pass to the model.",
+          "title_case": false,
+          "copy_field": false,
+          "type": "str",
+          "_input_type": "MultilineInput"
+        },
+        "tags": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "tags",
+          "value": "",
+          "display_name": "Tags",
+          "advanced": true,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "Comma-separated list of tags to add to the run trace.",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageTextInput"
+        },
+        "temperature": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": 0,
+            "max": 1,
+            "step": 0.01
           },
-          {
-            "allows_loop": false,
-            "cache": true,
-            "display_name": "Language Model",
-            "group_outputs": false,
-            "method": "build_model",
-            "name": "model_output",
-            "options": null,
-            "required_inputs": null,
-            "tool_mode": true,
-            "types": [
-              "LanguageModel"
-            ],
-            "value": "__UNDEFINED__"
-          }
-        ],
-        "pinned": false,
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "temperature",
+          "value": 0.1,
+          "display_name": "Temperature",
+          "advanced": true,
+          "dynamic": false,
+          "info": "",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
         "template": {
-          "_type": "Component",
-          "base_url": {
-            "_input_type": "MessageTextInput",
-            "advanced": false,
-            "display_name": "Base URL",
-            "dynamic": false,
-            "info": "Endpoint of the Ollama API.",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": true,
-            "name": "base_url",
-            "placeholder": "",
-            "real_time_refresh": true,
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": "OLLAMA_BASE_URL"
-          },
-          "code": {
-            "advanced": true,
-            "dynamic": true,
-            "fileTypes": [],
-            "file_path": "",
-            "info": "",
-            "list": false,
-            "load_from_db": false,
-            "multiline": true,
-            "name": "code",
-            "password": false,
-            "placeholder": "",
-            "required": true,
-            "show": true,
-            "title_case": false,
-            "type": "code",
-            "value": "import asyncio\nfrom typing import Any\nfrom urllib.parse import urljoin\n\nimport httpx\nfrom langchain_ollama import ChatOllama\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.ollama_constants import URL_LIST\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageTextInput, SliderInput\nfrom langflow.logging import logger\n\nHTTP_STATUS_OK = 200\n\n\nclass ChatOllamaComponent(LCModelComponent):\n    display_name = \"Ollama\"\n    description = \"Generate text using Ollama Local LLMs.\"\n    icon = \"Ollama\"\n    name = \"OllamaModel\"\n\n    # Define constants for JSON keys\n    JSON_MODELS_KEY = \"models\"\n    JSON_NAME_KEY = \"name\"\n    JSON_CAPABILITIES_KEY = \"capabilities\"\n    DESIRED_CAPABILITY = \"completion\"\n    TOOL_CALLING_CAPABILITY = \"tools\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"base_url\",\n            display_name=\"Base URL\",\n            info=\"Endpoint of the Ollama API.\",\n            value=\"\",\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            info=\"Refer to https://ollama.com/library for more models.\",\n            refresh_button=True,\n            real_time_refresh=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"format\", display_name=\"Format\", info=\"Specify the format of the output (e.g., json).\", advanced=True\n        ),\n        DictInput(name=\"metadata\", display_name=\"Metadata\", info=\"Metadata to add to the run trace.\", advanced=True),\n        DropdownInput(\n            name=\"mirostat\",\n            display_name=\"Mirostat\",\n            options=[\"Disabled\", \"Mirostat\", \"Mirostat 2.0\"],\n            info=\"Enable/disable Mirostat sampling for controlling perplexity.\",\n            value=\"Disabled\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        FloatInput(\n            name=\"mirostat_eta\",\n            display_name=\"Mirostat Eta\",\n            info=\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"mirostat_tau\",\n            display_name=\"Mirostat Tau\",\n            info=\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_ctx\",\n            display_name=\"Context Window Size\",\n            info=\"Size of the context window for generating tokens. (Default: 2048)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_gpu\",\n            display_name=\"Number of GPUs\",\n            info=\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"num_thread\",\n            display_name=\"Number of Threads\",\n            info=\"Number of threads to use during computation. (Default: detected for optimal performance)\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"repeat_last_n\",\n            display_name=\"Repeat Last N\",\n            info=\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"repeat_penalty\",\n            display_name=\"Repeat Penalty\",\n            info=\"Penalty for repetitions in generated text. (Default: 1.1)\",\n            advanced=True,\n        ),\n        FloatInput(name=\"tfs_z\", display_name=\"TFS Z\", info=\"Tail free sampling value. (Default: 1)\", advanced=True),\n        IntInput(name=\"timeout\", display_name=\"Timeout\", info=\"Timeout for the request stream.\", advanced=True),\n        IntInput(\n            name=\"top_k\", display_name=\"Top K\", info=\"Limits token selection to top K. (Default: 40)\", advanced=True\n        ),\n        FloatInput(name=\"top_p\", display_name=\"Top P\", info=\"Works together with top-k. (Default: 0.9)\", advanced=True),\n        BoolInput(name=\"verbose\", display_name=\"Verbose\", info=\"Whether to print out response text.\", advanced=True),\n        MessageTextInput(\n            name=\"tags\",\n            display_name=\"Tags\",\n            info=\"Comma-separated list of tags to add to the run trace.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"stop_tokens\",\n            display_name=\"Stop Tokens\",\n            info=\"Comma-separated list of tokens to signal the model to stop generating text.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"system\", display_name=\"System\", info=\"System to use for generating text.\", advanced=True\n        ),\n        BoolInput(\n            name=\"tool_model_enabled\",\n            display_name=\"Tool Model Enabled\",\n            info=\"Whether to enable tool calling in the model.\",\n            value=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"template\", display_name=\"Template\", info=\"Template to use for generating text.\", advanced=True\n        ),\n        *LCModelComponent._base_inputs,\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # Mapping mirostat settings to their corresponding values\n        mirostat_options = {\"Mirostat\": 1, \"Mirostat 2.0\": 2}\n\n        # Default to 0 for 'Disabled'\n        mirostat_value = mirostat_options.get(self.mirostat, 0)\n\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\n        if mirostat_value == 0:\n            mirostat_eta = None\n            mirostat_tau = None\n        else:\n            mirostat_eta = self.mirostat_eta\n            mirostat_tau = self.mirostat_tau\n\n        # Mapping system settings to their corresponding values\n        llm_params = {\n            \"base_url\": self.base_url,\n            \"model\": self.model_name,\n            \"mirostat\": mirostat_value,\n            \"format\": self.format,\n            \"metadata\": self.metadata,\n            \"tags\": self.tags.split(\",\") if self.tags else None,\n            \"mirostat_eta\": mirostat_eta,\n            \"mirostat_tau\": mirostat_tau,\n            \"num_ctx\": self.num_ctx or None,\n            \"num_gpu\": self.num_gpu or None,\n            \"num_thread\": self.num_thread or None,\n            \"repeat_last_n\": self.repeat_last_n or None,\n            \"repeat_penalty\": self.repeat_penalty or None,\n            \"temperature\": self.temperature or None,\n            \"stop\": self.stop_tokens.split(\",\") if self.stop_tokens else None,\n            \"system\": self.system,\n            \"tfs_z\": self.tfs_z or None,\n            \"timeout\": self.timeout or None,\n            \"top_k\": self.top_k or None,\n            \"top_p\": self.top_p or None,\n            \"verbose\": self.verbose,\n            \"template\": self.template,\n        }\n\n        # Remove parameters with None values\n        llm_params = {k: v for k, v in llm_params.items() if v is not None}\n\n        try:\n            output = ChatOllama(**llm_params)\n        except Exception as e:\n            msg = (\n                \"Unable to connect to the Ollama API. \",\n                \"Please verify the base URL, ensure the relevant Ollama model is pulled, and try again.\",\n            )\n            raise ValueError(msg) from e\n\n        return output\n\n    async def is_valid_ollama_url(self, url: str) -> bool:\n        try:\n            async with httpx.AsyncClient() as client:\n                return (await client.get(urljoin(url, \"api/tags\"))).status_code == HTTP_STATUS_OK\n        except httpx.RequestError:\n            return False\n\n    async def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None):\n        if field_name == \"mirostat\":\n            if field_value == \"Disabled\":\n                build_config[\"mirostat_eta\"][\"advanced\"] = True\n                build_config[\"mirostat_tau\"][\"advanced\"] = True\n                build_config[\"mirostat_eta\"][\"value\"] = None\n                build_config[\"mirostat_tau\"][\"value\"] = None\n\n            else:\n                build_config[\"mirostat_eta\"][\"advanced\"] = False\n                build_config[\"mirostat_tau\"][\"advanced\"] = False\n\n                if field_value == \"Mirostat 2.0\":\n                    build_config[\"mirostat_eta\"][\"value\"] = 0.2\n                    build_config[\"mirostat_tau\"][\"value\"] = 10\n                else:\n                    build_config[\"mirostat_eta\"][\"value\"] = 0.1\n                    build_config[\"mirostat_tau\"][\"value\"] = 5\n\n        if field_name in {\"base_url\", \"model_name\"}:\n            if build_config[\"base_url\"].get(\"load_from_db\", False):\n                base_url_value = await self.get_variables(build_config[\"base_url\"].get(\"value\", \"\"), \"base_url\")\n            else:\n                base_url_value = build_config[\"base_url\"].get(\"value\", \"\")\n\n            if not await self.is_valid_ollama_url(base_url_value):\n                # Check if any URL in the list is valid\n                valid_url = \"\"\n                check_urls = URL_LIST\n                if self.base_url:\n                    check_urls = [self.base_url, *URL_LIST]\n                for url in check_urls:\n                    if await self.is_valid_ollama_url(url):\n                        valid_url = url\n                        break\n                if valid_url != \"\":\n                    build_config[\"base_url\"][\"value\"] = valid_url\n                else:\n                    msg = \"No valid Ollama URL found.\"\n                    raise ValueError(msg)\n        if field_name in {\"model_name\", \"base_url\", \"tool_model_enabled\"}:\n            if await self.is_valid_ollama_url(self.base_url):\n                tool_model_enabled = build_config[\"tool_model_enabled\"].get(\"value\", False) or self.tool_model_enabled\n                build_config[\"model_name\"][\"options\"] = await self.get_models(\n                    self.base_url, tool_model_enabled=tool_model_enabled\n                )\n            elif await self.is_valid_ollama_url(build_config[\"base_url\"].get(\"value\", \"\")):\n                tool_model_enabled = build_config[\"tool_model_enabled\"].get(\"value\", False) or self.tool_model_enabled\n                build_config[\"model_name\"][\"options\"] = await self.get_models(\n                    build_config[\"base_url\"].get(\"value\", \"\"), tool_model_enabled=tool_model_enabled\n                )\n            else:\n                build_config[\"model_name\"][\"options\"] = []\n        if field_name == \"keep_alive_flag\":\n            if field_value == \"Keep\":\n                build_config[\"keep_alive\"][\"value\"] = \"-1\"\n                build_config[\"keep_alive\"][\"advanced\"] = True\n            elif field_value == \"Immediately\":\n                build_config[\"keep_alive\"][\"value\"] = \"0\"\n                build_config[\"keep_alive\"][\"advanced\"] = True\n            else:\n                build_config[\"keep_alive\"][\"advanced\"] = False\n\n        return build_config\n\n    async def get_models(self, base_url_value: str, *, tool_model_enabled: bool | None = None) -> list[str]:\n        \"\"\"Fetches a list of models from the Ollama API that do not have the \"embedding\" capability.\n\n        Args:\n            base_url_value (str): The base URL of the Ollama API.\n            tool_model_enabled (bool | None, optional): If True, filters the models further to include\n                only those that support tool calling. Defaults to None.\n\n        Returns:\n            list[str]: A list of model names that do not have the \"embedding\" capability. If\n                `tool_model_enabled` is True, only models supporting tool calling are included.\n\n        Raises:\n            ValueError: If there is an issue with the API request or response, or if the model\n                names cannot be retrieved.\n        \"\"\"\n        try:\n            # Normalize the base URL to avoid the repeated \"/\" at the end\n            base_url = base_url_value.rstrip(\"/\") + \"/\"\n\n            # Ollama REST API to return models\n            tags_url = urljoin(base_url, \"api/tags\")\n\n            # Ollama REST API to return model capabilities\n            show_url = urljoin(base_url, \"api/show\")\n\n            async with httpx.AsyncClient() as client:\n                # Fetch available models\n                tags_response = await client.get(tags_url)\n                tags_response.raise_for_status()\n                models = tags_response.json()\n                if asyncio.iscoroutine(models):\n                    models = await models\n                await logger.adebug(f\"Available models: {models}\")\n\n                # Filter models that are NOT embedding models\n                model_ids = []\n                for model in models[self.JSON_MODELS_KEY]:\n                    model_name = model[self.JSON_NAME_KEY]\n                    await logger.adebug(f\"Checking model: {model_name}\")\n\n                    payload = {\"model\": model_name}\n                    show_response = await client.post(show_url, json=payload)\n                    show_response.raise_for_status()\n                    json_data = show_response.json()\n                    if asyncio.iscoroutine(json_data):\n                        json_data = await json_data\n                    capabilities = json_data.get(self.JSON_CAPABILITIES_KEY, [])\n                    await logger.adebug(f\"Model: {model_name}, Capabilities: {capabilities}\")\n\n                    if self.DESIRED_CAPABILITY in capabilities and (\n                        not tool_model_enabled or self.TOOL_CALLING_CAPABILITY in capabilities\n                    ):\n                        model_ids.append(model_name)\n\n        except (httpx.RequestError, ValueError) as e:\n            msg = \"Could not get model names from Ollama.\"\n            raise ValueError(msg) from e\n\n        return model_ids\n"
-          },
-          "format": {
-            "_input_type": "MessageTextInput",
-            "advanced": true,
-            "display_name": "Format",
-            "dynamic": false,
-            "info": "Specify the format of the output (e.g., json).",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "format",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "input_value": {
-            "_input_type": "MessageInput",
-            "advanced": false,
-            "display_name": "Input",
-            "dynamic": false,
-            "info": "",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "input_value",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "metadata": {
-            "_input_type": "DictInput",
-            "advanced": true,
-            "display_name": "Metadata",
-            "dynamic": false,
-            "info": "Metadata to add to the run trace.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "metadata",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "type": "dict",
-            "value": {}
-          },
-          "mirostat": {
-            "_input_type": "DropdownInput",
-            "advanced": true,
-            "combobox": false,
-            "dialog_inputs": {},
-            "display_name": "Mirostat",
-            "dynamic": false,
-            "info": "Enable/disable Mirostat sampling for controlling perplexity.",
-            "name": "mirostat",
-            "options": [
-              "Disabled",
-              "Mirostat",
-              "Mirostat 2.0"
-            ],
-            "options_metadata": [],
-            "placeholder": "",
-            "real_time_refresh": true,
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "toggle": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": "Disabled"
-          },
-          "mirostat_eta": {
-            "_input_type": "FloatInput",
-            "advanced": true,
-            "display_name": "Mirostat Eta",
-            "dynamic": false,
-            "info": "Learning rate for Mirostat algorithm. (Default: 0.1)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "mirostat_eta",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "float",
-            "value": ""
-          },
-          "mirostat_tau": {
-            "_input_type": "FloatInput",
-            "advanced": true,
-            "display_name": "Mirostat Tau",
-            "dynamic": false,
-            "info": "Controls the balance between coherence and diversity of the output. (Default: 5.0)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "mirostat_tau",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "float",
-            "value": ""
-          },
-          "model_name": {
-            "_input_type": "DropdownInput",
-            "advanced": false,
-            "combobox": false,
-            "dialog_inputs": {},
-            "display_name": "Model Name",
-            "dynamic": false,
-            "info": "Refer to https://ollama.com/library for more models.",
-            "name": "model_name",
-            "options": [
-              "qwen3:4b"
-            ],
-            "options_metadata": [],
-            "placeholder": "",
-            "real_time_refresh": true,
-            "refresh_button": true,
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "toggle": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": "qwen3:4b"
-          },
-          "num_ctx": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Context Window Size",
-            "dynamic": false,
-            "info": "Size of the context window for generating tokens. (Default: 2048)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "num_ctx",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": ""
-          },
-          "num_gpu": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Number of GPUs",
-            "dynamic": false,
-            "info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "num_gpu",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": ""
-          },
-          "num_thread": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Number of Threads",
-            "dynamic": false,
-            "info": "Number of threads to use during computation. (Default: detected for optimal performance)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "num_thread",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": ""
-          },
-          "repeat_last_n": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Repeat Last N",
-            "dynamic": false,
-            "info": "How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "repeat_last_n",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": ""
-          },
-          "repeat_penalty": {
-            "_input_type": "FloatInput",
-            "advanced": true,
-            "display_name": "Repeat Penalty",
-            "dynamic": false,
-            "info": "Penalty for repetitions in generated text. (Default: 1.1)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "repeat_penalty",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "float",
-            "value": ""
-          },
-          "stop_tokens": {
-            "_input_type": "MessageTextInput",
-            "advanced": true,
-            "display_name": "Stop Tokens",
-            "dynamic": false,
-            "info": "Comma-separated list of tokens to signal the model to stop generating text.",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "stop_tokens",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "stream": {
-            "_input_type": "BoolInput",
-            "advanced": true,
-            "display_name": "Stream",
-            "dynamic": false,
-            "info": "Stream the response from the model. Streaming works only in Chat.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "stream",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "bool",
-            "value": false
-          },
-          "system": {
-            "_input_type": "MessageTextInput",
-            "advanced": true,
-            "display_name": "System",
-            "dynamic": false,
-            "info": "System to use for generating text.",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "system",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "system_message": {
-            "_input_type": "MultilineInput",
-            "advanced": false,
-            "copy_field": false,
-            "display_name": "System Message",
-            "dynamic": false,
-            "info": "System message to pass to the model.",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "multiline": true,
-            "name": "system_message",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "tags": {
-            "_input_type": "MessageTextInput",
-            "advanced": true,
-            "display_name": "Tags",
-            "dynamic": false,
-            "info": "Comma-separated list of tags to add to the run trace.",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "tags",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "temperature": {
-            "_input_type": "SliderInput",
-            "advanced": true,
-            "display_name": "Temperature",
-            "dynamic": false,
-            "info": "",
-            "max_label": "",
-            "max_label_icon": "",
-            "min_label": "",
-            "min_label_icon": "",
-            "name": "temperature",
-            "placeholder": "",
-            "range_spec": {
-              "max": 1,
-              "min": 0,
-              "step": 0.01,
-              "step_type": "float"
-            },
-            "required": false,
-            "show": true,
-            "slider_buttons": false,
-            "slider_buttons_options": [],
-            "slider_input": false,
-            "title_case": false,
-            "tool_mode": false,
-            "type": "slider",
-            "value": 0.1
-          },
-          "template": {
-            "_input_type": "MessageTextInput",
-            "advanced": true,
-            "display_name": "Template",
-            "dynamic": false,
-            "info": "Template to use for generating text.",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "template",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "tfs_z": {
-            "_input_type": "FloatInput",
-            "advanced": true,
-            "display_name": "TFS Z",
-            "dynamic": false,
-            "info": "Tail free sampling value. (Default: 1)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "tfs_z",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "float",
-            "value": ""
-          },
-          "timeout": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Timeout",
-            "dynamic": false,
-            "info": "Timeout for the request stream.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "timeout",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": ""
-          },
-          "tool_model_enabled": {
-            "_input_type": "BoolInput",
-            "advanced": false,
-            "display_name": "Tool Model Enabled",
-            "dynamic": false,
-            "info": "Whether to enable tool calling in the model.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "tool_model_enabled",
-            "placeholder": "",
-            "real_time_refresh": true,
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "bool",
-            "value": true
-          },
-          "top_k": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Top K",
-            "dynamic": false,
-            "info": "Limits token selection to top K. (Default: 40)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "top_k",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": ""
-          },
-          "top_p": {
-            "_input_type": "FloatInput",
-            "advanced": true,
-            "display_name": "Top P",
-            "dynamic": false,
-            "info": "Works together with top-k. (Default: 0.9)",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "top_p",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "float",
-            "value": ""
-          },
-          "verbose": {
-            "_input_type": "BoolInput",
-            "advanced": true,
-            "display_name": "Verbose",
-            "dynamic": false,
-            "info": "Whether to print out response text.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "verbose",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "bool",
-            "value": false
-          }
+          "tool_mode": false,
+          "trace_as_input": true,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "template",
+          "value": "",
+          "display_name": "Template",
+          "advanced": true,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "Template to use for generating text.",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageTextInput"
         },
-        "tool_mode": false
+        "tfs_z": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "tfs_z",
+          "value": "",
+          "display_name": "TFS Z",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Tail free sampling value. (Default: 1)",
+          "title_case": false,
+          "type": "float",
+          "_input_type": "FloatInput"
+        },
+        "timeout": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "timeout",
+          "value": "",
+          "display_name": "Timeout",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Timeout for the request stream.",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "tool_model_enabled": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "tool_model_enabled",
+          "value": true,
+          "display_name": "Tool Model Enabled",
+          "advanced": false,
+          "dynamic": false,
+          "info": "Whether to enable tool calling in the model.",
+          "real_time_refresh": true,
+          "title_case": false,
+          "type": "bool",
+          "_input_type": "BoolInput"
+        },
+        "top_k": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "top_k",
+          "value": "",
+          "display_name": "Top K",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Limits token selection to top K. (Default: 40)",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "top_p": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "top_p",
+          "value": "",
+          "display_name": "Top P",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Works together with top-k. (Default: 0.9)",
+          "title_case": false,
+          "type": "float",
+          "_input_type": "FloatInput"
+        },
+        "verbose": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "verbose",
+          "value": false,
+          "display_name": "Verbose",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Whether to print out response text.",
+          "title_case": false,
+          "type": "bool",
+          "_input_type": "BoolInput"
+        }
       },
-      "selected_output": "text_output",
-      "showNode": true,
-      "type": "OllamaModel"
+      "description": "Generate text using Ollama Local LLMs.",
+      "icon": "Ollama",
+      "base_classes": [
+        "LanguageModel",
+        "Message"
+      ],
+      "display_name": "Ollama",
+      "documentation": "",
+      "minimized": false,
+      "custom_fields": {},
+      "output_types": [],
+      "pinned": false,
+      "conditional_paths": [],
+      "frozen": false,
+      "outputs": [
+        {
+          "types": [
+            "Message"
+          ],
+          "selected": "Message",
+          "name": "text_output",
+          "display_name": "Model Response",
+          "method": "text_response",
+          "value": "__UNDEFINED__",
+          "cache": true,
+          "required_inputs": null,
+          "allows_loop": false,
+          "group_outputs": false,
+          "options": null,
+          "tool_mode": true
+        },
+        {
+          "types": [
+            "LanguageModel"
+          ],
+          "selected": "LanguageModel",
+          "name": "model_output",
+          "display_name": "Language Model",
+          "method": "build_model",
+          "value": "__UNDEFINED__",
+          "cache": true,
+          "required_inputs": null,
+          "allows_loop": false,
+          "group_outputs": false,
+          "options": null,
+          "tool_mode": true
+        }
+      ],
+      "field_order": [
+        "base_url",
+        "model_name",
+        "temperature",
+        "format",
+        "metadata",
+        "mirostat",
+        "mirostat_eta",
+        "mirostat_tau",
+        "num_ctx",
+        "num_gpu",
+        "num_thread",
+        "repeat_last_n",
+        "repeat_penalty",
+        "tfs_z",
+        "timeout",
+        "top_k",
+        "top_p",
+        "verbose",
+        "tags",
+        "stop_tokens",
+        "system",
+        "tool_model_enabled",
+        "template",
+        "input_value",
+        "system_message",
+        "stream"
+      ],
+      "beta": false,
+      "legacy": false,
+      "edited": false,
+      "metadata": {
+        "keywords": [
+          "model",
+          "llm",
+          "language model",
+          "large language model"
+        ],
+        "module": "lfx.components.ollama.ollama.ChatOllamaComponent",
+        "code_hash": "54de3b5da388",
+        "dependencies": {
+          "total_dependencies": 3,
+          "dependencies": [
+            {
+              "name": "httpx",
+              "version": "0.28.1"
+            },
+            {
+              "name": "langchain_ollama",
+              "version": "0.2.1"
+            },
+            {
+              "name": "lfx",
+              "version": null
+            }
+          ]
+        }
+      },
+      "tool_mode": false,
+      "last_updated": "2025-09-29T18:39:30.798Z",
+      "official": false
     },
-    "dragging": false,
-    "id": "OllamaModel-XDGqZ",
-    "measured": {
-      "height": 494,
-      "width": 320
-    },
-    "position": {
-      "x": 248.08287272472313,
-      "y": 216.98088326271431
-    },
-    "selected": false,
-    "type": "genericNode"
-  }
\ No newline at end of file
+    "showNode": true,
+    "type": "OllamaModel",
+    "id": "OllamaModel-8Re0J",
+    "selected_output": "text_output"
+  },
+  "id": "OllamaModel-8Re0J",
+  "position": {
+    "x": 0,
+    "y": 0
+  },
+  "type": "genericNode"
+}
\ No newline at end of file
diff --git a/flows/components/watsonx_embedding.json b/flows/components/watsonx_embedding.json
index 850cfb07..1d76c046 100644
--- a/flows/components/watsonx_embedding.json
+++ b/flows/components/watsonx_embedding.json
@@ -1,246 +1,207 @@
 {
-  "data": {
-    "id": "WatsonxEmbeddingsComponent-pJfXI",
+"data": {
     "node": {
-      "base_classes": [
-        "Embeddings"
-      ],
-      "beta": false,
-      "conditional_paths": [],
-      "custom_fields": {},
-      "description": "Generate embeddings using IBM watsonx.ai models.",
-      "display_name": "IBM watsonx.ai Embeddings",
-      "documentation": "",
-      "edited": false,
-      "field_order": [
-        "url",
-        "project_id",
-        "api_key",
-        "model_name",
-        "truncate_input_tokens",
-        "input_text"
-      ],
-      "frozen": false,
-      "icon": "WatsonxAI",
-      "last_updated": "2025-09-22T20:11:38.181Z",
-      "legacy": false,
-      "metadata": {
-        "code_hash": "b6c6d50cc7ed",
-        "dependencies": {
-          "dependencies": [
-            {
-              "name": "requests",
-              "version": "2.32.5"
-            },
-            {
-              "name": "ibm_watsonx_ai",
-              "version": "1.3.34"
-            },
-            {
-              "name": "langchain_ibm",
-              "version": "0.3.16"
-            },
-            {
-              "name": "pydantic",
-              "version": "2.10.6"
-            },
-            {
-              "name": "langflow",
-              "version": null
-            }
-          ],
-          "total_dependencies": 5
-        },
-        "module": "langflow.components.ibm.watsonx_embeddings.WatsonxEmbeddingsComponent"
-      },
-      "minimized": false,
-      "output_types": [],
-      "outputs": [
-        {
-          "allows_loop": false,
-          "cache": true,
-          "display_name": "Embedding Model",
-          "group_outputs": false,
-          "method": "build_embeddings",
-          "name": "embeddings",
-          "options": null,
-          "required_inputs": null,
-          "selected": "Embeddings",
-          "tool_mode": true,
-          "types": [
-            "Embeddings"
-          ],
-          "value": "__UNDEFINED__"
-        }
-      ],
-      "pinned": false,
-      "template": {
+    "template": {
         "_type": "Component",
         "api_key": {
-          "_input_type": "SecretStrInput",
-          "advanced": false,
-          "display_name": "API Key",
-          "dynamic": false,
-          "info": "The API Key to use for the model.",
-          "input_types": [],
-          "load_from_db": true,
-          "name": "api_key",
-          "password": true,
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "type": "str",
-          "value": "WATSONX_API_KEY"
+        "load_from_db": true,
+        "required": true,
+        "placeholder": "",
+        "show": true,
+        "name": "api_key",
+        "value": "WATSONX_API_KEY",
+        "display_name": "Watsonx API Key",
+        "advanced": false,
+        "input_types": [],
+        "dynamic": false,
+        "info": "The API Key to use for the model.",
+        "title_case": false,
+        "password": true,
+        "type": "str",
+        "_input_type": "SecretStrInput"
         },
         "code": {
-          "advanced": true,
-          "dynamic": true,
-          "fileTypes": [],
-          "file_path": "",
-          "info": "",
-          "list": false,
-          "load_from_db": false,
-          "multiline": true,
-          "name": "code",
-          "password": false,
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "type": "code",
-          "value": "from typing import Any\n\nimport requests\nfrom ibm_watsonx_ai import APIClient, Credentials\nfrom ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames\nfrom langchain_ibm import WatsonxEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DropdownInput, IntInput, SecretStrInput, StrInput\nfrom langflow.logging.logger import logger\nfrom langflow.schema.dotdict import dotdict\n\n\nclass WatsonxEmbeddingsComponent(LCEmbeddingsModel):\n    display_name = \"IBM watsonx.ai Embeddings\"\n    description = \"Generate embeddings using IBM watsonx.ai models.\"\n    icon = \"WatsonxAI\"\n    name = \"WatsonxEmbeddingsComponent\"\n\n    # models present in all the regions\n    _default_models = [\n        \"sentence-transformers/all-minilm-l12-v2\",\n        \"ibm/slate-125m-english-rtrvr-v2\",\n        \"ibm/slate-30m-english-rtrvr-v2\",\n        \"intfloat/multilingual-e5-large\",\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"url\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API.\",\n            value=None,\n            options=[\n                \"https://us-south.ml.cloud.ibm.com\",\n                \"https://eu-de.ml.cloud.ibm.com\",\n                \"https://eu-gb.ml.cloud.ibm.com\",\n                \"https://au-syd.ml.cloud.ibm.com\",\n                \"https://jp-tok.ml.cloud.ibm.com\",\n                \"https://ca-tor.ml.cloud.ibm.com\",\n            ],\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx project id\",\n            info=\"The project ID or deployment space ID that is associated with the foundation model.\",\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"The API Key to use for the model.\",\n            required=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            value=None,\n            dynamic=True,\n            required=True,\n        ),\n        IntInput(\n            name=\"truncate_input_tokens\",\n            display_name=\"Truncate Input Tokens\",\n            advanced=True,\n            value=200,\n        ),\n        BoolInput(\n            name=\"input_text\",\n            display_name=\"Include the original text in the output\",\n            value=True,\n            advanced=True,\n        ),\n    ]\n\n    @staticmethod\n    def fetch_models(base_url: str) -> list[str]:\n        \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n        try:\n            endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n            params = {\n                \"version\": \"2024-09-16\",\n                \"filters\": \"function_embedding,!lifecycle_withdrawn:and\",\n            }\n            response = requests.get(endpoint, params=params, timeout=10)\n            response.raise_for_status()\n            data = response.json()\n            models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n            return sorted(models)\n        except Exception:  # noqa: BLE001\n            logger.exception(\"Error fetching models\")\n            return WatsonxEmbeddingsComponent._default_models\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n        \"\"\"Update model options when URL or API key changes.\"\"\"\n        logger.debug(\n            \"Updating build config. Field name: %s, Field value: %s\",\n            field_name,\n            field_value,\n        )\n\n        if field_name == \"url\" and field_value:\n            try:\n                models = self.fetch_models(base_url=build_config.url.value)\n                build_config.model_name.options = models\n                if build_config.model_name.value:\n                    build_config.model_name.value = models[0]\n                info_message = f\"Updated model options: {len(models)} models found in {build_config.url.value}\"\n                logger.info(info_message)\n            except Exception:  # noqa: BLE001\n                logger.exception(\"Error updating model options.\")\n\n    def build_embeddings(self) -> Embeddings:\n        credentials = Credentials(\n            api_key=SecretStr(self.api_key).get_secret_value(),\n            url=self.url,\n        )\n\n        api_client = APIClient(credentials)\n\n        params = {\n            EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: self.truncate_input_tokens,\n            EmbedTextParamsMetaNames.RETURN_OPTIONS: {\"input_text\": self.input_text},\n        }\n\n        return WatsonxEmbeddings(\n            model_id=self.model_name,\n            params=params,\n            watsonx_client=api_client,\n            project_id=self.project_id,\n        )\n"
+        "type": "code",
+        "required": true,
+        "placeholder": "",
+        "list": false,
+        "show": true,
+        "multiline": true,
+        "value": "from typing import Any\n\nimport requests\nfrom ibm_watsonx_ai import APIClient, Credentials\nfrom ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames\nfrom langchain_ibm import WatsonxEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import BoolInput, DropdownInput, IntInput, SecretStrInput, StrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\n\n\nclass WatsonxEmbeddingsComponent(LCEmbeddingsModel):\n    display_name = \"IBM watsonx.ai Embeddings\"\n    description = \"Generate embeddings using IBM watsonx.ai models.\"\n    icon = \"WatsonxAI\"\n    name = \"WatsonxEmbeddingsComponent\"\n\n    # models present in all the regions\n    _default_models = [\n        \"sentence-transformers/all-minilm-l12-v2\",\n        \"ibm/slate-125m-english-rtrvr-v2\",\n        \"ibm/slate-30m-english-rtrvr-v2\",\n        \"intfloat/multilingual-e5-large\",\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"url\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API.\",\n            value=None,\n            options=[\n                \"https://us-south.ml.cloud.ibm.com\",\n                \"https://eu-de.ml.cloud.ibm.com\",\n                \"https://eu-gb.ml.cloud.ibm.com\",\n                \"https://au-syd.ml.cloud.ibm.com\",\n                \"https://jp-tok.ml.cloud.ibm.com\",\n                \"https://ca-tor.ml.cloud.ibm.com\",\n            ],\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx project id\",\n            info=\"The project ID or deployment space ID that is associated with the foundation model.\",\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Watsonx API Key\",\n            info=\"The API Key to use for the model.\",\n            required=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            value=None,\n            dynamic=True,\n            required=True,\n        ),\n        IntInput(\n            name=\"truncate_input_tokens\",\n            display_name=\"Truncate Input Tokens\",\n            advanced=True,\n            value=200,\n        ),\n        BoolInput(\n            name=\"input_text\",\n            display_name=\"Include the original text in the output\",\n            value=True,\n            advanced=True,\n        ),\n    ]\n\n    @staticmethod\n    def fetch_models(base_url: str) -> list[str]:\n        \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n        try:\n            endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n            params = {\n                \"version\": \"2024-09-16\",\n                \"filters\": \"function_embedding,!lifecycle_withdrawn:and\",\n            }\n            response = requests.get(endpoint, params=params, timeout=10)\n            response.raise_for_status()\n            data = response.json()\n            models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n            return sorted(models)\n        except Exception:  # noqa: BLE001\n            logger.exception(\"Error fetching models\")\n            return WatsonxEmbeddingsComponent._default_models\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n        \"\"\"Update model options when URL or API key changes.\"\"\"\n        logger.debug(\n            \"Updating build config. Field name: %s, Field value: %s\",\n            field_name,\n            field_value,\n        )\n\n        if field_name == \"url\" and field_value:\n            try:\n                models = self.fetch_models(base_url=build_config.url.value)\n                build_config.model_name.options = models\n                if build_config.model_name.value:\n                    build_config.model_name.value = models[0]\n                info_message = f\"Updated model options: {len(models)} models found in {build_config.url.value}\"\n                logger.info(info_message)\n            except Exception:  # noqa: BLE001\n                logger.exception(\"Error updating model options.\")\n\n    def build_embeddings(self) -> Embeddings:\n        credentials = Credentials(\n            api_key=SecretStr(self.api_key).get_secret_value(),\n            url=self.url,\n        )\n\n        api_client = APIClient(credentials)\n\n        params = {\n            EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: self.truncate_input_tokens,\n            EmbedTextParamsMetaNames.RETURN_OPTIONS: {\"input_text\": self.input_text},\n        }\n\n        return WatsonxEmbeddings(\n            model_id=self.model_name,\n            params=params,\n            watsonx_client=api_client,\n            project_id=self.project_id,\n        )\n",
+        "fileTypes": [],
+        "file_path": "",
+        "password": false,
+        "name": "code",
+        "advanced": true,
+        "dynamic": true,
+        "info": "",
+        "load_from_db": false,
+        "title_case": false
         },
         "input_text": {
-          "_input_type": "BoolInput",
-          "advanced": true,
-          "display_name": "Include the original text in the output",
-          "dynamic": false,
-          "info": "",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "input_text",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "bool",
-          "value": true
+        "tool_mode": false,
+        "trace_as_metadata": true,
+        "list": false,
+        "list_add_label": "Add More",
+        "required": false,
+        "placeholder": "",
+        "show": true,
+        "name": "input_text",
+        "value": true,
+        "display_name": "Include the original text in the output",
+        "advanced": true,
+        "dynamic": false,
+        "info": "",
+        "title_case": false,
+        "type": "bool",
+        "_input_type": "BoolInput"
         },
         "model_name": {
-          "_input_type": "DropdownInput",
-          "advanced": false,
-          "combobox": false,
-          "dialog_inputs": {},
-          "display_name": "Model Name",
-          "dynamic": true,
-          "info": "",
-          "name": "model_name",
-          "options": [
-            "ibm/granite-embedding-107m-multilingual",
-            "ibm/granite-embedding-278m-multilingual",
-            "ibm/slate-125m-english-rtrvr",
-            "ibm/slate-125m-english-rtrvr-v2",
-            "ibm/slate-30m-english-rtrvr",
-            "ibm/slate-30m-english-rtrvr-v2",
-            "intfloat/multilingual-e5-large",
-            "sentence-transformers/all-minilm-l6-v2"
-          ],
-          "options_metadata": [],
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "toggle": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "ibm/granite-embedding-107m-multilingual"
+        "tool_mode": false,
+        "trace_as_metadata": true,
+        "options": [],
+        "options_metadata": [],
+        "combobox": false,
+        "dialog_inputs": {},
+        "toggle": false,
+        "required": true,
+        "placeholder": "",
+        "show": true,
+        "name": "model_name",
+        "display_name": "Model Name",
+        "advanced": false,
+        "dynamic": true,
+        "info": "",
+        "title_case": false,
+        "external_options": {},
+        "type": "str",
+        "_input_type": "DropdownInput"
         },
         "project_id": {
-          "_input_type": "StrInput",
-          "advanced": false,
-          "display_name": "watsonx project id",
-          "dynamic": false,
-          "info": "The project ID or deployment space ID that is associated with the foundation model.",
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": true,
-          "name": "project_id",
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "WATSONX_PROJECT_ID"
+        "tool_mode": false,
+        "trace_as_metadata": true,
+        "load_from_db": true,
+        "list": false,
+        "list_add_label": "Add More",
+        "required": true,
+        "placeholder": "",
+        "show": true,
+        "name": "project_id",
+        "value": "WATSONX_PROJECT_ID",
+        "display_name": "watsonx project id",
+        "advanced": false,
+        "dynamic": false,
+        "info": "The project ID or deployment space ID that is associated with the foundation model.",
+        "title_case": false,
+        "type": "str",
+        "_input_type": "StrInput"
         },
         "truncate_input_tokens": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Truncate Input Tokens",
-          "dynamic": false,
-          "info": "",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "truncate_input_tokens",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": 200
+        "tool_mode": false,
+        "trace_as_metadata": true,
+        "list": false,
+        "list_add_label": "Add More",
+        "required": false,
+        "placeholder": "",
+        "show": true,
+        "name": "truncate_input_tokens",
+        "value": 200,
+        "display_name": "Truncate Input Tokens",
+        "advanced": true,
+        "dynamic": false,
+        "info": "",
+        "title_case": false,
+        "type": "int",
+        "_input_type": "IntInput"
         },
         "url": {
-          "_input_type": "DropdownInput",
-          "advanced": false,
-          "combobox": false,
-          "dialog_inputs": {},
-          "display_name": "watsonx API Endpoint",
-          "dynamic": false,
-          "info": "The base URL of the API.",
-          "name": "url",
-          "options": [
+        "tool_mode": false,
+        "trace_as_metadata": true,
+        "options": [
             "https://us-south.ml.cloud.ibm.com",
             "https://eu-de.ml.cloud.ibm.com",
             "https://eu-gb.ml.cloud.ibm.com",
             "https://au-syd.ml.cloud.ibm.com",
             "https://jp-tok.ml.cloud.ibm.com",
             "https://ca-tor.ml.cloud.ibm.com"
-          ],
-          "options_metadata": [],
-          "placeholder": "",
-          "real_time_refresh": true,
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "toggle": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "https://us-south.ml.cloud.ibm.com"
+        ],
+        "options_metadata": [],
+        "combobox": false,
+        "dialog_inputs": {},
+        "toggle": false,
+        "required": false,
+        "placeholder": "",
+        "show": true,
+        "name": "url",
+        "display_name": "watsonx API Endpoint",
+        "advanced": false,
+        "dynamic": false,
+        "info": "The base URL of the API.",
+        "real_time_refresh": true,
+        "title_case": false,
+        "external_options": {},
+        "type": "str",
+        "_input_type": "DropdownInput"
         }
-      },
-      "tool_mode": false
+    },
+    "description": "Generate embeddings using IBM watsonx.ai models.",
+    "icon": "WatsonxAI",
+    "base_classes": ["Embeddings"],
+    "display_name": "IBM watsonx.ai Embeddings",
+    "documentation": "",
+    "minimized": false,
+    "custom_fields": {},
+    "output_types": [],
+    "pinned": false,
+    "conditional_paths": [],
+    "frozen": false,
+    "outputs": [
+        {
+        "types": ["Embeddings"],
+        "selected": "Embeddings",
+        "name": "embeddings",
+        "display_name": "Embedding Model",
+        "method": "build_embeddings",
+        "value": "__UNDEFINED__",
+        "cache": true,
+        "allows_loop": false,
+        "group_outputs": false,
+        "tool_mode": true
+        }
+    ],
+    "field_order": [
+        "url",
+        "project_id",
+        "api_key",
+        "model_name",
+        "truncate_input_tokens",
+        "input_text"
+    ],
+    "beta": false,
+    "legacy": false,
+    "edited": false,
+    "metadata": {
+        "module": "lfx.components.ibm.watsonx_embeddings.WatsonxEmbeddingsComponent",
+        "code_hash": "ffded413ea90",
+        "dependencies": {
+        "total_dependencies": 5,
+        "dependencies": [
+            { "name": "requests", "version": "2.32.5" },
+            { "name": "ibm_watsonx_ai", "version": "1.3.34" },
+            { "name": "langchain_ibm", "version": "0.3.16" },
+            { "name": "pydantic", "version": "2.10.6" },
+            { "name": "lfx", "version": null }
+        ]
+        }
+    },
+    "tool_mode": false,
+    "official": false
     },
     "showNode": true,
-    "type": "WatsonxEmbeddingsComponent"
-  },
-  "dragging": false,
-  "id": "WatsonxEmbeddingsComponent-pJfXI",
-  "measured": {
-    "height": 467,
-    "width": 320
-  },
-  "position": {
-    "x": 364.4406919374723,
-    "y": 282.29319267029086
-  },
-  "selected": false,
-  "type": "genericNode"
+    "type": "WatsonxEmbeddingsComponent",
+    "id": "WatsonxEmbeddingsComponent-q67FN"
+},
+"id": "WatsonxEmbeddingsComponent-q67FN",
+"position": { "x": 0, "y": 0 },
+"type": "genericNode"
 }
\ No newline at end of file
diff --git a/flows/components/watsonx_llm.json b/flows/components/watsonx_llm.json
index 99a4a936..61baac42 100644
--- a/flows/components/watsonx_llm.json
+++ b/flows/components/watsonx_llm.json
@@ -1,18 +1,456 @@
 {
   "data": {
-    "id": "IBMwatsonxModel-jA4Nw",
     "node": {
+      "template": {
+        "_type": "Component",
+        "api_key": {
+          "load_from_db": true,
+          "required": true,
+          "placeholder": "",
+          "show": true,
+          "name": "api_key",
+          "value": "WATSONX_API_KEY",
+          "display_name": "Watsonx API Key",
+          "advanced": false,
+          "input_types": [],
+          "dynamic": false,
+          "info": "The API Key to use for the model.",
+          "title_case": false,
+          "password": true,
+          "type": "str",
+          "_input_type": "SecretStrInput"
+        },
+        "code": {
+          "type": "code",
+          "required": true,
+          "placeholder": "",
+          "list": false,
+          "show": true,
+          "multiline": true,
+          "value": "import json\nfrom typing import Any\n\nimport requests\nfrom langchain_ibm import ChatWatsonx\nfrom pydantic.v1 import SecretStr\n\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\n\n\nclass WatsonxAIComponent(LCModelComponent):\n    display_name = \"IBM watsonx.ai\"\n    description = \"Generate text using IBM watsonx.ai foundation models.\"\n    icon = \"WatsonxAI\"\n    name = \"IBMwatsonxModel\"\n    beta = False\n\n    _default_models = [\"ibm/granite-3-2b-instruct\", \"ibm/granite-3-8b-instruct\", \"ibm/granite-13b-instruct-v2\"]\n\n    inputs = [\n        *LCModelComponent.get_base_inputs(),\n        DropdownInput(\n            name=\"url\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API.\",\n            value=None,\n            options=[\n                \"https://us-south.ml.cloud.ibm.com\",\n                \"https://eu-de.ml.cloud.ibm.com\",\n                \"https://eu-gb.ml.cloud.ibm.com\",\n                \"https://au-syd.ml.cloud.ibm.com\",\n                \"https://jp-tok.ml.cloud.ibm.com\",\n                \"https://ca-tor.ml.cloud.ibm.com\",\n            ],\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            required=True,\n            info=\"The project ID or deployment space ID that is associated with the foundation model.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Watsonx API Key\",\n            info=\"The API Key to use for the model.\",\n            required=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            value=None,\n            dynamic=True,\n            required=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate.\",\n            range_spec=RangeSpec(min=1, max=4096),\n            value=1000,\n        ),\n        StrInput(\n            name=\"stop_sequence\",\n            display_name=\"Stop Sequence\",\n            advanced=True,\n            info=\"Sequence where generation should stop.\",\n            field_type=\"str\",\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            info=\"Controls randomness, higher values increase diversity.\",\n            value=0.1,\n            range_spec=RangeSpec(min=0, max=2, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"top_p\",\n            display_name=\"Top P\",\n            info=\"The cumulative probability cutoff for token selection. \"\n            \"Lower values mean sampling from a smaller, more top-weighted nucleus.\",\n            value=0.9,\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"frequency_penalty\",\n            display_name=\"Frequency Penalty\",\n            info=\"Penalty for frequency of token usage.\",\n            value=0.5,\n            range_spec=RangeSpec(min=-2.0, max=2.0, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"presence_penalty\",\n            display_name=\"Presence Penalty\",\n            info=\"Penalty for token presence in prior text.\",\n            value=0.3,\n            range_spec=RangeSpec(min=-2.0, max=2.0, step=0.01),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Random Seed\",\n            advanced=True,\n            info=\"The random seed for the model.\",\n            value=8,\n        ),\n        BoolInput(\n            name=\"logprobs\",\n            display_name=\"Log Probabilities\",\n            advanced=True,\n            info=\"Whether to return log probabilities of the output tokens.\",\n            value=True,\n        ),\n        IntInput(\n            name=\"top_logprobs\",\n            display_name=\"Top Log Probabilities\",\n            advanced=True,\n            info=\"Number of most likely tokens to return at each position.\",\n            value=3,\n            range_spec=RangeSpec(min=1, max=20),\n        ),\n        StrInput(\n            name=\"logit_bias\",\n            display_name=\"Logit Bias\",\n            advanced=True,\n            info='JSON string of token IDs to bias or suppress (e.g., {\"1003\": -100, \"1004\": 100}).',\n            field_type=\"str\",\n        ),\n    ]\n\n    @staticmethod\n    def fetch_models(base_url: str) -> list[str]:\n        \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n        try:\n            endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n            params = {\"version\": \"2024-09-16\", \"filters\": \"function_text_chat,!lifecycle_withdrawn\"}\n            response = requests.get(endpoint, params=params, timeout=10)\n            response.raise_for_status()\n            data = response.json()\n            models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n            return sorted(models)\n        except Exception:  # noqa: BLE001\n            logger.exception(\"Error fetching models. Using default models.\")\n            return WatsonxAIComponent._default_models\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n        \"\"\"Update model options when URL or API key changes.\"\"\"\n        logger.info(\"Updating build config. Field name: %s, Field value: %s\", field_name, field_value)\n\n        if field_name == \"url\" and field_value:\n            try:\n                models = self.fetch_models(base_url=build_config.url.value)\n                build_config.model_name.options = models\n                if build_config.model_name.value:\n                    build_config.model_name.value = models[0]\n                info_message = f\"Updated model options: {len(models)} models found in {build_config.url.value}\"\n                logger.info(info_message)\n            except Exception:  # noqa: BLE001\n                logger.exception(\"Error updating model options.\")\n\n    def build_model(self) -> LanguageModel:\n        # Parse logit_bias from JSON string if provided\n        logit_bias = None\n        if hasattr(self, \"logit_bias\") and self.logit_bias:\n            try:\n                logit_bias = json.loads(self.logit_bias)\n            except json.JSONDecodeError:\n                logger.warning(\"Invalid logit_bias JSON format. Using default instead.\")\n                logit_bias = {\"1003\": -100, \"1004\": -100}\n\n        chat_params = {\n            \"max_tokens\": getattr(self, \"max_tokens\", None),\n            \"temperature\": getattr(self, \"temperature\", None),\n            \"top_p\": getattr(self, \"top_p\", None),\n            \"frequency_penalty\": getattr(self, \"frequency_penalty\", None),\n            \"presence_penalty\": getattr(self, \"presence_penalty\", None),\n            \"seed\": getattr(self, \"seed\", None),\n            \"stop\": [self.stop_sequence] if self.stop_sequence else [],\n            \"n\": 1,\n            \"logprobs\": getattr(self, \"logprobs\", True),\n            \"top_logprobs\": getattr(self, \"top_logprobs\", None),\n            \"time_limit\": 600000,\n            \"logit_bias\": logit_bias,\n        }\n\n        return ChatWatsonx(\n            apikey=SecretStr(self.api_key).get_secret_value(),\n            url=self.url,\n            project_id=self.project_id,\n            model_id=self.model_name,\n            params=chat_params,\n            streaming=self.stream,\n        )\n",
+          "fileTypes": [],
+          "file_path": "",
+          "password": false,
+          "name": "code",
+          "advanced": true,
+          "dynamic": true,
+          "info": "",
+          "load_from_db": false,
+          "title_case": false
+        },
+        "frequency_penalty": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": -2,
+            "max": 2,
+            "step": 0.01
+          },
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "frequency_penalty",
+          "value": 0.5,
+          "display_name": "Frequency Penalty",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Penalty for frequency of token usage.",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
+        "input_value": {
+          "trace_as_input": true,
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "input_value",
+          "value": "",
+          "display_name": "Input",
+          "advanced": false,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageInput"
+        },
+        "logit_bias": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "logit_bias",
+          "value": "",
+          "display_name": "Logit Bias",
+          "advanced": true,
+          "dynamic": false,
+          "info": "JSON string of token IDs to bias or suppress (e.g., {\"1003\": -100, \"1004\": 100}).",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "StrInput"
+        },
+        "logprobs": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "logprobs",
+          "value": true,
+          "display_name": "Log Probabilities",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Whether to return log probabilities of the output tokens.",
+          "title_case": false,
+          "type": "bool",
+          "_input_type": "BoolInput"
+        },
+        "max_tokens": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "range_spec": {
+            "step_type": "float",
+            "min": 1,
+            "max": 4096,
+            "step": 0.1
+          },
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "max_tokens",
+          "value": 1000,
+          "display_name": "Max Tokens",
+          "advanced": true,
+          "dynamic": false,
+          "info": "The maximum number of tokens to generate.",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "model_name": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "options": [],
+          "options_metadata": [],
+          "combobox": false,
+          "dialog_inputs": {},
+          "toggle": false,
+          "required": true,
+          "placeholder": "",
+          "show": true,
+          "name": "model_name",
+          "display_name": "Model Name",
+          "advanced": false,
+          "dynamic": true,
+          "info": "",
+          "title_case": false,
+          "external_options": {},
+          "type": "str",
+          "_input_type": "DropdownInput"
+        },
+        "presence_penalty": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": -2,
+            "max": 2,
+            "step": 0.01
+          },
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "presence_penalty",
+          "value": 0.3,
+          "display_name": "Presence Penalty",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Penalty for token presence in prior text.",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
+        "project_id": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": true,
+          "placeholder": "",
+          "show": true,
+          "name": "project_id",
+          "value": "WATSONX_PROJECT_ID",
+          "display_name": "watsonx Project ID",
+          "advanced": false,
+          "dynamic": false,
+          "info": "The project ID or deployment space ID that is associated with the foundation model.",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "StrInput"
+        },
+        "seed": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "seed",
+          "value": 8,
+          "display_name": "Random Seed",
+          "advanced": true,
+          "dynamic": false,
+          "info": "The random seed for the model.",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "stop_sequence": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "stop_sequence",
+          "value": "",
+          "display_name": "Stop Sequence",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Sequence where generation should stop.",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "StrInput"
+        },
+        "stream": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "stream",
+          "value": false,
+          "display_name": "Stream",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Stream the response from the model. Streaming works only in Chat.",
+          "title_case": false,
+          "type": "bool",
+          "_input_type": "BoolInput"
+        },
+        "system_message": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "multiline": true,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "system_message",
+          "value": "",
+          "display_name": "System Message",
+          "advanced": false,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "System message to pass to the model.",
+          "title_case": false,
+          "copy_field": false,
+          "type": "str",
+          "_input_type": "MultilineInput"
+        },
+        "temperature": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": 0,
+            "max": 2,
+            "step": 0.01
+          },
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "temperature",
+          "value": 0.1,
+          "display_name": "Temperature",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Controls randomness, higher values increase diversity.",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
+        "top_logprobs": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "range_spec": {
+            "step_type": "float",
+            "min": 1,
+            "max": 20,
+            "step": 0.1
+          },
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "top_logprobs",
+          "value": 3,
+          "display_name": "Top Log Probabilities",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Number of most likely tokens to return at each position.",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "top_p": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": 0,
+            "max": 1,
+            "step": 0.01
+          },
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "top_p",
+          "value": 0.9,
+          "display_name": "Top P",
+          "advanced": true,
+          "dynamic": false,
+          "info": "The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus.",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
+        "url": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "options": [
+            "https://us-south.ml.cloud.ibm.com",
+            "https://eu-de.ml.cloud.ibm.com",
+            "https://eu-gb.ml.cloud.ibm.com",
+            "https://au-syd.ml.cloud.ibm.com",
+            "https://jp-tok.ml.cloud.ibm.com",
+            "https://ca-tor.ml.cloud.ibm.com"
+          ],
+          "options_metadata": [],
+          "combobox": false,
+          "dialog_inputs": {},
+          "toggle": false,
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "url",
+          "display_name": "watsonx API Endpoint",
+          "advanced": false,
+          "dynamic": false,
+          "info": "The base URL of the API.",
+          "real_time_refresh": true,
+          "title_case": false,
+          "external_options": {},
+          "type": "str",
+          "_input_type": "DropdownInput"
+        }
+      },
+      "description": "Generate text using IBM watsonx.ai foundation models.",
+      "icon": "WatsonxAI",
       "base_classes": [
         "LanguageModel",
         "Message"
       ],
-      "beta": false,
-      "conditional_paths": [],
-      "custom_fields": {},
-      "description": "Generate text using IBM watsonx.ai foundation models.",
       "display_name": "IBM watsonx.ai",
       "documentation": "",
-      "edited": false,
+      "minimized": false,
+      "custom_fields": {},
+      "output_types": [],
+      "pinned": false,
+      "conditional_paths": [],
+      "frozen": false,
+      "outputs": [
+        {
+          "types": [
+            "Message"
+          ],
+          "name": "text_output",
+          "display_name": "Model Response",
+          "method": "text_response",
+          "value": "__UNDEFINED__",
+          "cache": true,
+          "allows_loop": false,
+          "group_outputs": false,
+          "tool_mode": true
+        },
+        {
+          "types": [
+            "LanguageModel"
+          ],
+          "selected": "LanguageModel",
+          "name": "model_output",
+          "display_name": "Language Model",
+          "method": "build_model",
+          "value": "__UNDEFINED__",
+          "cache": true,
+          "allows_loop": false,
+          "group_outputs": false,
+          "tool_mode": true
+        }
+      ],
       "field_order": [
         "input_value",
         "system_message",
@@ -32,13 +470,20 @@
         "top_logprobs",
         "logit_bias"
       ],
-      "frozen": false,
-      "icon": "WatsonxAI",
-      "last_updated": "2025-09-22T20:03:31.248Z",
+      "beta": false,
       "legacy": false,
+      "edited": false,
       "metadata": {
-        "code_hash": "7767fd69a954",
+        "keywords": [
+          "model",
+          "llm",
+          "language model",
+          "large language model"
+        ],
+        "module": "lfx.components.ibm.watsonx.WatsonxAIComponent",
+        "code_hash": "85c24939214c",
         "dependencies": {
+          "total_dependencies": 4,
           "dependencies": [
             {
               "name": "requests",
@@ -53,498 +498,24 @@
               "version": "2.10.6"
             },
             {
-              "name": "langflow",
+              "name": "lfx",
               "version": null
             }
-          ],
-          "total_dependencies": 4
-        },
-        "keywords": [
-          "model",
-          "llm",
-          "language model",
-          "large language model"
-        ],
-        "module": "langflow.components.ibm.watsonx.WatsonxAIComponent"
-      },
-      "minimized": false,
-      "output_types": [],
-      "outputs": [
-        {
-          "allows_loop": false,
-          "cache": true,
-          "display_name": "Model Response",
-          "group_outputs": false,
-          "method": "text_response",
-          "name": "text_output",
-          "options": null,
-          "required_inputs": null,
-          "tool_mode": true,
-          "types": [
-            "Message"
-          ],
-          "value": "__UNDEFINED__"
-        },
-        {
-          "allows_loop": false,
-          "cache": true,
-          "display_name": "Language Model",
-          "group_outputs": false,
-          "method": "build_model",
-          "name": "model_output",
-          "options": null,
-          "required_inputs": null,
-          "selected": "LanguageModel",
-          "tool_mode": true,
-          "types": [
-            "LanguageModel"
-          ],
-          "value": "__UNDEFINED__"
-        }
-      ],
-      "pinned": false,
-      "template": {
-        "_type": "Component",
-        "api_key": {
-          "_input_type": "SecretStrInput",
-          "advanced": false,
-          "display_name": "API Key",
-          "dynamic": false,
-          "info": "The API Key to use for the model.",
-          "input_types": [],
-          "load_from_db": true,
-          "name": "api_key",
-          "password": true,
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "type": "str",
-          "value": "WATSONX_API_KEY"
-        },
-        "code": {
-          "advanced": true,
-          "dynamic": true,
-          "fileTypes": [],
-          "file_path": "",
-          "info": "",
-          "list": false,
-          "load_from_db": false,
-          "multiline": true,
-          "name": "code",
-          "password": false,
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "type": "code",
-          "value": "import json\nfrom typing import Any\n\nimport requests\nfrom langchain_ibm import ChatWatsonx\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs.inputs import BoolInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.logging.logger import logger\nfrom langflow.schema.dotdict import dotdict\n\n\nclass WatsonxAIComponent(LCModelComponent):\n    display_name = \"IBM watsonx.ai\"\n    description = \"Generate text using IBM watsonx.ai foundation models.\"\n    icon = \"WatsonxAI\"\n    name = \"IBMwatsonxModel\"\n    beta = False\n\n    _default_models = [\"ibm/granite-3-2b-instruct\", \"ibm/granite-3-8b-instruct\", \"ibm/granite-13b-instruct-v2\"]\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        DropdownInput(\n            name=\"url\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API.\",\n            value=None,\n            options=[\n                \"https://us-south.ml.cloud.ibm.com\",\n                \"https://eu-de.ml.cloud.ibm.com\",\n                \"https://eu-gb.ml.cloud.ibm.com\",\n                \"https://au-syd.ml.cloud.ibm.com\",\n                \"https://jp-tok.ml.cloud.ibm.com\",\n                \"https://ca-tor.ml.cloud.ibm.com\",\n            ],\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            required=True,\n            info=\"The project ID or deployment space ID that is associated with the foundation model.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"The API Key to use for the model.\",\n            required=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            value=None,\n            dynamic=True,\n            required=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate.\",\n            range_spec=RangeSpec(min=1, max=4096),\n            value=1000,\n        ),\n        StrInput(\n            name=\"stop_sequence\",\n            display_name=\"Stop Sequence\",\n            advanced=True,\n            info=\"Sequence where generation should stop.\",\n            field_type=\"str\",\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            info=\"Controls randomness, higher values increase diversity.\",\n            value=0.1,\n            range_spec=RangeSpec(min=0, max=2, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"top_p\",\n            display_name=\"Top P\",\n            info=\"The cumulative probability cutoff for token selection. \"\n            \"Lower values mean sampling from a smaller, more top-weighted nucleus.\",\n            value=0.9,\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"frequency_penalty\",\n            display_name=\"Frequency Penalty\",\n            info=\"Penalty for frequency of token usage.\",\n            value=0.5,\n            range_spec=RangeSpec(min=-2.0, max=2.0, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"presence_penalty\",\n            display_name=\"Presence Penalty\",\n            info=\"Penalty for token presence in prior text.\",\n            value=0.3,\n            range_spec=RangeSpec(min=-2.0, max=2.0, step=0.01),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Random Seed\",\n            advanced=True,\n            info=\"The random seed for the model.\",\n            value=8,\n        ),\n        BoolInput(\n            name=\"logprobs\",\n            display_name=\"Log Probabilities\",\n            advanced=True,\n            info=\"Whether to return log probabilities of the output tokens.\",\n            value=True,\n        ),\n        IntInput(\n            name=\"top_logprobs\",\n            display_name=\"Top Log Probabilities\",\n            advanced=True,\n            info=\"Number of most likely tokens to return at each position.\",\n            value=3,\n            range_spec=RangeSpec(min=1, max=20),\n        ),\n        StrInput(\n            name=\"logit_bias\",\n            display_name=\"Logit Bias\",\n            advanced=True,\n            info='JSON string of token IDs to bias or suppress (e.g., {\"1003\": -100, \"1004\": 100}).',\n            field_type=\"str\",\n        ),\n    ]\n\n    @staticmethod\n    def fetch_models(base_url: str) -> list[str]:\n        \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n        try:\n            endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n            params = {\"version\": \"2024-09-16\", \"filters\": \"function_text_chat,!lifecycle_withdrawn\"}\n            response = requests.get(endpoint, params=params, timeout=10)\n            response.raise_for_status()\n            data = response.json()\n            models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n            return sorted(models)\n        except Exception:  # noqa: BLE001\n            logger.exception(\"Error fetching models. Using default models.\")\n            return WatsonxAIComponent._default_models\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n        \"\"\"Update model options when URL or API key changes.\"\"\"\n        logger.info(\"Updating build config. Field name: %s, Field value: %s\", field_name, field_value)\n\n        if field_name == \"url\" and field_value:\n            try:\n                models = self.fetch_models(base_url=build_config.url.value)\n                build_config.model_name.options = models\n                if build_config.model_name.value:\n                    build_config.model_name.value = models[0]\n                info_message = f\"Updated model options: {len(models)} models found in {build_config.url.value}\"\n                logger.info(info_message)\n            except Exception:  # noqa: BLE001\n                logger.exception(\"Error updating model options.\")\n\n    def build_model(self) -> LanguageModel:\n        # Parse logit_bias from JSON string if provided\n        logit_bias = None\n        if hasattr(self, \"logit_bias\") and self.logit_bias:\n            try:\n                logit_bias = json.loads(self.logit_bias)\n            except json.JSONDecodeError:\n                logger.warning(\"Invalid logit_bias JSON format. Using default instead.\")\n                logit_bias = {\"1003\": -100, \"1004\": -100}\n\n        chat_params = {\n            \"max_tokens\": getattr(self, \"max_tokens\", None),\n            \"temperature\": getattr(self, \"temperature\", None),\n            \"top_p\": getattr(self, \"top_p\", None),\n            \"frequency_penalty\": getattr(self, \"frequency_penalty\", None),\n            \"presence_penalty\": getattr(self, \"presence_penalty\", None),\n            \"seed\": getattr(self, \"seed\", None),\n            \"stop\": [self.stop_sequence] if self.stop_sequence else [],\n            \"n\": 1,\n            \"logprobs\": getattr(self, \"logprobs\", True),\n            \"top_logprobs\": getattr(self, \"top_logprobs\", None),\n            \"time_limit\": 600000,\n            \"logit_bias\": logit_bias,\n        }\n\n        return ChatWatsonx(\n            apikey=SecretStr(self.api_key).get_secret_value(),\n            url=self.url,\n            project_id=self.project_id,\n            model_id=self.model_name,\n            params=chat_params,\n            streaming=self.stream,\n        )\n"
-        },
-        "frequency_penalty": {
-          "_input_type": "SliderInput",
-          "advanced": true,
-          "display_name": "Frequency Penalty",
-          "dynamic": false,
-          "info": "Penalty for frequency of token usage.",
-          "max_label": "",
-          "max_label_icon": "",
-          "min_label": "",
-          "min_label_icon": "",
-          "name": "frequency_penalty",
-          "placeholder": "",
-          "range_spec": {
-            "max": 2,
-            "min": -2,
-            "step": 0.01,
-            "step_type": "float"
-          },
-          "required": false,
-          "show": true,
-          "slider_buttons": false,
-          "slider_buttons_options": [],
-          "slider_input": false,
-          "title_case": false,
-          "tool_mode": false,
-          "type": "slider",
-          "value": 0.5
-        },
-        "input_value": {
-          "_input_type": "MessageInput",
-          "advanced": false,
-          "display_name": "Input",
-          "dynamic": false,
-          "info": "",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "input_value",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "logit_bias": {
-          "_input_type": "StrInput",
-          "advanced": true,
-          "display_name": "Logit Bias",
-          "dynamic": false,
-          "info": "JSON string of token IDs to bias or suppress (e.g., {\"1003\": -100, \"1004\": 100}).",
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "logit_bias",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "logprobs": {
-          "_input_type": "BoolInput",
-          "advanced": true,
-          "display_name": "Log Probabilities",
-          "dynamic": false,
-          "info": "Whether to return log probabilities of the output tokens.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "logprobs",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "bool",
-          "value": true
-        },
-        "max_tokens": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Max Tokens",
-          "dynamic": false,
-          "info": "The maximum number of tokens to generate.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "max_tokens",
-          "placeholder": "",
-          "range_spec": {
-            "max": 4096,
-            "min": 1,
-            "step": 0.1,
-            "step_type": "float"
-          },
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": 1000
-        },
-        "model_name": {
-          "_input_type": "DropdownInput",
-          "advanced": false,
-          "combobox": false,
-          "dialog_inputs": {},
-          "display_name": "Model Name",
-          "dynamic": true,
-          "info": "",
-          "name": "model_name",
-          "options": [
-            "ibm/granite-3-2-8b-instruct",
-            "ibm/granite-3-2b-instruct",
-            "ibm/granite-3-3-8b-instruct",
-            "ibm/granite-3-8b-instruct",
-            "ibm/granite-guardian-3-2b",
-            "ibm/granite-guardian-3-8b",
-            "ibm/granite-vision-3-2-2b",
-            "meta-llama/llama-3-2-11b-vision-instruct",
-            "meta-llama/llama-3-2-90b-vision-instruct",
-            "meta-llama/llama-3-3-70b-instruct",
-            "meta-llama/llama-3-405b-instruct",
-            "meta-llama/llama-4-maverick-17b-128e-instruct-fp8",
-            "meta-llama/llama-guard-3-11b-vision",
-            "mistralai/mistral-large",
-            "mistralai/mistral-medium-2505",
-            "mistralai/mistral-small-3-1-24b-instruct-2503",
-            "mistralai/pixtral-12b",
-            "openai/gpt-oss-120b"
-          ],
-          "options_metadata": [],
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "toggle": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "ibm/granite-3-2-8b-instruct"
-        },
-        "presence_penalty": {
-          "_input_type": "SliderInput",
-          "advanced": true,
-          "display_name": "Presence Penalty",
-          "dynamic": false,
-          "info": "Penalty for token presence in prior text.",
-          "max_label": "",
-          "max_label_icon": "",
-          "min_label": "",
-          "min_label_icon": "",
-          "name": "presence_penalty",
-          "placeholder": "",
-          "range_spec": {
-            "max": 2,
-            "min": -2,
-            "step": 0.01,
-            "step_type": "float"
-          },
-          "required": false,
-          "show": true,
-          "slider_buttons": false,
-          "slider_buttons_options": [],
-          "slider_input": false,
-          "title_case": false,
-          "tool_mode": false,
-          "type": "slider",
-          "value": 0.3
-        },
-        "project_id": {
-          "_input_type": "StrInput",
-          "advanced": false,
-          "display_name": "watsonx Project ID",
-          "dynamic": false,
-          "info": "The project ID or deployment space ID that is associated with the foundation model.",
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": true,
-          "name": "project_id",
-          "placeholder": "",
-          "required": true,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "WATSONX_PROJECT_ID"
-        },
-        "seed": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Random Seed",
-          "dynamic": false,
-          "info": "The random seed for the model.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "seed",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": 8
-        },
-        "stop_sequence": {
-          "_input_type": "StrInput",
-          "advanced": true,
-          "display_name": "Stop Sequence",
-          "dynamic": false,
-          "info": "Sequence where generation should stop.",
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "name": "stop_sequence",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "stream": {
-          "_input_type": "BoolInput",
-          "advanced": true,
-          "display_name": "Stream",
-          "dynamic": false,
-          "info": "Stream the response from the model. Streaming works only in Chat.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "stream",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "bool",
-          "value": false
-        },
-        "system_message": {
-          "_input_type": "MultilineInput",
-          "advanced": false,
-          "copy_field": false,
-          "display_name": "System Message",
-          "dynamic": false,
-          "info": "System message to pass to the model.",
-          "input_types": [
-            "Message"
-          ],
-          "list": false,
-          "list_add_label": "Add More",
-          "load_from_db": false,
-          "multiline": true,
-          "name": "system_message",
-          "placeholder": "",
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_input": true,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": ""
-        },
-        "temperature": {
-          "_input_type": "SliderInput",
-          "advanced": true,
-          "display_name": "Temperature",
-          "dynamic": false,
-          "info": "Controls randomness, higher values increase diversity.",
-          "max_label": "",
-          "max_label_icon": "",
-          "min_label": "",
-          "min_label_icon": "",
-          "name": "temperature",
-          "placeholder": "",
-          "range_spec": {
-            "max": 2,
-            "min": 0,
-            "step": 0.01,
-            "step_type": "float"
-          },
-          "required": false,
-          "show": true,
-          "slider_buttons": false,
-          "slider_buttons_options": [],
-          "slider_input": false,
-          "title_case": false,
-          "tool_mode": false,
-          "type": "slider",
-          "value": 0.1
-        },
-        "top_logprobs": {
-          "_input_type": "IntInput",
-          "advanced": true,
-          "display_name": "Top Log Probabilities",
-          "dynamic": false,
-          "info": "Number of most likely tokens to return at each position.",
-          "list": false,
-          "list_add_label": "Add More",
-          "name": "top_logprobs",
-          "placeholder": "",
-          "range_spec": {
-            "max": 20,
-            "min": 1,
-            "step": 0.1,
-            "step_type": "float"
-          },
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "int",
-          "value": 3
-        },
-        "top_p": {
-          "_input_type": "SliderInput",
-          "advanced": true,
-          "display_name": "Top P",
-          "dynamic": false,
-          "info": "The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus.",
-          "max_label": "",
-          "max_label_icon": "",
-          "min_label": "",
-          "min_label_icon": "",
-          "name": "top_p",
-          "placeholder": "",
-          "range_spec": {
-            "max": 1,
-            "min": 0,
-            "step": 0.01,
-            "step_type": "float"
-          },
-          "required": false,
-          "show": true,
-          "slider_buttons": false,
-          "slider_buttons_options": [],
-          "slider_input": false,
-          "title_case": false,
-          "tool_mode": false,
-          "type": "slider",
-          "value": 0.9
-        },
-        "url": {
-          "_input_type": "DropdownInput",
-          "advanced": false,
-          "combobox": false,
-          "dialog_inputs": {},
-          "display_name": "watsonx API Endpoint",
-          "dynamic": false,
-          "info": "The base URL of the API.",
-          "name": "url",
-          "options": [
-            "https://us-south.ml.cloud.ibm.com",
-            "https://eu-de.ml.cloud.ibm.com",
-            "https://eu-gb.ml.cloud.ibm.com",
-            "https://au-syd.ml.cloud.ibm.com",
-            "https://jp-tok.ml.cloud.ibm.com",
-            "https://ca-tor.ml.cloud.ibm.com"
-          ],
-          "options_metadata": [],
-          "placeholder": "",
-          "real_time_refresh": true,
-          "required": false,
-          "show": true,
-          "title_case": false,
-          "toggle": false,
-          "tool_mode": false,
-          "trace_as_metadata": true,
-          "type": "str",
-          "value": "https://us-south.ml.cloud.ibm.com"
+          ]
         }
       },
-      "tool_mode": false
+      "tool_mode": false,
+      "official": false
     },
-    "selected_output": "model_output",
     "showNode": true,
-    "type": "IBMwatsonxModel"
-  },
-  "dragging": false,
-  "id": "IBMwatsonxModel-jA4Nw",
-  "measured": {
-    "height": 632,
-    "width": 320
+    "type": "IBMwatsonxModel",
+    "id": "IBMwatsonxModel-qXZxc",
+    "selected_output": "model_output"
   },
+  "id": "IBMwatsonxModel-qXZxc",
   "position": {
-    "x": 371.93566807042805,
-    "y": 197.47711431325635
+    "x": 0,
+    "y": 0
   },
-  "selected": false,
   "type": "genericNode"
 }
\ No newline at end of file
diff --git a/flows/components/watsonx_llm_text.json b/flows/components/watsonx_llm_text.json
index a2966a48..f0f42455 100644
--- a/flows/components/watsonx_llm_text.json
+++ b/flows/components/watsonx_llm_text.json
@@ -1,551 +1,521 @@
 {
-    "data": {
-      "id": "IBMwatsonxModel-18kmA",
-      "node": {
-        "base_classes": [
-          "LanguageModel",
-          "Message"
-        ],
-        "beta": false,
-        "conditional_paths": [],
-        "custom_fields": {},
-        "description": "Generate text using IBM watsonx.ai foundation models.",
-        "display_name": "IBM watsonx.ai",
-        "documentation": "",
-        "edited": false,
-        "field_order": [
-          "input_value",
-          "system_message",
-          "stream",
-          "url",
-          "project_id",
-          "api_key",
-          "model_name",
-          "max_tokens",
-          "stop_sequence",
-          "temperature",
-          "top_p",
-          "frequency_penalty",
-          "presence_penalty",
-          "seed",
-          "logprobs",
-          "top_logprobs",
-          "logit_bias"
-        ],
-        "frozen": false,
-        "icon": "WatsonxAI",
-        "last_updated": "2025-09-22T20:03:31.248Z",
-        "legacy": false,
-        "metadata": {
-          "code_hash": "7767fd69a954",
-          "dependencies": {
-            "dependencies": [
-              {
-                "name": "requests",
-                "version": "2.32.5"
-              },
-              {
-                "name": "langchain_ibm",
-                "version": "0.3.16"
-              },
-              {
-                "name": "pydantic",
-                "version": "2.10.6"
-              },
-              {
-                "name": "langflow",
-                "version": null
-              }
-            ],
-            "total_dependencies": 4
+  "data": {
+    "node": {
+      "template": {
+        "_type": "Component",
+        "api_key": {
+          "load_from_db": true,
+          "required": true,
+          "placeholder": "",
+          "show": true,
+          "name": "api_key",
+          "value": "WATSONX_API_KEY",
+          "display_name": "Watsonx API Key",
+          "advanced": false,
+          "input_types": [],
+          "dynamic": false,
+          "info": "The API Key to use for the model.",
+          "title_case": false,
+          "password": true,
+          "type": "str",
+          "_input_type": "SecretStrInput"
+        },
+        "code": {
+          "type": "code",
+          "required": true,
+          "placeholder": "",
+          "list": false,
+          "show": true,
+          "multiline": true,
+          "value": "import json\nfrom typing import Any\n\nimport requests\nfrom langchain_ibm import ChatWatsonx\nfrom pydantic.v1 import SecretStr\n\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.dotdict import dotdict\n\n\nclass WatsonxAIComponent(LCModelComponent):\n    display_name = \"IBM watsonx.ai\"\n    description = \"Generate text using IBM watsonx.ai foundation models.\"\n    icon = \"WatsonxAI\"\n    name = \"IBMwatsonxModel\"\n    beta = False\n\n    _default_models = [\"ibm/granite-3-2b-instruct\", \"ibm/granite-3-8b-instruct\", \"ibm/granite-13b-instruct-v2\"]\n\n    inputs = [\n        *LCModelComponent.get_base_inputs(),\n        DropdownInput(\n            name=\"url\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API.\",\n            value=None,\n            options=[\n                \"https://us-south.ml.cloud.ibm.com\",\n                \"https://eu-de.ml.cloud.ibm.com\",\n                \"https://eu-gb.ml.cloud.ibm.com\",\n                \"https://au-syd.ml.cloud.ibm.com\",\n                \"https://jp-tok.ml.cloud.ibm.com\",\n                \"https://ca-tor.ml.cloud.ibm.com\",\n            ],\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            required=True,\n            info=\"The project ID or deployment space ID that is associated with the foundation model.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Watsonx API Key\",\n            info=\"The API Key to use for the model.\",\n            required=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            value=None,\n            dynamic=True,\n            required=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate.\",\n            range_spec=RangeSpec(min=1, max=4096),\n            value=1000,\n        ),\n        StrInput(\n            name=\"stop_sequence\",\n            display_name=\"Stop Sequence\",\n            advanced=True,\n            info=\"Sequence where generation should stop.\",\n            field_type=\"str\",\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            info=\"Controls randomness, higher values increase diversity.\",\n            value=0.1,\n            range_spec=RangeSpec(min=0, max=2, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"top_p\",\n            display_name=\"Top P\",\n            info=\"The cumulative probability cutoff for token selection. \"\n            \"Lower values mean sampling from a smaller, more top-weighted nucleus.\",\n            value=0.9,\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"frequency_penalty\",\n            display_name=\"Frequency Penalty\",\n            info=\"Penalty for frequency of token usage.\",\n            value=0.5,\n            range_spec=RangeSpec(min=-2.0, max=2.0, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"presence_penalty\",\n            display_name=\"Presence Penalty\",\n            info=\"Penalty for token presence in prior text.\",\n            value=0.3,\n            range_spec=RangeSpec(min=-2.0, max=2.0, step=0.01),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Random Seed\",\n            advanced=True,\n            info=\"The random seed for the model.\",\n            value=8,\n        ),\n        BoolInput(\n            name=\"logprobs\",\n            display_name=\"Log Probabilities\",\n            advanced=True,\n            info=\"Whether to return log probabilities of the output tokens.\",\n            value=True,\n        ),\n        IntInput(\n            name=\"top_logprobs\",\n            display_name=\"Top Log Probabilities\",\n            advanced=True,\n            info=\"Number of most likely tokens to return at each position.\",\n            value=3,\n            range_spec=RangeSpec(min=1, max=20),\n        ),\n        StrInput(\n            name=\"logit_bias\",\n            display_name=\"Logit Bias\",\n            advanced=True,\n            info='JSON string of token IDs to bias or suppress (e.g., {\"1003\": -100, \"1004\": 100}).',\n            field_type=\"str\",\n        ),\n    ]\n\n    @staticmethod\n    def fetch_models(base_url: str) -> list[str]:\n        \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n        try:\n            endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n            params = {\"version\": \"2024-09-16\", \"filters\": \"function_text_chat,!lifecycle_withdrawn\"}\n            response = requests.get(endpoint, params=params, timeout=10)\n            response.raise_for_status()\n            data = response.json()\n            models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n            return sorted(models)\n        except Exception:  # noqa: BLE001\n            logger.exception(\"Error fetching models. Using default models.\")\n            return WatsonxAIComponent._default_models\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n        \"\"\"Update model options when URL or API key changes.\"\"\"\n        logger.info(\"Updating build config. Field name: %s, Field value: %s\", field_name, field_value)\n\n        if field_name == \"url\" and field_value:\n            try:\n                models = self.fetch_models(base_url=build_config.url.value)\n                build_config.model_name.options = models\n                if build_config.model_name.value:\n                    build_config.model_name.value = models[0]\n                info_message = f\"Updated model options: {len(models)} models found in {build_config.url.value}\"\n                logger.info(info_message)\n            except Exception:  # noqa: BLE001\n                logger.exception(\"Error updating model options.\")\n\n    def build_model(self) -> LanguageModel:\n        # Parse logit_bias from JSON string if provided\n        logit_bias = None\n        if hasattr(self, \"logit_bias\") and self.logit_bias:\n            try:\n                logit_bias = json.loads(self.logit_bias)\n            except json.JSONDecodeError:\n                logger.warning(\"Invalid logit_bias JSON format. Using default instead.\")\n                logit_bias = {\"1003\": -100, \"1004\": -100}\n\n        chat_params = {\n            \"max_tokens\": getattr(self, \"max_tokens\", None),\n            \"temperature\": getattr(self, \"temperature\", None),\n            \"top_p\": getattr(self, \"top_p\", None),\n            \"frequency_penalty\": getattr(self, \"frequency_penalty\", None),\n            \"presence_penalty\": getattr(self, \"presence_penalty\", None),\n            \"seed\": getattr(self, \"seed\", None),\n            \"stop\": [self.stop_sequence] if self.stop_sequence else [],\n            \"n\": 1,\n            \"logprobs\": getattr(self, \"logprobs\", True),\n            \"top_logprobs\": getattr(self, \"top_logprobs\", None),\n            \"time_limit\": 600000,\n            \"logit_bias\": logit_bias,\n        }\n\n        return ChatWatsonx(\n            apikey=SecretStr(self.api_key).get_secret_value(),\n            url=self.url,\n            project_id=self.project_id,\n            model_id=self.model_name,\n            params=chat_params,\n            streaming=self.stream,\n        )\n",
+          "fileTypes": [],
+          "file_path": "",
+          "password": false,
+          "name": "code",
+          "advanced": true,
+          "dynamic": true,
+          "info": "",
+          "load_from_db": false,
+          "title_case": false
+        },
+        "frequency_penalty": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": -2,
+            "max": 2,
+            "step": 0.01
           },
-          "keywords": [
-            "model",
-            "llm",
-            "language model",
-            "large language model"
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "frequency_penalty",
+          "value": 0.5,
+          "display_name": "Frequency Penalty",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Penalty for frequency of token usage.",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
+        "input_value": {
+          "trace_as_input": true,
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "input_value",
+          "value": "",
+          "display_name": "Input",
+          "advanced": false,
+          "input_types": [
+            "Message"
           ],
-          "module": "langflow.components.ibm.watsonx.WatsonxAIComponent"
+          "dynamic": false,
+          "info": "",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "MessageInput"
         },
-        "minimized": false,
-        "output_types": [],
-        "outputs": [
-          {
-            "allows_loop": false,
-            "cache": true,
-            "display_name": "Model Response",
-            "group_outputs": false,
-            "method": "text_response",
-            "name": "text_output",
-            "options": null,
-            "required_inputs": null,
-            "selected": "Message",
-            "tool_mode": true,
-            "types": [
-              "Message"
-            ],
-            "value": "__UNDEFINED__"
-          },
-          {
-            "allows_loop": false,
-            "cache": true,
-            "display_name": "Language Model",
-            "group_outputs": false,
-            "method": "build_model",
-            "name": "model_output",
-            "options": null,
-            "required_inputs": null,
-            "selected": "LanguageModel",
-            "tool_mode": true,
-            "types": [
-              "LanguageModel"
-            ],
-            "value": "__UNDEFINED__"
-          }
-        ],
-        "pinned": false,
-        "template": {
-          "_type": "Component",
-          "api_key": {
-            "_input_type": "SecretStrInput",
-            "advanced": false,
-            "display_name": "API Key",
-            "dynamic": false,
-            "info": "The API Key to use for the model.",
-            "input_types": [],
-            "load_from_db": true,
-            "name": "api_key",
-            "password": true,
-            "placeholder": "",
-            "required": true,
-            "show": true,
-            "title_case": false,
-            "type": "str",
-            "value": "WATSONX_API_KEY"
-          },
-          "code": {
-            "advanced": true,
-            "dynamic": true,
-            "fileTypes": [],
-            "file_path": "",
-            "info": "",
-            "list": false,
-            "load_from_db": false,
-            "multiline": true,
-            "name": "code",
-            "password": false,
-            "placeholder": "",
-            "required": true,
-            "show": true,
-            "title_case": false,
-            "type": "code",
-            "value": "import json\nfrom typing import Any\n\nimport requests\nfrom langchain_ibm import ChatWatsonx\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs.inputs import BoolInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.logging.logger import logger\nfrom langflow.schema.dotdict import dotdict\n\n\nclass WatsonxAIComponent(LCModelComponent):\n    display_name = \"IBM watsonx.ai\"\n    description = \"Generate text using IBM watsonx.ai foundation models.\"\n    icon = \"WatsonxAI\"\n    name = \"IBMwatsonxModel\"\n    beta = False\n\n    _default_models = [\"ibm/granite-3-2b-instruct\", \"ibm/granite-3-8b-instruct\", \"ibm/granite-13b-instruct-v2\"]\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        DropdownInput(\n            name=\"url\",\n            display_name=\"watsonx API Endpoint\",\n            info=\"The base URL of the API.\",\n            value=None,\n            options=[\n                \"https://us-south.ml.cloud.ibm.com\",\n                \"https://eu-de.ml.cloud.ibm.com\",\n                \"https://eu-gb.ml.cloud.ibm.com\",\n                \"https://au-syd.ml.cloud.ibm.com\",\n                \"https://jp-tok.ml.cloud.ibm.com\",\n                \"https://ca-tor.ml.cloud.ibm.com\",\n            ],\n            real_time_refresh=True,\n        ),\n        StrInput(\n            name=\"project_id\",\n            display_name=\"watsonx Project ID\",\n            required=True,\n            info=\"The project ID or deployment space ID that is associated with the foundation model.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"API Key\",\n            info=\"The API Key to use for the model.\",\n            required=True,\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=[],\n            value=None,\n            dynamic=True,\n            required=True,\n        ),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate.\",\n            range_spec=RangeSpec(min=1, max=4096),\n            value=1000,\n        ),\n        StrInput(\n            name=\"stop_sequence\",\n            display_name=\"Stop Sequence\",\n            advanced=True,\n            info=\"Sequence where generation should stop.\",\n            field_type=\"str\",\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            info=\"Controls randomness, higher values increase diversity.\",\n            value=0.1,\n            range_spec=RangeSpec(min=0, max=2, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"top_p\",\n            display_name=\"Top P\",\n            info=\"The cumulative probability cutoff for token selection. \"\n            \"Lower values mean sampling from a smaller, more top-weighted nucleus.\",\n            value=0.9,\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"frequency_penalty\",\n            display_name=\"Frequency Penalty\",\n            info=\"Penalty for frequency of token usage.\",\n            value=0.5,\n            range_spec=RangeSpec(min=-2.0, max=2.0, step=0.01),\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"presence_penalty\",\n            display_name=\"Presence Penalty\",\n            info=\"Penalty for token presence in prior text.\",\n            value=0.3,\n            range_spec=RangeSpec(min=-2.0, max=2.0, step=0.01),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Random Seed\",\n            advanced=True,\n            info=\"The random seed for the model.\",\n            value=8,\n        ),\n        BoolInput(\n            name=\"logprobs\",\n            display_name=\"Log Probabilities\",\n            advanced=True,\n            info=\"Whether to return log probabilities of the output tokens.\",\n            value=True,\n        ),\n        IntInput(\n            name=\"top_logprobs\",\n            display_name=\"Top Log Probabilities\",\n            advanced=True,\n            info=\"Number of most likely tokens to return at each position.\",\n            value=3,\n            range_spec=RangeSpec(min=1, max=20),\n        ),\n        StrInput(\n            name=\"logit_bias\",\n            display_name=\"Logit Bias\",\n            advanced=True,\n            info='JSON string of token IDs to bias or suppress (e.g., {\"1003\": -100, \"1004\": 100}).',\n            field_type=\"str\",\n        ),\n    ]\n\n    @staticmethod\n    def fetch_models(base_url: str) -> list[str]:\n        \"\"\"Fetch available models from the watsonx.ai API.\"\"\"\n        try:\n            endpoint = f\"{base_url}/ml/v1/foundation_model_specs\"\n            params = {\"version\": \"2024-09-16\", \"filters\": \"function_text_chat,!lifecycle_withdrawn\"}\n            response = requests.get(endpoint, params=params, timeout=10)\n            response.raise_for_status()\n            data = response.json()\n            models = [model[\"model_id\"] for model in data.get(\"resources\", [])]\n            return sorted(models)\n        except Exception:  # noqa: BLE001\n            logger.exception(\"Error fetching models. Using default models.\")\n            return WatsonxAIComponent._default_models\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):\n        \"\"\"Update model options when URL or API key changes.\"\"\"\n        logger.info(\"Updating build config. Field name: %s, Field value: %s\", field_name, field_value)\n\n        if field_name == \"url\" and field_value:\n            try:\n                models = self.fetch_models(base_url=build_config.url.value)\n                build_config.model_name.options = models\n                if build_config.model_name.value:\n                    build_config.model_name.value = models[0]\n                info_message = f\"Updated model options: {len(models)} models found in {build_config.url.value}\"\n                logger.info(info_message)\n            except Exception:  # noqa: BLE001\n                logger.exception(\"Error updating model options.\")\n\n    def build_model(self) -> LanguageModel:\n        # Parse logit_bias from JSON string if provided\n        logit_bias = None\n        if hasattr(self, \"logit_bias\") and self.logit_bias:\n            try:\n                logit_bias = json.loads(self.logit_bias)\n            except json.JSONDecodeError:\n                logger.warning(\"Invalid logit_bias JSON format. Using default instead.\")\n                logit_bias = {\"1003\": -100, \"1004\": -100}\n\n        chat_params = {\n            \"max_tokens\": getattr(self, \"max_tokens\", None),\n            \"temperature\": getattr(self, \"temperature\", None),\n            \"top_p\": getattr(self, \"top_p\", None),\n            \"frequency_penalty\": getattr(self, \"frequency_penalty\", None),\n            \"presence_penalty\": getattr(self, \"presence_penalty\", None),\n            \"seed\": getattr(self, \"seed\", None),\n            \"stop\": [self.stop_sequence] if self.stop_sequence else [],\n            \"n\": 1,\n            \"logprobs\": getattr(self, \"logprobs\", True),\n            \"top_logprobs\": getattr(self, \"top_logprobs\", None),\n            \"time_limit\": 600000,\n            \"logit_bias\": logit_bias,\n        }\n\n        return ChatWatsonx(\n            apikey=SecretStr(self.api_key).get_secret_value(),\n            url=self.url,\n            project_id=self.project_id,\n            model_id=self.model_name,\n            params=chat_params,\n            streaming=self.stream,\n        )\n"
-          },
-          "frequency_penalty": {
-            "_input_type": "SliderInput",
-            "advanced": true,
-            "display_name": "Frequency Penalty",
-            "dynamic": false,
-            "info": "Penalty for frequency of token usage.",
-            "max_label": "",
-            "max_label_icon": "",
-            "min_label": "",
-            "min_label_icon": "",
-            "name": "frequency_penalty",
-            "placeholder": "",
-            "range_spec": {
-              "max": 2,
-              "min": -2,
-              "step": 0.01,
-              "step_type": "float"
-            },
-            "required": false,
-            "show": true,
-            "slider_buttons": false,
-            "slider_buttons_options": [],
-            "slider_input": false,
-            "title_case": false,
-            "tool_mode": false,
-            "type": "slider",
-            "value": 0.5
-          },
-          "input_value": {
-            "_input_type": "MessageInput",
-            "advanced": false,
-            "display_name": "Input",
-            "dynamic": false,
-            "info": "",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "input_value",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "logit_bias": {
-            "_input_type": "StrInput",
-            "advanced": true,
-            "display_name": "Logit Bias",
-            "dynamic": false,
-            "info": "JSON string of token IDs to bias or suppress (e.g., {\"1003\": -100, \"1004\": 100}).",
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "logit_bias",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "logprobs": {
-            "_input_type": "BoolInput",
-            "advanced": true,
-            "display_name": "Log Probabilities",
-            "dynamic": false,
-            "info": "Whether to return log probabilities of the output tokens.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "logprobs",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "bool",
-            "value": true
-          },
-          "max_tokens": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Max Tokens",
-            "dynamic": false,
-            "info": "The maximum number of tokens to generate.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "max_tokens",
-            "placeholder": "",
-            "range_spec": {
-              "max": 4096,
-              "min": 1,
-              "step": 0.1,
-              "step_type": "float"
-            },
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": 1000
-          },
-          "model_name": {
-            "_input_type": "DropdownInput",
-            "advanced": false,
-            "combobox": false,
-            "dialog_inputs": {},
-            "display_name": "Model Name",
-            "dynamic": true,
-            "info": "",
-            "name": "model_name",
-            "options": [
-              "ibm/granite-3-2-8b-instruct",
-              "ibm/granite-3-2b-instruct",
-              "ibm/granite-3-3-8b-instruct",
-              "ibm/granite-3-8b-instruct",
-              "ibm/granite-guardian-3-2b",
-              "ibm/granite-guardian-3-8b",
-              "ibm/granite-vision-3-2-2b",
-              "meta-llama/llama-3-2-11b-vision-instruct",
-              "meta-llama/llama-3-2-90b-vision-instruct",
-              "meta-llama/llama-3-3-70b-instruct",
-              "meta-llama/llama-3-405b-instruct",
-              "meta-llama/llama-4-maverick-17b-128e-instruct-fp8",
-              "meta-llama/llama-guard-3-11b-vision",
-              "mistralai/mistral-large",
-              "mistralai/mistral-medium-2505",
-              "mistralai/mistral-small-3-1-24b-instruct-2503",
-              "mistralai/pixtral-12b",
-              "openai/gpt-oss-120b"
-            ],
-            "options_metadata": [],
-            "placeholder": "",
-            "required": true,
-            "show": true,
-            "title_case": false,
-            "toggle": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": "ibm/granite-3-2-8b-instruct"
-          },
-          "presence_penalty": {
-            "_input_type": "SliderInput",
-            "advanced": true,
-            "display_name": "Presence Penalty",
-            "dynamic": false,
-            "info": "Penalty for token presence in prior text.",
-            "max_label": "",
-            "max_label_icon": "",
-            "min_label": "",
-            "min_label_icon": "",
-            "name": "presence_penalty",
-            "placeholder": "",
-            "range_spec": {
-              "max": 2,
-              "min": -2,
-              "step": 0.01,
-              "step_type": "float"
-            },
-            "required": false,
-            "show": true,
-            "slider_buttons": false,
-            "slider_buttons_options": [],
-            "slider_input": false,
-            "title_case": false,
-            "tool_mode": false,
-            "type": "slider",
-            "value": 0.3
-          },
-          "project_id": {
-            "_input_type": "StrInput",
-            "advanced": false,
-            "display_name": "watsonx Project ID",
-            "dynamic": false,
-            "info": "The project ID or deployment space ID that is associated with the foundation model.",
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": true,
-            "name": "project_id",
-            "placeholder": "",
-            "required": true,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": "WATSONX_PROJECT_ID"
-          },
-          "seed": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Random Seed",
-            "dynamic": false,
-            "info": "The random seed for the model.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "seed",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": 8
-          },
-          "stop_sequence": {
-            "_input_type": "StrInput",
-            "advanced": true,
-            "display_name": "Stop Sequence",
-            "dynamic": false,
-            "info": "Sequence where generation should stop.",
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "name": "stop_sequence",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "stream": {
-            "_input_type": "BoolInput",
-            "advanced": true,
-            "display_name": "Stream",
-            "dynamic": false,
-            "info": "Stream the response from the model. Streaming works only in Chat.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "stream",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "bool",
-            "value": false
-          },
-          "system_message": {
-            "_input_type": "MultilineInput",
-            "advanced": false,
-            "copy_field": false,
-            "display_name": "System Message",
-            "dynamic": false,
-            "info": "System message to pass to the model.",
-            "input_types": [
-              "Message"
-            ],
-            "list": false,
-            "list_add_label": "Add More",
-            "load_from_db": false,
-            "multiline": true,
-            "name": "system_message",
-            "placeholder": "",
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_input": true,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": ""
-          },
-          "temperature": {
-            "_input_type": "SliderInput",
-            "advanced": true,
-            "display_name": "Temperature",
-            "dynamic": false,
-            "info": "Controls randomness, higher values increase diversity.",
-            "max_label": "",
-            "max_label_icon": "",
-            "min_label": "",
-            "min_label_icon": "",
-            "name": "temperature",
-            "placeholder": "",
-            "range_spec": {
-              "max": 2,
-              "min": 0,
-              "step": 0.01,
-              "step_type": "float"
-            },
-            "required": false,
-            "show": true,
-            "slider_buttons": false,
-            "slider_buttons_options": [],
-            "slider_input": false,
-            "title_case": false,
-            "tool_mode": false,
-            "type": "slider",
-            "value": 0.1
-          },
-          "top_logprobs": {
-            "_input_type": "IntInput",
-            "advanced": true,
-            "display_name": "Top Log Probabilities",
-            "dynamic": false,
-            "info": "Number of most likely tokens to return at each position.",
-            "list": false,
-            "list_add_label": "Add More",
-            "name": "top_logprobs",
-            "placeholder": "",
-            "range_spec": {
-              "max": 20,
-              "min": 1,
-              "step": 0.1,
-              "step_type": "float"
-            },
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "int",
-            "value": 3
-          },
-          "top_p": {
-            "_input_type": "SliderInput",
-            "advanced": true,
-            "display_name": "Top P",
-            "dynamic": false,
-            "info": "The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus.",
-            "max_label": "",
-            "max_label_icon": "",
-            "min_label": "",
-            "min_label_icon": "",
-            "name": "top_p",
-            "placeholder": "",
-            "range_spec": {
-              "max": 1,
-              "min": 0,
-              "step": 0.01,
-              "step_type": "float"
-            },
-            "required": false,
-            "show": true,
-            "slider_buttons": false,
-            "slider_buttons_options": [],
-            "slider_input": false,
-            "title_case": false,
-            "tool_mode": false,
-            "type": "slider",
-            "value": 0.9
-          },
-          "url": {
-            "_input_type": "DropdownInput",
-            "advanced": false,
-            "combobox": false,
-            "dialog_inputs": {},
-            "display_name": "watsonx API Endpoint",
-            "dynamic": false,
-            "info": "The base URL of the API.",
-            "name": "url",
-            "options": [
-              "https://us-south.ml.cloud.ibm.com",
-              "https://eu-de.ml.cloud.ibm.com",
-              "https://eu-gb.ml.cloud.ibm.com",
-              "https://au-syd.ml.cloud.ibm.com",
-              "https://jp-tok.ml.cloud.ibm.com",
-              "https://ca-tor.ml.cloud.ibm.com"
-            ],
-            "options_metadata": [],
-            "placeholder": "",
-            "real_time_refresh": true,
-            "required": false,
-            "show": true,
-            "title_case": false,
-            "toggle": false,
-            "tool_mode": false,
-            "trace_as_metadata": true,
-            "type": "str",
-            "value": "https://us-south.ml.cloud.ibm.com"
-          }
+        "logit_bias": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "logit_bias",
+          "value": "",
+          "display_name": "Logit Bias",
+          "advanced": true,
+          "dynamic": false,
+          "info": "JSON string of token IDs to bias or suppress (e.g., {\"1003\": -100, \"1004\": 100}).",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "StrInput"
         },
-        "tool_mode": false
+        "logprobs": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "logprobs",
+          "value": true,
+          "display_name": "Log Probabilities",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Whether to return log probabilities of the output tokens.",
+          "title_case": false,
+          "type": "bool",
+          "_input_type": "BoolInput"
+        },
+        "max_tokens": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "range_spec": {
+            "step_type": "float",
+            "min": 1,
+            "max": 4096,
+            "step": 0.1
+          },
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "max_tokens",
+          "value": 1000,
+          "display_name": "Max Tokens",
+          "advanced": true,
+          "dynamic": false,
+          "info": "The maximum number of tokens to generate.",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "model_name": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "options": [],
+          "options_metadata": [],
+          "combobox": false,
+          "dialog_inputs": {},
+          "toggle": false,
+          "required": true,
+          "placeholder": "",
+          "show": true,
+          "name": "model_name",
+          "display_name": "Model Name",
+          "advanced": false,
+          "dynamic": true,
+          "info": "",
+          "title_case": false,
+          "external_options": {},
+          "type": "str",
+          "_input_type": "DropdownInput"
+        },
+        "presence_penalty": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": -2,
+            "max": 2,
+            "step": 0.01
+          },
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "presence_penalty",
+          "value": 0.3,
+          "display_name": "Presence Penalty",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Penalty for token presence in prior text.",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
+        "project_id": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": true,
+          "placeholder": "",
+          "show": true,
+          "name": "project_id",
+          "value": "WATSONX_PROJECT_ID",
+          "display_name": "watsonx Project ID",
+          "advanced": false,
+          "dynamic": false,
+          "info": "The project ID or deployment space ID that is associated with the foundation model.",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "StrInput"
+        },
+        "seed": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "seed",
+          "value": 8,
+          "display_name": "Random Seed",
+          "advanced": true,
+          "dynamic": false,
+          "info": "The random seed for the model.",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "stop_sequence": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "stop_sequence",
+          "value": "",
+          "display_name": "Stop Sequence",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Sequence where generation should stop.",
+          "title_case": false,
+          "type": "str",
+          "_input_type": "StrInput"
+        },
+        "stream": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "stream",
+          "value": false,
+          "display_name": "Stream",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Stream the response from the model. Streaming works only in Chat.",
+          "title_case": false,
+          "type": "bool",
+          "_input_type": "BoolInput"
+        },
+        "system_message": {
+          "tool_mode": false,
+          "trace_as_input": true,
+          "multiline": true,
+          "trace_as_metadata": true,
+          "load_from_db": false,
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "system_message",
+          "value": "",
+          "display_name": "System Message",
+          "advanced": false,
+          "input_types": [
+            "Message"
+          ],
+          "dynamic": false,
+          "info": "System message to pass to the model.",
+          "title_case": false,
+          "copy_field": false,
+          "type": "str",
+          "_input_type": "MultilineInput"
+        },
+        "temperature": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": 0,
+            "max": 2,
+            "step": 0.01
+          },
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "temperature",
+          "value": 0.1,
+          "display_name": "Temperature",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Controls randomness, higher values increase diversity.",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
+        "top_logprobs": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "range_spec": {
+            "step_type": "float",
+            "min": 1,
+            "max": 20,
+            "step": 0.1
+          },
+          "list": false,
+          "list_add_label": "Add More",
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "top_logprobs",
+          "value": 3,
+          "display_name": "Top Log Probabilities",
+          "advanced": true,
+          "dynamic": false,
+          "info": "Number of most likely tokens to return at each position.",
+          "title_case": false,
+          "type": "int",
+          "_input_type": "IntInput"
+        },
+        "top_p": {
+          "tool_mode": false,
+          "min_label": "",
+          "max_label": "",
+          "min_label_icon": "",
+          "max_label_icon": "",
+          "slider_buttons": false,
+          "slider_buttons_options": [],
+          "slider_input": false,
+          "range_spec": {
+            "step_type": "float",
+            "min": 0,
+            "max": 1,
+            "step": 0.01
+          },
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "top_p",
+          "value": 0.9,
+          "display_name": "Top P",
+          "advanced": true,
+          "dynamic": false,
+          "info": "The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus.",
+          "title_case": false,
+          "type": "slider",
+          "_input_type": "SliderInput"
+        },
+        "url": {
+          "tool_mode": false,
+          "trace_as_metadata": true,
+          "options": [
+            "https://us-south.ml.cloud.ibm.com",
+            "https://eu-de.ml.cloud.ibm.com",
+            "https://eu-gb.ml.cloud.ibm.com",
+            "https://au-syd.ml.cloud.ibm.com",
+            "https://jp-tok.ml.cloud.ibm.com",
+            "https://ca-tor.ml.cloud.ibm.com"
+          ],
+          "options_metadata": [],
+          "combobox": false,
+          "dialog_inputs": {},
+          "toggle": false,
+          "required": false,
+          "placeholder": "",
+          "show": true,
+          "name": "url",
+          "display_name": "watsonx API Endpoint",
+          "advanced": false,
+          "dynamic": false,
+          "info": "The base URL of the API.",
+          "real_time_refresh": true,
+          "title_case": false,
+          "external_options": {},
+          "type": "str",
+          "_input_type": "DropdownInput"
+        }
       },
-      "selected_output": "text_output",
-      "showNode": true,
-      "type": "IBMwatsonxModel"
+      "description": "Generate text using IBM watsonx.ai foundation models.",
+      "icon": "WatsonxAI",
+      "base_classes": [
+        "LanguageModel",
+        "Message"
+      ],
+      "display_name": "IBM watsonx.ai",
+      "documentation": "",
+      "minimized": false,
+      "custom_fields": {},
+      "output_types": [],
+      "pinned": false,
+      "conditional_paths": [],
+      "frozen": false,
+      "outputs": [
+        {
+          "types": [
+            "Message"
+          ],
+          "selected": "Message",
+          "name": "text_output",
+          "display_name": "Model Response",
+          "method": "text_response",
+          "value": "__UNDEFINED__",
+          "cache": true,
+          "allows_loop": false,
+          "group_outputs": false,
+          "tool_mode": true
+        },
+        {
+          "types": [
+            "LanguageModel"
+          ],
+          "name": "model_output",
+          "display_name": "Language Model",
+          "method": "build_model",
+          "value": "__UNDEFINED__",
+          "cache": true,
+          "allows_loop": false,
+          "group_outputs": false,
+          "tool_mode": true
+        }
+      ],
+      "field_order": [
+        "input_value",
+        "system_message",
+        "stream",
+        "url",
+        "project_id",
+        "api_key",
+        "model_name",
+        "max_tokens",
+        "stop_sequence",
+        "temperature",
+        "top_p",
+        "frequency_penalty",
+        "presence_penalty",
+        "seed",
+        "logprobs",
+        "top_logprobs",
+        "logit_bias"
+      ],
+      "beta": false,
+      "legacy": false,
+      "edited": false,
+      "metadata": {
+        "keywords": [
+          "model",
+          "llm",
+          "language model",
+          "large language model"
+        ],
+        "module": "lfx.components.ibm.watsonx.WatsonxAIComponent",
+        "code_hash": "85c24939214c",
+        "dependencies": {
+          "total_dependencies": 4,
+          "dependencies": [
+            {
+              "name": "requests",
+              "version": "2.32.5"
+            },
+            {
+              "name": "langchain_ibm",
+              "version": "0.3.16"
+            },
+            {
+              "name": "pydantic",
+              "version": "2.10.6"
+            },
+            {
+              "name": "lfx",
+              "version": null
+            }
+          ]
+        }
+      },
+      "tool_mode": false,
+      "official": false
     },
-    "dragging": false,
-    "id": "IBMwatsonxModel-18kmA",
-    "measured": {
-      "height": 632,
-      "width": 320
-    },
-    "position": {
-      "x": 370.8989669694083,
-      "y": 184
-    },
-    "selected": true,
-    "type": "genericNode"
-  }
\ No newline at end of file
+    "showNode": true,
+    "type": "IBMwatsonxModel",
+    "id": "IBMwatsonxModel-qXZxc",
+    "selected_output": "text_output"
+  },
+  "id": "IBMwatsonxModel-qXZxc",
+  "position": {
+    "x": 0,
+    "y": 0
+  },
+  "type": "genericNode"
+}
\ No newline at end of file
diff --git a/flows/ingestion_flow.json b/flows/ingestion_flow.json
index fb155916..12cf5b63 100644
--- a/flows/ingestion_flow.json
+++ b/flows/ingestion_flow.json
@@ -61,6 +61,8 @@
         "targetHandle": "{œfieldNameœ:œdata_inputsœ,œidœ:œSplitText-QIKhgœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}"
       },
       {
+        "animated": false,
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "EmbeddingModel",
@@ -80,10 +82,155 @@
           }
         },
         "id": "xy-edge__EmbeddingModel-eZ6bT{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-eZ6bTœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-OpenSearchHybrid-Ve6bS{œfieldNameœ:œembeddingœ,œidœ:œOpenSearchHybrid-Ve6bSœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
+        "selected": false,
         "source": "EmbeddingModel-eZ6bT",
         "sourceHandle": "{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-eZ6bTœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}",
         "target": "OpenSearchHybrid-Ve6bS",
         "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œOpenSearchHybrid-Ve6bSœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}"
+      },
+      {
+        "animated": false,
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "SecretInput",
+            "id": "SecretInput-F34VJ",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "dynamic_connector_type",
+            "id": "AdvancedDynamicFormBuilder-81Exw",
+            "inputTypes": [
+              "Text",
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "xy-edge__SecretInput-F34VJ{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-F34VJœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-AdvancedDynamicFormBuilder-81Exw{œfieldNameœ:œdynamic_connector_typeœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "SecretInput-F34VJ",
+        "sourceHandle": "{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-F34VJœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "AdvancedDynamicFormBuilder-81Exw",
+        "targetHandle": "{œfieldNameœ:œdynamic_connector_typeœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "animated": false,
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "SecretInput",
+            "id": "SecretInput-b2cab",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "dynamic_owner",
+            "id": "AdvancedDynamicFormBuilder-81Exw",
+            "inputTypes": [
+              "Text",
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "xy-edge__SecretInput-b2cab{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-b2cabœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-AdvancedDynamicFormBuilder-81Exw{œfieldNameœ:œdynamic_ownerœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "SecretInput-b2cab",
+        "sourceHandle": "{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-b2cabœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "AdvancedDynamicFormBuilder-81Exw",
+        "targetHandle": "{œfieldNameœ:œdynamic_ownerœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "animated": false,
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "SecretInput",
+            "id": "SecretInput-ZVfuS",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "dynamic_owner_email",
+            "id": "AdvancedDynamicFormBuilder-81Exw",
+            "inputTypes": [
+              "Text",
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "xy-edge__SecretInput-ZVfuS{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-ZVfuSœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-AdvancedDynamicFormBuilder-81Exw{œfieldNameœ:œdynamic_owner_emailœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "SecretInput-ZVfuS",
+        "sourceHandle": "{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-ZVfuSœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "AdvancedDynamicFormBuilder-81Exw",
+        "targetHandle": "{œfieldNameœ:œdynamic_owner_emailœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "animated": false,
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "SecretInput",
+            "id": "SecretInput-Iqtxd",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "dynamic_owner_name",
+            "id": "AdvancedDynamicFormBuilder-81Exw",
+            "inputTypes": [
+              "Text",
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "xy-edge__SecretInput-Iqtxd{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-Iqtxdœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-AdvancedDynamicFormBuilder-81Exw{œfieldNameœ:œdynamic_owner_nameœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "SecretInput-Iqtxd",
+        "sourceHandle": "{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-Iqtxdœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "AdvancedDynamicFormBuilder-81Exw",
+        "targetHandle": "{œfieldNameœ:œdynamic_owner_nameœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "animated": false,
+        "className": "not-running",
+        "data": {
+          "sourceHandle": {
+            "dataType": "AdvancedDynamicFormBuilder",
+            "id": "AdvancedDynamicFormBuilder-81Exw",
+            "name": "form_data",
+            "output_types": [
+              "Data"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "docs_metadata",
+            "id": "OpenSearchHybrid-Ve6bS",
+            "inputTypes": [
+              "Data"
+            ],
+            "type": "table"
+          }
+        },
+        "id": "xy-edge__AdvancedDynamicFormBuilder-81Exw{œdataTypeœ:œAdvancedDynamicFormBuilderœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œnameœ:œform_dataœ,œoutput_typesœ:[œDataœ]}-OpenSearchHybrid-Ve6bS{œfieldNameœ:œdocs_metadataœ,œidœ:œOpenSearchHybrid-Ve6bSœ,œinputTypesœ:[œDataœ],œtypeœ:œtableœ}",
+        "selected": false,
+        "source": "AdvancedDynamicFormBuilder-81Exw",
+        "sourceHandle": "{œdataTypeœ:œAdvancedDynamicFormBuilderœ,œidœ:œAdvancedDynamicFormBuilder-81Exwœ,œnameœ:œform_dataœ,œoutput_typesœ:[œDataœ]}",
+        "target": "OpenSearchHybrid-Ve6bS",
+        "targetHandle": "{œfieldNameœ:œdocs_metadataœ,œidœ:œOpenSearchHybrid-Ve6bSœ,œinputTypesœ:[œDataœ],œtypeœ:œtableœ}"
       }
     ],
     "nodes": [
@@ -114,9 +261,9 @@
             "frozen": false,
             "icon": "scissors-line-dashed",
             "legacy": false,
-            "lf_version": "1.5.0.post2",
+            "lf_version": "1.6.0",
             "metadata": {
-              "code_hash": "65a90e1f4fe6",
+              "code_hash": "f2867efda61f",
               "dependencies": {
                 "dependencies": [
                   {
@@ -124,8 +271,8 @@
                     "version": "0.3.9"
                   },
                   {
-                    "name": "langflow",
-                    "version": "1.5.0.post2"
+                    "name": "lfx",
+                    "version": null
                   }
                 ],
                 "total_dependencies": 2
@@ -208,7 +355,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_text_splitters import CharacterTextSplitter\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.io import DropdownInput, HandleInput, IntInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n    display_name: str = \"Split Text\"\n    description: str = \"Split text into chunks based on specified criteria.\"\n    documentation: str = \"https://docs.langflow.org/components-processing#split-text\"\n    icon = \"scissors-line-dashed\"\n    name = \"SplitText\"\n\n    inputs = [\n        HandleInput(\n            name=\"data_inputs\",\n            display_name=\"Input\",\n            info=\"The data with texts to split in chunks.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        IntInput(\n            name=\"chunk_overlap\",\n            display_name=\"Chunk Overlap\",\n            info=\"Number of characters to overlap between chunks.\",\n            value=200,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=(\n                \"The maximum length of each chunk. Text is first split by separator, \"\n                \"then chunks are merged up to this size. \"\n                \"Individual splits larger than this won't be further divided.\"\n            ),\n            value=1000,\n        ),\n        MessageTextInput(\n            name=\"separator\",\n            display_name=\"Separator\",\n            info=(\n                \"The character to split on. Use \\\\n for newline. \"\n                \"Examples: \\\\n\\\\n for paragraphs, \\\\n for lines, . for sentences\"\n            ),\n            value=\"\\n\",\n        ),\n        MessageTextInput(\n            name=\"text_key\",\n            display_name=\"Text Key\",\n            info=\"The key to use for the text column.\",\n            value=\"text\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"keep_separator\",\n            display_name=\"Keep Separator\",\n            info=\"Whether to keep the separator in the output chunks and where to place it.\",\n            options=[\"False\", \"True\", \"Start\", \"End\"],\n            value=\"False\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Chunks\", name=\"dataframe\", method=\"split_text\"),\n    ]\n\n    def _docs_to_data(self, docs) -> list[Data]:\n        data_list = [Data(text=doc.page_content, data=doc.metadata) for doc in docs]\n        return data_list\n\n    def _fix_separator(self, separator: str) -> str:\n        \"\"\"Fix common separator issues and convert to proper format.\"\"\"\n        if separator == \"/n\":\n            return \"\\n\"\n        if separator == \"/t\":\n            return \"\\t\"\n        return separator\n\n    def split_text_base(self):\n        separator = self._fix_separator(self.separator)\n        separator = unescape_string(separator)\n\n        if isinstance(self.data_inputs, DataFrame):\n            if not len(self.data_inputs):\n                msg = \"DataFrame is empty\"\n                raise TypeError(msg)\n\n            self.data_inputs.text_key = self.text_key\n            try:\n                documents = self.data_inputs.to_lc_documents()\n            except Exception as e:\n                msg = f\"Error converting DataFrame to documents: {e}\"\n                raise TypeError(msg) from e\n        elif isinstance(self.data_inputs, Message):\n            self.data_inputs = [self.data_inputs.to_data()]\n            return self.split_text_base()\n        else:\n            if not self.data_inputs:\n                msg = \"No data inputs provided\"\n                raise TypeError(msg)\n\n            documents = []\n            if isinstance(self.data_inputs, Data):\n                self.data_inputs.text_key = self.text_key\n                documents = [self.data_inputs.to_lc_document()]\n            else:\n                try:\n                    documents = [input_.to_lc_document() for input_ in self.data_inputs if isinstance(input_, Data)]\n                    if not documents:\n                        msg = f\"No valid Data inputs found in {type(self.data_inputs)}\"\n                        raise TypeError(msg)\n                except AttributeError as e:\n                    msg = f\"Invalid input type in collection: {e}\"\n                    raise TypeError(msg) from e\n        try:\n            # Convert string 'False'/'True' to boolean\n            keep_sep = self.keep_separator\n            if isinstance(keep_sep, str):\n                if keep_sep.lower() == \"false\":\n                    keep_sep = False\n                elif keep_sep.lower() == \"true\":\n                    keep_sep = True\n                # 'start' and 'end' are kept as strings\n            self.log(documents)\n            splitter = CharacterTextSplitter(\n                chunk_overlap=self.chunk_overlap,\n                chunk_size=self.chunk_size,\n                separator=separator,\n                keep_separator=keep_sep,\n            )\n            return splitter.split_documents(documents)\n        except Exception as e:\n            msg = f\"Error splitting text: {e}\"\n            raise TypeError(msg) from e\n\n    def split_text(self) -> DataFrame:\n        return DataFrame(self._docs_to_data(self.split_text_base()))\n"
+                "value": "from langchain_text_splitters import CharacterTextSplitter\n\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.io import DropdownInput, HandleInput, IntInput, MessageTextInput, Output\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n    display_name: str = \"Split Text\"\n    description: str = \"Split text into chunks based on specified criteria.\"\n    documentation: str = \"https://docs.langflow.org/components-processing#split-text\"\n    icon = \"scissors-line-dashed\"\n    name = \"SplitText\"\n\n    inputs = [\n        HandleInput(\n            name=\"data_inputs\",\n            display_name=\"Input\",\n            info=\"The data with texts to split in chunks.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        IntInput(\n            name=\"chunk_overlap\",\n            display_name=\"Chunk Overlap\",\n            info=\"Number of characters to overlap between chunks.\",\n            value=200,\n        ),\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=(\n                \"The maximum length of each chunk. Text is first split by separator, \"\n                \"then chunks are merged up to this size. \"\n                \"Individual splits larger than this won't be further divided.\"\n            ),\n            value=1000,\n        ),\n        MessageTextInput(\n            name=\"separator\",\n            display_name=\"Separator\",\n            info=(\n                \"The character to split on. Use \\\\n for newline. \"\n                \"Examples: \\\\n\\\\n for paragraphs, \\\\n for lines, . for sentences\"\n            ),\n            value=\"\\n\",\n        ),\n        MessageTextInput(\n            name=\"text_key\",\n            display_name=\"Text Key\",\n            info=\"The key to use for the text column.\",\n            value=\"text\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"keep_separator\",\n            display_name=\"Keep Separator\",\n            info=\"Whether to keep the separator in the output chunks and where to place it.\",\n            options=[\"False\", \"True\", \"Start\", \"End\"],\n            value=\"False\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Chunks\", name=\"dataframe\", method=\"split_text\"),\n    ]\n\n    def _docs_to_data(self, docs) -> list[Data]:\n        return [Data(text=doc.page_content, data=doc.metadata) for doc in docs]\n\n    def _fix_separator(self, separator: str) -> str:\n        \"\"\"Fix common separator issues and convert to proper format.\"\"\"\n        if separator == \"/n\":\n            return \"\\n\"\n        if separator == \"/t\":\n            return \"\\t\"\n        return separator\n\n    def split_text_base(self):\n        separator = self._fix_separator(self.separator)\n        separator = unescape_string(separator)\n\n        if isinstance(self.data_inputs, DataFrame):\n            if not len(self.data_inputs):\n                msg = \"DataFrame is empty\"\n                raise TypeError(msg)\n\n            self.data_inputs.text_key = self.text_key\n            try:\n                documents = self.data_inputs.to_lc_documents()\n            except Exception as e:\n                msg = f\"Error converting DataFrame to documents: {e}\"\n                raise TypeError(msg) from e\n        elif isinstance(self.data_inputs, Message):\n            self.data_inputs = [self.data_inputs.to_data()]\n            return self.split_text_base()\n        else:\n            if not self.data_inputs:\n                msg = \"No data inputs provided\"\n                raise TypeError(msg)\n\n            documents = []\n            if isinstance(self.data_inputs, Data):\n                self.data_inputs.text_key = self.text_key\n                documents = [self.data_inputs.to_lc_document()]\n            else:\n                try:\n                    documents = [input_.to_lc_document() for input_ in self.data_inputs if isinstance(input_, Data)]\n                    if not documents:\n                        msg = f\"No valid Data inputs found in {type(self.data_inputs)}\"\n                        raise TypeError(msg)\n                except AttributeError as e:\n                    msg = f\"Invalid input type in collection: {e}\"\n                    raise TypeError(msg) from e\n        try:\n            # Convert string 'False'/'True' to boolean\n            keep_sep = self.keep_separator\n            if isinstance(keep_sep, str):\n                if keep_sep.lower() == \"false\":\n                    keep_sep = False\n                elif keep_sep.lower() == \"true\":\n                    keep_sep = True\n                # 'start' and 'end' are kept as strings\n\n            splitter = CharacterTextSplitter(\n                chunk_overlap=self.chunk_overlap,\n                chunk_size=self.chunk_size,\n                separator=separator,\n                keep_separator=keep_sep,\n            )\n            return splitter.split_documents(documents)\n        except Exception as e:\n            msg = f\"Error splitting text: {e}\"\n            raise TypeError(msg) from e\n\n    def split_text(self) -> DataFrame:\n        return DataFrame(self._docs_to_data(self.split_text_base()))\n"
               },
               "data_inputs": {
                 "_input_type": "HandleInput",
@@ -239,6 +386,7 @@
                 "dialog_inputs": {},
                 "display_name": "Keep Separator",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Whether to keep the separator in the output chunks and where to place it.",
                 "name": "keep_separator",
                 "options": [
@@ -339,7 +487,7 @@
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
-            "description": "Loads content from files with optional advanced document processing and export using Docling.",
+            "description": "Loads content from one or more files.",
             "display_name": "File",
             "documentation": "https://docs.langflow.org/components-data#file",
             "edited": true,
@@ -363,23 +511,19 @@
             ],
             "frozen": false,
             "icon": "file-text",
-            "last_updated": "2025-09-22T15:54:40.920Z",
+            "last_updated": "2025-09-26T14:37:42.811Z",
             "legacy": false,
-            "lf_version": "1.5.0.post2",
+            "lf_version": "1.6.0",
             "metadata": {
-              "code_hash": "086578fbbd54",
+              "code_hash": "9a1d497f4f91",
               "dependencies": {
                 "dependencies": [
                   {
-                    "name": "langflow",
-                    "version": "1.5.0.post2"
-                  },
-                  {
-                    "name": "anyio",
-                    "version": "4.10.0"
+                    "name": "lfx",
+                    "version": null
                   }
                 ],
-                "total_dependencies": 2
+                "total_dependencies": 1
               },
               "module": "custom_components.file"
             },
@@ -412,7 +556,7 @@
                 "advanced": false,
                 "display_name": "Advanced Parser",
                 "dynamic": false,
-                "info": "Enable advanced document processing and export with Docling for PDFs, images, and office documents. Available only for single file processing.",
+                "info": "Enable advanced document processing and export with Docling for PDFs, images, and office documents. Available only for single file processing.Note that advanced document processing can consume significant resources.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "advanced_mode",
@@ -442,7 +586,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "\"\"\"Enhanced file component with clearer structure and Docling isolation.\n\nNotes:\n-----\n- Functionality is preserved with minimal behavioral changes.\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities (and optional threading via `parallel_load_data`).\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport subprocess\nimport sys\nimport textwrap\nfrom copy import deepcopy\nfrom typing import TYPE_CHECKING, Any\n\nfrom langflow.base.data.base_file import BaseFileComponent\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parallel_load_data, parse_text_file_to_data\nfrom langflow.io import (\n    BoolInput,\n    DropdownInput,\n    FileInput,\n    IntInput,\n    MessageTextInput,\n    Output,\n    StrInput,\n)\nfrom langflow.schema.data import Data\nfrom langflow.schema.message import Message\nimport anyio\nfrom langflow.services.storage.utils import build_content_type_from_extension\nif TYPE_CHECKING:\n    from langflow.schema import DataFrame\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"File\"\n    description = \"Loads content from files with optional advanced document processing and export using Docling.\"\n    documentation: str = \"https://docs.langflow.org/components-data#file\"\n    icon = \"file-text\"\n    name = \"File\"\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"csv\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"docx\",\n        \"htm\",\n        \"html\",\n        \"jpeg\",\n        \"json\",\n        \"md\",\n        \"pdf\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"txt\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"xml\",\n        \"webp\",\n        *TEXT_FILE_TYPES,\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    # ---- Inputs / Outputs (kept as close to original as possible) -------------------\n    _base_inputs = deepcopy(BaseFileComponent._base_inputs)\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            break\n\n    inputs = [\n        *_base_inputs,\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Available only for single file processing.\"\n            ),\n            show=False,\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"\", \"easyocr\"],\n            value=\"\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\"),\n    ]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n            file_path = paths[0] if paths else \"\"\n            file_count = len(field_value) if field_value else 0\n\n            # Advanced mode only for single (non-tabular) file\n            allow_advanced = file_count == 1 and not file_path.endswith((\".csv\", \".xlsx\", \".parquet\"))\n            build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n            if not allow_advanced:\n                build_config[\"advanced_mode\"][\"value\"] = False\n                for f in (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\"):\n                    if f in build_config:\n                        build_config[f][\"show\"] = False\n\n        elif field_name == \"advanced_mode\":\n            for f in (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\"):\n                if f in build_config:\n                    build_config[f][\"show\"] = bool(field_value)\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Structured Content\", name=\"dataframe\", method=\"load_files_structured\"),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Structured Content\", name=\"json\", method=\"load_files_json\"),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Structured Output\", name=\"advanced\", method=\"load_files_advanced\"),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Markdown\", name=\"markdown\", method=\"load_files_markdown\"),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"File Path\", name=\"path\", method=\"load_files_path\"),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\"),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"File Path\", name=\"path\", method=\"load_files_path\"),\n                )\n        else:\n            # Multiple files => DataFrame output; advanced parser disabled\n            frontend_node[\"outputs\"].append(Output(display_name=\"Files\", name=\"dataframe\", method=\"load_files\"))\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    def _process_docling_in_subprocess(self, file_path: str) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n        \"\"\"\n        if not file_path:\n            return None\n\n        args: dict[str, Any] = {\n            \"file_path\": file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": str(self.ocr_engine) if getattr(self, \"ocr_engine\", \"\") else None,\n        }\n\n        # The child is a tiny, self-contained script to keep memory/state isolated.\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                # Strategy 1: latest layout\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception:\n                    pass\n                # Strategy 2: alternative layout\n                try:\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    try:\n                        from docling_core.types import ConversionStatus, InputFormat  # type: ignore\n                    except Exception:\n                        try:\n                            from docling.datamodel import ConversionStatus, InputFormat  # type: ignore\n                        except Exception:\n                            class ConversionStatus: SUCCESS = \"success\"\n                            class InputFormat:\n                                PDF=\"pdf\"; IMAGE=\"image\"\n                    try:\n                        from docling_core.types.doc import ImageRefMode  # type: ignore\n                    except Exception:\n                        class ImageRefMode:\n                            PLACEHOLDER=\"placeholder\"; EMBEDDED=\"embedded\"\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"alternative\"\n                except Exception:\n                    pass\n                # Strategy 3: basic converter only\n                try:\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    class ConversionStatus: SUCCESS = \"success\"\n                    class InputFormat:\n                        PDF=\"pdf\"; IMAGE=\"image\"\n                    class ImageRefMode:\n                        PLACEHOLDER=\"placeholder\"; EMBEDDED=\"embedded\"\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"basic\"\n                except Exception as e:\n                    raise ImportError(f\"Docling imports failed: {e}\") from e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                if strategy == \"latest\" and pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n                        pipe = PdfPipelineOptions()\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                pipe.do_ocr = False\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # Validate file_path to avoid command injection or unsafe input\n        if not isinstance(args[\"file_path\"], str) or any(c in args[\"file_path\"] for c in [\";\", \"|\", \"&\", \"$\", \"`\"]):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        proc = subprocess.run(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            input=json.dumps(args).encode(\"utf-8\"),\n            capture_output=True,\n            check=False,\n        )\n\n        if not proc.stdout:\n            err_msg = proc.stderr.decode(\"utf-8\", errors=\"replace\") or \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": file_path})\n\n        try:\n            result = json.loads(proc.stdout.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = proc.stderr.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\", \"file_path\": file_path},\n            )\n\n        if not result.get(\"ok\"):\n            return Data(data={\"error\": result.get(\"error\", \"Unknown Docling error\"), **result.get(\"meta\", {})})\n\n        meta = result.get(\"meta\", {})\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - Single file + advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing in current process (optionally threaded).\n        \"\"\"\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                return parse_text_file_to_data(file_path, silent_errors=silent_errors)\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n\n        # Advanced path: only for a single Docling-compatible file\n        if len(file_list) == 1:\n            file_path = str(file_list[0].path)\n            if self.advanced_mode and self._is_docling_compatible(file_path):\n                advanced_data: Data | None = self._process_docling_in_subprocess(file_path)\n\n                # --- UNNEST: expand each element in `doc` to its own Data row\n                payload = getattr(advanced_data, \"data\", {}) or {}\n                doc_rows = payload.get(\"doc\")\n                if isinstance(doc_rows, list):\n                    rows: list[Data | None] = [\n                        Data(\n                            data={\n                                \"file_path\": file_path,\n                                **(item if isinstance(item, dict) else {\"value\": item}),\n                            },\n                        )\n                        for item in doc_rows\n                    ]\n                    return self.rollup_data(file_list, rows)\n\n                # If not structured, keep as-is (e.g., markdown export or error dict)\n                return self.rollup_data(file_list, [advanced_data])\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = 1 if not self.use_multithreading else max(1, self.concurrency_multithreading)\n        file_paths = [str(f.path) for f in file_list]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = parallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        return self.rollup_data(file_list, my_data)\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    def load_files_advanced(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to an advanced format.\"\"\"\n        self.markdown = False\n        return self.load_files()\n\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = self.load_files()\n        return Message(text=str(result.text[0]))\n    \n    async def load_files_message(self) -> Message:\n        \"\"\"Load files and return as Message.\n        \n        Returns:\n          Message: Message containing all file data\n        \"\"\"\n        data_list = self.load_files_core()\n        if not data_list:\n          return Message()  # No data -> empty message\n        \n        sep: str = getattr(self, \"separator\", \"\\n\\n\") or \"\\n\\n\"\n        \n        parts: list[str] = []\n        metadata = {}  # Initialize as empty dict instead of None\n        \n        for d in data_list:\n          # Prefer explicit text if available, fall back to full dict, lastly str()\n          text = (getattr(d, \"get_text\", lambda: None)() or d.data.get(\"text\")) if isinstance(d.data, dict) else None\n          parts.append(text if text is not None else str(d))\n        \n          # Set metadata from first file (or you could combine metadata from all files)\n          if not metadata and hasattr(d, 'file_path'):\n              file_path = d.file_path\n              # Get filename\n              file_path_obj = anyio.Path(file_path)\n              file_size_stat = await file_path_obj.stat()\n              filesize = file_size_stat.st_size\n              filename = file_path_obj.name\n              metadata[\"filename\"] = filename\n              metadata[\"file_size\"] = filesize\n              extension = filename.split(\".\")[-1]\n              if extension:\n                metadata[\"mimetype\"] = build_content_type_from_extension(filename.split(\".\")[-1])\n        \n              # Add other common metadata fields if available\n              if hasattr(d, 'data') and isinstance(d.data, dict):\n                  # Copy relevant metadata fields\n                  for field in ['mimetype', 'file_size', 'created_time', 'modified_time']:\n                      if field in d.data:\n                          metadata[field] = d.data[field]\n        self.log(metadata)\n        return Message(text=sep.join(parts), **metadata)\n"
+                "value": "\"\"\"Enhanced file component with Docling support and process isolation.\n\nNotes:\n-----\n- ALL Docling parsing/export runs in a separate OS process to prevent memory\n  growth and native library state from impacting the main Langflow process.\n- Standard text/structured parsing continues to use existing BaseFileComponent\n  utilities (and optional threading via `parallel_load_data`).\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport subprocess\nimport sys\nimport textwrap\nfrom copy import deepcopy\nfrom typing import Any\n\nfrom lfx.base.data.base_file import BaseFileComponent\nfrom lfx.base.data.utils import TEXT_FILE_TYPES, parallel_load_data, parse_text_file_to_data\nfrom lfx.inputs.inputs import DropdownInput, MessageTextInput, StrInput\nfrom lfx.io import BoolInput, FileInput, IntInput, Output\nfrom lfx.schema import DataFrame  # noqa: TC001\nfrom lfx.schema.data import Data\nfrom lfx.schema.message import Message\n\n\nclass FileComponent(BaseFileComponent):\n    \"\"\"File component with optional Docling processing (isolated in a subprocess).\"\"\"\n\n    display_name = \"File\"\n    description = \"Loads content from one or more files.\"\n    documentation: str = \"https://docs.langflow.org/components-data#file\"\n    icon = \"file-text\"\n    name = \"File\"\n\n    # Docling-supported/compatible extensions; TEXT_FILE_TYPES are supported by the base loader.\n    VALID_EXTENSIONS = [\n        *TEXT_FILE_TYPES,\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"jpeg\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"webp\",\n    ]\n\n    # Fixed export settings used when markdown export is requested.\n    EXPORT_FORMAT = \"Markdown\"\n    IMAGE_MODE = \"placeholder\"\n\n    _base_inputs = deepcopy(BaseFileComponent.get_base_inputs())\n\n    for input_item in _base_inputs:\n        if isinstance(input_item, FileInput) and input_item.name == \"path\":\n            input_item.real_time_refresh = True\n            break\n\n    inputs = [\n        *_base_inputs,\n        BoolInput(\n            name=\"advanced_mode\",\n            display_name=\"Advanced Parser\",\n            value=False,\n            real_time_refresh=True,\n            info=(\n                \"Enable advanced document processing and export with Docling for PDFs, images, and office documents. \"\n                \"Available only for single file processing.\"\n                \"Note that advanced document processing can consume significant resources.\"\n            ),\n            show=False,\n        ),\n        DropdownInput(\n            name=\"pipeline\",\n            display_name=\"Pipeline\",\n            info=\"Docling pipeline to use\",\n            options=[\"standard\", \"vlm\"],\n            value=\"standard\",\n            advanced=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"ocr_engine\",\n            display_name=\"OCR Engine\",\n            info=\"OCR engine to use. Only available when pipeline is set to 'standard'.\",\n            options=[\"None\", \"easyocr\"],\n            value=\"easyocr\",\n            show=False,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n            show=False,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder between pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n            show=False,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n            show=False,\n        ),\n        # Deprecated input retained for backward-compatibility.\n        BoolInput(\n            name=\"use_multithreading\",\n            display_name=\"[Deprecated] Use Multithreading\",\n            advanced=True,\n            value=True,\n            info=\"Set 'Processing Concurrency' greater than 1 to enable multithreading.\",\n        ),\n        IntInput(\n            name=\"concurrency_multithreading\",\n            display_name=\"Processing Concurrency\",\n            advanced=True,\n            info=\"When multiple files are being processed, the number of files to process concurrently.\",\n            value=1,\n        ),\n        BoolInput(\n            name=\"markdown\",\n            display_name=\"Markdown Export\",\n            info=\"Export processed documents to Markdown format. Only available when advanced mode is enabled.\",\n            value=False,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\"),\n    ]\n\n    # ------------------------------ UI helpers --------------------------------------\n\n    def _path_value(self, template: dict) -> list[str]:\n        \"\"\"Return the list of currently selected file paths from the template.\"\"\"\n        return template.get(\"path\", {}).get(\"file_path\", [])\n\n    def update_build_config(\n        self,\n        build_config: dict[str, Any],\n        field_value: Any,\n        field_name: str | None = None,\n    ) -> dict[str, Any]:\n        \"\"\"Show/hide Advanced Parser and related fields based on selection context.\"\"\"\n        if field_name == \"path\":\n            paths = self._path_value(build_config)\n            file_path = paths[0] if paths else \"\"\n            file_count = len(field_value) if field_value else 0\n\n            # Advanced mode only for single (non-tabular) file\n            allow_advanced = file_count == 1 and not file_path.endswith((\".csv\", \".xlsx\", \".parquet\"))\n            build_config[\"advanced_mode\"][\"show\"] = allow_advanced\n            if not allow_advanced:\n                build_config[\"advanced_mode\"][\"value\"] = False\n                for f in (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\"):\n                    if f in build_config:\n                        build_config[f][\"show\"] = False\n\n        # Docling Processing\n        elif field_name == \"advanced_mode\":\n            for f in (\"pipeline\", \"ocr_engine\", \"doc_key\", \"md_image_placeholder\", \"md_page_break_placeholder\"):\n                if f in build_config:\n                    build_config[f][\"show\"] = bool(field_value)\n\n        elif field_name == \"pipeline\":\n            if field_value == \"standard\":\n                build_config[\"ocr_engine\"][\"show\"] = True\n                build_config[\"ocr_engine\"][\"value\"] = \"easyocr\"\n            else:\n                build_config[\"ocr_engine\"][\"show\"] = False\n                build_config[\"ocr_engine\"][\"value\"] = \"None\"\n\n        return build_config\n\n    def update_outputs(self, frontend_node: dict[str, Any], field_name: str, field_value: Any) -> dict[str, Any]:  # noqa: ARG002\n        \"\"\"Dynamically show outputs based on file count/type and advanced mode.\"\"\"\n        if field_name not in [\"path\", \"advanced_mode\", \"pipeline\"]:\n            return frontend_node\n\n        template = frontend_node.get(\"template\", {})\n        paths = self._path_value(template)\n        if not paths:\n            return frontend_node\n\n        frontend_node[\"outputs\"] = []\n        if len(paths) == 1:\n            file_path = paths[0] if field_name == \"path\" else frontend_node[\"template\"][\"path\"][\"file_path\"][0]\n            if file_path.endswith((\".csv\", \".xlsx\", \".parquet\")):\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Structured Content\", name=\"dataframe\", method=\"load_files_structured\"),\n                )\n            elif file_path.endswith(\".json\"):\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Structured Content\", name=\"json\", method=\"load_files_json\"),\n                )\n\n            advanced_mode = frontend_node.get(\"template\", {}).get(\"advanced_mode\", {}).get(\"value\", False)\n            if advanced_mode:\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Structured Output\", name=\"advanced_dataframe\", method=\"load_files_dataframe\"),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Markdown\", name=\"advanced_markdown\", method=\"load_files_markdown\"),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"File Path\", name=\"path\", method=\"load_files_path\"),\n                )\n            else:\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"Raw Content\", name=\"message\", method=\"load_files_message\"),\n                )\n                frontend_node[\"outputs\"].append(\n                    Output(display_name=\"File Path\", name=\"path\", method=\"load_files_path\"),\n                )\n        else:\n            # Multiple files => DataFrame output; advanced parser disabled\n            frontend_node[\"outputs\"].append(Output(display_name=\"Files\", name=\"dataframe\", method=\"load_files\"))\n\n        return frontend_node\n\n    # ------------------------------ Core processing ----------------------------------\n\n    def _is_docling_compatible(self, file_path: str) -> bool:\n        \"\"\"Lightweight extension gate for Docling-compatible types.\"\"\"\n        docling_exts = (\n            \".adoc\",\n            \".asciidoc\",\n            \".asc\",\n            \".bmp\",\n            \".csv\",\n            \".dotx\",\n            \".dotm\",\n            \".docm\",\n            \".docx\",\n            \".htm\",\n            \".html\",\n            \".jpeg\",\n            \".json\",\n            \".md\",\n            \".pdf\",\n            \".png\",\n            \".potx\",\n            \".ppsx\",\n            \".pptm\",\n            \".potm\",\n            \".ppsm\",\n            \".pptx\",\n            \".tiff\",\n            \".txt\",\n            \".xls\",\n            \".xlsx\",\n            \".xhtml\",\n            \".xml\",\n            \".webp\",\n        )\n        return file_path.lower().endswith(docling_exts)\n\n    def _process_docling_in_subprocess(self, file_path: str) -> Data | None:\n        \"\"\"Run Docling in a separate OS process and map the result to a Data object.\n\n        We avoid multiprocessing pickling by launching `python -c \"<script>\"` and\n        passing JSON config via stdin. The child prints a JSON result to stdout.\n        \"\"\"\n        if not file_path:\n            return None\n\n        args: dict[str, Any] = {\n            \"file_path\": file_path,\n            \"markdown\": bool(self.markdown),\n            \"image_mode\": str(self.IMAGE_MODE),\n            \"md_image_placeholder\": str(self.md_image_placeholder),\n            \"md_page_break_placeholder\": str(self.md_page_break_placeholder),\n            \"pipeline\": str(self.pipeline),\n            \"ocr_engine\": (\n                self.ocr_engine if self.ocr_engine and self.ocr_engine != \"None\" and self.pipeline != \"vlm\" else None\n            ),\n        }\n\n        self.log(f\"Starting Docling subprocess for file: {file_path}\")\n        self.log(args)\n\n        # Child script for isolating the docling processing\n        child_script = textwrap.dedent(\n            r\"\"\"\n            import json, sys\n\n            def try_imports():\n                # Strategy 1: latest layout\n                try:\n                    from docling.datamodel.base_models import ConversionStatus, InputFormat  # type: ignore\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    from docling_core.types.doc import ImageRefMode  # type: ignore\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"latest\"\n                except Exception:\n                    pass\n                # Strategy 2: alternative layout\n                try:\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    try:\n                        from docling_core.types import ConversionStatus, InputFormat  # type: ignore\n                    except Exception:\n                        try:\n                            from docling.datamodel import ConversionStatus, InputFormat  # type: ignore\n                        except Exception:\n                            class ConversionStatus: SUCCESS = \"success\"\n                            class InputFormat:\n                                PDF=\"pdf\"; IMAGE=\"image\"\n                    try:\n                        from docling_core.types.doc import ImageRefMode  # type: ignore\n                    except Exception:\n                        class ImageRefMode:\n                            PLACEHOLDER=\"placeholder\"; EMBEDDED=\"embedded\"\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"alternative\"\n                except Exception:\n                    pass\n                # Strategy 3: basic converter only\n                try:\n                    from docling.document_converter import DocumentConverter  # type: ignore\n                    class ConversionStatus: SUCCESS = \"success\"\n                    class InputFormat:\n                        PDF=\"pdf\"; IMAGE=\"image\"\n                    class ImageRefMode:\n                        PLACEHOLDER=\"placeholder\"; EMBEDDED=\"embedded\"\n                    return ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, \"basic\"\n                except Exception as e:\n                    raise ImportError(f\"Docling imports failed: {e}\") from e\n\n            def create_converter(strategy, input_format, DocumentConverter, pipeline, ocr_engine):\n                # --- Standard PDF/IMAGE pipeline (your existing behavior), with optional OCR ---\n                if pipeline == \"standard\":\n                    try:\n                        from docling.datamodel.pipeline_options import PdfPipelineOptions  # type: ignore\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        pipe = PdfPipelineOptions()\n                        pipe.do_ocr = False\n\n                        if ocr_engine:\n                            try:\n                                from docling.models.factories import get_ocr_factory  # type: ignore\n                                pipe.do_ocr = True\n                                fac = get_ocr_factory(allow_external_plugins=False)\n                                pipe.ocr_options = fac.create_options(kind=ocr_engine)\n                            except Exception:\n                                # If OCR setup fails, disable it\n                                pipe.do_ocr = False\n\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_options=pipe)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_options=pipe)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Vision-Language Model (VLM) pipeline ---\n                if pipeline == \"vlm\":\n                    try:\n                        from docling.pipeline.vlm_pipeline import VlmPipeline\n                        from docling.document_converter import PdfFormatOption  # type: ignore\n\n                        vl_pipe = VlmPipelineOptions()\n\n                        # VLM paths generally don't need OCR; keep OCR off by default here.\n                        fmt = {}\n                        if hasattr(input_format, \"PDF\"):\n                            fmt[getattr(input_format, \"PDF\")] = PdfFormatOption(pipeline_cls=VlmPipeline)\n                        if hasattr(input_format, \"IMAGE\"):\n                            fmt[getattr(input_format, \"IMAGE\")] = PdfFormatOption(pipeline_cls=VlmPipeline)\n\n                        return DocumentConverter(format_options=fmt)\n                    except Exception:\n                        return DocumentConverter()\n\n                # --- Fallback: default converter with no special options ---\n                return DocumentConverter()\n\n            def export_markdown(document, ImageRefMode, image_mode, img_ph, pg_ph):\n                try:\n                    mode = getattr(ImageRefMode, image_mode.upper(), image_mode)\n                    return document.export_to_markdown(\n                        image_mode=mode,\n                        image_placeholder=img_ph,\n                        page_break_placeholder=pg_ph,\n                    )\n                except Exception:\n                    try:\n                        return document.export_to_text()\n                    except Exception:\n                        return str(document)\n\n            def to_rows(doc_dict):\n                rows = []\n                for t in doc_dict.get(\"texts\", []):\n                    prov = t.get(\"prov\") or []\n                    page_no = None\n                    if prov and isinstance(prov, list) and isinstance(prov[0], dict):\n                        page_no = prov[0].get(\"page_no\")\n                    rows.append({\n                        \"page_no\": page_no,\n                        \"label\": t.get(\"label\"),\n                        \"text\": t.get(\"text\"),\n                        \"level\": t.get(\"level\"),\n                    })\n                return rows\n\n            def main():\n                cfg = json.loads(sys.stdin.read())\n                file_path = cfg[\"file_path\"]\n                markdown = cfg[\"markdown\"]\n                image_mode = cfg[\"image_mode\"]\n                img_ph = cfg[\"md_image_placeholder\"]\n                pg_ph = cfg[\"md_page_break_placeholder\"]\n                pipeline = cfg[\"pipeline\"]\n                ocr_engine = cfg.get(\"ocr_engine\")\n                meta = {\"file_path\": file_path}\n\n                try:\n                    ConversionStatus, InputFormat, DocumentConverter, ImageRefMode, strategy = try_imports()\n                    converter = create_converter(strategy, InputFormat, DocumentConverter, pipeline, ocr_engine)\n                    try:\n                        res = converter.convert(file_path)\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling conversion error: {e}\", \"meta\": meta}))\n                        return\n\n                    ok = False\n                    if hasattr(res, \"status\"):\n                        try:\n                            ok = (res.status == ConversionStatus.SUCCESS) or (str(res.status).lower() == \"success\")\n                        except Exception:\n                            ok = (str(res.status).lower() == \"success\")\n                    if not ok and hasattr(res, \"document\"):\n                        ok = getattr(res, \"document\", None) is not None\n                    if not ok:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling conversion failed\", \"meta\": meta}))\n                        return\n\n                    doc = getattr(res, \"document\", None)\n                    if doc is None:\n                        print(json.dumps({\"ok\": False, \"error\": \"Docling produced no document\", \"meta\": meta}))\n                        return\n\n                    if markdown:\n                        text = export_markdown(doc, ImageRefMode, image_mode, img_ph, pg_ph)\n                        print(json.dumps({\"ok\": True, \"mode\": \"markdown\", \"text\": text, \"meta\": meta}))\n                        return\n\n                    # structured\n                    try:\n                        doc_dict = doc.export_to_dict()\n                    except Exception as e:\n                        print(json.dumps({\"ok\": False, \"error\": f\"Docling export_to_dict failed: {e}\", \"meta\": meta}))\n                        return\n\n                    rows = to_rows(doc_dict)\n                    print(json.dumps({\"ok\": True, \"mode\": \"structured\", \"doc\": rows, \"meta\": meta}))\n                except Exception as e:\n                    print(\n                        json.dumps({\n                            \"ok\": False,\n                            \"error\": f\"Docling processing error: {e}\",\n                            \"meta\": {\"file_path\": file_path},\n                        })\n                    )\n\n            if __name__ == \"__main__\":\n                main()\n            \"\"\"\n        )\n\n        # Validate file_path to avoid command injection or unsafe input\n        if not isinstance(args[\"file_path\"], str) or any(c in args[\"file_path\"] for c in [\";\", \"|\", \"&\", \"$\", \"`\"]):\n            return Data(data={\"error\": \"Unsafe file path detected.\", \"file_path\": args[\"file_path\"]})\n\n        proc = subprocess.run(  # noqa: S603\n            [sys.executable, \"-u\", \"-c\", child_script],\n            input=json.dumps(args).encode(\"utf-8\"),\n            capture_output=True,\n            check=False,\n        )\n\n        if not proc.stdout:\n            err_msg = proc.stderr.decode(\"utf-8\", errors=\"replace\") or \"no output from child process\"\n            return Data(data={\"error\": f\"Docling subprocess error: {err_msg}\", \"file_path\": file_path})\n\n        try:\n            result = json.loads(proc.stdout.decode(\"utf-8\"))\n        except Exception as e:  # noqa: BLE001\n            err_msg = proc.stderr.decode(\"utf-8\", errors=\"replace\")\n            return Data(\n                data={\"error\": f\"Invalid JSON from Docling subprocess: {e}. stderr={err_msg}\", \"file_path\": file_path},\n            )\n\n        if not result.get(\"ok\"):\n            return Data(data={\"error\": result.get(\"error\", \"Unknown Docling error\"), **result.get(\"meta\", {})})\n\n        meta = result.get(\"meta\", {})\n        if result.get(\"mode\") == \"markdown\":\n            exported_content = str(result.get(\"text\", \"\"))\n            return Data(\n                text=exported_content,\n                data={\"exported_content\": exported_content, \"export_format\": self.EXPORT_FORMAT, **meta},\n            )\n\n        rows = list(result.get(\"doc\", []))\n        return Data(data={\"doc\": rows, \"export_format\": self.EXPORT_FORMAT, **meta})\n\n    def process_files(\n        self,\n        file_list: list[BaseFileComponent.BaseFile],\n    ) -> list[BaseFileComponent.BaseFile]:\n        \"\"\"Process input files.\n\n        - Single file + advanced_mode => Docling in a separate process.\n        - Otherwise => standard parsing in current process (optionally threaded).\n        \"\"\"\n        if not file_list:\n            msg = \"No files to process.\"\n            raise ValueError(msg)\n\n        def process_file_standard(file_path: str, *, silent_errors: bool = False) -> Data | None:\n            try:\n                return parse_text_file_to_data(file_path, silent_errors=silent_errors)\n            except FileNotFoundError as e:\n                self.log(f\"File not found: {file_path}. Error: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n            except Exception as e:\n                self.log(f\"Unexpected error processing {file_path}: {e}\")\n                if not silent_errors:\n                    raise\n                return None\n\n        # Advanced path: only for a single Docling-compatible file\n        if len(file_list) == 1:\n            file_path = str(file_list[0].path)\n            if self.advanced_mode and self._is_docling_compatible(file_path):\n                advanced_data: Data | None = self._process_docling_in_subprocess(file_path)\n\n                # --- UNNEST: expand each element in `doc` to its own Data row\n                payload = getattr(advanced_data, \"data\", {}) or {}\n                doc_rows = payload.get(\"doc\")\n                if isinstance(doc_rows, list):\n                    rows: list[Data | None] = [\n                        Data(\n                            data={\n                                \"file_path\": file_path,\n                                **(item if isinstance(item, dict) else {\"value\": item}),\n                            },\n                        )\n                        for item in doc_rows\n                    ]\n                    return self.rollup_data(file_list, rows)\n\n                # If not structured, keep as-is (e.g., markdown export or error dict)\n                return self.rollup_data(file_list, [advanced_data])\n\n        # Standard multi-file (or single non-advanced) path\n        concurrency = 1 if not self.use_multithreading else max(1, self.concurrency_multithreading)\n        file_paths = [str(f.path) for f in file_list]\n        self.log(f\"Starting parallel processing of {len(file_paths)} files with concurrency: {concurrency}.\")\n        my_data = parallel_load_data(\n            file_paths,\n            silent_errors=self.silent_errors,\n            load_function=process_file_standard,\n            max_concurrency=concurrency,\n        )\n        return self.rollup_data(file_list, my_data)\n\n    # ------------------------------ Output helpers -----------------------------------\n\n    def load_files_helper(self) -> DataFrame:\n        result = self.load_files()\n\n        # Error condition - raise error if no text and an error is present\n        if not hasattr(result, \"text\"):\n            if hasattr(result, \"error\"):\n                raise ValueError(result.error[0])\n            msg = \"No content generated.\"\n            raise ValueError(msg)\n\n        return result\n\n    def load_files_dataframe(self) -> DataFrame:\n        \"\"\"Load files using advanced Docling processing and export to DataFrame format.\"\"\"\n        self.markdown = False\n        return self.load_files_helper()\n\n    def load_files_markdown(self) -> Message:\n        \"\"\"Load files using advanced Docling processing and export to Markdown format.\"\"\"\n        self.markdown = True\n        result = self.load_files_helper()\n        return Message(text=str(result.text[0]))\n"
               },
               "concurrency_multithreading": {
                 "_input_type": "IntInput",
@@ -623,10 +767,12 @@
                 "dialog_inputs": {},
                 "display_name": "OCR Engine",
                 "dynamic": false,
+                "external_options": {},
                 "info": "OCR engine to use. Only available when pipeline is set to 'standard'.",
+                "load_from_db": false,
                 "name": "ocr_engine",
                 "options": [
-                  "",
+                  "None",
                   "easyocr"
                 ],
                 "options_metadata": [],
@@ -646,21 +792,32 @@
                 "display_name": "Files",
                 "dynamic": false,
                 "fileTypes": [
+                  "csv",
+                  "json",
+                  "pdf",
+                  "txt",
+                  "md",
+                  "mdx",
+                  "yaml",
+                  "yml",
+                  "xml",
+                  "html",
+                  "htm",
+                  "docx",
+                  "py",
+                  "sh",
+                  "sql",
+                  "js",
+                  "ts",
+                  "tsx",
                   "adoc",
                   "asciidoc",
                   "asc",
                   "bmp",
-                  "csv",
                   "dotx",
                   "dotm",
                   "docm",
-                  "docx",
-                  "htm",
-                  "html",
                   "jpeg",
-                  "json",
-                  "md",
-                  "pdf",
                   "png",
                   "potx",
                   "ppsx",
@@ -669,30 +826,10 @@
                   "ppsm",
                   "pptx",
                   "tiff",
-                  "txt",
                   "xls",
                   "xlsx",
                   "xhtml",
-                  "xml",
                   "webp",
-                  "txt",
-                  "md",
-                  "mdx",
-                  "csv",
-                  "json",
-                  "yaml",
-                  "yml",
-                  "xml",
-                  "html",
-                  "htm",
-                  "pdf",
-                  "docx",
-                  "py",
-                  "sh",
-                  "sql",
-                  "js",
-                  "ts",
-                  "tsx",
                   "zip",
                   "tar",
                   "tgz",
@@ -700,10 +837,9 @@
                   "gz"
                 ],
                 "file_path": [],
-                "info": "Supported file extensions: adoc, asciidoc, asc, bmp, csv, dotx, dotm, docm, docx, htm, html, jpeg, json, md, pdf, png, potx, ppsx, pptm, potm, ppsm, pptx, tiff, txt, xls, xlsx, xhtml, xml, webp, txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx; optionally bundled in file extensions: zip, tar, tgz, bz2, gz",
+                "info": "Supported file extensions: csv, json, pdf, txt, md, mdx, yaml, yml, xml, html, htm, docx, py, sh, sql, js, ts, tsx, adoc, asciidoc, asc, bmp, dotx, dotm, docm, jpeg, png, potx, ppsx, pptm, potm, ppsm, pptx, tiff, xls, xlsx, xhtml, webp; optionally bundled in file extensions: zip, tar, tgz, bz2, gz",
                 "list": true,
                 "list_add_label": "Add More",
-                "load_from_db": false,
                 "name": "path",
                 "placeholder": "",
                 "real_time_refresh": true,
@@ -722,6 +858,7 @@
                 "dialog_inputs": {},
                 "display_name": "Pipeline",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Docling pipeline to use",
                 "name": "pipeline",
                 "options": [
@@ -730,6 +867,7 @@
                 ],
                 "options_metadata": [],
                 "placeholder": "",
+                "real_time_refresh": true,
                 "required": false,
                 "show": false,
                 "title_case": false,
@@ -804,12 +942,12 @@
         "dragging": false,
         "id": "File-PSU37",
         "measured": {
-          "height": 230,
+          "height": 214,
           "width": 320
         },
         "position": {
-          "x": 1330.7650978046952,
-          "y": 1431.5905495627503
+          "x": 1270.0395728258152,
+          "y": 1372.889208749833
         },
         "selected": false,
         "type": "genericNode"
@@ -826,8 +964,8 @@
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
-            "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
-            "display_name": "OpenSearch (Hybrid)",
+            "description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
+            "display_name": "OpenSearch",
             "documentation": "",
             "edited": true,
             "field_order": [
@@ -858,21 +996,21 @@
             "icon": "OpenSearch",
             "legacy": false,
             "metadata": {
-              "code_hash": "deee3f04cb47",
+              "code_hash": "c81b23acb81a",
               "dependencies": {
                 "dependencies": [
-                  {
-                    "name": "langflow",
-                    "version": "1.5.0.post2"
-                  },
                   {
                     "name": "opensearchpy",
                     "version": "2.8.0"
+                  },
+                  {
+                    "name": "lfx",
+                    "version": null
                   }
                 ],
                 "total_dependencies": 2
               },
-              "module": "custom_components.opensearch_hybrid"
+              "module": "custom_components.opensearch"
             },
             "minimized": false,
             "output_types": [],
@@ -916,7 +1054,7 @@
                 "cache": true,
                 "display_name": "Vector Store Connection",
                 "group_outputs": false,
-                "hidden": true,
+                "hidden": false,
                 "method": "as_vector_store",
                 "name": "vectorstoreconnection",
                 "options": null,
@@ -937,9 +1075,10 @@
                 "advanced": false,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Auth Mode",
+                "display_name": "Authentication Mode",
                 "dynamic": false,
-                "info": "Choose Basic (username/password) or JWT (Bearer token).",
+                "external_options": {},
+                "info": "Authentication method: 'basic' for username/password authentication, or 'jwt' for JSON Web Token (Bearer) authentication.",
                 "load_from_db": false,
                 "name": "auth_mode",
                 "options": [
@@ -992,14 +1131,17 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any, Dict, List, Optional\n\nfrom langflow.base.vectorstores.model import (\n    LCVectorStoreComponent,\n    check_cached_vector_store,\n)\nfrom langflow.base.vectorstores.vector_store_connection_decorator import (\n    vector_store_connection,\n)\nfrom langflow.io import (\n    BoolInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n    TableInput,\n)\nfrom langflow.logging import logger\nfrom langflow.schema.data import Data\nfrom opensearchpy import OpenSearch, helpers\n\n\n@vector_store_connection\nclass OpenSearchHybridComponent(LCVectorStoreComponent):\n    \"\"\"OpenSearch hybrid search: KNN (k=10, boost=0.7) + multi_match (boost=0.3) with optional filters & min_score.\"\"\"\n\n    display_name: str = \"OpenSearch (Hybrid)\"\n    name: str = \"OpenSearchHybrid\"\n    icon: str = \"OpenSearch\"\n    description: str = \"Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.\"\n\n    # Keys we consider baseline\n    default_keys: list[str] = [\n        \"opensearch_url\",\n        \"index_name\",\n        *[\n            i.name for i in LCVectorStoreComponent.inputs\n        ],  # search_query, add_documents, etc.\n        \"embedding\",\n        \"vector_field\",\n        \"number_of_results\",\n        \"auth_mode\",\n        \"username\",\n        \"password\",\n        \"jwt_token\",\n        \"jwt_header\",\n        \"bearer_prefix\",\n        \"use_ssl\",\n        \"verify_certs\",\n        \"filter_expression\",\n        \"engine\",\n        \"space_type\",\n        \"ef_construction\",\n        \"m\",\n        \"docs_metadata\",\n    ]\n\n    inputs = [\n        TableInput(\n            name=\"docs_metadata\",\n            display_name=\"Ingestion Metadata\",\n            info=\"Key value pairs to be inserted into each ingested document.\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Key\",\n                    \"type\": \"str\",\n                    \"description\": \"Key name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Value of the metadata\",\n                },\n            ],\n            value=[],\n            advanced=True,\n        ),\n        StrInput(\n            name=\"opensearch_url\",\n            display_name=\"OpenSearch URL\",\n            value=\"http://localhost:9200\",\n            info=\"URL for your OpenSearch cluster.\",\n        ),\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow\",\n            info=\"The index to search.\",\n        ),\n        DropdownInput(\n            name=\"engine\",\n            display_name=\"Engine\",\n            options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n            value=\"jvector\",\n            info=\"Vector search engine to use.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"space_type\",\n            display_name=\"Space Type\",\n            options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n            value=\"l2\",\n            info=\"Distance metric for vector similarity.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"ef_construction\",\n            display_name=\"EF Construction\",\n            value=512,\n            info=\"Size of the dynamic list used during k-NN graph creation.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"m\",\n            display_name=\"M Parameter\",\n            value=16,\n            info=\"Number of bidirectional links created for each new element.\",\n            advanced=True,\n        ),\n        *LCVectorStoreComponent.inputs,  # includes search_query, add_documents, etc.\n        HandleInput(\n            name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]\n        ),\n        StrInput(\n            name=\"vector_field\",\n            display_name=\"Vector Field\",\n            value=\"chunk_embedding\",\n            advanced=True,\n            info=\"Vector field used for KNN.\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Default Size (limit)\",\n            value=10,\n            advanced=True,\n            info=\"Default number of hits when no limit provided in filter_expression.\",\n        ),\n        MultilineInput(\n            name=\"filter_expression\",\n            display_name=\"Filter Expression (JSON)\",\n            value=\"\",\n            info=(\n                \"Optional JSON to control filters/limit/score threshold.\\n\"\n                \"Accepted shapes:\\n\"\n                '1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\\n'\n                '2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\\n'\n                \"Placeholders with __IMPOSSIBLE_VALUE__ are ignored.\"\n            ),\n        ),\n        # ----- Auth controls (dynamic) -----\n        DropdownInput(\n            name=\"auth_mode\",\n            display_name=\"Auth Mode\",\n            value=\"basic\",\n            options=[\"basic\", \"jwt\"],\n            info=\"Choose Basic (username/password) or JWT (Bearer token).\",\n            real_time_refresh=True,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"username\",\n            display_name=\"Username\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"password\",\n            display_name=\"Password\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"jwt_token\",\n            display_name=\"JWT Token\",\n            value=\"JWT\",\n            load_from_db=True,\n            show=True,\n            info=\"Paste a valid JWT (sent as a header).\",\n        ),\n        StrInput(\n            name=\"jwt_header\",\n            display_name=\"JWT Header Name\",\n            value=\"Authorization\",\n            show=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"bearer_prefix\",\n            display_name=\"Prefix 'Bearer '\",\n            value=True,\n            show=False,\n            advanced=True,\n        ),\n        # ----- TLS -----\n        BoolInput(name=\"use_ssl\", display_name=\"Use SSL\", value=True, advanced=True),\n        BoolInput(\n            name=\"verify_certs\",\n            display_name=\"Verify Certificates\",\n            value=False,\n            advanced=True,\n        ),\n    ]\n\n    # ---------- helper functions for index management ----------\n    def _default_text_mapping(\n        self,\n        dim: int,\n        engine: str = \"jvector\",\n        space_type: str = \"l2\",\n        ef_search: int = 512,\n        ef_construction: int = 100,\n        m: int = 16,\n        vector_field: str = \"vector_field\",\n    ) -> Dict[str, Any]:\n        \"\"\"For Approximate k-NN Search, this is the default mapping to create index.\"\"\"\n        return {\n            \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n            \"mappings\": {\n                \"properties\": {\n                    vector_field: {\n                        \"type\": \"knn_vector\",\n                        \"dimension\": dim,\n                        \"method\": {\n                            \"name\": \"disk_ann\",\n                            \"space_type\": space_type,\n                            \"engine\": engine,\n                            \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n                        },\n                    }\n                }\n            },\n        }\n\n    def _validate_aoss_with_engines(self, is_aoss: bool, engine: str) -> None:\n        \"\"\"Validate AOSS with the engine.\"\"\"\n        if is_aoss and engine != \"nmslib\" and engine != \"faiss\":\n            raise ValueError(\n                \"Amazon OpenSearch Service Serverless only \"\n                \"supports `nmslib` or `faiss` engines\"\n            )\n\n    def _is_aoss_enabled(self, http_auth: Any) -> bool:\n        \"\"\"Check if the service is http_auth is set as `aoss`.\"\"\"\n        if (\n            http_auth is not None\n            and hasattr(http_auth, \"service\")\n            and http_auth.service == \"aoss\"\n        ):\n            return True\n        return False\n\n    def _bulk_ingest_embeddings(\n        self,\n        client: OpenSearch,\n        index_name: str,\n        embeddings: List[List[float]],\n        texts: List[str],\n        metadatas: Optional[List[dict]] = None,\n        ids: Optional[List[str]] = None,\n        vector_field: str = \"vector_field\",\n        text_field: str = \"text\",\n        mapping: Optional[Dict] = None,\n        max_chunk_bytes: Optional[int] = 1 * 1024 * 1024,\n        is_aoss: bool = False,\n    ) -> List[str]:\n        \"\"\"Bulk Ingest Embeddings into given index.\"\"\"\n        if not mapping:\n            mapping = dict()\n\n        requests = []\n        return_ids = []\n\n        for i, text in enumerate(texts):\n            metadata = metadatas[i] if metadatas else {}\n            _id = ids[i] if ids else str(uuid.uuid4())\n            request = {\n                \"_op_type\": \"index\",\n                \"_index\": index_name,\n                vector_field: embeddings[i],\n                text_field: text,\n                **metadata,\n            }\n            if is_aoss:\n                request[\"id\"] = _id\n            else:\n                request[\"_id\"] = _id\n            requests.append(request)\n            return_ids.append(_id)\n        self.log(metadatas[i])\n        helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n        return return_ids\n\n    # ---------- auth / client ----------\n    def _build_auth_kwargs(self) -> Dict[str, Any]:\n        mode = (self.auth_mode or \"basic\").strip().lower()\n        if mode == \"jwt\":\n            token = (self.jwt_token or \"\").strip()\n            if not token:\n                raise ValueError(\"Auth Mode is 'jwt' but no jwt_token was provided.\")\n            header_name = (self.jwt_header or \"Authorization\").strip()\n            header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n            return {\"headers\": {header_name: header_value}}\n        user = (self.username or \"\").strip()\n        pwd = (self.password or \"\").strip()\n        if not user or not pwd:\n            raise ValueError(\"Auth Mode is 'basic' but username/password are missing.\")\n        return {\"http_auth\": (user, pwd)}\n\n    def build_client(self) -> OpenSearch:\n        auth_kwargs = self._build_auth_kwargs()\n        return OpenSearch(\n            hosts=[self.opensearch_url],\n            use_ssl=self.use_ssl,\n            verify_certs=self.verify_certs,\n            ssl_assert_hostname=False,\n            ssl_show_warn=False,\n            **auth_kwargs,\n        )\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> OpenSearch:\n        # Return raw OpenSearch client as our “vector store.”\n        self.log(self.ingest_data)\n        client = self.build_client()\n        self._add_documents_to_vector_store(client=client)\n        return client\n\n    # ---------- ingest ----------\n    def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n        # Convert DataFrame to Data if needed using parent's method\n        self.ingest_data = self._prepare_ingest_data()\n\n        docs = self.ingest_data or []\n        if not docs:\n            self.log(\"No documents to ingest.\")\n            return\n\n        # Extract texts and metadata from documents\n        texts = []\n        metadatas = []\n        # Process docs_metadata table input into a dict\n        additional_metadata = {}\n        if hasattr(self, \"docs_metadata\") and self.docs_metadata:\n            for item in self.docs_metadata:\n                if isinstance(item, dict) and \"key\" in item and \"value\" in item:\n                    additional_metadata[item[\"key\"]] = item[\"value\"]\n\n        for doc_obj in docs:\n            data_copy = json.loads(doc_obj.model_dump_json())\n            text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n            texts.append(text)\n\n            # Merge additional metadata from table input\n            data_copy.update(additional_metadata)\n\n            metadatas.append(data_copy)\n        self.log(metadatas)\n        if not self.embedding:\n            raise ValueError(\"Embedding handle is required to embed documents.\")\n\n        # Generate embeddings\n        vectors = self.embedding.embed_documents(texts)\n\n        if not vectors:\n            self.log(\"No vectors generated from documents.\")\n            return\n\n        # Get vector dimension for mapping\n        dim = len(vectors[0]) if vectors else 768  # default fallback\n\n        # Check for AOSS\n        auth_kwargs = self._build_auth_kwargs()\n        is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n        # Validate engine with AOSS\n        engine = getattr(self, \"engine\", \"jvector\")\n        self._validate_aoss_with_engines(is_aoss, engine)\n\n        # Create mapping with proper KNN settings\n        space_type = getattr(self, \"space_type\", \"l2\")\n        ef_construction = getattr(self, \"ef_construction\", 512)\n        m = getattr(self, \"m\", 16)\n\n        mapping = self._default_text_mapping(\n            dim=dim,\n            engine=engine,\n            space_type=space_type,\n            ef_construction=ef_construction,\n            m=m,\n            vector_field=self.vector_field,\n        )\n\n        self.log(\n            f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\"\n        )\n\n        # Use the LangChain-style bulk ingestion\n        return_ids = self._bulk_ingest_embeddings(\n            client=client,\n            index_name=self.index_name,\n            embeddings=vectors,\n            texts=texts,\n            metadatas=metadatas,\n            vector_field=self.vector_field,\n            text_field=\"text\",\n            mapping=mapping,\n            is_aoss=is_aoss,\n        )\n        self.log(metadatas)\n\n        self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n    # ---------- helpers for filters ----------\n    def _is_placeholder_term(self, term_obj: dict) -> bool:\n        # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n        return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n    def _coerce_filter_clauses(self, filter_obj: dict | None) -> List[dict]:\n        \"\"\"\n        Accepts either:\n          A) {\"filter\":[ ...term/terms objects... ], \"limit\":..., \"score_threshold\":...}\n          B) Context-style: {\"data_sources\":[...], \"document_types\":[...], \"owners\":[...]}\n        Returns a list of OS filter clauses (term/terms), skipping placeholders and empty terms.\n        \"\"\"\n\n        if not filter_obj:\n            return []\n\n        # If it’s a string, try to parse it once\n        if isinstance(filter_obj, str):\n            try:\n                filter_obj = json.loads(filter_obj)\n            except Exception:\n                # Not valid JSON → treat as no filters\n                return []\n\n        # Case A: already an explicit list/dict under \"filter\"\n        if \"filter\" in filter_obj:\n            raw = filter_obj[\"filter\"]\n            if isinstance(raw, dict):\n                raw = [raw]\n            clauses: List[dict] = []\n            for f in raw or []:\n                if (\n                    \"term\" in f\n                    and isinstance(f[\"term\"], dict)\n                    and not self._is_placeholder_term(f[\"term\"])\n                ):\n                    clauses.append(f)\n                elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n                    field, vals = next(iter(f[\"terms\"].items()))\n                    if isinstance(vals, list) and len(vals) > 0:\n                        clauses.append(f)\n            return clauses\n\n        # Case B: convert context-style maps into clauses\n        field_mapping = {\n            \"data_sources\": \"filename\",\n            \"document_types\": \"mimetype\",\n            \"owners\": \"owner\",\n        }\n        clauses: List[dict] = []\n        for k, values in filter_obj.items():\n            if not isinstance(values, list):\n                continue\n            field = field_mapping.get(k, k)\n            if len(values) == 0:\n                # Match-nothing placeholder (kept to mirror your tool semantics)\n                clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n            elif len(values) == 1:\n                if values[0] != \"__IMPOSSIBLE_VALUE__\":\n                    clauses.append({\"term\": {field: values[0]}})\n            else:\n                clauses.append({\"terms\": {field: values}})\n        return clauses\n\n    # ---------- search (single hybrid path matching your tool) ----------\n    def search(self, query: str | None = None) -> list[dict[str, Any]]:\n        logger.info(self.ingest_data)\n        client = self.build_client()\n        q = (query or \"\").strip()\n\n        # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n        filter_obj = None\n        if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n            try:\n                filter_obj = json.loads(self.filter_expression)\n            except json.JSONDecodeError as e:\n                raise ValueError(f\"Invalid filter_expression JSON: {e}\") from e\n\n        if not self.embedding:\n            raise ValueError(\n                \"Embedding is required to run hybrid search (KNN + keyword).\"\n            )\n\n        # Embed the query\n        vec = self.embedding.embed_query(q)\n\n        # Build filter clauses (accept both shapes)\n        clauses = self._coerce_filter_clauses(filter_obj)\n\n        # Respect the tool's limit/threshold defaults\n        limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n        score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n        # Build the same hybrid body as your SearchService\n        body = {\n            \"query\": {\n                \"bool\": {\n                    \"should\": [\n                        {\n                            \"knn\": {\n                                self.vector_field: {\n                                    \"vector\": vec,\n                                    \"k\": 10,  # fixed to match the tool\n                                    \"boost\": 0.7,\n                                }\n                            }\n                        },\n                        {\n                            \"multi_match\": {\n                                \"query\": q,\n                                \"fields\": [\"text^2\", \"filename^1.5\"],\n                                \"type\": \"best_fields\",\n                                \"fuzziness\": \"AUTO\",\n                                \"boost\": 0.3,\n                            }\n                        },\n                    ],\n                    \"minimum_should_match\": 1,\n                }\n            },\n            \"aggs\": {\n                \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n                \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n                \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n            },\n            \"_source\": [\n                \"filename\",\n                \"mimetype\",\n                \"page\",\n                \"text\",\n                \"source_url\",\n                \"owner\",\n                \"allowed_users\",\n                \"allowed_groups\",\n            ],\n            \"size\": limit,\n        }\n        if clauses:\n            body[\"query\"][\"bool\"][\"filter\"] = clauses\n\n        if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n            # top-level min_score (matches your tool)\n            body[\"min_score\"] = score_threshold\n\n        resp = client.search(index=self.index_name, body=body)\n        hits = resp.get(\"hits\", {}).get(\"hits\", [])\n        return [\n            {\n                \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n                \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n                \"score\": hit.get(\"_score\"),\n            }\n            for hit in hits\n        ]\n\n    def search_documents(self) -> list[Data]:\n        try:\n            raw = self.search(self.search_query or \"\")\n            return [Data(text=hit[\"page_content\"], **hit[\"metadata\"]) for hit in raw]\n            self.log(self.ingest_data)\n        except Exception as e:\n            self.log(f\"search_documents error: {e}\")\n            raise\n\n    # -------- dynamic UI handling (auth switch) --------\n    async def update_build_config(\n        self, build_config: dict, field_value: str, field_name: str | None = None\n    ) -> dict:\n        try:\n            if field_name == \"auth_mode\":\n                mode = (field_value or \"basic\").strip().lower()\n                is_basic = mode == \"basic\"\n                is_jwt = mode == \"jwt\"\n\n                build_config[\"username\"][\"show\"] = is_basic\n                build_config[\"password\"][\"show\"] = is_basic\n\n                build_config[\"jwt_token\"][\"show\"] = is_jwt\n                build_config[\"jwt_header\"][\"show\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n                build_config[\"username\"][\"required\"] = is_basic\n                build_config[\"password\"][\"required\"] = is_basic\n\n                build_config[\"jwt_token\"][\"required\"] = is_jwt\n                build_config[\"jwt_header\"][\"required\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"required\"] = False\n\n                if is_basic:\n                    build_config[\"jwt_token\"][\"value\"] = \"\"\n\n                return build_config\n\n            return build_config\n\n        except Exception as e:\n            self.log(f\"update_build_config error: {e}\")\n            return build_config\n"
+                "value": "from __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput, TableInput\nfrom lfx.log import logger\nfrom lfx.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchVectorStoreComponent(LCVectorStoreComponent):\n    \"\"\"OpenSearch Vector Store Component with Hybrid Search Capabilities.\n\n    This component provides vector storage and retrieval using OpenSearch, combining semantic\n    similarity search (KNN) with keyword-based search for optimal results. It supports document\n    ingestion, vector embeddings, and advanced filtering with authentication options.\n\n    Features:\n    - Vector storage with configurable engines (jvector, nmslib, faiss, lucene)\n    - Hybrid search combining KNN vector similarity and keyword matching\n    - Flexible authentication (Basic auth, JWT tokens)\n    - Advanced filtering and aggregations\n    - Metadata injection during document ingestion\n    \"\"\"\n\n    display_name: str = \"OpenSearch\"\n    icon: str = \"OpenSearch\"\n    description: str = (\n        \"Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.\"\n    )\n\n    # Keys we consider baseline\n    default_keys: list[str] = [\n        \"opensearch_url\",\n        \"index_name\",\n        *[i.name for i in LCVectorStoreComponent.inputs],  # search_query, add_documents, etc.\n        \"embedding\",\n        \"vector_field\",\n        \"number_of_results\",\n        \"auth_mode\",\n        \"username\",\n        \"password\",\n        \"jwt_token\",\n        \"jwt_header\",\n        \"bearer_prefix\",\n        \"use_ssl\",\n        \"verify_certs\",\n        \"filter_expression\",\n        \"engine\",\n        \"space_type\",\n        \"ef_construction\",\n        \"m\",\n        \"docs_metadata\",\n    ]\n\n    inputs = [\n        TableInput(\n            name=\"docs_metadata\",\n            display_name=\"Document Metadata\",\n            info=(\n                \"Additional metadata key-value pairs to be added to all ingested documents. \"\n                \"Useful for tagging documents with source information, categories, or other custom attributes.\"\n            ),\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Key\",\n                    \"type\": \"str\",\n                    \"description\": \"Key name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Value of the metadata\",\n                },\n            ],\n            value=[],\n            # advanced=True,\n            input_types=[\"Data\"]\n        ),\n        StrInput(\n            name=\"opensearch_url\",\n            display_name=\"OpenSearch URL\",\n            value=\"http://localhost:9200\",\n            info=(\n                \"The connection URL for your OpenSearch cluster \"\n                \"(e.g., http://localhost:9200 for local development or your cloud endpoint).\"\n            ),\n        ),\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow\",\n            info=(\n                \"The OpenSearch index name where documents will be stored and searched. \"\n                \"Will be created automatically if it doesn't exist.\"\n            ),\n        ),\n        DropdownInput(\n            name=\"engine\",\n            display_name=\"Vector Engine\",\n            options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n            value=\"jvector\",\n            info=(\n                \"Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. \"\n                \"Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.\"\n            ),\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"space_type\",\n            display_name=\"Distance Metric\",\n            options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n            value=\"l2\",\n            info=(\n                \"Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, \"\n                \"'cosinesimil' for cosine similarity, 'innerproduct' for dot product.\"\n            ),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"ef_construction\",\n            display_name=\"EF Construction\",\n            value=512,\n            info=(\n                \"Size of the dynamic candidate list during index construction. \"\n                \"Higher values improve recall but increase indexing time and memory usage.\"\n            ),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"m\",\n            display_name=\"M Parameter\",\n            value=16,\n            info=(\n                \"Number of bidirectional connections for each vector in the HNSW graph. \"\n                \"Higher values improve search quality but increase memory usage and indexing time.\"\n            ),\n            advanced=True,\n        ),\n        *LCVectorStoreComponent.inputs,  # includes search_query, add_documents, etc.\n        HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n        StrInput(\n            name=\"vector_field\",\n            display_name=\"Vector Field Name\",\n            value=\"chunk_embedding\",\n            advanced=True,\n            info=\"Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Default Result Limit\",\n            value=10,\n            advanced=True,\n            info=(\n                \"Default maximum number of search results to return when no limit is \"\n                \"specified in the filter expression.\"\n            ),\n        ),\n        MultilineInput(\n            name=\"filter_expression\",\n            display_name=\"Search Filters (JSON)\",\n            value=\"\",\n            info=(\n                \"Optional JSON configuration for search filtering, result limits, and score thresholds.\\n\\n\"\n                \"Format 1 - Explicit filters:\\n\"\n                '{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, '\n                '{\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\\n\\n'\n                \"Format 2 - Context-style mapping:\\n\"\n                '{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\\n\\n'\n                \"Use __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.\"\n            ),\n        ),\n        # ----- Auth controls (dynamic) -----\n        DropdownInput(\n            name=\"auth_mode\",\n            display_name=\"Authentication Mode\",\n            value=\"basic\",\n            options=[\"basic\", \"jwt\"],\n            info=(\n                \"Authentication method: 'basic' for username/password authentication, \"\n                \"or 'jwt' for JSON Web Token (Bearer) authentication.\"\n            ),\n            real_time_refresh=True,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"username\",\n            display_name=\"Username\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"password\",\n            display_name=\"OpenSearch Password\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"jwt_token\",\n            display_name=\"JWT Token\",\n            value=\"JWT\",\n            load_from_db=False,\n            show=True,\n            info=(\n                \"Valid JSON Web Token for authentication. \"\n                \"Will be sent in the Authorization header (with optional 'Bearer ' prefix).\"\n            ),\n        ),\n        StrInput(\n            name=\"jwt_header\",\n            display_name=\"JWT Header Name\",\n            value=\"Authorization\",\n            show=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"bearer_prefix\",\n            display_name=\"Prefix 'Bearer '\",\n            value=True,\n            show=False,\n            advanced=True,\n        ),\n        # ----- TLS -----\n        BoolInput(\n            name=\"use_ssl\",\n            display_name=\"Use SSL/TLS\",\n            value=True,\n            advanced=True,\n            info=\"Enable SSL/TLS encryption for secure connections to OpenSearch.\",\n        ),\n        BoolInput(\n            name=\"verify_certs\",\n            display_name=\"Verify SSL Certificates\",\n            value=False,\n            advanced=True,\n            info=(\n                \"Verify SSL certificates when connecting. \"\n                \"Disable for self-signed certificates in development environments.\"\n            ),\n        ),\n    ]\n\n    # ---------- helper functions for index management ----------\n    def _default_text_mapping(\n        self,\n        dim: int,\n        engine: str = \"jvector\",\n        space_type: str = \"l2\",\n        ef_search: int = 512,\n        ef_construction: int = 100,\n        m: int = 16,\n        vector_field: str = \"vector_field\",\n    ) -> dict[str, Any]:\n        \"\"\"Create the default OpenSearch index mapping for vector search.\n\n        This method generates the index configuration with k-NN settings optimized\n        for approximate nearest neighbor search using the specified vector engine.\n\n        Args:\n            dim: Dimensionality of the vector embeddings\n            engine: Vector search engine (jvector, nmslib, faiss, lucene)\n            space_type: Distance metric for similarity calculation\n            ef_search: Size of dynamic list used during search\n            ef_construction: Size of dynamic list used during index construction\n            m: Number of bidirectional links for each vector\n            vector_field: Name of the field storing vector embeddings\n\n        Returns:\n            Dictionary containing OpenSearch index mapping configuration\n        \"\"\"\n        return {\n            \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n            \"mappings\": {\n                \"properties\": {\n                    vector_field: {\n                        \"type\": \"knn_vector\",\n                        \"dimension\": dim,\n                        \"method\": {\n                            \"name\": \"disk_ann\",\n                            \"space_type\": space_type,\n                            \"engine\": engine,\n                            \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n                        },\n                    }\n                }\n            },\n        }\n\n    def _validate_aoss_with_engines(self, *, is_aoss: bool, engine: str) -> None:\n        \"\"\"Validate engine compatibility with Amazon OpenSearch Serverless (AOSS).\n\n        Amazon OpenSearch Serverless has restrictions on which vector engines\n        can be used. This method ensures the selected engine is compatible.\n\n        Args:\n            is_aoss: Whether the connection is to Amazon OpenSearch Serverless\n            engine: The selected vector search engine\n\n        Raises:\n            ValueError: If AOSS is used with an incompatible engine\n        \"\"\"\n        if is_aoss and engine not in {\"nmslib\", \"faiss\"}:\n            msg = \"Amazon OpenSearch Service Serverless only supports `nmslib` or `faiss` engines\"\n            raise ValueError(msg)\n\n    def _is_aoss_enabled(self, http_auth: Any) -> bool:\n        \"\"\"Determine if Amazon OpenSearch Serverless (AOSS) is being used.\n\n        Args:\n            http_auth: The HTTP authentication object\n\n        Returns:\n            True if AOSS is enabled, False otherwise\n        \"\"\"\n        return http_auth is not None and hasattr(http_auth, \"service\") and http_auth.service == \"aoss\"\n\n    def _bulk_ingest_embeddings(\n        self,\n        client: OpenSearch,\n        index_name: str,\n        embeddings: list[list[float]],\n        texts: list[str],\n        metadatas: list[dict] | None = None,\n        ids: list[str] | None = None,\n        vector_field: str = \"vector_field\",\n        text_field: str = \"text\",\n        mapping: dict | None = None,\n        max_chunk_bytes: int | None = 1 * 1024 * 1024,\n        *,\n        is_aoss: bool = False,\n    ) -> list[str]:\n        \"\"\"Efficiently ingest multiple documents with embeddings into OpenSearch.\n\n        This method uses bulk operations to insert documents with their vector\n        embeddings and metadata into the specified OpenSearch index.\n\n        Args:\n            client: OpenSearch client instance\n            index_name: Target index for document storage\n            embeddings: List of vector embeddings for each document\n            texts: List of document texts\n            metadatas: Optional metadata dictionaries for each document\n            ids: Optional document IDs (UUIDs generated if not provided)\n            vector_field: Field name for storing vector embeddings\n            text_field: Field name for storing document text\n            mapping: Optional index mapping configuration\n            max_chunk_bytes: Maximum size per bulk request chunk\n            is_aoss: Whether using Amazon OpenSearch Serverless\n\n        Returns:\n            List of document IDs that were successfully ingested\n        \"\"\"\n        if not mapping:\n            mapping = {}\n\n        requests = []\n        return_ids = []\n\n        for i, text in enumerate(texts):\n            metadata = metadatas[i] if metadatas else {}\n            _id = ids[i] if ids else str(uuid.uuid4())\n            request = {\n                \"_op_type\": \"index\",\n                \"_index\": index_name,\n                vector_field: embeddings[i],\n                text_field: text,\n                **metadata,\n            }\n            if is_aoss:\n                request[\"id\"] = _id\n            else:\n                request[\"_id\"] = _id\n            requests.append(request)\n            return_ids.append(_id)\n        if metadatas:\n            self.log(f\"Sample metadata: {metadatas[0] if metadatas else {}}\")\n        helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n        return return_ids\n\n    # ---------- auth / client ----------\n    def _build_auth_kwargs(self) -> dict[str, Any]:\n        \"\"\"Build authentication configuration for OpenSearch client.\n\n        Constructs the appropriate authentication parameters based on the\n        selected auth mode (basic username/password or JWT token).\n\n        Returns:\n            Dictionary containing authentication configuration\n\n        Raises:\n            ValueError: If required authentication parameters are missing\n        \"\"\"\n        mode = (self.auth_mode or \"basic\").strip().lower()\n        if mode == \"jwt\":\n            token = (self.jwt_token or \"\").strip()\n            if not token:\n                msg = \"Auth Mode is 'jwt' but no jwt_token was provided.\"\n                raise ValueError(msg)\n            header_name = (self.jwt_header or \"Authorization\").strip()\n            header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n            return {\"headers\": {header_name: header_value}}\n        user = (self.username or \"\").strip()\n        pwd = (self.password or \"\").strip()\n        if not user or not pwd:\n            msg = \"Auth Mode is 'basic' but username/password are missing.\"\n            raise ValueError(msg)\n        return {\"http_auth\": (user, pwd)}\n\n    def build_client(self) -> OpenSearch:\n        \"\"\"Create and configure an OpenSearch client instance.\n\n        Returns:\n            Configured OpenSearch client ready for operations\n        \"\"\"\n        auth_kwargs = self._build_auth_kwargs()\n        return OpenSearch(\n            hosts=[self.opensearch_url],\n            use_ssl=self.use_ssl,\n            verify_certs=self.verify_certs,\n            ssl_assert_hostname=False,\n            ssl_show_warn=False,\n            **auth_kwargs,\n        )\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> OpenSearch:\n        # Return raw OpenSearch client as our “vector store.”\n        self.log(self.ingest_data)\n        client = self.build_client()\n        self._add_documents_to_vector_store(client=client)\n        return client\n\n    # ---------- ingest ----------\n    def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n        \"\"\"Process and ingest documents into the OpenSearch vector store.\n\n        This method handles the complete document ingestion pipeline:\n        - Prepares document data and metadata\n        - Generates vector embeddings\n        - Creates appropriate index mappings\n        - Bulk inserts documents with vectors\n\n        Args:\n            client: OpenSearch client for performing operations\n        \"\"\"\n        # Convert DataFrame to Data if needed using parent's method\n        self.ingest_data = self._prepare_ingest_data()\n\n        docs = self.ingest_data or []\n        if not docs:\n            self.log(\"No documents to ingest.\")\n            return\n\n        # Extract texts and metadata from documents\n        texts = []\n        metadatas = []\n        # Process docs_metadata table input into a dict\n        additional_metadata = {}\n        if hasattr(self, \"docs_metadata\") and self.docs_metadata:\n            logger.info(f\"[LF] Docs metadata {self.docs_metadata}\")\n            if isinstance(self.docs_metadata[-1], Data):\n                logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n                self.docs_metadata = self.docs_metadata[-1].data\n                logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n                additional_metadata.update(self.docs_metadata)\n            else:\n                for item in self.docs_metadata:\n                    if isinstance(item, dict) and \"key\" in item and \"value\" in item:\n                        additional_metadata[item[\"key\"]] = item[\"value\"]\n        # Replace string \"None\" values with actual None\n        for key, value in additional_metadata.items():\n            if value == \"None\":\n                additional_metadata[key] = None\n        logger.info(f\"[LF] Additional metadata {additional_metadata}\")\n        for doc_obj in docs:\n            data_copy = json.loads(doc_obj.model_dump_json())\n            text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n            texts.append(text)\n\n            # Merge additional metadata from table input\n            data_copy.update(additional_metadata)\n\n            metadatas.append(data_copy)\n        self.log(metadatas)\n        if not self.embedding:\n            msg = \"Embedding handle is required to embed documents.\"\n            raise ValueError(msg)\n\n        # Generate embeddings\n        vectors = self.embedding.embed_documents(texts)\n\n        if not vectors:\n            self.log(\"No vectors generated from documents.\")\n            return\n\n        # Get vector dimension for mapping\n        dim = len(vectors[0]) if vectors else 768  # default fallback\n\n        # Check for AOSS\n        auth_kwargs = self._build_auth_kwargs()\n        is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n        # Validate engine with AOSS\n        engine = getattr(self, \"engine\", \"jvector\")\n        self._validate_aoss_with_engines(is_aoss=is_aoss, engine=engine)\n\n        # Create mapping with proper KNN settings\n        space_type = getattr(self, \"space_type\", \"l2\")\n        ef_construction = getattr(self, \"ef_construction\", 512)\n        m = getattr(self, \"m\", 16)\n\n        mapping = self._default_text_mapping(\n            dim=dim,\n            engine=engine,\n            space_type=space_type,\n            ef_construction=ef_construction,\n            m=m,\n            vector_field=self.vector_field,\n        )\n\n        self.log(f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\")\n\n        # Use the LangChain-style bulk ingestion\n        return_ids = self._bulk_ingest_embeddings(\n            client=client,\n            index_name=self.index_name,\n            embeddings=vectors,\n            texts=texts,\n            metadatas=metadatas,\n            vector_field=self.vector_field,\n            text_field=\"text\",\n            mapping=mapping,\n            is_aoss=is_aoss,\n        )\n        self.log(metadatas)\n\n        self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n    # ---------- helpers for filters ----------\n    def _is_placeholder_term(self, term_obj: dict) -> bool:\n        # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n        return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n    def _coerce_filter_clauses(self, filter_obj: dict | None) -> list[dict]:\n        \"\"\"Convert filter expressions into OpenSearch-compatible filter clauses.\n\n        This method accepts two filter formats and converts them to standardized\n        OpenSearch query clauses:\n\n        Format A - Explicit filters:\n        {\"filter\": [{\"term\": {\"field\": \"value\"}}, {\"terms\": {\"field\": [\"val1\", \"val2\"]}}],\n         \"limit\": 10, \"score_threshold\": 1.5}\n\n        Format B - Context-style mapping:\n        {\"data_sources\": [\"file1.pdf\"], \"document_types\": [\"pdf\"], \"owners\": [\"user1\"]}\n\n        Args:\n            filter_obj: Filter configuration dictionary or None\n\n        Returns:\n            List of OpenSearch filter clauses (term/terms objects)\n            Placeholder values with \"__IMPOSSIBLE_VALUE__\" are ignored\n        \"\"\"\n        if not filter_obj:\n            return []\n\n        # If it is a string, try to parse it once\n        if isinstance(filter_obj, str):\n            try:\n                filter_obj = json.loads(filter_obj)\n            except json.JSONDecodeError:\n                # Not valid JSON - treat as no filters\n                return []\n\n        # Case A: already an explicit list/dict under \"filter\"\n        if \"filter\" in filter_obj:\n            raw = filter_obj[\"filter\"]\n            if isinstance(raw, dict):\n                raw = [raw]\n            explicit_clauses: list[dict] = []\n            for f in raw or []:\n                if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n                    explicit_clauses.append(f)\n                elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n                    field, vals = next(iter(f[\"terms\"].items()))\n                    if isinstance(vals, list) and len(vals) > 0:\n                        explicit_clauses.append(f)\n            return explicit_clauses\n\n        # Case B: convert context-style maps into clauses\n        field_mapping = {\n            \"data_sources\": \"filename\",\n            \"document_types\": \"mimetype\",\n            \"owners\": \"owner\",\n        }\n        context_clauses: list[dict] = []\n        for k, values in filter_obj.items():\n            if not isinstance(values, list):\n                continue\n            field = field_mapping.get(k, k)\n            if len(values) == 0:\n                # Match-nothing placeholder (kept to mirror your tool semantics)\n                context_clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n            elif len(values) == 1:\n                if values[0] != \"__IMPOSSIBLE_VALUE__\":\n                    context_clauses.append({\"term\": {field: values[0]}})\n            else:\n                context_clauses.append({\"terms\": {field: values}})\n        return context_clauses\n\n    # ---------- search (single hybrid path matching your tool) ----------\n    def search(self, query: str | None = None) -> list[dict[str, Any]]:\n        \"\"\"Perform hybrid search combining vector similarity and keyword matching.\n\n        This method executes a sophisticated search that combines:\n        - K-nearest neighbor (KNN) vector similarity search (70% weight)\n        - Multi-field keyword search with fuzzy matching (30% weight)\n        - Optional filtering and score thresholds\n        - Aggregations for faceted search results\n\n        Args:\n            query: Search query string (used for both vector embedding and keyword search)\n\n        Returns:\n            List of search results with page_content, metadata, and relevance scores\n\n        Raises:\n            ValueError: If embedding component is not provided or filter JSON is invalid\n        \"\"\"\n        logger.info(self.ingest_data)\n        client = self.build_client()\n        q = (query or \"\").strip()\n\n        # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n        filter_obj = None\n        if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n            try:\n                filter_obj = json.loads(self.filter_expression)\n            except json.JSONDecodeError as e:\n                msg = f\"Invalid filter_expression JSON: {e}\"\n                raise ValueError(msg) from e\n\n        if not self.embedding:\n            msg = \"Embedding is required to run hybrid search (KNN + keyword).\"\n            raise ValueError(msg)\n\n        # Embed the query\n        vec = self.embedding.embed_query(q)\n\n        # Build filter clauses (accept both shapes)\n        filter_clauses = self._coerce_filter_clauses(filter_obj)\n\n        # Respect the tool's limit/threshold defaults\n        limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n        score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n        # Build the same hybrid body as your SearchService\n        body = {\n            \"query\": {\n                \"bool\": {\n                    \"should\": [\n                        {\n                            \"knn\": {\n                                self.vector_field: {\n                                    \"vector\": vec,\n                                    \"k\": 10,  # fixed to match the tool\n                                    \"boost\": 0.7,\n                                }\n                            }\n                        },\n                        {\n                            \"multi_match\": {\n                                \"query\": q,\n                                \"fields\": [\"text^2\", \"filename^1.5\"],\n                                \"type\": \"best_fields\",\n                                \"fuzziness\": \"AUTO\",\n                                \"boost\": 0.3,\n                            }\n                        },\n                    ],\n                    \"minimum_should_match\": 1,\n                }\n            },\n            \"aggs\": {\n                \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n                \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n                \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n            },\n            \"_source\": [\n                \"filename\",\n                \"mimetype\",\n                \"page\",\n                \"text\",\n                \"source_url\",\n                \"owner\",\n                \"allowed_users\",\n                \"allowed_groups\",\n            ],\n            \"size\": limit,\n        }\n        if filter_clauses:\n            body[\"query\"][\"bool\"][\"filter\"] = filter_clauses\n\n        if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n            # top-level min_score (matches your tool)\n            body[\"min_score\"] = score_threshold\n\n        resp = client.search(index=self.index_name, body=body)\n        hits = resp.get(\"hits\", {}).get(\"hits\", [])\n        return [\n            {\n                \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n                \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n                \"score\": hit.get(\"_score\"),\n            }\n            for hit in hits\n        ]\n\n    def search_documents(self) -> list[Data]:\n        \"\"\"Search documents and return results as Data objects.\n\n        This is the main interface method that performs the search using the\n        configured search_query and returns results in Langflow's Data format.\n\n        Returns:\n            List of Data objects containing search results with text and metadata\n\n        Raises:\n            Exception: If search operation fails\n        \"\"\"\n        try:\n            raw = self.search(self.search_query or \"\")\n            return [Data(text=hit[\"page_content\"], **hit[\"metadata\"]) for hit in raw]\n            self.log(self.ingest_data)\n        except Exception as e:\n            self.log(f\"search_documents error: {e}\")\n            raise\n\n    # -------- dynamic UI handling (auth switch) --------\n    async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n        \"\"\"Dynamically update component configuration based on field changes.\n\n        This method handles real-time UI updates, particularly for authentication\n        mode changes that show/hide relevant input fields.\n\n        Args:\n            build_config: Current component configuration\n            field_value: New value for the changed field\n            field_name: Name of the field that changed\n\n        Returns:\n            Updated build configuration with appropriate field visibility\n        \"\"\"\n        try:\n            if field_name == \"auth_mode\":\n                mode = (field_value or \"basic\").strip().lower()\n                is_basic = mode == \"basic\"\n                is_jwt = mode == \"jwt\"\n\n                build_config[\"username\"][\"show\"] = is_basic\n                build_config[\"password\"][\"show\"] = is_basic\n\n                build_config[\"jwt_token\"][\"show\"] = is_jwt\n                build_config[\"jwt_header\"][\"show\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n                build_config[\"username\"][\"required\"] = is_basic\n                build_config[\"password\"][\"required\"] = is_basic\n\n                build_config[\"jwt_token\"][\"required\"] = is_jwt\n                build_config[\"jwt_header\"][\"required\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"required\"] = False\n\n                if is_basic:\n                    build_config[\"jwt_token\"][\"value\"] = \"\"\n\n                return build_config\n\n        except (KeyError, ValueError) as e:\n            self.log(f\"update_build_config error: {e}\")\n\n        return build_config\n"
               },
               "docs_metadata": {
                 "_input_type": "TableInput",
-                "advanced": true,
-                "display_name": "Ingestion Metadata",
+                "advanced": false,
+                "display_name": "Document Metadata",
                 "dynamic": false,
-                "info": "Key value pairs to be inserted into each ingested document.",
+                "info": "Additional metadata key-value pairs to be added to all ingested documents. Useful for tagging documents with source information, categories, or other custom attributes.",
+                "input_types": [
+                  "Data"
+                ],
                 "is_list": true,
                 "list_add_label": "Add More",
                 "name": "docs_metadata",
@@ -1007,36 +1149,22 @@
                 "required": false,
                 "show": true,
                 "table_icon": "Table",
-                "table_schema": {
-                  "columns": [
-                    {
-                      "default": "None",
-                      "description": "Key name",
-                      "disable_edit": false,
-                      "display_name": "Key",
-                      "edit_mode": "popover",
-                      "filterable": true,
-                      "formatter": "text",
-                      "hidden": false,
-                      "name": "key",
-                      "sortable": true,
-                      "type": "str"
-                    },
-                    {
-                      "default": "None",
-                      "description": "Value of the metadata",
-                      "disable_edit": false,
-                      "display_name": "Value",
-                      "edit_mode": "popover",
-                      "filterable": true,
-                      "formatter": "text",
-                      "hidden": false,
-                      "name": "value",
-                      "sortable": true,
-                      "type": "str"
-                    }
-                  ]
-                },
+                "table_schema": [
+                  {
+                    "description": "Key name",
+                    "display_name": "Key",
+                    "formatter": "text",
+                    "name": "key",
+                    "type": "str"
+                  },
+                  {
+                    "description": "Value of the metadata",
+                    "display_name": "Value",
+                    "formatter": "text",
+                    "name": "value",
+                    "type": "str"
+                  }
+                ],
                 "title_case": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
@@ -1050,7 +1178,7 @@
                 "advanced": true,
                 "display_name": "EF Construction",
                 "dynamic": false,
-                "info": "Size of the dynamic list used during k-NN graph creation.",
+                "info": "Size of the dynamic candidate list during index construction. Higher values improve recall but increase indexing time and memory usage.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "ef_construction",
@@ -1088,9 +1216,10 @@
                 "advanced": true,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Engine",
+                "display_name": "Vector Engine",
                 "dynamic": false,
-                "info": "Vector search engine to use.",
+                "external_options": {},
+                "info": "Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.",
                 "load_from_db": false,
                 "name": "engine",
                 "options": [
@@ -1114,9 +1243,9 @@
                 "_input_type": "MultilineInput",
                 "advanced": false,
                 "copy_field": false,
-                "display_name": "Filter Expression (JSON)",
+                "display_name": "Search Filters (JSON)",
                 "dynamic": false,
-                "info": "Optional JSON to control filters/limit/score threshold.\nAccepted shapes:\n1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\n2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\nPlaceholders with __IMPOSSIBLE_VALUE__ are ignored.",
+                "info": "Optional JSON configuration for search filtering, result limits, and score thresholds.\n\nFormat 1 - Explicit filters:\n{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, {\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\n\nFormat 2 - Context-style mapping:\n{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\n\nUse __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.",
                 "input_types": [
                   "Message"
                 ],
@@ -1140,7 +1269,7 @@
                 "advanced": false,
                 "display_name": "Index Name",
                 "dynamic": false,
-                "info": "The index to search.",
+                "info": "The OpenSearch index name where documents will be stored and searched. Will be created automatically if it doesn't exist.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1199,7 +1328,7 @@
                 "advanced": false,
                 "display_name": "JWT Token",
                 "dynamic": false,
-                "info": "Paste a valid JWT (sent as a header).",
+                "info": "Valid JSON Web Token for authentication. Will be sent in the Authorization header (with optional 'Bearer ' prefix).",
                 "input_types": [],
                 "load_from_db": false,
                 "name": "jwt_token",
@@ -1216,7 +1345,7 @@
                 "advanced": true,
                 "display_name": "M Parameter",
                 "dynamic": false,
-                "info": "Number of bidirectional links created for each new element.",
+                "info": "Number of bidirectional connections for each vector in the HNSW graph. Higher values improve search quality but increase memory usage and indexing time.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "m",
@@ -1232,9 +1361,9 @@
               "number_of_results": {
                 "_input_type": "IntInput",
                 "advanced": true,
-                "display_name": "Default Size (limit)",
+                "display_name": "Default Result Limit",
                 "dynamic": false,
-                "info": "Default number of hits when no limit provided in filter_expression.",
+                "info": "Default maximum number of search results to return when no limit is specified in the filter expression.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1253,7 +1382,7 @@
                 "advanced": false,
                 "display_name": "OpenSearch URL",
                 "dynamic": false,
-                "info": "URL for your OpenSearch cluster.",
+                "info": "The connection URL for your OpenSearch cluster (e.g., http://localhost:9200 for local development or your cloud endpoint).",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1270,7 +1399,7 @@
               "password": {
                 "_input_type": "SecretStrInput",
                 "advanced": false,
-                "display_name": "Password",
+                "display_name": "OpenSearch Password",
                 "dynamic": false,
                 "info": "",
                 "input_types": [],
@@ -1330,9 +1459,10 @@
                 "advanced": true,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Space Type",
+                "display_name": "Distance Metric",
                 "dynamic": false,
-                "info": "Distance metric for vector similarity.",
+                "external_options": {},
+                "info": "Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, 'cosinesimil' for cosine similarity, 'innerproduct' for dot product.",
                 "name": "space_type",
                 "options": [
                   "l2",
@@ -1355,9 +1485,9 @@
               "use_ssl": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Use SSL",
+                "display_name": "Use SSL/TLS",
                 "dynamic": false,
-                "info": "",
+                "info": "Enable SSL/TLS encryption for secure connections to OpenSearch.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "use_ssl",
@@ -1392,9 +1522,9 @@
               "vector_field": {
                 "_input_type": "StrInput",
                 "advanced": true,
-                "display_name": "Vector Field",
+                "display_name": "Vector Field Name",
                 "dynamic": false,
-                "info": "Vector field used for KNN.",
+                "info": "Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1411,9 +1541,9 @@
               "verify_certs": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Verify Certificates",
+                "display_name": "Verify SSL Certificates",
                 "dynamic": false,
-                "info": "",
+                "info": "Verify SSL certificates when connecting. Disable for self-signed certificates in development environments.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "verify_certs",
@@ -1431,12 +1561,12 @@
           },
           "selected_output": "search_results",
           "showNode": true,
-          "type": "OpenSearchHybrid"
+          "type": "OpenSearchVectorStoreComponent"
         },
         "dragging": false,
         "id": "OpenSearchHybrid-Ve6bS",
         "measured": {
-          "height": 765,
+          "height": 822,
           "width": 320
         },
         "position": {
@@ -1448,6 +1578,8 @@
       },
       {
         "data": {
+          "description": "Generate embeddings using a specified provider.",
+          "display_name": "Embedding Model",
           "id": "EmbeddingModel-eZ6bT",
           "node": {
             "base_classes": [
@@ -1474,10 +1606,11 @@
             ],
             "frozen": false,
             "icon": "binary",
-            "last_updated": "2025-09-22T15:54:52.885Z",
+            "last_updated": "2025-09-26T14:37:42.699Z",
             "legacy": false,
+            "lf_version": "1.6.0",
             "metadata": {
-              "code_hash": "93faf11517da",
+              "code_hash": "8607e963fdef",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1485,13 +1618,13 @@
                     "version": "0.3.23"
                   },
                   {
-                    "name": "langflow",
+                    "name": "lfx",
                     "version": null
                   }
                 ],
                 "total_dependencies": 2
               },
-              "module": "langflow.components.models.embedding_model.EmbeddingModelComponent"
+              "module": "custom_components.embedding_model"
             },
             "minimized": false,
             "output_types": [],
@@ -1591,7 +1724,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageTextInput,\n    SecretStrInput,\n)\nfrom langflow.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n    display_name = \"Embedding Model\"\n    description = \"Generate embeddings using a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n    icon = \"binary\"\n    name = \"EmbeddingModel\"\n    category = \"models\"\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\"],\n            value=\"OpenAI\",\n            info=\"Select the embedding model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}],\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model Name\",\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n            info=\"Select the embedding model to use\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=True,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"api_base\",\n            display_name=\"API Base URL\",\n            info=\"Base URL for the API. Leave empty for default.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"dimensions\",\n            display_name=\"Dimensions\",\n            info=\"The number of dimensions the resulting output embeddings should have. \"\n            \"Only supported by certain models.\",\n            advanced=True,\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        provider = self.provider\n        model = self.model\n        api_key = self.api_key\n        api_base = self.api_base\n        dimensions = self.dimensions\n        chunk_size = self.chunk_size\n        request_timeout = self.request_timeout\n        max_retries = self.max_retries\n        show_progress_bar = self.show_progress_bar\n        model_kwargs = self.model_kwargs or {}\n\n        if provider == \"OpenAI\":\n            if not api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n            return OpenAIEmbeddings(\n                model=model,\n                dimensions=dimensions or None,\n                base_url=api_base or None,\n                api_key=api_key,\n                chunk_size=chunk_size,\n                max_retries=max_retries,\n                timeout=request_timeout or None,\n                show_progress_bar=show_progress_bar,\n                model_kwargs=model_kwargs,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\" and field_value == \"OpenAI\":\n            build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n            build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n            build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n        return build_config\n"
+                "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageTextInput,\n    SecretStrInput,\n)\nfrom lfx.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n    display_name = \"Embedding Model\"\n    description = \"Generate embeddings using a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n    icon = \"binary\"\n    name = \"EmbeddingModel\"\n    category = \"models\"\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\"],\n            value=\"OpenAI\",\n            info=\"Select the embedding model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}],\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model Name\",\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n            info=\"Select the embedding model to use\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=True,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"api_base\",\n            display_name=\"API Base URL\",\n            info=\"Base URL for the API. Leave empty for default.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"dimensions\",\n            display_name=\"Dimensions\",\n            info=\"The number of dimensions the resulting output embeddings should have. \"\n            \"Only supported by certain models.\",\n            advanced=True,\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        provider = self.provider\n        model = self.model\n        api_key = self.api_key\n        api_base = self.api_base\n        dimensions = self.dimensions\n        chunk_size = self.chunk_size\n        request_timeout = self.request_timeout\n        max_retries = self.max_retries\n        show_progress_bar = self.show_progress_bar\n        model_kwargs = self.model_kwargs or {}\n\n        if provider == \"OpenAI\":\n            if not api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n            return OpenAIEmbeddings(\n                model=model,\n                dimensions=dimensions or None,\n                base_url=api_base or None,\n                api_key=api_key,\n                chunk_size=chunk_size,\n                max_retries=max_retries,\n                timeout=request_timeout or None,\n                show_progress_bar=show_progress_bar,\n                model_kwargs=model_kwargs,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\" and field_value == \"OpenAI\":\n            build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n            build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n            build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n        return build_config\n"
               },
               "dimensions": {
                 "_input_type": "IntInput",
@@ -1636,6 +1769,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Name",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the embedding model to use",
                 "name": "model",
                 "options": [
@@ -1679,6 +1813,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Provider",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the embedding model provider",
                 "name": "provider",
                 "options": [
@@ -1752,21 +1887,740 @@
           "x": 1726.6943524438122,
           "y": 1800.5330404375484
         },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "AdvancedDynamicFormBuilder-81Exw",
+          "node": {
+            "base_classes": [
+              "Data",
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Creates dynamic input fields that can receive data from other components or manual input.",
+            "display_name": "Create Data",
+            "documentation": "",
+            "edited": true,
+            "field_order": [
+              "form_fields",
+              "include_metadata"
+            ],
+            "frozen": false,
+            "icon": "braces",
+            "last_updated": "2025-09-26T14:37:42.700Z",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Data",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "process_form",
+                "name": "form_data",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Data",
+                "tool_mode": true,
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Message",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "get_message",
+                "name": "message",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from typing import Any\r\n\r\nfrom langflow.custom import Component\r\nfrom langflow.io import (\r\n    BoolInput,\r\n    FloatInput,\r\n    HandleInput,\r\n    IntInput,\r\n    MultilineInput,\r\n    Output,\r\n    StrInput,\r\n    TableInput,\r\n)\r\nfrom langflow.schema.data import Data\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass CrateData(Component):\r\n    \"\"\"Dynamic Form Component\r\n\r\n    This component creates dynamic inputs that can receive data from other components\r\n    or be filled manually. It demonstrates advanced dynamic input functionality with\r\n    component connectivity.\r\n\r\n    ## Features\r\n    - **Dynamic Input Generation**: Create inputs based on table configuration\r\n    - **Component Connectivity**: Inputs can receive data from other components\r\n    - **Multiple Input Types**: Support for text, number, boolean, and handle inputs\r\n    - **Flexible Data Sources**: Manual input OR component connections\r\n    - **Real-time Updates**: Form fields update immediately when table changes\r\n         - **Multiple Output Formats**: Data and formatted Message outputs\r\n    - **JSON Output**: Collects all dynamic inputs into a structured JSON response\r\n\r\n    ## Use Cases\r\n    - Dynamic API parameter collection from multiple sources\r\n    - Variable data aggregation from different components\r\n    - Flexible pipeline configuration\r\n    - Multi-source data processing\r\n\r\n    ## Field Types Available\r\n    - **text**: Single-line text input (can connect to Text/String outputs)\r\n    - **multiline**: Multi-line text input (can connect to Text outputs)\r\n    - **number**: Integer input (can connect to Number outputs)\r\n    - **float**: Decimal number input (can connect to Number outputs)\r\n    - **boolean**: True/false checkbox (can connect to Boolean outputs)\r\n    - **handle**: Generic data input (can connect to any component output)\r\n    - **data**: Structured data input (can connect to Data outputs)\r\n\r\n    ## Input Types for Connections\r\n    - **Text**: Text/String data from components\r\n    - **Data**: Structured data objects\r\n    - **Message**: Message objects with text content\r\n    - **Number**: Numeric values\r\n    - **Boolean**: True/false values\r\n    - **Any**: Accepts any type of connection\r\n    - **Combinations**: Text,Message | Data,Text | Text,Data,Message | etc.\r\n    \"\"\"\r\n\r\n    display_name = \"Create Data\"\r\n    description = \"Creates dynamic input fields that can receive data from other components or manual input.\"\r\n    icon = \"braces\"\r\n    name = \"AdvancedDynamicFormBuilder\"\r\n\r\n    def __init__(self, **kwargs):\r\n        super().__init__(**kwargs)\r\n        self._dynamic_inputs = {}\r\n\r\n    inputs = [\r\n        TableInput(\r\n            name=\"form_fields\",\r\n            display_name=\"Input Configuration\",\r\n            info=\"Define the dynamic form fields. Each row creates a new input field that can connect to other components.\",\r\n            table_schema=[\r\n                {\r\n                    \"name\": \"field_name\",\r\n                    \"display_name\": \"Field Name\",\r\n                    \"type\": \"str\",\r\n                    \"description\": \"Name for the field (used as both internal name and display label)\",\r\n                },\r\n                {\r\n                    \"name\": \"field_type\",\r\n                    \"display_name\": \"Field Type\",\r\n                    \"type\": \"str\",\r\n                    \"description\": \"Type of input field to create\",\r\n                    \"options\": [\"Text\", \"Data\", \"Number\", \"Handle\", \"Boolean\"],\r\n                    \"value\": \"Text\",\r\n                },\r\n            ],\r\n            value=[{\"field_name\": \"field_name\", \"field_type\": \"Text\"}],\r\n            real_time_refresh=True,\r\n        ),\r\n        BoolInput(\r\n            name=\"include_metadata\",\r\n            display_name=\"Include Metadata\",\r\n            info=\"Include form configuration metadata in the output.\",\r\n            value=False,\r\n            advanced=True,\r\n        ),\r\n    ]\r\n\r\n    outputs = [\r\n        Output(display_name=\"Data\", name=\"form_data\", method=\"process_form\"),\r\n        Output(display_name=\"Message\", name=\"message\", method=\"get_message\"),\r\n    ]\r\n\r\n    def update_build_config(self, build_config: dict, field_value: Any, field_name: str = None) -> dict:\r\n        \"\"\"Update build configuration to add dynamic inputs that can connect to other components.\"\"\"\r\n        if field_name == \"form_fields\":\r\n            # Store current values before clearing dynamic inputs\r\n            current_values = {}\r\n            keys_to_remove = [key for key in build_config if key.startswith(\"dynamic_\")]\r\n            for key in keys_to_remove:\r\n                # Preserve the current value before deletion\r\n                if hasattr(self, key):\r\n                    current_values[key] = getattr(self, key)\r\n                del build_config[key]\r\n\r\n            # Add dynamic inputs based on table configuration\r\n            # Safety check to ensure field_value is not None and is iterable\r\n            if field_value is None:\r\n                field_value = []\r\n\r\n            for i, field_config in enumerate(field_value):\r\n                # Safety check to ensure field_config is not None\r\n                if field_config is None:\r\n                    continue\r\n\r\n                field_name = field_config.get(\"field_name\", f\"field_{i}\")\r\n                display_name = field_name  # Use field_name as display_name\r\n                field_type_option = field_config.get(\"field_type\", \"Text\")\r\n                default_value = \"\"  # All fields have empty default value\r\n                required = False  # All fields are optional by default\r\n                help_text = \"\"  # All fields have empty help text\r\n\r\n                # Map field type options to actual field types and input types\r\n                field_type_mapping = {\r\n                    \"Text\": {\"field_type\": \"multiline\", \"input_types\": [\"Text\", \"Message\"]},\r\n                    \"Data\": {\"field_type\": \"data\", \"input_types\": [\"Data\"]},\r\n                    \"Number\": {\"field_type\": \"number\", \"input_types\": [\"Text\", \"Message\"]},\r\n                    \"Handle\": {\"field_type\": \"handle\", \"input_types\": [\"Text\", \"Data\", \"Message\"]},\r\n                    \"Boolean\": {\"field_type\": \"boolean\", \"input_types\": None},\r\n                }\r\n\r\n                field_config_mapped = field_type_mapping.get(\r\n                    field_type_option, {\"field_type\": \"text\", \"input_types\": []}\r\n                )\r\n                field_type = field_config_mapped[\"field_type\"]\r\n                input_types_list = field_config_mapped[\"input_types\"]\r\n\r\n                # Create the appropriate input type based on field_type\r\n                dynamic_input_name = f\"dynamic_{field_name}\"\r\n\r\n                if field_type == \"text\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    if input_types_list:\r\n                        build_config[dynamic_input_name] = StrInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=f\"{help_text} (Can connect to: {', '.join(input_types_list)})\",\r\n                            value=current_value,\r\n                            required=required,\r\n                            input_types=input_types_list,\r\n                        )\r\n                    else:\r\n                        build_config[dynamic_input_name] = StrInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=help_text,\r\n                            value=current_value,\r\n                            required=required,\r\n                        )\r\n\r\n                elif field_type == \"multiline\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    if input_types_list:\r\n                        build_config[dynamic_input_name] = MultilineInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=f\"{help_text} (Can connect to: {', '.join(input_types_list)})\",\r\n                            value=current_value,\r\n                            required=required,\r\n                            input_types=input_types_list,\r\n                        )\r\n                    else:\r\n                        build_config[dynamic_input_name] = MultilineInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=help_text,\r\n                            value=current_value,\r\n                            required=required,\r\n                        )\r\n\r\n                elif field_type == \"number\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    try:\r\n                        if current_value:\r\n                            current_int = int(current_value)\r\n                        else:\r\n                            current_int = 0\r\n                    except (ValueError, TypeError):\r\n                        try:\r\n                            current_int = int(default_value) if default_value else 0\r\n                        except ValueError:\r\n                            current_int = 0\r\n\r\n                    if input_types_list:\r\n                        build_config[dynamic_input_name] = IntInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=f\"{help_text} (Can connect to: {', '.join(input_types_list)})\",\r\n                            value=current_int,\r\n                            required=required,\r\n                            input_types=input_types_list,\r\n                        )\r\n                    else:\r\n                        build_config[dynamic_input_name] = IntInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=help_text,\r\n                            value=current_int,\r\n                            required=required,\r\n                        )\r\n\r\n                elif field_type == \"float\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    try:\r\n                        if current_value:\r\n                            current_float = float(current_value)\r\n                        else:\r\n                            current_float = 0.0\r\n                    except (ValueError, TypeError):\r\n                        try:\r\n                            current_float = float(default_value) if default_value else 0.0\r\n                        except ValueError:\r\n                            current_float = 0.0\r\n\r\n                    if input_types_list:\r\n                        build_config[dynamic_input_name] = FloatInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=f\"{help_text} (Can connect to: {', '.join(input_types_list)})\",\r\n                            value=current_float,\r\n                            required=required,\r\n                            input_types=input_types_list,\r\n                        )\r\n                    else:\r\n                        build_config[dynamic_input_name] = FloatInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=help_text,\r\n                            value=current_float,\r\n                            required=required,\r\n                        )\r\n\r\n                elif field_type == \"boolean\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    # Convert current value to boolean\r\n                    if isinstance(current_value, bool):\r\n                        current_bool = current_value\r\n                    else:\r\n                        current_bool = str(current_value).lower() in [\"true\", \"1\", \"yes\"] if current_value else False\r\n\r\n                    # Boolean fields don't use input_types parameter to avoid errors\r\n                    build_config[dynamic_input_name] = BoolInput(\r\n                        name=dynamic_input_name,\r\n                        display_name=display_name,\r\n                        info=help_text,\r\n                        value=current_bool,\r\n                        input_types=[],\r\n                        required=required,\r\n                    )\r\n\r\n                elif field_type == \"handle\":\r\n                    # HandleInput for generic data connections\r\n                    build_config[dynamic_input_name] = HandleInput(\r\n                        name=dynamic_input_name,\r\n                        display_name=display_name,\r\n                        info=f\"{help_text} (Accepts: {', '.join(input_types_list) if input_types_list else 'Any'})\",\r\n                        input_types=input_types_list if input_types_list else [\"Data\", \"Text\", \"Message\"],\r\n                        required=required,\r\n                    )\r\n\r\n                elif field_type == \"data\":\r\n                    # Specialized for Data type connections\r\n                    build_config[dynamic_input_name] = HandleInput(\r\n                        name=dynamic_input_name,\r\n                        display_name=display_name,\r\n                        info=f\"{help_text} (Data input)\",\r\n                        input_types=[\"Data\"] if not input_types_list else input_types_list,\r\n                        required=required,\r\n                    )\r\n\r\n                else:\r\n                    # Default to text input for unknown types\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    build_config[dynamic_input_name] = StrInput(\r\n                        name=dynamic_input_name,\r\n                        display_name=display_name,\r\n                        info=f\"{help_text} (Unknown type '{field_type}', defaulting to text)\",\r\n                        value=current_value,\r\n                        required=required,\r\n                    )\r\n\r\n        return build_config\r\n\r\n    def get_dynamic_values(self) -> dict[str, Any]:\r\n        \"\"\"Extract simple values from all dynamic inputs, handling both manual and connected inputs.\"\"\"\r\n        dynamic_values = {}\r\n        connection_info = {}\r\n        form_fields = getattr(self, \"form_fields\", [])\r\n\r\n        for field_config in form_fields:\r\n            # Safety check to ensure field_config is not None\r\n            if field_config is None:\r\n                continue\r\n\r\n            field_name = field_config.get(\"field_name\", \"\")\r\n            if field_name:\r\n                dynamic_input_name = f\"dynamic_{field_name}\"\r\n                value = getattr(self, dynamic_input_name, None)\r\n\r\n                # Extract simple values from connections or manual input\r\n                if value is not None:\r\n                    try:\r\n                        extracted_value = self._extract_simple_value(value)\r\n                        dynamic_values[field_name] = extracted_value\r\n\r\n                        # Determine connection type for status\r\n                        if hasattr(value, \"text\") and hasattr(value, \"timestamp\"):\r\n                            connection_info[field_name] = \"Connected (Message)\"\r\n                        elif hasattr(value, \"data\"):\r\n                            connection_info[field_name] = \"Connected (Data)\"\r\n                        elif isinstance(value, (str, int, float, bool, list, dict)):\r\n                            connection_info[field_name] = \"Manual input\"\r\n                        else:\r\n                            connection_info[field_name] = \"Connected (Object)\"\r\n\r\n                    except Exception:\r\n                        # Fallback to string representation if all else fails\r\n                        dynamic_values[field_name] = str(value)\r\n                        connection_info[field_name] = \"Error\"\r\n                else:\r\n                    # Use empty default value if nothing connected\r\n                    dynamic_values[field_name] = \"\"\r\n                    connection_info[field_name] = \"Empty default\"\r\n\r\n        # Store connection info for status output\r\n        self._connection_info = connection_info\r\n        return dynamic_values\r\n\r\n    def _extract_simple_value(self, value: Any) -> Any:\r\n        \"\"\"Extract the simplest, most useful value from any input type.\"\"\"\r\n        # Handle None\r\n        if value is None:\r\n            return None\r\n\r\n        # Handle simple types directly\r\n        if isinstance(value, (str, int, float, bool)):\r\n            return value\r\n\r\n        # Handle lists and tuples - keep simple\r\n        if isinstance(value, (list, tuple)):\r\n            return [self._extract_simple_value(item) for item in value]\r\n\r\n        # Handle dictionaries - keep simple\r\n        if isinstance(value, dict):\r\n            return {str(k): self._extract_simple_value(v) for k, v in value.items()}\r\n\r\n        # Handle Message objects - extract only the text\r\n        if hasattr(value, \"text\"):\r\n            return str(value.text) if value.text is not None else \"\"\r\n\r\n        # Handle Data objects - extract the data content\r\n        if hasattr(value, \"data\") and value.data is not None:\r\n            return self._extract_simple_value(value.data)\r\n\r\n        # For any other object, convert to string\r\n        return str(value)\r\n\r\n    def process_form(self) -> Data:\r\n        \"\"\"Process all dynamic form inputs and return clean data with just field values.\"\"\"\r\n        # Get all dynamic values (just the key:value pairs)\r\n        dynamic_values = self.get_dynamic_values()\r\n\r\n        # Update status with connection info\r\n        connected_fields = len([v for v in getattr(self, \"_connection_info\", {}).values() if \"Connected\" in v])\r\n        total_fields = len(dynamic_values)\r\n\r\n        self.status = f\"Form processed successfully. {connected_fields}/{total_fields} fields connected to components.\"\r\n\r\n        # Return clean Data object with just the field values\r\n        return Data(data=dynamic_values)\r\n\r\n    def get_message(self) -> Message:\r\n        \"\"\"Return form data as a formatted text message.\"\"\"\r\n        # Get all dynamic values\r\n        dynamic_values = self.get_dynamic_values()\r\n\r\n        if not dynamic_values:\r\n            return Message(text=\"No form data available\")\r\n\r\n        # Format as text message\r\n        message_lines = [\"📋 Form Data:\"]\r\n        message_lines.append(\"=\" * 40)\r\n\r\n        for field_name, value in dynamic_values.items():\r\n            # Use field_name as display_name\r\n            display_name = field_name\r\n\r\n            message_lines.append(f\"• {display_name}: {value}\")\r\n\r\n        message_lines.append(\"=\" * 40)\r\n        message_lines.append(f\"Total fields: {len(dynamic_values)}\")\r\n\r\n        message_text = \"\\n\".join(message_lines)\r\n        self.status = f\"Message formatted with {len(dynamic_values)} fields\"\r\n\r\n        return Message(text=message_text)"
+              },
+              "dynamic_connector_type": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "copy_field": false,
+                "display_name": "connector_type",
+                "dynamic": false,
+                "helper_text": null,
+                "info": " (Can connect to: Text, Message)",
+                "input_types": [
+                  "Text",
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "dynamic_connector_type",
+                "placeholder": "",
+                "real_time_refresh": null,
+                "refresh_button": null,
+                "refresh_button_text": null,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "dynamic_owner": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "copy_field": false,
+                "display_name": "owner",
+                "dynamic": false,
+                "helper_text": null,
+                "info": " (Can connect to: Text, Message)",
+                "input_types": [
+                  "Text",
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "dynamic_owner",
+                "placeholder": "",
+                "real_time_refresh": null,
+                "refresh_button": null,
+                "refresh_button_text": null,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "dynamic_owner_email": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "copy_field": false,
+                "display_name": "owner_email",
+                "dynamic": false,
+                "helper_text": null,
+                "info": " (Can connect to: Text, Message)",
+                "input_types": [
+                  "Text",
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "dynamic_owner_email",
+                "placeholder": "",
+                "real_time_refresh": null,
+                "refresh_button": null,
+                "refresh_button_text": null,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "dynamic_owner_name": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "copy_field": false,
+                "display_name": "owner_name",
+                "dynamic": false,
+                "helper_text": null,
+                "info": " (Can connect to: Text, Message)",
+                "input_types": [
+                  "Text",
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "dynamic_owner_name",
+                "placeholder": "",
+                "real_time_refresh": null,
+                "refresh_button": null,
+                "refresh_button_text": null,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "form_fields": {
+                "_input_type": "TableInput",
+                "advanced": false,
+                "display_name": "Input Configuration",
+                "dynamic": false,
+                "info": "Define the dynamic form fields. Each row creates a new input field that can connect to other components.",
+                "is_list": true,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "form_fields",
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": false,
+                "show": true,
+                "table_icon": "Table",
+                "table_schema": {
+                  "columns": [
+                    {
+                      "default": "None",
+                      "description": "Name for the field (used as both internal name and display label)",
+                      "disable_edit": false,
+                      "display_name": "Field Name",
+                      "edit_mode": "popover",
+                      "filterable": true,
+                      "formatter": "text",
+                      "hidden": false,
+                      "name": "field_name",
+                      "sortable": true,
+                      "type": "str"
+                    },
+                    {
+                      "default": "None",
+                      "description": "Type of input field to create",
+                      "disable_edit": false,
+                      "display_name": "Field Type",
+                      "edit_mode": "popover",
+                      "filterable": true,
+                      "formatter": "text",
+                      "hidden": false,
+                      "name": "field_type",
+                      "options": [
+                        "Text",
+                        "Data",
+                        "Number",
+                        "Handle",
+                        "Boolean"
+                      ],
+                      "sortable": true,
+                      "type": "str"
+                    }
+                  ]
+                },
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "trigger_icon": "Table",
+                "trigger_text": "Open table",
+                "type": "table",
+                "value": [
+                  {
+                    "field_name": "owner",
+                    "field_type": "Text"
+                  },
+                  {
+                    "field_name": "owner_name",
+                    "field_type": "Text"
+                  },
+                  {
+                    "field_name": "owner_email",
+                    "field_type": "Text"
+                  },
+                  {
+                    "field_name": "connector_type",
+                    "field_type": "Text"
+                  }
+                ]
+              },
+              "include_metadata": {
+                "_input_type": "BoolInput",
+                "advanced": true,
+                "display_name": "Include Metadata",
+                "dynamic": false,
+                "info": "Include form configuration metadata in the output.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "include_metadata",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "bool",
+                "value": false
+              }
+            },
+            "tool_mode": false
+          },
+          "selected_output": "form_data",
+          "showNode": true,
+          "type": "AdvancedDynamicFormBuilder"
+        },
+        "dragging": false,
+        "id": "AdvancedDynamicFormBuilder-81Exw",
+        "measured": {
+          "height": 552,
+          "width": 320
+        },
+        "position": {
+          "x": 1363.7188885586695,
+          "y": 1810.433145275832
+        },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "SecretInput-F34VJ",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Allows the selection of a secret to be generated as output..",
+            "display_name": "Secret Input",
+            "documentation": "https://docs.langflow.org/components-io#text-input",
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
+            "frozen": false,
+            "icon": "type",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Output Text",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "text_response",
+                "name": "text",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.text import TextComponent\r\nfrom langflow.io import MultilineInput, Output, SecretStrInput\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass SecretInputComponent(TextComponent):\r\n    display_name = \"Secret Input\"\r\n    description = \"Allows the selection of a secret to be generated as output..\"\r\n    documentation: str = \"https://docs.langflow.org/components-io#text-input\"\r\n    icon = \"type\"\r\n    name = \"SecretInput\"\r\n\r\n    inputs = [\r\n        SecretStrInput(\r\n            name=\"input_value\",\r\n            display_name=\"Secret\",\r\n            info=\"Secret to be passed as input.\",\r\n        ),\r\n    ]\r\n    outputs = [\r\n        Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\r\n    ]\r\n\r\n    def text_response(self) -> Message:\r\n        return Message(\r\n            text=self.input_value,\r\n        )\r\n"
+              },
+              "input_value": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Secret",
+                "dynamic": false,
+                "info": "Secret to be passed as input.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "input_value",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "CONNECTOR_TYPE"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "SecretInput"
+        },
+        "dragging": false,
+        "id": "SecretInput-F34VJ",
+        "measured": {
+          "height": 220,
+          "width": 320
+        },
+        "position": {
+          "x": 714.0622664079099,
+          "y": 1763.5050239191407
+        },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "SecretInput-b2cab",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Allows the selection of a secret to be generated as output..",
+            "display_name": "Secret Input",
+            "documentation": "https://docs.langflow.org/components-io#text-input",
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
+            "frozen": false,
+            "icon": "type",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Output Text",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "text_response",
+                "name": "text",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.text import TextComponent\r\nfrom langflow.io import MultilineInput, Output, SecretStrInput\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass SecretInputComponent(TextComponent):\r\n    display_name = \"Secret Input\"\r\n    description = \"Allows the selection of a secret to be generated as output..\"\r\n    documentation: str = \"https://docs.langflow.org/components-io#text-input\"\r\n    icon = \"type\"\r\n    name = \"SecretInput\"\r\n\r\n    inputs = [\r\n        SecretStrInput(\r\n            name=\"input_value\",\r\n            display_name=\"Secret\",\r\n            info=\"Secret to be passed as input.\",\r\n        ),\r\n    ]\r\n    outputs = [\r\n        Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\r\n    ]\r\n\r\n    def text_response(self) -> Message:\r\n        return Message(\r\n            text=self.input_value,\r\n        )\r\n"
+              },
+              "input_value": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Secret",
+                "dynamic": false,
+                "info": "Secret to be passed as input.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "input_value",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "OWNER"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "SecretInput"
+        },
+        "dragging": false,
+        "id": "SecretInput-b2cab",
+        "measured": {
+          "height": 220,
+          "width": 320
+        },
+        "position": {
+          "x": 714.4587372144712,
+          "y": 2004.1002386954729
+        },
         "selected": true,
         "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "SecretInput-ZVfuS",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Allows the selection of a secret to be generated as output..",
+            "display_name": "Secret Input",
+            "documentation": "https://docs.langflow.org/components-io#text-input",
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
+            "frozen": false,
+            "icon": "type",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Output Text",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "text_response",
+                "name": "text",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.text import TextComponent\r\nfrom langflow.io import MultilineInput, Output, SecretStrInput\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass SecretInputComponent(TextComponent):\r\n    display_name = \"Secret Input\"\r\n    description = \"Allows the selection of a secret to be generated as output..\"\r\n    documentation: str = \"https://docs.langflow.org/components-io#text-input\"\r\n    icon = \"type\"\r\n    name = \"SecretInput\"\r\n\r\n    inputs = [\r\n        SecretStrInput(\r\n            name=\"input_value\",\r\n            display_name=\"Secret\",\r\n            info=\"Secret to be passed as input.\",\r\n        ),\r\n    ]\r\n    outputs = [\r\n        Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\r\n    ]\r\n\r\n    def text_response(self) -> Message:\r\n        return Message(\r\n            text=self.input_value,\r\n        )\r\n"
+              },
+              "input_value": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Secret",
+                "dynamic": false,
+                "info": "Secret to be passed as input.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "input_value",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "OWNER_EMAIL"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "SecretInput"
+        },
+        "dragging": false,
+        "id": "SecretInput-ZVfuS",
+        "measured": {
+          "height": 220,
+          "width": 320
+        },
+        "position": {
+          "x": 709.8548852366719,
+          "y": 2250.972699431992
+        },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "SecretInput-Iqtxd",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Allows the selection of a secret to be generated as output..",
+            "display_name": "Secret Input",
+            "documentation": "https://docs.langflow.org/components-io#text-input",
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
+            "frozen": false,
+            "icon": "type",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Output Text",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "text_response",
+                "name": "text",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.text import TextComponent\r\nfrom langflow.io import MultilineInput, Output, SecretStrInput\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass SecretInputComponent(TextComponent):\r\n    display_name = \"Secret Input\"\r\n    description = \"Allows the selection of a secret to be generated as output..\"\r\n    documentation: str = \"https://docs.langflow.org/components-io#text-input\"\r\n    icon = \"type\"\r\n    name = \"SecretInput\"\r\n\r\n    inputs = [\r\n        SecretStrInput(\r\n            name=\"input_value\",\r\n            display_name=\"Secret\",\r\n            info=\"Secret to be passed as input.\",\r\n        ),\r\n    ]\r\n    outputs = [\r\n        Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\r\n    ]\r\n\r\n    def text_response(self) -> Message:\r\n        return Message(\r\n            text=self.input_value,\r\n        )\r\n"
+              },
+              "input_value": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Secret",
+                "dynamic": false,
+                "info": "Secret to be passed as input.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "input_value",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "OWNER_NAME"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "SecretInput"
+        },
+        "dragging": false,
+        "id": "SecretInput-Iqtxd",
+        "measured": {
+          "height": 220,
+          "width": 320
+        },
+        "position": {
+          "x": 712.1292482141275,
+          "y": 2505.6122587806585
+        },
+        "selected": false,
+        "type": "genericNode"
       }
     ],
     "viewport": {
-      "x": -816.160132796055,
-      "y": -860.347743678783,
-      "zoom": 0.7338647578262055
+      "x": -311.42857930212404,
+      "y": -532.9060284457172,
+      "zoom": 0.5361317364942912
     }
   },
   "description": "Load your data for chat context with Retrieval Augmented Generation.",
   "endpoint_name": null,
   "id": "5488df7c-b93f-4f87-a446-b67028bc0813",
   "is_component": false,
-  "last_tested_version": "1.5.0.post2",
+  "last_tested_version": "1.6.0",
   "name": "OpenSearch Ingestion Flow",
   "tags": [
     "openai",
diff --git a/flows/openrag_agent.json b/flows/openrag_agent.json
index ae9ac1d1..9e1f33cc 100644
--- a/flows/openrag_agent.json
+++ b/flows/openrag_agent.json
@@ -57,34 +57,6 @@
         "target": "Agent-crjWf",
         "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"
       },
-      {
-        "animated": false,
-        "className": "",
-        "data": {
-          "sourceHandle": {
-            "dataType": "TextInput",
-            "id": "TextInput-aHsQb",
-            "name": "text",
-            "output_types": [
-              "Message"
-            ]
-          },
-          "targetHandle": {
-            "fieldName": "filter_expression",
-            "id": "OpenSearch-iYfjf",
-            "inputTypes": [
-              "Message"
-            ],
-            "type": "str"
-          }
-        },
-        "id": "xy-edge__TextInput-aHsQb{œdataTypeœ:œTextInputœ,œidœ:œTextInput-aHsQbœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-OpenSearch-iYfjf{œfieldNameœ:œfilter_expressionœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
-        "selected": false,
-        "source": "TextInput-aHsQb",
-        "sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-aHsQbœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
-        "target": "OpenSearch-iYfjf",
-        "targetHandle": "{œfieldNameœ:œfilter_expressionœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"
-      },
       {
         "animated": false,
         "className": "",
@@ -120,7 +92,7 @@
         "className": "",
         "data": {
           "sourceHandle": {
-            "dataType": "OpenSearchHybrid",
+            "dataType": "OpenSearchVectorStoreComponent",
             "id": "OpenSearch-iYfjf",
             "name": "component_as_tool",
             "output_types": [
@@ -136,15 +108,16 @@
             "type": "other"
           }
         },
-        "id": "xy-edge__OpenSearch-iYfjf{œdataTypeœ:œOpenSearchHybridœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-crjWf{œfieldNameœ:œtoolsœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+        "id": "xy-edge__OpenSearch-iYfjf{œdataTypeœ:œOpenSearchVectorStoreComponentœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-crjWf{œfieldNameœ:œtoolsœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
         "selected": false,
         "source": "OpenSearch-iYfjf",
-        "sourceHandle": "{œdataTypeœ:œOpenSearchHybridœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}",
+        "sourceHandle": "{œdataTypeœ:œOpenSearchVectorStoreComponentœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}",
         "target": "Agent-crjWf",
         "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}"
       },
       {
         "animated": false,
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "LanguageModelComponent",
@@ -169,6 +142,31 @@
         "sourceHandle": "{œdataTypeœ:œLanguageModelComponentœ,œidœ:œLanguageModelComponent-0YME7œ,œnameœ:œmodel_outputœ,œoutput_typesœ:[œLanguageModelœ]}",
         "target": "Agent-crjWf",
         "targetHandle": "{œfieldNameœ:œagent_llmœ,œidœ:œAgent-crjWfœ,œinputTypesœ:[œLanguageModelœ],œtypeœ:œstrœ}"
+      },
+      {
+        "data": {
+          "sourceHandle": {
+            "dataType": "TextInput",
+            "id": "TextInput-aHsQb",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "filter_expression",
+            "id": "OpenSearch-iYfjf",
+            "inputTypes": [
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "xy-edge__TextInput-aHsQb{œdataTypeœ:œTextInputœ,œidœ:œTextInput-aHsQbœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-OpenSearch-iYfjf{œfieldNameœ:œfilter_expressionœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "TextInput-aHsQb",
+        "sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-aHsQbœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "OpenSearch-iYfjf",
+        "targetHandle": "{œfieldNameœ:œfilter_expressionœ,œidœ:œOpenSearch-iYfjfœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"
       }
     ],
     "nodes": [
@@ -180,12 +178,11 @@
               "Message"
             ],
             "beta": false,
-            "category": "inputs",
             "conditional_paths": [],
             "custom_fields": {},
             "description": "Get chat inputs from the Playground.",
             "display_name": "Chat Input",
-            "documentation": "",
+            "documentation": "https://docs.langflow.org/components-io#chat-input",
             "edited": false,
             "field_order": [
               "input_value",
@@ -193,17 +190,25 @@
               "sender",
               "sender_name",
               "session_id",
-              "files",
-              "background_color",
-              "chat_icon",
-              "text_color"
+              "files"
             ],
             "frozen": false,
             "icon": "MessagesSquare",
-            "key": "ChatInput",
             "legacy": false,
-            "lf_version": "1.5.0.post1",
-            "metadata": {},
+            "lf_version": "1.6.0",
+            "metadata": {
+              "code_hash": "f701f686b325",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "lfx",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 1
+              },
+              "module": "custom_components.chat_input"
+            },
             "minimized": true,
             "output_types": [],
             "outputs": [
@@ -214,6 +219,8 @@
                 "group_outputs": false,
                 "method": "message_response",
                 "name": "message",
+                "options": null,
+                "required_inputs": null,
                 "selected": "Message",
                 "tool_mode": true,
                 "types": [
@@ -223,55 +230,8 @@
               }
             ],
             "pinned": false,
-            "score": 0.0020353564437605998,
             "template": {
               "_type": "Component",
-              "background_color": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Background Color",
-                "dynamic": false,
-                "info": "The background color of the icon.",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "background_color",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "chat_icon": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Icon",
-                "dynamic": false,
-                "info": "The icon of the message.",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "chat_icon",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -288,7 +248,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs.inputs import BoolInput\nfrom langflow.io import (\n    DropdownInput,\n    FileInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n)\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_USER,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n    minimized = True\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n            input_types=[],\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n            temp_file=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\n                \"background_color\": background_color,\n                \"text_color\": text_color,\n                \"icon\": icon,\n            },\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from lfx.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import (\n    DropdownInput,\n    FileInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n)\nfrom lfx.schema.message import Message\nfrom lfx.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_USER,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n    minimized = True\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n            input_types=[],\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n            temp_file=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        # Ensure files is a list and filter out empty/None values\n        files = self.files if self.files else []\n        if files and not isinstance(files, list):\n            files = [files]\n        # Filter out None/empty values\n        files = [f for f in files if f is not None and f != \"\"]\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=files,\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -296,17 +256,17 @@
                 "display_name": "Files",
                 "dynamic": false,
                 "fileTypes": [
+                  "csv",
+                  "json",
+                  "pdf",
                   "txt",
                   "md",
                   "mdx",
-                  "csv",
-                  "json",
                   "yaml",
                   "yml",
                   "xml",
                   "html",
                   "htm",
-                  "pdf",
                   "docx",
                   "py",
                   "sh",
@@ -364,6 +324,7 @@
                 "dialog_inputs": {},
                 "display_name": "Sender Type",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Type of sender.",
                 "name": "sender",
                 "options": [
@@ -375,6 +336,7 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
+                "toggle": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
                 "type": "str",
@@ -443,29 +405,6 @@
                 "trace_as_metadata": true,
                 "type": "bool",
                 "value": true
-              },
-              "text_color": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Text Color",
-                "dynamic": false,
-                "info": "The text color of the name",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "text_color",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
               }
             },
             "tool_mode": false
@@ -495,12 +434,11 @@
               "Message"
             ],
             "beta": false,
-            "category": "outputs",
             "conditional_paths": [],
             "custom_fields": {},
             "description": "Display a chat message in the Playground.",
             "display_name": "Chat Output",
-            "documentation": "",
+            "documentation": "https://docs.langflow.org/components-io#chat-output",
             "edited": false,
             "field_order": [
               "input_value",
@@ -509,17 +447,33 @@
               "sender_name",
               "session_id",
               "data_template",
-              "background_color",
-              "chat_icon",
-              "text_color",
               "clean_data"
             ],
             "frozen": false,
             "icon": "MessagesSquare",
-            "key": "ChatOutput",
             "legacy": false,
-            "lf_version": "1.5.0.post1",
-            "metadata": {},
+            "lf_version": "1.6.0",
+            "metadata": {
+              "code_hash": "9647f4d2f4b4",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "orjson",
+                    "version": "3.10.15"
+                  },
+                  {
+                    "name": "fastapi",
+                    "version": "0.117.1"
+                  },
+                  {
+                    "name": "lfx",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 3
+              },
+              "module": "custom_components.chat_output"
+            },
             "minimized": true,
             "output_types": [],
             "outputs": [
@@ -530,6 +484,8 @@
                 "group_outputs": false,
                 "method": "message_response",
                 "name": "message",
+                "options": null,
+                "required_inputs": null,
                 "selected": "Message",
                 "tool_mode": true,
                 "types": [
@@ -539,61 +495,14 @@
               }
             ],
             "pinned": false,
-            "score": 0.003169567463043492,
             "template": {
               "_type": "Component",
-              "background_color": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Background Color",
-                "dynamic": false,
-                "info": "The background color of the icon.",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "background_color",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "chat_icon": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Icon",
-                "dynamic": false,
-                "info": "The icon of the message.",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "chat_icon",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
               "clean_data": {
                 "_input_type": "BoolInput",
                 "advanced": true,
                 "display_name": "Basic Clean Data",
                 "dynamic": false,
-                "info": "Whether to clean the data",
+                "info": "Whether to clean data before converting to string.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "clean_data",
@@ -622,7 +531,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.helpers.data import safe_convert\nfrom langflow.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.template.field.base import Output\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Inputs\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Output Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Inputs\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            advanced=True,\n            info=\"Whether to clean data before converting to string.\",\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Output Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n\n        # Get source properties\n        source, _, display_name, source_id = self.get_properties_from_source_component()\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            clean_data: bool = getattr(self, \"clean_data\", False)\n            return \"\\n\".join([safe_convert(item, clean_data=clean_data) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -676,6 +585,7 @@
                 "dialog_inputs": {},
                 "display_name": "Sender Type",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Type of sender.",
                 "name": "sender",
                 "options": [
@@ -687,6 +597,7 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
+                "toggle": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
                 "type": "str",
@@ -755,29 +666,6 @@
                 "trace_as_metadata": true,
                 "type": "bool",
                 "value": true
-              },
-              "text_color": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Text Color",
-                "dynamic": false,
-                "info": "The text color of the name",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "text_color",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
               }
             },
             "tool_mode": false
@@ -809,13 +697,18 @@
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
-            "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
-            "display_name": "OpenSearch (Hybrid)",
+            "description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
+            "display_name": "OpenSearch",
             "documentation": "",
             "edited": true,
             "field_order": [
+              "docs_metadata",
               "opensearch_url",
               "index_name",
+              "engine",
+              "space_type",
+              "ef_construction",
+              "m",
               "ingest_data",
               "search_query",
               "should_cache_vector_store",
@@ -834,9 +727,26 @@
             ],
             "frozen": false,
             "icon": "OpenSearch",
-            "last_updated": "2025-09-22T15:52:59.128Z",
+            "last_updated": "2025-10-02T20:05:34.814Z",
             "legacy": false,
-            "metadata": {},
+            "lf_version": "1.6.0",
+            "metadata": {
+              "code_hash": "07eef12db820",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "opensearchpy",
+                    "version": "2.8.0"
+                  },
+                  {
+                    "name": "lfx",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 2
+              },
+              "module": "custom_components.opensearch"
+            },
             "minimized": false,
             "output_types": [],
             "outputs": [
@@ -866,9 +776,10 @@
                 "advanced": false,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Auth Mode",
+                "display_name": "Authentication Mode",
                 "dynamic": false,
-                "info": "Choose Basic (username/password) or JWT (Bearer token).",
+                "external_options": {},
+                "info": "Authentication method: 'basic' for username/password authentication, or 'jwt' for JSON Web Token (Bearer) authentication.",
                 "load_from_db": false,
                 "name": "auth_mode",
                 "options": [
@@ -898,7 +809,7 @@
                 "name": "bearer_prefix",
                 "placeholder": "",
                 "required": false,
-                "show": true,
+                "show": false,
                 "title_case": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
@@ -921,7 +832,65 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport json\nfrom typing import Any, Dict, List\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom langflow.io import (\n    BoolInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n    DropdownInput,\n)\nfrom langflow.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchHybridComponent(LCVectorStoreComponent):\n    \"\"\"OpenSearch hybrid search: KNN (k=10, boost=0.7) + multi_match (boost=0.3) with optional filters & min_score.\"\"\"\n    display_name: str = \"OpenSearch (Hybrid)\"\n    name: str = \"OpenSearchHybrid\"\n    icon: str = \"OpenSearch\"\n    description: str = \"Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.\"\n\n    # Keys we consider baseline\n    default_keys: list[str] = [\n        \"opensearch_url\",\n        \"index_name\",\n        *[i.name for i in LCVectorStoreComponent.inputs],  # search_query, add_documents, etc.\n        \"embedding\",\n        \"vector_field\",\n        \"number_of_results\",\n        \"auth_mode\",\n        \"username\",\n        \"password\",\n        \"jwt_token\",\n        \"jwt_header\",\n        \"bearer_prefix\",\n        \"use_ssl\",\n        \"verify_certs\",\n        \"filter_expression\",\n    ]\n\n    inputs = [\n        StrInput(\n            name=\"opensearch_url\",\n            display_name=\"OpenSearch URL\",\n            value=\"http://localhost:9200\",\n            info=\"URL for your OpenSearch cluster.\"\n        ),\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow\",\n            info=\"The index to search.\"\n        ),\n        *LCVectorStoreComponent.inputs,  # includes search_query, add_documents, etc.\n        HandleInput(\n            name=\"embedding\",\n            display_name=\"Embedding\",\n            input_types=[\"Embeddings\"]\n        ),\n        StrInput(\n            name=\"vector_field\",\n            display_name=\"Vector Field\",\n            value=\"chunk_embedding\",\n            advanced=True,\n            info=\"Vector field used for KNN.\"\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Default Size (limit)\",\n            value=10,\n            advanced=True,\n            info=\"Default number of hits when no limit provided in filter_expression.\"\n        ),\n        MultilineInput(\n            name=\"filter_expression\",\n            display_name=\"Filter Expression (JSON)\",\n            value=\"\",\n            info=(\n                \"Optional JSON to control filters/limit/score threshold.\\n\"\n                \"Accepted shapes:\\n\"\n                '1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\\n'\n                '2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\\n'\n                \"Placeholders with __IMPOSSIBLE_VALUE__ are ignored.\"\n            )\n        ),\n\n        # ----- Auth controls (dynamic) -----\n        DropdownInput(\n            name=\"auth_mode\",\n            display_name=\"Auth Mode\",\n            value=\"basic\",\n            options=[\"basic\", \"jwt\"],\n            info=\"Choose Basic (username/password) or JWT (Bearer token).\",\n            real_time_refresh=True,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"username\",\n            display_name=\"Username\",\n            value=\"admin\",\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"password\",\n            display_name=\"Password\",\n            value=\"admin\",\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"jwt_token\",\n            display_name=\"JWT Token\",\n            value=\"\",\n            show=False,\n            info=\"Paste a valid JWT (sent as a header).\",\n        ),\n        StrInput(\n            name=\"jwt_header\",\n            display_name=\"JWT Header Name\",\n            value=\"Authorization\",\n            show=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"bearer_prefix\",\n            display_name=\"Prefix 'Bearer '\",\n            value=True,\n            show=False,\n            advanced=True,\n        ),\n\n        # ----- TLS -----\n        BoolInput(\n            name=\"use_ssl\",\n            display_name=\"Use SSL\",\n            value=True,\n            advanced=True\n        ),\n        BoolInput(\n            name=\"verify_certs\",\n            display_name=\"Verify Certificates\",\n            value=False,\n            advanced=True\n        ),\n    ]\n\n    # ---------- auth / client ----------\n    def _build_auth_kwargs(self) -> Dict[str, Any]:\n        mode = (self.auth_mode or \"basic\").strip().lower()\n        if mode == \"jwt\":\n            token = (self.jwt_token or \"\").strip()\n            if not token:\n                raise ValueError(\"Auth Mode is 'jwt' but no jwt_token was provided.\")\n            header_name = (self.jwt_header or \"Authorization\").strip()\n            header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n            return {\"headers\": {header_name: header_value}}\n        user = (self.username or \"\").strip()\n        pwd = (self.password or \"\").strip()\n        if not user or not pwd:\n            raise ValueError(\"Auth Mode is 'basic' but username/password are missing.\")\n        return {\"http_auth\": (user, pwd)}\n\n    def build_client(self) -> OpenSearch:\n        auth_kwargs = self._build_auth_kwargs()\n        return OpenSearch(\n            hosts=[self.opensearch_url],\n            use_ssl=self.use_ssl,\n            verify_certs=self.verify_certs,\n            ssl_assert_hostname=False,\n            ssl_show_warn=False,\n            **auth_kwargs,\n        )\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> OpenSearch:\n        # Return raw OpenSearch client as our “vector store.”\n        return self.build_client()\n\n    # ---------- ingest ----------\n    def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n        docs = self._prepare_ingest_data() or []\n        if not docs:\n            self.log(\"No documents to ingest.\")\n            return\n\n        texts = [d.to_lc_document().page_content for d in docs]\n        if not self.embedding:\n            raise ValueError(\"Embedding handle is required to embed documents.\")\n        vectors = self.embedding.embed_documents(texts)\n\n        actions = []\n        for doc_obj, vec in zip(docs, vectors):\n            lc_doc = doc_obj.to_lc_document()\n            body = {\n                **lc_doc.metadata,\n                \"text\": lc_doc.page_content,\n                self.vector_field: vec,\n            }\n            actions.append({\n                \"_op_type\": \"index\",\n                \"_index\": self.index_name,\n                \"_source\": body,\n            })\n\n        self.log(f\"Indexing {len(actions)} docs into '{self.index_name}'…\")\n        helpers.bulk(client, actions)\n\n    # ---------- helpers for filters ----------\n    def _is_placeholder_term(self, term_obj: dict) -> bool:\n        # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n        return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n    def _coerce_filter_clauses(self, filter_obj: dict | None) -> List[dict]:\n        \"\"\"\n        Accepts either:\n          A) {\"filter\":[ ...term/terms objects... ], \"limit\":..., \"score_threshold\":...}\n          B) Context-style: {\"data_sources\":[...], \"document_types\":[...], \"owners\":[...]}\n        Returns a list of OS filter clauses (term/terms), skipping placeholders and empty terms.\n        \"\"\"\n        \n        if not filter_obj:\n            return []\n\n        # If it’s a string, try to parse it once\n        if isinstance(filter_obj, str):\n            try:\n                filter_obj = json.loads(filter_obj)\n            except Exception:\n                # Not valid JSON → treat as no filters\n                return []\n            \n        # Case A: already an explicit list/dict under \"filter\"\n        if \"filter\" in filter_obj:\n            raw = filter_obj[\"filter\"]\n            if isinstance(raw, dict):\n                raw = [raw]\n            clauses: List[dict] = []\n            for f in (raw or []):\n                if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n                    clauses.append(f)\n                elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n                    field, vals = next(iter(f[\"terms\"].items()))\n                    if isinstance(vals, list) and len(vals) > 0:\n                        clauses.append(f)\n            return clauses\n\n        # Case B: convert context-style maps into clauses\n        field_mapping = {\"data_sources\": \"filename\", \"document_types\": \"mimetype\", \"owners\": \"owner\"}\n        print(f\"filter_obj {filter_obj}\")\n        clauses: List[dict] = []\n        for k, values in filter_obj.items():\n            if not isinstance(values, list):\n                continue\n            field = field_mapping.get(k, k)\n            if len(values) == 0:\n                # Match-nothing placeholder (kept to mirror your tool semantics)\n                clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n            elif len(values) == 1:\n                if values[0] != \"__IMPOSSIBLE_VALUE__\":\n                    clauses.append({\"term\": {field: values[0]}})\n            else:\n                clauses.append({\"terms\": {field: values}})\n        return clauses\n\n    # ---------- search (single hybrid path matching your tool) ----------\n    def search(self, query: str | None = None) -> list[dict[str, Any]]:\n        print(\"search method\")\n        client = self.build_client()\n        q = (query or \"\").strip()\n\n        # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n        filter_obj = None\n        print(f\"DEBUG q {q}\")\n        if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n            try:\n                self.log(f\"DEBUG FILTER EXPRESSION {self.filter_expression}\")\n            except json.JSONDecodeError as e:\n                raise ValueError(f\"Invalid filter_expression JSON: {e}\") from e\n\n        if not self.embedding:\n            raise ValueError(\"Embedding is required to run hybrid search (KNN + keyword).\")\n\n        # Embed the query\n        vec = self.embedding.embed_query(q)\n\n        # Build filter clauses (accept both shapes)\n        clauses = self._coerce_filter_clauses(filter_obj)\n\n        # Respect the tool's limit/threshold defaults\n        limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n        score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n        print(f\"DEBUG clauses {clauses}\")\n        # Build the same hybrid body as your SearchService\n        body = {\n            \"query\": {\n                \"bool\": {\n                    \"should\": [\n                        {\n                            \"knn\": {\n                                self.vector_field: {\n                                    \"vector\": vec,\n                                    \"k\": 10,          # fixed to match the tool\n                                    \"boost\": 0.7\n                                }\n                            }\n                        },\n                        {\n                            \"multi_match\": {\n                                \"query\": q,\n                                \"fields\": [\"text^2\", \"filename^1.5\"],\n                                \"type\": \"best_fields\",\n                                \"fuzziness\": \"AUTO\",\n                                \"boost\": 0.3\n                            }\n                        }\n                    ],\n                    \"minimum_should_match\": 1\n                }\n            },\n            \"aggs\": {\n                \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n                \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n                \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}}\n            },\n            \"_source\": [\n                \"filename\", \"mimetype\", \"page\", \"text\", \"source_url\",\n                \"owner\", \"allowed_users\", \"allowed_groups\"\n            ],\n            \"size\": limit\n        }\n        print(f\"DEBUG BODY {body}\")\n        if clauses:\n            body[\"query\"][\"bool\"][\"filter\"] = clauses\n\n        if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n            # top-level min_score (matches your tool)\n            body[\"min_score\"] = score_threshold\n\n        print(f\"DEBUG HYBRID BODY {body}\")\n        resp = client.search(index=self.index_name, body=body)\n        hits = resp.get(\"hits\", {}).get(\"hits\", [])\n        return [\n            {\n                \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n                \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n                \"score\": hit.get(\"_score\"),\n            }\n            for hit in hits\n        ]\n\n    def search_documents(self) -> list[Data]:\n        try:\n            raw = self.search(self.search_query or \"\")\n            return [\n                Data(file_path=hit[\"metadata\"].get(\"file_path\", \"\"), text=hit[\"page_content\"])\n                for hit in raw\n            ]\n        except Exception as e:\n            print(f\"ERROR search_documents: {e}\")\n            self.log(f\"search_documents error: {e}\")\n            raise\n\n    # -------- dynamic UI handling (auth switch) --------\n    async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n        try:\n            if field_name == \"auth_mode\":\n                mode = (field_value or \"basic\").strip().lower()\n                is_basic = mode == \"basic\"\n                is_jwt = mode == \"jwt\"\n\n                build_config[\"username\"][\"show\"] = is_basic\n                build_config[\"password\"][\"show\"] = is_basic\n\n                build_config[\"jwt_token\"][\"show\"] = is_jwt\n                build_config[\"jwt_header\"][\"show\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n                build_config[\"username\"][\"required\"] = is_basic\n                build_config[\"password\"][\"required\"] = is_basic\n\n                build_config[\"jwt_token\"][\"required\"] = is_jwt\n                build_config[\"jwt_header\"][\"required\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"required\"] = False\n\n                if is_basic:\n                    build_config[\"jwt_token\"][\"value\"] = \"\"\n\n                return build_config\n\n            return build_config\n\n        except Exception as e:\n            self.log(f\"update_build_config error: {e}\")\n            return build_config\n"
+                "value": "from __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput, TableInput\nfrom lfx.log import logger\nfrom lfx.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchVectorStoreComponent(LCVectorStoreComponent):\n    \"\"\"OpenSearch Vector Store Component with Hybrid Search Capabilities.\n\n    This component provides vector storage and retrieval using OpenSearch, combining semantic\n    similarity search (KNN) with keyword-based search for optimal results. It supports document\n    ingestion, vector embeddings, and advanced filtering with authentication options.\n\n    Features:\n    - Vector storage with configurable engines (jvector, nmslib, faiss, lucene)\n    - Hybrid search combining KNN vector similarity and keyword matching\n    - Flexible authentication (Basic auth, JWT tokens)\n    - Advanced filtering and aggregations\n    - Metadata injection during document ingestion\n    \"\"\"\n\n    display_name: str = \"OpenSearch\"\n    icon: str = \"OpenSearch\"\n    description: str = (\n        \"Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.\"\n    )\n\n    # Keys we consider baseline\n    default_keys: list[str] = [\n        \"opensearch_url\",\n        \"index_name\",\n        *[i.name for i in LCVectorStoreComponent.inputs],  # search_query, add_documents, etc.\n        \"embedding\",\n        \"vector_field\",\n        \"number_of_results\",\n        \"auth_mode\",\n        \"username\",\n        \"password\",\n        \"jwt_token\",\n        \"jwt_header\",\n        \"bearer_prefix\",\n        \"use_ssl\",\n        \"verify_certs\",\n        \"filter_expression\",\n        \"engine\",\n        \"space_type\",\n        \"ef_construction\",\n        \"m\",\n        \"docs_metadata\",\n    ]\n\n    inputs = [\n        TableInput(\n            name=\"docs_metadata\",\n            display_name=\"Document Metadata\",\n            info=(\n                \"Additional metadata key-value pairs to be added to all ingested documents. \"\n                \"Useful for tagging documents with source information, categories, or other custom attributes.\"\n            ),\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Key\",\n                    \"type\": \"str\",\n                    \"description\": \"Key name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Value of the metadata\",\n                },\n            ],\n            value=[],\n            # advanced=True,\n            input_types=[\"Data\"]\n        ),\n        StrInput(\n            name=\"opensearch_url\",\n            display_name=\"OpenSearch URL\",\n            value=\"http://localhost:9200\",\n            info=(\n                \"The connection URL for your OpenSearch cluster \"\n                \"(e.g., http://localhost:9200 for local development or your cloud endpoint).\"\n            ),\n        ),\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow\",\n            info=(\n                \"The OpenSearch index name where documents will be stored and searched. \"\n                \"Will be created automatically if it doesn't exist.\"\n            ),\n        ),\n        DropdownInput(\n            name=\"engine\",\n            display_name=\"Vector Engine\",\n            options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n            value=\"jvector\",\n            info=(\n                \"Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. \"\n                \"Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.\"\n            ),\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"space_type\",\n            display_name=\"Distance Metric\",\n            options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n            value=\"l2\",\n            info=(\n                \"Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, \"\n                \"'cosinesimil' for cosine similarity, 'innerproduct' for dot product.\"\n            ),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"ef_construction\",\n            display_name=\"EF Construction\",\n            value=512,\n            info=(\n                \"Size of the dynamic candidate list during index construction. \"\n                \"Higher values improve recall but increase indexing time and memory usage.\"\n            ),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"m\",\n            display_name=\"M Parameter\",\n            value=16,\n            info=(\n                \"Number of bidirectional connections for each vector in the HNSW graph. \"\n                \"Higher values improve search quality but increase memory usage and indexing time.\"\n            ),\n            advanced=True,\n        ),\n        *LCVectorStoreComponent.inputs,  # includes search_query, add_documents, etc.\n        HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n        StrInput(\n            name=\"vector_field\",\n            display_name=\"Vector Field Name\",\n            value=\"chunk_embedding\",\n            advanced=True,\n            info=\"Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Default Result Limit\",\n            value=10,\n            advanced=True,\n            info=(\n                \"Default maximum number of search results to return when no limit is \"\n                \"specified in the filter expression.\"\n            ),\n        ),\n        MultilineInput(\n            name=\"filter_expression\",\n            display_name=\"Search Filters (JSON)\",\n            value=\"\",\n            info=(\n                \"Optional JSON configuration for search filtering, result limits, and score thresholds.\\n\\n\"\n                \"Format 1 - Explicit filters:\\n\"\n                '{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, '\n                '{\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\\n\\n'\n                \"Format 2 - Context-style mapping:\\n\"\n                '{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\\n\\n'\n                \"Use __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.\"\n            ),\n        ),\n        # ----- Auth controls (dynamic) -----\n        DropdownInput(\n            name=\"auth_mode\",\n            display_name=\"Authentication Mode\",\n            value=\"basic\",\n            options=[\"basic\", \"jwt\"],\n            info=(\n                \"Authentication method: 'basic' for username/password authentication, \"\n                \"or 'jwt' for JSON Web Token (Bearer) authentication.\"\n            ),\n            real_time_refresh=True,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"username\",\n            display_name=\"Username\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"password\",\n            display_name=\"OpenSearch Password\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"jwt_token\",\n            display_name=\"JWT Token\",\n            value=\"JWT\",\n            load_from_db=False,\n            show=True,\n            info=(\n                \"Valid JSON Web Token for authentication. \"\n                \"Will be sent in the Authorization header (with optional 'Bearer ' prefix).\"\n            ),\n        ),\n        StrInput(\n            name=\"jwt_header\",\n            display_name=\"JWT Header Name\",\n            value=\"Authorization\",\n            show=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"bearer_prefix\",\n            display_name=\"Prefix 'Bearer '\",\n            value=True,\n            show=False,\n            advanced=True,\n        ),\n        # ----- TLS -----\n        BoolInput(\n            name=\"use_ssl\",\n            display_name=\"Use SSL/TLS\",\n            value=True,\n            advanced=True,\n            info=\"Enable SSL/TLS encryption for secure connections to OpenSearch.\",\n        ),\n        BoolInput(\n            name=\"verify_certs\",\n            display_name=\"Verify SSL Certificates\",\n            value=False,\n            advanced=True,\n            info=(\n                \"Verify SSL certificates when connecting. \"\n                \"Disable for self-signed certificates in development environments.\"\n            ),\n        ),\n    ]\n\n    # ---------- helper functions for index management ----------\n    def _default_text_mapping(\n        self,\n        dim: int,\n        engine: str = \"jvector\",\n        space_type: str = \"l2\",\n        ef_search: int = 512,\n        ef_construction: int = 100,\n        m: int = 16,\n        vector_field: str = \"vector_field\",\n    ) -> dict[str, Any]:\n        \"\"\"Create the default OpenSearch index mapping for vector search.\n\n        This method generates the index configuration with k-NN settings optimized\n        for approximate nearest neighbor search using the specified vector engine.\n\n        Args:\n            dim: Dimensionality of the vector embeddings\n            engine: Vector search engine (jvector, nmslib, faiss, lucene)\n            space_type: Distance metric for similarity calculation\n            ef_search: Size of dynamic list used during search\n            ef_construction: Size of dynamic list used during index construction\n            m: Number of bidirectional links for each vector\n            vector_field: Name of the field storing vector embeddings\n\n        Returns:\n            Dictionary containing OpenSearch index mapping configuration\n        \"\"\"\n        return {\n            \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n            \"mappings\": {\n                \"properties\": {\n                    vector_field: {\n                        \"type\": \"knn_vector\",\n                        \"dimension\": dim,\n                        \"method\": {\n                            \"name\": \"disk_ann\",\n                            \"space_type\": space_type,\n                            \"engine\": engine,\n                            \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n                        },\n                    }\n                }\n            },\n        }\n\n    def _validate_aoss_with_engines(self, *, is_aoss: bool, engine: str) -> None:\n        \"\"\"Validate engine compatibility with Amazon OpenSearch Serverless (AOSS).\n\n        Amazon OpenSearch Serverless has restrictions on which vector engines\n        can be used. This method ensures the selected engine is compatible.\n\n        Args:\n            is_aoss: Whether the connection is to Amazon OpenSearch Serverless\n            engine: The selected vector search engine\n\n        Raises:\n            ValueError: If AOSS is used with an incompatible engine\n        \"\"\"\n        if is_aoss and engine not in {\"nmslib\", \"faiss\"}:\n            msg = \"Amazon OpenSearch Service Serverless only supports `nmslib` or `faiss` engines\"\n            raise ValueError(msg)\n\n    def _is_aoss_enabled(self, http_auth: Any) -> bool:\n        \"\"\"Determine if Amazon OpenSearch Serverless (AOSS) is being used.\n\n        Args:\n            http_auth: The HTTP authentication object\n\n        Returns:\n            True if AOSS is enabled, False otherwise\n        \"\"\"\n        return http_auth is not None and hasattr(http_auth, \"service\") and http_auth.service == \"aoss\"\n\n    def _bulk_ingest_embeddings(\n        self,\n        client: OpenSearch,\n        index_name: str,\n        embeddings: list[list[float]],\n        texts: list[str],\n        metadatas: list[dict] | None = None,\n        ids: list[str] | None = None,\n        vector_field: str = \"vector_field\",\n        text_field: str = \"text\",\n        mapping: dict | None = None,\n        max_chunk_bytes: int | None = 1 * 1024 * 1024,\n        *,\n        is_aoss: bool = False,\n    ) -> list[str]:\n        \"\"\"Efficiently ingest multiple documents with embeddings into OpenSearch.\n\n        This method uses bulk operations to insert documents with their vector\n        embeddings and metadata into the specified OpenSearch index.\n\n        Args:\n            client: OpenSearch client instance\n            index_name: Target index for document storage\n            embeddings: List of vector embeddings for each document\n            texts: List of document texts\n            metadatas: Optional metadata dictionaries for each document\n            ids: Optional document IDs (UUIDs generated if not provided)\n            vector_field: Field name for storing vector embeddings\n            text_field: Field name for storing document text\n            mapping: Optional index mapping configuration\n            max_chunk_bytes: Maximum size per bulk request chunk\n            is_aoss: Whether using Amazon OpenSearch Serverless\n\n        Returns:\n            List of document IDs that were successfully ingested\n        \"\"\"\n        if not mapping:\n            mapping = {}\n\n        requests = []\n        return_ids = []\n\n        for i, text in enumerate(texts):\n            metadata = metadatas[i] if metadatas else {}\n            _id = ids[i] if ids else str(uuid.uuid4())\n            request = {\n                \"_op_type\": \"index\",\n                \"_index\": index_name,\n                vector_field: embeddings[i],\n                text_field: text,\n                **metadata,\n            }\n            if is_aoss:\n                request[\"id\"] = _id\n            else:\n                request[\"_id\"] = _id\n            requests.append(request)\n            return_ids.append(_id)\n        if metadatas:\n            self.log(f\"Sample metadata: {metadatas[0] if metadatas else {}}\")\n        helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n        return return_ids\n\n    # ---------- auth / client ----------\n    def _build_auth_kwargs(self) -> dict[str, Any]:\n        \"\"\"Build authentication configuration for OpenSearch client.\n\n        Constructs the appropriate authentication parameters based on the\n        selected auth mode (basic username/password or JWT token).\n\n        Returns:\n            Dictionary containing authentication configuration\n\n        Raises:\n            ValueError: If required authentication parameters are missing\n        \"\"\"\n        mode = (self.auth_mode or \"basic\").strip().lower()\n        if mode == \"jwt\":\n            token = (self.jwt_token or \"\").strip()\n            if not token:\n                msg = \"Auth Mode is 'jwt' but no jwt_token was provided.\"\n                raise ValueError(msg)\n            header_name = (self.jwt_header or \"Authorization\").strip()\n            header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n            return {\"headers\": {header_name: header_value}}\n        user = (self.username or \"\").strip()\n        pwd = (self.password or \"\").strip()\n        if not user or not pwd:\n            msg = \"Auth Mode is 'basic' but username/password are missing.\"\n            raise ValueError(msg)\n        return {\"http_auth\": (user, pwd)}\n\n    def build_client(self) -> OpenSearch:\n        \"\"\"Create and configure an OpenSearch client instance.\n\n        Returns:\n            Configured OpenSearch client ready for operations\n        \"\"\"\n        auth_kwargs = self._build_auth_kwargs()\n        return OpenSearch(\n            hosts=[self.opensearch_url],\n            use_ssl=self.use_ssl,\n            verify_certs=self.verify_certs,\n            ssl_assert_hostname=False,\n            ssl_show_warn=False,\n            **auth_kwargs,\n        )\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> OpenSearch:\n        # Return raw OpenSearch client as our “vector store.”\n        self.log(self.ingest_data)\n        client = self.build_client()\n        self._add_documents_to_vector_store(client=client)\n        return client\n\n    # ---------- ingest ----------\n    def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n        \"\"\"Process and ingest documents into the OpenSearch vector store.\n\n        This method handles the complete document ingestion pipeline:\n        - Prepares document data and metadata\n        - Generates vector embeddings\n        - Creates appropriate index mappings\n        - Bulk inserts documents with vectors\n\n        Args:\n            client: OpenSearch client for performing operations\n        \"\"\"\n        # Convert DataFrame to Data if needed using parent's method\n        self.ingest_data = self._prepare_ingest_data()\n\n        docs = self.ingest_data or []\n        if not docs:\n            self.log(\"No documents to ingest.\")\n            return\n\n        # Extract texts and metadata from documents\n        texts = []\n        metadatas = []\n        # Process docs_metadata table input into a dict\n        additional_metadata = {}\n        if hasattr(self, \"docs_metadata\") and self.docs_metadata:\n            logger.info(f\"[LF] Docs metadata {self.docs_metadata}\")\n            if isinstance(self.docs_metadata[-1], Data):\n                logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n                self.docs_metadata = self.docs_metadata[-1].data\n                logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n                additional_metadata.update(self.docs_metadata)\n            else:\n                for item in self.docs_metadata:\n                    if isinstance(item, dict) and \"key\" in item and \"value\" in item:\n                        additional_metadata[item[\"key\"]] = item[\"value\"]\n        logger.info(f\"[LF] Additional metadata {additional_metadata}\")\n        for doc_obj in docs:\n            data_copy = json.loads(doc_obj.model_dump_json())\n            text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n            texts.append(text)\n\n            # Merge additional metadata from table input\n            data_copy.update(additional_metadata)\n\n            metadatas.append(data_copy)\n        self.log(metadatas)\n        if not self.embedding:\n            msg = \"Embedding handle is required to embed documents.\"\n            raise ValueError(msg)\n\n        # Generate embeddings\n        vectors = self.embedding.embed_documents(texts)\n\n        if not vectors:\n            self.log(\"No vectors generated from documents.\")\n            return\n\n        # Get vector dimension for mapping\n        dim = len(vectors[0]) if vectors else 768  # default fallback\n\n        # Check for AOSS\n        auth_kwargs = self._build_auth_kwargs()\n        is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n        # Validate engine with AOSS\n        engine = getattr(self, \"engine\", \"jvector\")\n        self._validate_aoss_with_engines(is_aoss=is_aoss, engine=engine)\n\n        # Create mapping with proper KNN settings\n        space_type = getattr(self, \"space_type\", \"l2\")\n        ef_construction = getattr(self, \"ef_construction\", 512)\n        m = getattr(self, \"m\", 16)\n\n        mapping = self._default_text_mapping(\n            dim=dim,\n            engine=engine,\n            space_type=space_type,\n            ef_construction=ef_construction,\n            m=m,\n            vector_field=self.vector_field,\n        )\n\n        self.log(f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\")\n\n        # Use the LangChain-style bulk ingestion\n        return_ids = self._bulk_ingest_embeddings(\n            client=client,\n            index_name=self.index_name,\n            embeddings=vectors,\n            texts=texts,\n            metadatas=metadatas,\n            vector_field=self.vector_field,\n            text_field=\"text\",\n            mapping=mapping,\n            is_aoss=is_aoss,\n        )\n        self.log(metadatas)\n\n        self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n    # ---------- helpers for filters ----------\n    def _is_placeholder_term(self, term_obj: dict) -> bool:\n        # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n        return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n    def _coerce_filter_clauses(self, filter_obj: dict | None) -> list[dict]:\n        \"\"\"Convert filter expressions into OpenSearch-compatible filter clauses.\n\n        This method accepts two filter formats and converts them to standardized\n        OpenSearch query clauses:\n\n        Format A - Explicit filters:\n        {\"filter\": [{\"term\": {\"field\": \"value\"}}, {\"terms\": {\"field\": [\"val1\", \"val2\"]}}],\n         \"limit\": 10, \"score_threshold\": 1.5}\n\n        Format B - Context-style mapping:\n        {\"data_sources\": [\"file1.pdf\"], \"document_types\": [\"pdf\"], \"owners\": [\"user1\"]}\n\n        Args:\n            filter_obj: Filter configuration dictionary or None\n\n        Returns:\n            List of OpenSearch filter clauses (term/terms objects)\n            Placeholder values with \"__IMPOSSIBLE_VALUE__\" are ignored\n        \"\"\"\n        if not filter_obj:\n            return []\n\n        # If it is a string, try to parse it once\n        if isinstance(filter_obj, str):\n            try:\n                filter_obj = json.loads(filter_obj)\n            except json.JSONDecodeError:\n                # Not valid JSON - treat as no filters\n                return []\n\n        # Case A: already an explicit list/dict under \"filter\"\n        if \"filter\" in filter_obj:\n            raw = filter_obj[\"filter\"]\n            if isinstance(raw, dict):\n                raw = [raw]\n            explicit_clauses: list[dict] = []\n            for f in raw or []:\n                if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n                    explicit_clauses.append(f)\n                elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n                    field, vals = next(iter(f[\"terms\"].items()))\n                    if isinstance(vals, list) and len(vals) > 0:\n                        explicit_clauses.append(f)\n            return explicit_clauses\n\n        # Case B: convert context-style maps into clauses\n        field_mapping = {\n            \"data_sources\": \"filename\",\n            \"document_types\": \"mimetype\",\n            \"owners\": \"owner\",\n        }\n        context_clauses: list[dict] = []\n        for k, values in filter_obj.items():\n            if not isinstance(values, list):\n                continue\n            field = field_mapping.get(k, k)\n            if len(values) == 0:\n                # Match-nothing placeholder (kept to mirror your tool semantics)\n                context_clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n            elif len(values) == 1:\n                if values[0] != \"__IMPOSSIBLE_VALUE__\":\n                    context_clauses.append({\"term\": {field: values[0]}})\n            else:\n                context_clauses.append({\"terms\": {field: values}})\n        return context_clauses\n\n    # ---------- search (single hybrid path matching your tool) ----------\n    def search(self, query: str | None = None) -> list[dict[str, Any]]:\n        \"\"\"Perform hybrid search combining vector similarity and keyword matching.\n\n        This method executes a sophisticated search that combines:\n        - K-nearest neighbor (KNN) vector similarity search (70% weight)\n        - Multi-field keyword search with fuzzy matching (30% weight)\n        - Optional filtering and score thresholds\n        - Aggregations for faceted search results\n\n        Args:\n            query: Search query string (used for both vector embedding and keyword search)\n\n        Returns:\n            List of search results with page_content, metadata, and relevance scores\n\n        Raises:\n            ValueError: If embedding component is not provided or filter JSON is invalid\n        \"\"\"\n        logger.info(self.ingest_data)\n        client = self.build_client()\n        q = (query or \"\").strip()\n\n        # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n        filter_obj = None\n        if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n            try:\n                filter_obj = json.loads(self.filter_expression)\n            except json.JSONDecodeError as e:\n                msg = f\"Invalid filter_expression JSON: {e}\"\n                raise ValueError(msg) from e\n\n        if not self.embedding:\n            msg = \"Embedding is required to run hybrid search (KNN + keyword).\"\n            raise ValueError(msg)\n\n        # Embed the query\n        vec = self.embedding.embed_query(q)\n\n        # Build filter clauses (accept both shapes)\n        filter_clauses = self._coerce_filter_clauses(filter_obj)\n\n        # Respect the tool's limit/threshold defaults\n        limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n        score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n        # Build the same hybrid body as your SearchService\n        body = {\n            \"query\": {\n                \"bool\": {\n                    \"should\": [\n                        {\n                            \"knn\": {\n                                self.vector_field: {\n                                    \"vector\": vec,\n                                    \"k\": 10,  # fixed to match the tool\n                                    \"boost\": 0.7,\n                                }\n                            }\n                        },\n                        {\n                            \"multi_match\": {\n                                \"query\": q,\n                                \"fields\": [\"text^2\", \"filename^1.5\"],\n                                \"type\": \"best_fields\",\n                                \"fuzziness\": \"AUTO\",\n                                \"boost\": 0.3,\n                            }\n                        },\n                    ],\n                    \"minimum_should_match\": 1,\n                }\n            },\n            \"aggs\": {\n                \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n                \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n                \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n            },\n            \"_source\": [\n                \"filename\",\n                \"mimetype\",\n                \"page\",\n                \"text\",\n                \"source_url\",\n                \"owner\",\n                \"allowed_users\",\n                \"allowed_groups\",\n            ],\n            \"size\": limit,\n        }\n        if filter_clauses:\n            body[\"query\"][\"bool\"][\"filter\"] = filter_clauses\n\n        if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n            # top-level min_score (matches your tool)\n            body[\"min_score\"] = score_threshold\n\n        resp = client.search(index=self.index_name, body=body)\n        hits = resp.get(\"hits\", {}).get(\"hits\", [])\n        return [\n            {\n                \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n                \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n                \"score\": hit.get(\"_score\"),\n            }\n            for hit in hits\n        ]\n\n    def search_documents(self) -> list[Data]:\n        \"\"\"Search documents and return results as Data objects.\n\n        This is the main interface method that performs the search using the\n        configured search_query and returns results in Langflow's Data format.\n\n        Returns:\n            List of Data objects containing search results with text and metadata\n\n        Raises:\n            Exception: If search operation fails\n        \"\"\"\n        try:\n            raw = self.search(self.search_query or \"\")\n            return [Data(text=hit[\"page_content\"], **hit[\"metadata\"]) for hit in raw]\n            self.log(self.ingest_data)\n        except Exception as e:\n            self.log(f\"search_documents error: {e}\")\n            raise\n\n    # -------- dynamic UI handling (auth switch) --------\n    async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n        \"\"\"Dynamically update component configuration based on field changes.\n\n        This method handles real-time UI updates, particularly for authentication\n        mode changes that show/hide relevant input fields.\n\n        Args:\n            build_config: Current component configuration\n            field_value: New value for the changed field\n            field_name: Name of the field that changed\n\n        Returns:\n            Updated build configuration with appropriate field visibility\n        \"\"\"\n        try:\n            if field_name == \"auth_mode\":\n                mode = (field_value or \"basic\").strip().lower()\n                is_basic = mode == \"basic\"\n                is_jwt = mode == \"jwt\"\n\n                build_config[\"username\"][\"show\"] = is_basic\n                build_config[\"password\"][\"show\"] = is_basic\n\n                build_config[\"jwt_token\"][\"show\"] = is_jwt\n                build_config[\"jwt_header\"][\"show\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n                build_config[\"username\"][\"required\"] = is_basic\n                build_config[\"password\"][\"required\"] = is_basic\n\n                build_config[\"jwt_token\"][\"required\"] = is_jwt\n                build_config[\"jwt_header\"][\"required\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"required\"] = False\n\n                if is_basic:\n                    build_config[\"jwt_token\"][\"value\"] = \"\"\n\n                return build_config\n\n        except (KeyError, ValueError) as e:\n            self.log(f\"update_build_config error: {e}\")\n\n        return build_config\n"
+              },
+              "docs_metadata": {
+                "_input_type": "TableInput",
+                "advanced": false,
+                "display_name": "Document Metadata",
+                "dynamic": false,
+                "info": "Additional metadata key-value pairs to be added to all ingested documents. Useful for tagging documents with source information, categories, or other custom attributes.",
+                "input_types": [
+                  "Data"
+                ],
+                "is_list": true,
+                "list_add_label": "Add More",
+                "name": "docs_metadata",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "table_icon": "Table",
+                "table_schema": [
+                  {
+                    "description": "Key name",
+                    "display_name": "Key",
+                    "formatter": "text",
+                    "name": "key",
+                    "type": "str"
+                  },
+                  {
+                    "description": "Value of the metadata",
+                    "display_name": "Value",
+                    "formatter": "text",
+                    "name": "value",
+                    "type": "str"
+                  }
+                ],
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "trigger_icon": "Table",
+                "trigger_text": "Open table",
+                "type": "table",
+                "value": []
+              },
+              "ef_construction": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "EF Construction",
+                "dynamic": false,
+                "info": "Size of the dynamic candidate list during index construction. Higher values improve recall but increase indexing time and memory usage.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "ef_construction",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "int",
+                "value": 512
               },
               "embedding": {
                 "_input_type": "HandleInput",
@@ -943,13 +912,40 @@
                 "type": "other",
                 "value": ""
               },
+              "engine": {
+                "_input_type": "DropdownInput",
+                "advanced": true,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Vector Engine",
+                "dynamic": false,
+                "external_options": {},
+                "info": "Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.",
+                "name": "engine",
+                "options": [
+                  "jvector",
+                  "nmslib",
+                  "faiss",
+                  "lucene"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "toggle": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "jvector"
+              },
               "filter_expression": {
                 "_input_type": "MultilineInput",
                 "advanced": false,
                 "copy_field": false,
-                "display_name": "Filter Expression (JSON)",
+                "display_name": "Search Filters (JSON)",
                 "dynamic": false,
-                "info": "Optional JSON to control filters/limit/score threshold.\nAccepted shapes:\n1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\n2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\nPlaceholders with __IMPOSSIBLE_VALUE__ are ignored.",
+                "info": "Optional JSON configuration for search filtering, result limits, and score thresholds.\n\nFormat 1 - Explicit filters:\n{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, {\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\n\nFormat 2 - Context-style mapping:\n{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\n\nUse __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.",
                 "input_types": [
                   "Message"
                 ],
@@ -973,7 +969,7 @@
                 "advanced": false,
                 "display_name": "Index Name",
                 "dynamic": false,
-                "info": "The index to search.",
+                "info": "The OpenSearch index name where documents will be stored and searched. Will be created automatically if it doesn't exist.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1019,8 +1015,8 @@
                 "load_from_db": false,
                 "name": "jwt_header",
                 "placeholder": "",
-                "required": true,
-                "show": true,
+                "required": false,
+                "show": false,
                 "title_case": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
@@ -1032,24 +1028,42 @@
                 "advanced": false,
                 "display_name": "JWT Token",
                 "dynamic": false,
-                "info": "Paste a valid JWT (sent as a header).",
+                "info": "Valid JSON Web Token for authentication. Will be sent in the Authorization header (with optional 'Bearer ' prefix).",
                 "input_types": [],
                 "load_from_db": true,
                 "name": "jwt_token",
                 "password": true,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
                 "value": "JWT"
               },
+              "m": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "M Parameter",
+                "dynamic": false,
+                "info": "Number of bidirectional connections for each vector in the HNSW graph. Higher values improve search quality but increase memory usage and indexing time.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "m",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "int",
+                "value": 16
+              },
               "number_of_results": {
                 "_input_type": "IntInput",
                 "advanced": true,
-                "display_name": "Default Size (limit)",
+                "display_name": "Default Result Limit",
                 "dynamic": false,
-                "info": "Default number of hits when no limit provided in filter_expression.",
+                "info": "Default maximum number of search results to return when no limit is specified in the filter expression.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1068,7 +1082,7 @@
                 "advanced": false,
                 "display_name": "OpenSearch URL",
                 "dynamic": false,
-                "info": "URL for your OpenSearch cluster.",
+                "info": "The connection URL for your OpenSearch cluster (e.g., http://localhost:9200 for local development or your cloud endpoint).",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1085,7 +1099,7 @@
               "password": {
                 "_input_type": "SecretStrInput",
                 "advanced": false,
-                "display_name": "Password",
+                "display_name": "OpenSearch Password",
                 "dynamic": false,
                 "info": "",
                 "input_types": [],
@@ -1140,6 +1154,34 @@
                 "type": "bool",
                 "value": true
               },
+              "space_type": {
+                "_input_type": "DropdownInput",
+                "advanced": true,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Distance Metric",
+                "dynamic": false,
+                "external_options": {},
+                "info": "Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, 'cosinesimil' for cosine similarity, 'innerproduct' for dot product.",
+                "name": "space_type",
+                "options": [
+                  "l2",
+                  "l1",
+                  "cosinesimil",
+                  "linf",
+                  "innerproduct"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "toggle": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "l2"
+              },
               "tools_metadata": {
                 "_input_type": "ToolsInput",
                 "advanced": false,
@@ -1167,8 +1209,8 @@
                         "type": "string"
                       }
                     },
-                    "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
-                    "display_description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
+                    "description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
+                    "display_description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
                     "display_name": "search_documents",
                     "name": "search_documents",
                     "readonly": false,
@@ -1186,8 +1228,8 @@
                         "type": "string"
                       }
                     },
-                    "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
-                    "display_description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
+                    "description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
+                    "display_description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
                     "display_name": "as_dataframe",
                     "name": "as_dataframe",
                     "readonly": false,
@@ -1205,8 +1247,8 @@
                         "type": "string"
                       }
                     },
-                    "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
-                    "display_description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
+                    "description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
+                    "display_description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
                     "display_name": "as_vector_store",
                     "name": "as_vector_store",
                     "readonly": false,
@@ -1220,9 +1262,9 @@
               "use_ssl": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Use SSL",
+                "display_name": "Use SSL/TLS",
                 "dynamic": false,
-                "info": "",
+                "info": "Enable SSL/TLS encryption for secure connections to OpenSearch.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "use_ssl",
@@ -1257,9 +1299,9 @@
               "vector_field": {
                 "_input_type": "StrInput",
                 "advanced": true,
-                "display_name": "Vector Field",
+                "display_name": "Vector Field Name",
                 "dynamic": false,
-                "info": "Vector field used for KNN.",
+                "info": "Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1276,9 +1318,9 @@
               "verify_certs": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Verify Certificates",
+                "display_name": "Verify SSL Certificates",
                 "dynamic": false,
-                "info": "",
+                "info": "Verify SSL certificates when connecting. Disable for self-signed certificates in development environments.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "verify_certs",
@@ -1296,12 +1338,12 @@
           },
           "selected_output": "search_results",
           "showNode": true,
-          "type": "OpenSearchHybrid"
+          "type": "OpenSearchVectorStoreComponent"
         },
         "dragging": false,
         "id": "OpenSearch-iYfjf",
         "measured": {
-          "height": 747,
+          "height": 848,
           "width": 320
         },
         "position": {
@@ -1319,7 +1361,6 @@
               "Embeddings"
             ],
             "beta": false,
-            "category": "models",
             "conditional_paths": [],
             "custom_fields": {},
             "description": "Generate embeddings using a specified provider.",
@@ -1340,11 +1381,26 @@
             ],
             "frozen": false,
             "icon": "binary",
-            "key": "EmbeddingModel",
-            "last_updated": "2025-09-22T15:52:59.132Z",
+            "last_updated": "2025-10-02T20:05:34.815Z",
             "legacy": false,
-            "lf_version": "1.5.0.post1",
-            "metadata": {},
+            "lf_version": "1.6.0",
+            "metadata": {
+              "code_hash": "8607e963fdef",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "langchain_openai",
+                    "version": "0.3.23"
+                  },
+                  {
+                    "name": "lfx",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 2
+              },
+              "module": "custom_components.embedding_model"
+            },
             "minimized": false,
             "output_types": [],
             "outputs": [
@@ -1366,7 +1422,6 @@
               }
             ],
             "pinned": false,
-            "score": 0.002833550413469189,
             "template": {
               "_type": "Component",
               "api_base": {
@@ -1444,7 +1499,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageTextInput,\n    SecretStrInput,\n)\nfrom langflow.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n    display_name = \"Embedding Model\"\n    description = \"Generate embeddings using a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n    icon = \"binary\"\n    name = \"EmbeddingModel\"\n    category = \"models\"\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\"],\n            value=\"OpenAI\",\n            info=\"Select the embedding model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}],\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model Name\",\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n            info=\"Select the embedding model to use\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=True,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"api_base\",\n            display_name=\"API Base URL\",\n            info=\"Base URL for the API. Leave empty for default.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"dimensions\",\n            display_name=\"Dimensions\",\n            info=\"The number of dimensions the resulting output embeddings should have. \"\n            \"Only supported by certain models.\",\n            advanced=True,\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        provider = self.provider\n        model = self.model\n        api_key = self.api_key\n        api_base = self.api_base\n        dimensions = self.dimensions\n        chunk_size = self.chunk_size\n        request_timeout = self.request_timeout\n        max_retries = self.max_retries\n        show_progress_bar = self.show_progress_bar\n        model_kwargs = self.model_kwargs or {}\n\n        if provider == \"OpenAI\":\n            if not api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n            return OpenAIEmbeddings(\n                model=model,\n                dimensions=dimensions or None,\n                base_url=api_base or None,\n                api_key=api_key,\n                chunk_size=chunk_size,\n                max_retries=max_retries,\n                timeout=request_timeout or None,\n                show_progress_bar=show_progress_bar,\n                model_kwargs=model_kwargs,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\" and field_value == \"OpenAI\":\n            build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n            build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n            build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n        return build_config\n"
+                "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageTextInput,\n    SecretStrInput,\n)\nfrom lfx.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n    display_name = \"Embedding Model\"\n    description = \"Generate embeddings using a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n    icon = \"binary\"\n    name = \"EmbeddingModel\"\n    category = \"models\"\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\"],\n            value=\"OpenAI\",\n            info=\"Select the embedding model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}],\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model Name\",\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n            info=\"Select the embedding model to use\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=True,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"api_base\",\n            display_name=\"API Base URL\",\n            info=\"Base URL for the API. Leave empty for default.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"dimensions\",\n            display_name=\"Dimensions\",\n            info=\"The number of dimensions the resulting output embeddings should have. \"\n            \"Only supported by certain models.\",\n            advanced=True,\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        provider = self.provider\n        model = self.model\n        api_key = self.api_key\n        api_base = self.api_base\n        dimensions = self.dimensions\n        chunk_size = self.chunk_size\n        request_timeout = self.request_timeout\n        max_retries = self.max_retries\n        show_progress_bar = self.show_progress_bar\n        model_kwargs = self.model_kwargs or {}\n\n        if provider == \"OpenAI\":\n            if not api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n            return OpenAIEmbeddings(\n                model=model,\n                dimensions=dimensions or None,\n                base_url=api_base or None,\n                api_key=api_key,\n                chunk_size=chunk_size,\n                max_retries=max_retries,\n                timeout=request_timeout or None,\n                show_progress_bar=show_progress_bar,\n                model_kwargs=model_kwargs,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\" and field_value == \"OpenAI\":\n            build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n            build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n            build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n        return build_config\n"
               },
               "dimensions": {
                 "_input_type": "IntInput",
@@ -1489,6 +1544,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Name",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the embedding model to use",
                 "name": "model",
                 "options": [
@@ -1532,6 +1588,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Provider",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the embedding model provider",
                 "name": "provider",
                 "options": [
@@ -1615,7 +1672,6 @@
           "id": "Agent-crjWf",
           "node": {
             "base_classes": [
-              "Data",
               "Message"
             ],
             "beta": false,
@@ -1650,22 +1706,23 @@
             ],
             "frozen": false,
             "icon": "bot",
-            "last_updated": "2025-09-22T15:54:25.247Z",
+            "last_updated": "2025-10-02T20:05:34.872Z",
             "legacy": false,
+            "lf_version": "1.6.0",
             "metadata": {
-              "code_hash": "964ea0b7bc96",
+              "code_hash": "81467945381f",
               "dependencies": {
                 "dependencies": [
                   {
                     "name": "langchain_core",
-                    "version": "0.3.72"
+                    "version": "0.3.76"
                   },
                   {
                     "name": "pydantic",
                     "version": "2.10.6"
                   },
                   {
-                    "name": "langflow",
+                    "name": "lfx",
                     "version": null
                   }
                 ],
@@ -1691,22 +1748,6 @@
                   "Message"
                 ],
                 "value": "__UNDEFINED__"
-              },
-              {
-                "allows_loop": false,
-                "cache": true,
-                "display_name": "Structured Response",
-                "group_outputs": false,
-                "method": "json_response",
-                "name": "structured_response",
-                "options": null,
-                "required_inputs": null,
-                "selected": "Data",
-                "tool_mode": false,
-                "types": [
-                  "Data"
-                ],
-                "value": "__UNDEFINED__"
               }
             ],
             "pinned": false,
@@ -1763,7 +1804,18 @@
                 "dialog_inputs": {},
                 "display_name": "Language Model",
                 "dynamic": false,
-                "info": "",
+                "external_options": {
+                  "fields": {
+                    "data": {
+                      "node": {
+                        "display_name": "Connect other models",
+                        "icon": "CornerDownLeft",
+                        "name": "connect_other_models"
+                      }
+                    }
+                  }
+                },
+                "info": "The provider of the language model that the agent will use to generate responses.",
                 "input_types": [
                   "LanguageModel"
                 ],
@@ -1771,9 +1823,7 @@
                 "options": [
                   "Anthropic",
                   "Google Generative AI",
-                  "Groq",
-                  "OpenAI",
-                  "Custom"
+                  "OpenAI"
                 ],
                 "options_metadata": [
                   {
@@ -1782,18 +1832,13 @@
                   {
                     "icon": "GoogleGenerativeAI"
                   },
-                  {
-                    "icon": "Groq"
-                  },
                   {
                     "icon": "OpenAI"
-                  },
-                  {
-                    "icon": "brain"
                   }
                 ],
-                "placeholder": "",
+                "placeholder": "Awaiting model input.",
                 "real_time_refresh": true,
+                "refresh_button": false,
                 "required": false,
                 "show": true,
                 "title_case": false,
@@ -1820,7 +1865,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import json\nimport re\n\nfrom langchain_core.tools import StructuredTool\nfrom pydantic import ValidationError\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.agents.events import ExceptionWithMessageError\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_DYNAMIC_UPDATE_FIELDS,\n    MODEL_PROVIDERS,\n    MODEL_PROVIDERS_DICT,\n    MODELS_METADATA,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers.current_date import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.custom.custom_component.component import _get_component_toolkit\nfrom langflow.custom.utils import update_component_build_config\nfrom langflow.field_typing import Tool\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, DropdownInput, IntInput, MultilineInput, Output, TableInput\nfrom langflow.logging import logger\nfrom langflow.schema.data import Data\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nMODEL_PROVIDERS_LIST = [\"Anthropic\", \"Google Generative AI\", \"Groq\", \"OpenAI\"]\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    # Filter out json_mode from OpenAI inputs since we handle structured output differently\n    openai_inputs_filtered = [\n        input_field\n        for input_field in MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"]\n        if not (hasattr(input_field, \"name\") and input_field.name == \"json_mode\")\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*MODEL_PROVIDERS_LIST, \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n            options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST] + [{\"icon\": \"brain\"}],\n        ),\n        *openai_inputs_filtered,\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n        Output(name=\"structured_response\", display_name=\"Structured Response\", method=\"json_response\", tool_mode=False),\n    ]\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        llm_model, display_name = await self.get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            self.tools.append(current_date_tool)\n        return llm_model, self.chat_history, self.tools\n\n    async def message_response(self) -> Message:\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            # Set up and run agent\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self.system_prompt,\n            )\n            agent = self.create_agent_runnable()\n            result = await self.run_agent(agent)\n\n            # Store result for potential JSON output\n            self._agent_result = result\n\n        except (ValueError, TypeError, KeyError) as e:\n            await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n            raise\n        except ExceptionWithMessageError as e:\n            await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n            raise\n        # Avoid catching blind Exception; let truly unexpected exceptions propagate\n        except Exception as e:\n            await logger.aerror(f\"Unexpected error: {e!s}\")\n            raise\n        else:\n            return result\n\n    def _preprocess_schema(self, schema):\n        \"\"\"Preprocess schema to ensure correct data types for build_model_from_schema.\"\"\"\n        processed_schema = []\n        for field in schema:\n            processed_field = {\n                \"name\": str(field.get(\"name\", \"field\")),\n                \"type\": str(field.get(\"type\", \"str\")),\n                \"description\": str(field.get(\"description\", \"\")),\n                \"multiple\": field.get(\"multiple\", False),\n            }\n            # Ensure multiple is handled correctly\n            if isinstance(processed_field[\"multiple\"], str):\n                processed_field[\"multiple\"] = processed_field[\"multiple\"].lower() in [\"true\", \"1\", \"t\", \"y\", \"yes\"]\n            processed_schema.append(processed_field)\n        return processed_schema\n\n    async def build_structured_output_base(self, content: str):\n        \"\"\"Build structured output with optional BaseModel validation.\"\"\"\n        json_pattern = r\"\\{.*\\}\"\n        schema_error_msg = \"Try setting an output schema\"\n\n        # Try to parse content as JSON first\n        json_data = None\n        try:\n            json_data = json.loads(content)\n        except json.JSONDecodeError:\n            json_match = re.search(json_pattern, content, re.DOTALL)\n            if json_match:\n                try:\n                    json_data = json.loads(json_match.group())\n                except json.JSONDecodeError:\n                    return {\"content\": content, \"error\": schema_error_msg}\n            else:\n                return {\"content\": content, \"error\": schema_error_msg}\n\n        # If no output schema provided, return parsed JSON without validation\n        if not hasattr(self, \"output_schema\") or not self.output_schema or len(self.output_schema) == 0:\n            return json_data\n\n        # Use BaseModel validation with schema\n        try:\n            processed_schema = self._preprocess_schema(self.output_schema)\n            output_model = build_model_from_schema(processed_schema)\n\n            # Validate against the schema\n            if isinstance(json_data, list):\n                # Multiple objects\n                validated_objects = []\n                for item in json_data:\n                    try:\n                        validated_obj = output_model.model_validate(item)\n                        validated_objects.append(validated_obj.model_dump())\n                    except ValidationError as e:\n                        await logger.aerror(f\"Validation error for item: {e}\")\n                        # Include invalid items with error info\n                        validated_objects.append({\"data\": item, \"validation_error\": str(e)})\n                return validated_objects\n\n            # Single object\n            try:\n                validated_obj = output_model.model_validate(json_data)\n                return [validated_obj.model_dump()]  # Return as list for consistency\n            except ValidationError as e:\n                await logger.aerror(f\"Validation error: {e}\")\n                return [{\"data\": json_data, \"validation_error\": str(e)}]\n\n        except (TypeError, ValueError) as e:\n            await logger.aerror(f\"Error building structured output: {e}\")\n            # Fallback to parsed JSON without validation\n            return json_data\n\n    async def json_response(self) -> Data:\n        \"\"\"Convert agent response to structured JSON Data output with schema validation.\"\"\"\n        # Always use structured chat agent for JSON response mode for better JSON formatting\n        try:\n            system_components = []\n\n            # 1. Agent Instructions (system_prompt)\n            agent_instructions = getattr(self, \"system_prompt\", \"\") or \"\"\n            if agent_instructions:\n                system_components.append(f\"{agent_instructions}\")\n\n            # 2. Format Instructions\n            format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n            if format_instructions:\n                system_components.append(f\"Format instructions: {format_instructions}\")\n\n            # 3. Schema Information from BaseModel\n            if hasattr(self, \"output_schema\") and self.output_schema and len(self.output_schema) > 0:\n                try:\n                    processed_schema = self._preprocess_schema(self.output_schema)\n                    output_model = build_model_from_schema(processed_schema)\n                    schema_dict = output_model.model_json_schema()\n                    schema_info = (\n                        \"You are given some text that may include format instructions, \"\n                        \"explanations, or other content alongside a JSON schema.\\n\\n\"\n                        \"Your task:\\n\"\n                        \"- Extract only the JSON schema.\\n\"\n                        \"- Return it as valid JSON.\\n\"\n                        \"- Do not include format instructions, explanations, or extra text.\\n\\n\"\n                        \"Input:\\n\"\n                        f\"{json.dumps(schema_dict, indent=2)}\\n\\n\"\n                        \"Output (only JSON schema):\"\n                    )\n                    system_components.append(schema_info)\n                except (ValidationError, ValueError, TypeError, KeyError) as e:\n                    await logger.aerror(f\"Could not build schema for prompt: {e}\", exc_info=True)\n\n            # Combine all components\n            combined_instructions = \"\\n\\n\".join(system_components) if system_components else \"\"\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=combined_instructions,\n            )\n\n            # Create and run structured chat agent\n            try:\n                structured_agent = self.create_agent_runnable()\n            except (NotImplementedError, ValueError, TypeError) as e:\n                await logger.aerror(f\"Error with structured chat agent: {e}\")\n                raise\n            try:\n                result = await self.run_agent(structured_agent)\n            except (ExceptionWithMessageError, ValueError, TypeError, RuntimeError) as e:\n                await logger.aerror(f\"Error with structured agent result: {e}\")\n                raise\n            # Extract content from structured agent result\n            if hasattr(result, \"content\"):\n                content = result.content\n            elif hasattr(result, \"text\"):\n                content = result.text\n            else:\n                content = str(result)\n\n        except (ExceptionWithMessageError, ValueError, TypeError, NotImplementedError, AttributeError) as e:\n            await logger.aerror(f\"Error with structured chat agent: {e}\")\n            # Fallback to regular agent\n            content_str = \"No content returned from agent\"\n            return Data(data={\"content\": content_str, \"error\": str(e)})\n\n        # Process with structured output validation\n        try:\n            structured_output = await self.build_structured_output_base(content)\n\n            # Handle different output formats\n            if isinstance(structured_output, list) and structured_output:\n                if len(structured_output) == 1:\n                    return Data(data=structured_output[0])\n                return Data(data={\"results\": structured_output})\n            if isinstance(structured_output, dict):\n                return Data(data=structured_output)\n            return Data(data={\"content\": content})\n\n        except (ValueError, TypeError) as e:\n            await logger.aerror(f\"Error in structured output processing: {e}\")\n            return Data(data={\"content\": content, \"error\": str(e)})\n\n    async def get_memory_data(self):\n        # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n        messages = (\n            await MemoryComponent(**self.get_base_args())\n            .set(session_id=self.graph.session_id, order=\"Ascending\", n_messages=self.n_messages)\n            .retrieve_messages()\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    async def get_llm(self):\n        if not isinstance(self.agent_llm, str):\n            return self.agent_llm, None\n\n        try:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if not provider_info:\n                msg = f\"Invalid model provider: {self.agent_llm}\"\n                raise ValueError(msg)\n\n            component_class = provider_info.get(\"component_class\")\n            display_name = component_class.display_name\n            inputs = provider_info.get(\"inputs\")\n            prefix = provider_info.get(\"prefix\", \"\")\n\n            return self._build_llm_model(component_class, inputs, prefix), display_name\n\n        except (AttributeError, ValueError, TypeError, RuntimeError) as e:\n            await logger.aerror(f\"Error building {self.agent_llm} language model: {e!s}\")\n            msg = f\"Failed to initialize language model: {e!s}\"\n            raise ValueError(msg) from e\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {}\n        for input_ in inputs:\n            if hasattr(self, f\"{prefix}{input_.name}\"):\n                model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n        return component.set(**model_kwargs).build_model()\n\n    def set_component_params(self, component):\n        provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n        if provider_info:\n            inputs = provider_info.get(\"inputs\")\n            prefix = provider_info.get(\"prefix\")\n            # Filter out json_mode and only use attributes that exist on this component\n            model_kwargs = {}\n            for input_ in inputs:\n                if hasattr(self, f\"{prefix}{input_.name}\"):\n                    model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n\n            return component.set(**model_kwargs)\n        return component\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name in (\"agent_llm\",):\n            build_config[\"agent_llm\"][\"value\"] = field_value\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = await update_component_build_config(\n                        component_class, build_config, field_value, \"model_name\"\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                    options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())]\n                    + [{\"icon\": \"brain\"}],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if (\n            isinstance(self.agent_llm, str)\n            and self.agent_llm in MODEL_PROVIDERS_DICT\n            and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n        ):\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                component_class = self.set_component_params(component_class)\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = await update_component_build_config(\n                        component_class, build_config, field_value, \"model_name\"\n                    )\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = _get_component_toolkit()\n        tools_names = self._build_tools_names()\n        agent_description = self.get_tool_description()\n        # TODO: Agent Description Depreciated Feature to be removed\n        description = f\"{agent_description}{tools_names}\"\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\", tool_description=description, callbacks=self.get_langchain_callbacks()\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n        return tools\n"
+                "value": "import json\nimport re\n\nfrom langchain_core.tools import StructuredTool, Tool\nfrom pydantic import ValidationError\n\nfrom lfx.base.agents.agent import LCToolsAgentComponent\nfrom lfx.base.agents.events import ExceptionWithMessageError\nfrom lfx.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_DYNAMIC_UPDATE_FIELDS,\n    MODEL_PROVIDERS_DICT,\n    MODEL_PROVIDERS_LIST,\n    MODELS_METADATA,\n)\nfrom lfx.base.models.model_utils import get_model_name\nfrom lfx.components.helpers.current_date import CurrentDateComponent\nfrom lfx.components.helpers.memory import MemoryComponent\nfrom lfx.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom lfx.custom.custom_component.component import get_component_toolkit\nfrom lfx.custom.utils import update_component_build_config\nfrom lfx.helpers.base_model import build_model_from_schema\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import DropdownInput, IntInput, MultilineInput, Output, TableInput\nfrom lfx.log.logger import logger\nfrom lfx.schema.data import Data\nfrom lfx.schema.dotdict import dotdict\nfrom lfx.schema.message import Message\nfrom lfx.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    documentation: str = \"https://docs.langflow.org/agents\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    # Filter out json_mode from OpenAI inputs since we handle structured output differently\n    if \"OpenAI\" in MODEL_PROVIDERS_DICT:\n        openai_inputs_filtered = [\n            input_field\n            for input_field in MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"]\n            if not (hasattr(input_field, \"name\") and input_field.name == \"json_mode\")\n        ]\n    else:\n        openai_inputs_filtered = []\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*MODEL_PROVIDERS_LIST],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            refresh_button=False,\n            input_types=[],\n            options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST if key in MODELS_METADATA],\n            external_options={\n                \"fields\": {\n                    \"data\": {\n                        \"node\": {\n                            \"name\": \"connect_other_models\",\n                            \"display_name\": \"Connect other models\",\n                            \"icon\": \"CornerDownLeft\",\n                        }\n                    }\n                },\n            },\n        ),\n        *openai_inputs_filtered,\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Chat History Messages\",\n            value=100,\n            info=\"Number of chat history messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        MultilineInput(\n            name=\"format_instructions\",\n            display_name=\"Output Format Instructions\",\n            info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n            value=(\n                \"You are an AI that extracts structured JSON objects from unstructured text. \"\n                \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n                \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n                \"Fill missing or ambiguous values with defaults: null for missing values. \"\n                \"Remove exact duplicates but keep variations that have different field values. \"\n                \"Always return valid JSON in the expected format, never throw errors. \"\n                \"If multiple objects can be extracted, return them all in the structured format.\"\n            ),\n            advanced=True,\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=(\n                \"Schema Validation: Define the structure and data types for structured output. \"\n                \"No validation if no output schema.\"\n            ),\n            advanced=True,\n            required=False,\n            value=[],\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                    \"default\": \"field\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                    \"default\": \"description of field\",\n                    \"edit_mode\": EditMode.POPOVER,\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"edit_mode\": EditMode.INLINE,\n                    \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n                    \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n                    \"default\": \"str\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"As List\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                    \"edit_mode\": EditMode.INLINE,\n                },\n            ],\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        # removed memory inputs from agent component\n        # *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n    ]\n\n    async def get_agent_requirements(self):\n        \"\"\"Get the agent requirements for the agent.\"\"\"\n        llm_model, display_name = await self.get_llm()\n        if llm_model is None:\n            msg = \"No language model selected. Please choose a model to proceed.\"\n            raise ValueError(msg)\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n\n        # Get memory data\n        self.chat_history = await self.get_memory_data()\n        if isinstance(self.chat_history, Message):\n            self.chat_history = [self.chat_history]\n\n        # Add current date tool if enabled\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n            if not isinstance(current_date_tool, StructuredTool):\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise TypeError(msg)\n            self.tools.append(current_date_tool)\n        return llm_model, self.chat_history, self.tools\n\n    async def message_response(self) -> Message:\n        try:\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            # Set up and run agent\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=self.system_prompt,\n            )\n            agent = self.create_agent_runnable()\n            result = await self.run_agent(agent)\n\n            # Store result for potential JSON output\n            self._agent_result = result\n\n        except (ValueError, TypeError, KeyError) as e:\n            await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n            raise\n        except ExceptionWithMessageError as e:\n            await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n            raise\n        # Avoid catching blind Exception; let truly unexpected exceptions propagate\n        except Exception as e:\n            await logger.aerror(f\"Unexpected error: {e!s}\")\n            raise\n        else:\n            return result\n\n    def _preprocess_schema(self, schema):\n        \"\"\"Preprocess schema to ensure correct data types for build_model_from_schema.\"\"\"\n        processed_schema = []\n        for field in schema:\n            processed_field = {\n                \"name\": str(field.get(\"name\", \"field\")),\n                \"type\": str(field.get(\"type\", \"str\")),\n                \"description\": str(field.get(\"description\", \"\")),\n                \"multiple\": field.get(\"multiple\", False),\n            }\n            # Ensure multiple is handled correctly\n            if isinstance(processed_field[\"multiple\"], str):\n                processed_field[\"multiple\"] = processed_field[\"multiple\"].lower() in [\n                    \"true\",\n                    \"1\",\n                    \"t\",\n                    \"y\",\n                    \"yes\",\n                ]\n            processed_schema.append(processed_field)\n        return processed_schema\n\n    async def build_structured_output_base(self, content: str):\n        \"\"\"Build structured output with optional BaseModel validation.\"\"\"\n        json_pattern = r\"\\{.*\\}\"\n        schema_error_msg = \"Try setting an output schema\"\n\n        # Try to parse content as JSON first\n        json_data = None\n        try:\n            json_data = json.loads(content)\n        except json.JSONDecodeError:\n            json_match = re.search(json_pattern, content, re.DOTALL)\n            if json_match:\n                try:\n                    json_data = json.loads(json_match.group())\n                except json.JSONDecodeError:\n                    return {\"content\": content, \"error\": schema_error_msg}\n            else:\n                return {\"content\": content, \"error\": schema_error_msg}\n\n        # If no output schema provided, return parsed JSON without validation\n        if not hasattr(self, \"output_schema\") or not self.output_schema or len(self.output_schema) == 0:\n            return json_data\n\n        # Use BaseModel validation with schema\n        try:\n            processed_schema = self._preprocess_schema(self.output_schema)\n            output_model = build_model_from_schema(processed_schema)\n\n            # Validate against the schema\n            if isinstance(json_data, list):\n                # Multiple objects\n                validated_objects = []\n                for item in json_data:\n                    try:\n                        validated_obj = output_model.model_validate(item)\n                        validated_objects.append(validated_obj.model_dump())\n                    except ValidationError as e:\n                        await logger.aerror(f\"Validation error for item: {e}\")\n                        # Include invalid items with error info\n                        validated_objects.append({\"data\": item, \"validation_error\": str(e)})\n                return validated_objects\n\n            # Single object\n            try:\n                validated_obj = output_model.model_validate(json_data)\n                return [validated_obj.model_dump()]  # Return as list for consistency\n            except ValidationError as e:\n                await logger.aerror(f\"Validation error: {e}\")\n                return [{\"data\": json_data, \"validation_error\": str(e)}]\n\n        except (TypeError, ValueError) as e:\n            await logger.aerror(f\"Error building structured output: {e}\")\n            # Fallback to parsed JSON without validation\n            return json_data\n\n    async def json_response(self) -> Data:\n        \"\"\"Convert agent response to structured JSON Data output with schema validation.\"\"\"\n        # Always use structured chat agent for JSON response mode for better JSON formatting\n        try:\n            system_components = []\n\n            # 1. Agent Instructions (system_prompt)\n            agent_instructions = getattr(self, \"system_prompt\", \"\") or \"\"\n            if agent_instructions:\n                system_components.append(f\"{agent_instructions}\")\n\n            # 2. Format Instructions\n            format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n            if format_instructions:\n                system_components.append(f\"Format instructions: {format_instructions}\")\n\n            # 3. Schema Information from BaseModel\n            if hasattr(self, \"output_schema\") and self.output_schema and len(self.output_schema) > 0:\n                try:\n                    processed_schema = self._preprocess_schema(self.output_schema)\n                    output_model = build_model_from_schema(processed_schema)\n                    schema_dict = output_model.model_json_schema()\n                    schema_info = (\n                        \"You are given some text that may include format instructions, \"\n                        \"explanations, or other content alongside a JSON schema.\\n\\n\"\n                        \"Your task:\\n\"\n                        \"- Extract only the JSON schema.\\n\"\n                        \"- Return it as valid JSON.\\n\"\n                        \"- Do not include format instructions, explanations, or extra text.\\n\\n\"\n                        \"Input:\\n\"\n                        f\"{json.dumps(schema_dict, indent=2)}\\n\\n\"\n                        \"Output (only JSON schema):\"\n                    )\n                    system_components.append(schema_info)\n                except (ValidationError, ValueError, TypeError, KeyError) as e:\n                    await logger.aerror(f\"Could not build schema for prompt: {e}\", exc_info=True)\n\n            # Combine all components\n            combined_instructions = \"\\n\\n\".join(system_components) if system_components else \"\"\n            llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n            self.set(\n                llm=llm_model,\n                tools=self.tools or [],\n                chat_history=self.chat_history,\n                input_value=self.input_value,\n                system_prompt=combined_instructions,\n            )\n\n            # Create and run structured chat agent\n            try:\n                structured_agent = self.create_agent_runnable()\n            except (NotImplementedError, ValueError, TypeError) as e:\n                await logger.aerror(f\"Error with structured chat agent: {e}\")\n                raise\n            try:\n                result = await self.run_agent(structured_agent)\n            except (\n                ExceptionWithMessageError,\n                ValueError,\n                TypeError,\n                RuntimeError,\n            ) as e:\n                await logger.aerror(f\"Error with structured agent result: {e}\")\n                raise\n            # Extract content from structured agent result\n            if hasattr(result, \"content\"):\n                content = result.content\n            elif hasattr(result, \"text\"):\n                content = result.text\n            else:\n                content = str(result)\n\n        except (\n            ExceptionWithMessageError,\n            ValueError,\n            TypeError,\n            NotImplementedError,\n            AttributeError,\n        ) as e:\n            await logger.aerror(f\"Error with structured chat agent: {e}\")\n            # Fallback to regular agent\n            content_str = \"No content returned from agent\"\n            return Data(data={\"content\": content_str, \"error\": str(e)})\n\n        # Process with structured output validation\n        try:\n            structured_output = await self.build_structured_output_base(content)\n\n            # Handle different output formats\n            if isinstance(structured_output, list) and structured_output:\n                if len(structured_output) == 1:\n                    return Data(data=structured_output[0])\n                return Data(data={\"results\": structured_output})\n            if isinstance(structured_output, dict):\n                return Data(data=structured_output)\n            return Data(data={\"content\": content})\n\n        except (ValueError, TypeError) as e:\n            await logger.aerror(f\"Error in structured output processing: {e}\")\n            return Data(data={\"content\": content, \"error\": str(e)})\n\n    async def get_memory_data(self):\n        # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n        messages = (\n            await MemoryComponent(**self.get_base_args())\n            .set(\n                session_id=self.graph.session_id,\n                order=\"Ascending\",\n                n_messages=self.n_messages,\n            )\n            .retrieve_messages()\n        )\n        return [\n            message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n        ]\n\n    async def get_llm(self):\n        if not isinstance(self.agent_llm, str):\n            return self.agent_llm, None\n\n        try:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if not provider_info:\n                msg = f\"Invalid model provider: {self.agent_llm}\"\n                raise ValueError(msg)\n\n            component_class = provider_info.get(\"component_class\")\n            display_name = component_class.display_name\n            inputs = provider_info.get(\"inputs\")\n            prefix = provider_info.get(\"prefix\", \"\")\n\n            return self._build_llm_model(component_class, inputs, prefix), display_name\n\n        except (AttributeError, ValueError, TypeError, RuntimeError) as e:\n            await logger.aerror(f\"Error building {self.agent_llm} language model: {e!s}\")\n            msg = f\"Failed to initialize language model: {e!s}\"\n            raise ValueError(msg) from e\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {}\n        for input_ in inputs:\n            if hasattr(self, f\"{prefix}{input_.name}\"):\n                model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n        return component.set(**model_kwargs).build_model()\n\n    def set_component_params(self, component):\n        provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n        if provider_info:\n            inputs = provider_info.get(\"inputs\")\n            prefix = provider_info.get(\"prefix\")\n            # Filter out json_mode and only use attributes that exist on this component\n            model_kwargs = {}\n            for input_ in inputs:\n                if hasattr(self, f\"{prefix}{input_.name}\"):\n                    model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n\n            return component.set(**model_kwargs)\n        return component\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    async def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name in (\"agent_llm\",):\n            build_config[\"agent_llm\"][\"value\"] = field_value\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = await update_component_build_config(\n                        component_class, build_config, field_value, \"model_name\"\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n                build_config[\"agent_llm\"][\"display_name\"] = \"Model Provider\"\n            elif field_value == \"connect_other_models\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    info=\"The provider of the language model that the agent will use to generate responses.\",\n                    options=[*MODEL_PROVIDERS_LIST],\n                    real_time_refresh=True,\n                    refresh_button=False,\n                    input_types=[\"LanguageModel\"],\n                    placeholder=\"Awaiting model input.\",\n                    options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST if key in MODELS_METADATA],\n                    external_options={\n                        \"fields\": {\n                            \"data\": {\n                                \"node\": {\n                                    \"name\": \"connect_other_models\",\n                                    \"display_name\": \"Connect other models\",\n                                    \"icon\": \"CornerDownLeft\",\n                                },\n                            }\n                        },\n                    },\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if (\n            isinstance(self.agent_llm, str)\n            and self.agent_llm in MODEL_PROVIDERS_DICT\n            and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n        ):\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                component_class = self.set_component_params(component_class)\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = await update_component_build_config(\n                        component_class, build_config, field_value, \"model_name\"\n                    )\n        return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n    async def _get_tools(self) -> list[Tool]:\n        component_toolkit = get_component_toolkit()\n        tools_names = self._build_tools_names()\n        agent_description = self.get_tool_description()\n        # TODO: Agent Description Depreciated Feature to be removed\n        description = f\"{agent_description}{tools_names}\"\n        tools = component_toolkit(component=self).get_tools(\n            tool_name=\"Call_Agent\",\n            tool_description=description,\n            callbacks=self.get_langchain_callbacks(),\n        )\n        if hasattr(self, \"tools_metadata\"):\n            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n        return tools\n"
               },
               "format_instructions": {
                 "_input_type": "MultilineInput",
@@ -1941,69 +1986,47 @@
                 "required": false,
                 "show": true,
                 "table_icon": "Table",
-                "table_schema": {
-                  "columns": [
-                    {
-                      "default": "field",
-                      "description": "Specify the name of the output field.",
-                      "disable_edit": false,
-                      "display_name": "Name",
-                      "edit_mode": "inline",
-                      "filterable": true,
-                      "formatter": "text",
-                      "hidden": false,
-                      "name": "name",
-                      "sortable": true,
-                      "type": "str"
-                    },
-                    {
-                      "default": "description of field",
-                      "description": "Describe the purpose of the output field.",
-                      "disable_edit": false,
-                      "display_name": "Description",
-                      "edit_mode": "popover",
-                      "filterable": true,
-                      "formatter": "text",
-                      "hidden": false,
-                      "name": "description",
-                      "sortable": true,
-                      "type": "str"
-                    },
-                    {
-                      "default": "str",
-                      "description": "Indicate the data type of the output field (e.g., str, int, float, bool, dict).",
-                      "disable_edit": false,
-                      "display_name": "Type",
-                      "edit_mode": "inline",
-                      "filterable": true,
-                      "formatter": "text",
-                      "hidden": false,
-                      "name": "type",
-                      "options": [
-                        "str",
-                        "int",
-                        "float",
-                        "bool",
-                        "dict"
-                      ],
-                      "sortable": true,
-                      "type": "str"
-                    },
-                    {
-                      "default": false,
-                      "description": "Set to True if this output field should be a list of the specified type.",
-                      "disable_edit": false,
-                      "display_name": "As List",
-                      "edit_mode": "inline",
-                      "filterable": true,
-                      "formatter": "boolean",
-                      "hidden": false,
-                      "name": "multiple",
-                      "sortable": true,
-                      "type": "boolean"
-                    }
-                  ]
-                },
+                "table_schema": [
+                  {
+                    "default": "field",
+                    "description": "Specify the name of the output field.",
+                    "display_name": "Name",
+                    "edit_mode": "inline",
+                    "name": "name",
+                    "type": "str"
+                  },
+                  {
+                    "default": "description of field",
+                    "description": "Describe the purpose of the output field.",
+                    "display_name": "Description",
+                    "edit_mode": "popover",
+                    "name": "description",
+                    "type": "str"
+                  },
+                  {
+                    "default": "str",
+                    "description": "Indicate the data type of the output field (e.g., str, int, float, bool, dict).",
+                    "display_name": "Type",
+                    "edit_mode": "inline",
+                    "name": "type",
+                    "options": [
+                      "str",
+                      "int",
+                      "float",
+                      "bool",
+                      "dict"
+                    ],
+                    "type": "str"
+                  },
+                  {
+                    "default": "False",
+                    "description": "Set to True if this output field should be a list of the specified type.",
+                    "display_name": "As List",
+                    "edit_mode": "inline",
+                    "name": "multiple",
+                    "type": "boolean"
+                  }
+                ],
                 "title_case": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
@@ -2116,7 +2139,7 @@
             "frozen": false,
             "icon": "type",
             "legacy": false,
-            "lf_version": "1.5.0.post1",
+            "lf_version": "1.6.0",
             "metadata": {},
             "minimized": false,
             "output_types": [],
@@ -2222,10 +2245,11 @@
             ],
             "frozen": false,
             "icon": "brain-circuit",
-            "last_updated": "2025-09-22T15:54:29.406Z",
+            "last_updated": "2025-10-02T20:05:34.815Z",
             "legacy": false,
+            "lf_version": "1.6.0",
             "metadata": {
-              "code_hash": "6ac42a7167a4",
+              "code_hash": "bb5f8714781b",
               "dependencies": {
                 "dependencies": [
                   {
@@ -2241,7 +2265,7 @@
                     "version": "0.3.23"
                   },
                   {
-                    "name": "langflow",
+                    "name": "lfx",
                     "version": null
                   }
                 ],
@@ -2253,7 +2277,7 @@
                 "language model",
                 "large language model"
               ],
-              "module": "langflow.components.models.language_model.LanguageModelComponent"
+              "module": "custom_components.language_model"
             },
             "minimized": false,
             "output_types": [],
@@ -2329,7 +2353,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_CHAT_MODEL_NAMES, OPENAI_REASONING_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass LanguageModelComponent(LCModelComponent):\n    display_name = \"Language Model\"\n    description = \"Runs a language model given a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-models\"\n    icon = \"brain-circuit\"\n    category = \"models\"\n    priority = 0  # Set priority to 0 to make it appear first\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\", \"Anthropic\", \"Google\"],\n            value=\"OpenAI\",\n            info=\"Select the model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Anthropic\"}, {\"icon\": \"GoogleGenerativeAI\"}],\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n            value=OPENAI_CHAT_MODEL_NAMES[0],\n            info=\"Select the model to use\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=False,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            info=\"The input text to send to the model\",\n        ),\n        MultilineInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"A system message that helps set the behavior of the assistant\",\n            advanced=False,\n        ),\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=\"Whether to stream the response\",\n            value=False,\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            info=\"Controls randomness in responses\",\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        provider = self.provider\n        model_name = self.model_name\n        temperature = self.temperature\n        stream = self.stream\n\n        if provider == \"OpenAI\":\n            if not self.api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n\n            if model_name in OPENAI_REASONING_MODEL_NAMES:\n                # reasoning models do not support temperature (yet)\n                temperature = None\n\n            return ChatOpenAI(\n                model_name=model_name,\n                temperature=temperature,\n                streaming=stream,\n                openai_api_key=self.api_key,\n            )\n        if provider == \"Anthropic\":\n            if not self.api_key:\n                msg = \"Anthropic API key is required when using Anthropic provider\"\n                raise ValueError(msg)\n            return ChatAnthropic(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                anthropic_api_key=self.api_key,\n            )\n        if provider == \"Google\":\n            if not self.api_key:\n                msg = \"Google API key is required when using Google provider\"\n                raise ValueError(msg)\n            return ChatGoogleGenerativeAI(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                google_api_key=self.api_key,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\":\n            if field_value == \"OpenAI\":\n                build_config[\"model_name\"][\"options\"] = OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES\n                build_config[\"model_name\"][\"value\"] = OPENAI_CHAT_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            elif field_value == \"Anthropic\":\n                build_config[\"model_name\"][\"options\"] = ANTHROPIC_MODELS\n                build_config[\"model_name\"][\"value\"] = ANTHROPIC_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Anthropic API Key\"\n            elif field_value == \"Google\":\n                build_config[\"model_name\"][\"options\"] = GOOGLE_GENERATIVE_AI_MODELS\n                build_config[\"model_name\"][\"value\"] = GOOGLE_GENERATIVE_AI_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Google API Key\"\n        elif field_name == \"model_name\" and field_value.startswith(\"o1\") and self.provider == \"OpenAI\":\n            # Hide system_message for o1 models - currently unsupported\n            if \"system_message\" in build_config:\n                build_config[\"system_message\"][\"show\"] = False\n        elif field_name == \"model_name\" and not field_value.startswith(\"o1\") and \"system_message\" in build_config:\n            build_config[\"system_message\"][\"show\"] = True\n        return build_config\n"
+                "value": "from typing import Any\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom langchain_openai import ChatOpenAI\n\nfrom lfx.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom lfx.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.openai_constants import OPENAI_CHAT_MODEL_NAMES, OPENAI_REASONING_MODEL_NAMES\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput\nfrom lfx.schema.dotdict import dotdict\n\n\nclass LanguageModelComponent(LCModelComponent):\n    display_name = \"Language Model\"\n    description = \"Runs a language model given a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-models\"\n    icon = \"brain-circuit\"\n    category = \"models\"\n    priority = 0  # Set priority to 0 to make it appear first\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\", \"Anthropic\", \"Google\"],\n            value=\"OpenAI\",\n            info=\"Select the model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Anthropic\"}, {\"icon\": \"GoogleGenerativeAI\"}],\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n            value=OPENAI_CHAT_MODEL_NAMES[0],\n            info=\"Select the model to use\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=False,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            info=\"The input text to send to the model\",\n        ),\n        MultilineInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"A system message that helps set the behavior of the assistant\",\n            advanced=False,\n        ),\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=\"Whether to stream the response\",\n            value=False,\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            info=\"Controls randomness in responses\",\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        provider = self.provider\n        model_name = self.model_name\n        temperature = self.temperature\n        stream = self.stream\n\n        if provider == \"OpenAI\":\n            if not self.api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n\n            if model_name in OPENAI_REASONING_MODEL_NAMES:\n                # reasoning models do not support temperature (yet)\n                temperature = None\n\n            return ChatOpenAI(\n                model_name=model_name,\n                temperature=temperature,\n                streaming=stream,\n                openai_api_key=self.api_key,\n            )\n        if provider == \"Anthropic\":\n            if not self.api_key:\n                msg = \"Anthropic API key is required when using Anthropic provider\"\n                raise ValueError(msg)\n            return ChatAnthropic(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                anthropic_api_key=self.api_key,\n            )\n        if provider == \"Google\":\n            if not self.api_key:\n                msg = \"Google API key is required when using Google provider\"\n                raise ValueError(msg)\n            return ChatGoogleGenerativeAI(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                google_api_key=self.api_key,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\":\n            if field_value == \"OpenAI\":\n                build_config[\"model_name\"][\"options\"] = OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES\n                build_config[\"model_name\"][\"value\"] = OPENAI_CHAT_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            elif field_value == \"Anthropic\":\n                build_config[\"model_name\"][\"options\"] = ANTHROPIC_MODELS\n                build_config[\"model_name\"][\"value\"] = ANTHROPIC_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Anthropic API Key\"\n            elif field_value == \"Google\":\n                build_config[\"model_name\"][\"options\"] = GOOGLE_GENERATIVE_AI_MODELS\n                build_config[\"model_name\"][\"value\"] = GOOGLE_GENERATIVE_AI_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Google API Key\"\n        elif field_name == \"model_name\" and field_value.startswith(\"o1\") and self.provider == \"OpenAI\":\n            # Hide system_message for o1 models - currently unsupported\n            if \"system_message\" in build_config:\n                build_config[\"system_message\"][\"show\"] = False\n        elif field_name == \"model_name\" and not field_value.startswith(\"o1\") and \"system_message\" in build_config:\n            build_config[\"system_message\"][\"show\"] = True\n        return build_config\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -2361,6 +2385,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Name",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the model to use",
                 "name": "model_name",
                 "options": [
@@ -2403,6 +2428,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Provider",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the model provider",
                 "name": "provider",
                 "options": [
@@ -2525,16 +2551,16 @@
       }
     ],
     "viewport": {
-      "x": -171.9254522389558,
-      "y": 141.41747076743695,
-      "zoom": 0.5223218120388116
+      "x": -237.0727605845459,
+      "y": 154.6885920024542,
+      "zoom": 0.602433700773958
     }
   },
   "description": "OpenRAG Open Search Agent",
   "endpoint_name": null,
   "id": "1098eea1-6649-4e1d-aed1-b77249fb8dd0",
   "is_component": false,
-  "last_tested_version": "1.5.0.post2",
+  "last_tested_version": "1.6.0",
   "name": "OpenRAG Open Search Agent",
   "tags": [
     "assistants",
diff --git a/flows/openrag_ingest_docling.json b/flows/openrag_ingest_docling.json
index cce73398..f0e8b164 100644
--- a/flows/openrag_ingest_docling.json
+++ b/flows/openrag_ingest_docling.json
@@ -116,6 +116,143 @@
         "sourceHandle": "{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-eZ6bTœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}",
         "target": "OpenSearchHybrid-XtKoA",
         "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œOpenSearchHybrid-XtKoAœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}"
+      },
+      {
+        "animated": false,
+        "data": {
+          "sourceHandle": {
+            "dataType": "SecretInput",
+            "id": "SecretInput-GddHQ",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "dynamic_connector_type",
+            "id": "AdvancedDynamicFormBuilder-rDFKw",
+            "inputTypes": [
+              "Text",
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-SecretInput-GddHQ{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-GddHQœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-AdvancedDynamicFormBuilder-rDFKw{œfieldNameœ:œdynamic_connector_typeœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "SecretInput-GddHQ",
+        "sourceHandle": "{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-GddHQœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "AdvancedDynamicFormBuilder-rDFKw",
+        "targetHandle": "{œfieldNameœ:œdynamic_connector_typeœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "animated": false,
+        "data": {
+          "sourceHandle": {
+            "dataType": "SecretInput",
+            "id": "SecretInput-8QSeL",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "dynamic_owner",
+            "id": "AdvancedDynamicFormBuilder-rDFKw",
+            "inputTypes": [
+              "Text",
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-SecretInput-8QSeL{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-8QSeLœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-AdvancedDynamicFormBuilder-rDFKw{œfieldNameœ:œdynamic_ownerœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "SecretInput-8QSeL",
+        "sourceHandle": "{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-8QSeLœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "AdvancedDynamicFormBuilder-rDFKw",
+        "targetHandle": "{œfieldNameœ:œdynamic_ownerœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "animated": false,
+        "data": {
+          "sourceHandle": {
+            "dataType": "SecretInput",
+            "id": "SecretInput-qdu4S",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "dynamic_owner_email",
+            "id": "AdvancedDynamicFormBuilder-rDFKw",
+            "inputTypes": [
+              "Text",
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-SecretInput-qdu4S{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-qdu4Sœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-AdvancedDynamicFormBuilder-rDFKw{œfieldNameœ:œdynamic_owner_emailœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "SecretInput-qdu4S",
+        "sourceHandle": "{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-qdu4Sœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "AdvancedDynamicFormBuilder-rDFKw",
+        "targetHandle": "{œfieldNameœ:œdynamic_owner_emailœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "animated": false,
+        "data": {
+          "sourceHandle": {
+            "dataType": "SecretInput",
+            "id": "SecretInput-p9iHD",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "dynamic_owner_name",
+            "id": "AdvancedDynamicFormBuilder-rDFKw",
+            "inputTypes": [
+              "Text",
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-SecretInput-p9iHD{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-p9iHDœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-AdvancedDynamicFormBuilder-rDFKw{œfieldNameœ:œdynamic_owner_nameœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "SecretInput-p9iHD",
+        "sourceHandle": "{œdataTypeœ:œSecretInputœ,œidœ:œSecretInput-p9iHDœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "AdvancedDynamicFormBuilder-rDFKw",
+        "targetHandle": "{œfieldNameœ:œdynamic_owner_nameœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œinputTypesœ:[œTextœ,œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "data": {
+          "sourceHandle": {
+            "dataType": "AdvancedDynamicFormBuilder",
+            "id": "AdvancedDynamicFormBuilder-rDFKw",
+            "name": "form_data",
+            "output_types": [
+              "Data"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "docs_metadata",
+            "id": "OpenSearchHybrid-XtKoA",
+            "inputTypes": [
+              "Data"
+            ],
+            "type": "table"
+          }
+        },
+        "id": "xy-edge__AdvancedDynamicFormBuilder-rDFKw{œdataTypeœ:œAdvancedDynamicFormBuilderœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œnameœ:œform_dataœ,œoutput_typesœ:[œDataœ]}-OpenSearchHybrid-XtKoA{œfieldNameœ:œdocs_metadataœ,œidœ:œOpenSearchHybrid-XtKoAœ,œinputTypesœ:[œDataœ],œtypeœ:œtableœ}",
+        "source": "AdvancedDynamicFormBuilder-rDFKw",
+        "sourceHandle": "{œdataTypeœ:œAdvancedDynamicFormBuilderœ,œidœ:œAdvancedDynamicFormBuilder-rDFKwœ,œnameœ:œform_dataœ,œoutput_typesœ:[œDataœ]}",
+        "target": "OpenSearchHybrid-XtKoA",
+        "targetHandle": "{œfieldNameœ:œdocs_metadataœ,œidœ:œOpenSearchHybrid-XtKoAœ,œinputTypesœ:[œDataœ],œtypeœ:œtableœ}"
       }
     ],
     "nodes": [
@@ -373,8 +510,8 @@
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
-            "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
-            "display_name": "OpenSearch (Hybrid)",
+            "description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
+            "display_name": "OpenSearch",
             "documentation": "",
             "edited": true,
             "field_order": [
@@ -405,21 +542,21 @@
             "icon": "OpenSearch",
             "legacy": false,
             "metadata": {
-              "code_hash": "deee3f04cb47",
+              "code_hash": "c81b23acb81a",
               "dependencies": {
                 "dependencies": [
-                  {
-                    "name": "langflow",
-                    "version": "1.5.0.post2"
-                  },
                   {
                     "name": "opensearchpy",
                     "version": "2.8.0"
+                  },
+                  {
+                    "name": "lfx",
+                    "version": null
                   }
                 ],
                 "total_dependencies": 2
               },
-              "module": "custom_components.opensearch_hybrid"
+              "module": "custom_components.opensearch"
             },
             "minimized": false,
             "output_types": [],
@@ -463,7 +600,7 @@
                 "cache": true,
                 "display_name": "Vector Store Connection",
                 "group_outputs": false,
-                "hidden": true,
+                "hidden": false,
                 "method": "as_vector_store",
                 "name": "vectorstoreconnection",
                 "options": null,
@@ -484,9 +621,10 @@
                 "advanced": false,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Auth Mode",
+                "display_name": "Authentication Mode",
                 "dynamic": false,
-                "info": "Choose Basic (username/password) or JWT (Bearer token).",
+                "external_options": {},
+                "info": "Authentication method: 'basic' for username/password authentication, or 'jwt' for JSON Web Token (Bearer) authentication.",
                 "load_from_db": false,
                 "name": "auth_mode",
                 "options": [
@@ -539,14 +677,17 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any, Dict, List, Optional\n\nfrom langflow.base.vectorstores.model import (\n    LCVectorStoreComponent,\n    check_cached_vector_store,\n)\nfrom langflow.base.vectorstores.vector_store_connection_decorator import (\n    vector_store_connection,\n)\nfrom langflow.io import (\n    BoolInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n    TableInput,\n)\nfrom langflow.logging import logger\nfrom langflow.schema.data import Data\nfrom opensearchpy import OpenSearch, helpers\n\n\n@vector_store_connection\nclass OpenSearchHybridComponent(LCVectorStoreComponent):\n    \"\"\"OpenSearch hybrid search: KNN (k=10, boost=0.7) + multi_match (boost=0.3) with optional filters & min_score.\"\"\"\n\n    display_name: str = \"OpenSearch (Hybrid)\"\n    name: str = \"OpenSearchHybrid\"\n    icon: str = \"OpenSearch\"\n    description: str = \"Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.\"\n\n    # Keys we consider baseline\n    default_keys: list[str] = [\n        \"opensearch_url\",\n        \"index_name\",\n        *[\n            i.name for i in LCVectorStoreComponent.inputs\n        ],  # search_query, add_documents, etc.\n        \"embedding\",\n        \"vector_field\",\n        \"number_of_results\",\n        \"auth_mode\",\n        \"username\",\n        \"password\",\n        \"jwt_token\",\n        \"jwt_header\",\n        \"bearer_prefix\",\n        \"use_ssl\",\n        \"verify_certs\",\n        \"filter_expression\",\n        \"engine\",\n        \"space_type\",\n        \"ef_construction\",\n        \"m\",\n        \"docs_metadata\",\n    ]\n\n    inputs = [\n        TableInput(\n            name=\"docs_metadata\",\n            display_name=\"Ingestion Metadata\",\n            info=\"Key value pairs to be inserted into each ingested document.\",\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Key\",\n                    \"type\": \"str\",\n                    \"description\": \"Key name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Value of the metadata\",\n                },\n            ],\n            value=[],\n            advanced=True,\n        ),\n        StrInput(\n            name=\"opensearch_url\",\n            display_name=\"OpenSearch URL\",\n            value=\"http://localhost:9200\",\n            info=\"URL for your OpenSearch cluster.\",\n        ),\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow\",\n            info=\"The index to search.\",\n        ),\n        DropdownInput(\n            name=\"engine\",\n            display_name=\"Engine\",\n            options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n            value=\"jvector\",\n            info=\"Vector search engine to use.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"space_type\",\n            display_name=\"Space Type\",\n            options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n            value=\"l2\",\n            info=\"Distance metric for vector similarity.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"ef_construction\",\n            display_name=\"EF Construction\",\n            value=512,\n            info=\"Size of the dynamic list used during k-NN graph creation.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"m\",\n            display_name=\"M Parameter\",\n            value=16,\n            info=\"Number of bidirectional links created for each new element.\",\n            advanced=True,\n        ),\n        *LCVectorStoreComponent.inputs,  # includes search_query, add_documents, etc.\n        HandleInput(\n            name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]\n        ),\n        StrInput(\n            name=\"vector_field\",\n            display_name=\"Vector Field\",\n            value=\"chunk_embedding\",\n            advanced=True,\n            info=\"Vector field used for KNN.\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Default Size (limit)\",\n            value=10,\n            advanced=True,\n            info=\"Default number of hits when no limit provided in filter_expression.\",\n        ),\n        MultilineInput(\n            name=\"filter_expression\",\n            display_name=\"Filter Expression (JSON)\",\n            value=\"\",\n            info=(\n                \"Optional JSON to control filters/limit/score threshold.\\n\"\n                \"Accepted shapes:\\n\"\n                '1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\\n'\n                '2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\\n'\n                \"Placeholders with __IMPOSSIBLE_VALUE__ are ignored.\"\n            ),\n        ),\n        # ----- Auth controls (dynamic) -----\n        DropdownInput(\n            name=\"auth_mode\",\n            display_name=\"Auth Mode\",\n            value=\"basic\",\n            options=[\"basic\", \"jwt\"],\n            info=\"Choose Basic (username/password) or JWT (Bearer token).\",\n            real_time_refresh=True,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"username\",\n            display_name=\"Username\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"password\",\n            display_name=\"Password\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"jwt_token\",\n            display_name=\"JWT Token\",\n            value=\"JWT\",\n            load_from_db=True,\n            show=True,\n            info=\"Paste a valid JWT (sent as a header).\",\n        ),\n        StrInput(\n            name=\"jwt_header\",\n            display_name=\"JWT Header Name\",\n            value=\"Authorization\",\n            show=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"bearer_prefix\",\n            display_name=\"Prefix 'Bearer '\",\n            value=True,\n            show=False,\n            advanced=True,\n        ),\n        # ----- TLS -----\n        BoolInput(name=\"use_ssl\", display_name=\"Use SSL\", value=True, advanced=True),\n        BoolInput(\n            name=\"verify_certs\",\n            display_name=\"Verify Certificates\",\n            value=False,\n            advanced=True,\n        ),\n    ]\n\n    # ---------- helper functions for index management ----------\n    def _default_text_mapping(\n        self,\n        dim: int,\n        engine: str = \"jvector\",\n        space_type: str = \"l2\",\n        ef_search: int = 512,\n        ef_construction: int = 100,\n        m: int = 16,\n        vector_field: str = \"vector_field\",\n    ) -> Dict[str, Any]:\n        \"\"\"For Approximate k-NN Search, this is the default mapping to create index.\"\"\"\n        return {\n            \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n            \"mappings\": {\n                \"properties\": {\n                    vector_field: {\n                        \"type\": \"knn_vector\",\n                        \"dimension\": dim,\n                        \"method\": {\n                            \"name\": \"disk_ann\",\n                            \"space_type\": space_type,\n                            \"engine\": engine,\n                            \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n                        },\n                    }\n                }\n            },\n        }\n\n    def _validate_aoss_with_engines(self, is_aoss: bool, engine: str) -> None:\n        \"\"\"Validate AOSS with the engine.\"\"\"\n        if is_aoss and engine != \"nmslib\" and engine != \"faiss\":\n            raise ValueError(\n                \"Amazon OpenSearch Service Serverless only \"\n                \"supports `nmslib` or `faiss` engines\"\n            )\n\n    def _is_aoss_enabled(self, http_auth: Any) -> bool:\n        \"\"\"Check if the service is http_auth is set as `aoss`.\"\"\"\n        if (\n            http_auth is not None\n            and hasattr(http_auth, \"service\")\n            and http_auth.service == \"aoss\"\n        ):\n            return True\n        return False\n\n    def _bulk_ingest_embeddings(\n        self,\n        client: OpenSearch,\n        index_name: str,\n        embeddings: List[List[float]],\n        texts: List[str],\n        metadatas: Optional[List[dict]] = None,\n        ids: Optional[List[str]] = None,\n        vector_field: str = \"vector_field\",\n        text_field: str = \"text\",\n        mapping: Optional[Dict] = None,\n        max_chunk_bytes: Optional[int] = 1 * 1024 * 1024,\n        is_aoss: bool = False,\n    ) -> List[str]:\n        \"\"\"Bulk Ingest Embeddings into given index.\"\"\"\n        if not mapping:\n            mapping = dict()\n\n        requests = []\n        return_ids = []\n\n        for i, text in enumerate(texts):\n            metadata = metadatas[i] if metadatas else {}\n            _id = ids[i] if ids else str(uuid.uuid4())\n            request = {\n                \"_op_type\": \"index\",\n                \"_index\": index_name,\n                vector_field: embeddings[i],\n                text_field: text,\n                **metadata,\n            }\n            if is_aoss:\n                request[\"id\"] = _id\n            else:\n                request[\"_id\"] = _id\n            requests.append(request)\n            return_ids.append(_id)\n        self.log(metadatas[i])\n        helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n        return return_ids\n\n    # ---------- auth / client ----------\n    def _build_auth_kwargs(self) -> Dict[str, Any]:\n        mode = (self.auth_mode or \"basic\").strip().lower()\n        if mode == \"jwt\":\n            token = (self.jwt_token or \"\").strip()\n            if not token:\n                raise ValueError(\"Auth Mode is 'jwt' but no jwt_token was provided.\")\n            header_name = (self.jwt_header or \"Authorization\").strip()\n            header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n            return {\"headers\": {header_name: header_value}}\n        user = (self.username or \"\").strip()\n        pwd = (self.password or \"\").strip()\n        if not user or not pwd:\n            raise ValueError(\"Auth Mode is 'basic' but username/password are missing.\")\n        return {\"http_auth\": (user, pwd)}\n\n    def build_client(self) -> OpenSearch:\n        auth_kwargs = self._build_auth_kwargs()\n        return OpenSearch(\n            hosts=[self.opensearch_url],\n            use_ssl=self.use_ssl,\n            verify_certs=self.verify_certs,\n            ssl_assert_hostname=False,\n            ssl_show_warn=False,\n            **auth_kwargs,\n        )\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> OpenSearch:\n        # Return raw OpenSearch client as our “vector store.”\n        self.log(self.ingest_data)\n        client = self.build_client()\n        self._add_documents_to_vector_store(client=client)\n        return client\n\n    # ---------- ingest ----------\n    def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n        # Convert DataFrame to Data if needed using parent's method\n        self.ingest_data = self._prepare_ingest_data()\n\n        docs = self.ingest_data or []\n        if not docs:\n            self.log(\"No documents to ingest.\")\n            return\n\n        # Extract texts and metadata from documents\n        texts = []\n        metadatas = []\n        # Process docs_metadata table input into a dict\n        additional_metadata = {}\n        if hasattr(self, \"docs_metadata\") and self.docs_metadata:\n            for item in self.docs_metadata:\n                if isinstance(item, dict) and \"key\" in item and \"value\" in item:\n                    additional_metadata[item[\"key\"]] = item[\"value\"]\n\n        for doc_obj in docs:\n            data_copy = json.loads(doc_obj.model_dump_json())\n            text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n            texts.append(text)\n\n            # Merge additional metadata from table input\n            data_copy.update(additional_metadata)\n\n            metadatas.append(data_copy)\n        self.log(metadatas)\n        if not self.embedding:\n            raise ValueError(\"Embedding handle is required to embed documents.\")\n\n        # Generate embeddings\n        vectors = self.embedding.embed_documents(texts)\n\n        if not vectors:\n            self.log(\"No vectors generated from documents.\")\n            return\n\n        # Get vector dimension for mapping\n        dim = len(vectors[0]) if vectors else 768  # default fallback\n\n        # Check for AOSS\n        auth_kwargs = self._build_auth_kwargs()\n        is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n        # Validate engine with AOSS\n        engine = getattr(self, \"engine\", \"jvector\")\n        self._validate_aoss_with_engines(is_aoss, engine)\n\n        # Create mapping with proper KNN settings\n        space_type = getattr(self, \"space_type\", \"l2\")\n        ef_construction = getattr(self, \"ef_construction\", 512)\n        m = getattr(self, \"m\", 16)\n\n        mapping = self._default_text_mapping(\n            dim=dim,\n            engine=engine,\n            space_type=space_type,\n            ef_construction=ef_construction,\n            m=m,\n            vector_field=self.vector_field,\n        )\n\n        self.log(\n            f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\"\n        )\n\n        # Use the LangChain-style bulk ingestion\n        return_ids = self._bulk_ingest_embeddings(\n            client=client,\n            index_name=self.index_name,\n            embeddings=vectors,\n            texts=texts,\n            metadatas=metadatas,\n            vector_field=self.vector_field,\n            text_field=\"text\",\n            mapping=mapping,\n            is_aoss=is_aoss,\n        )\n        self.log(metadatas)\n\n        self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n    # ---------- helpers for filters ----------\n    def _is_placeholder_term(self, term_obj: dict) -> bool:\n        # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n        return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n    def _coerce_filter_clauses(self, filter_obj: dict | None) -> List[dict]:\n        \"\"\"\n        Accepts either:\n          A) {\"filter\":[ ...term/terms objects... ], \"limit\":..., \"score_threshold\":...}\n          B) Context-style: {\"data_sources\":[...], \"document_types\":[...], \"owners\":[...]}\n        Returns a list of OS filter clauses (term/terms), skipping placeholders and empty terms.\n        \"\"\"\n\n        if not filter_obj:\n            return []\n\n        # If it’s a string, try to parse it once\n        if isinstance(filter_obj, str):\n            try:\n                filter_obj = json.loads(filter_obj)\n            except Exception:\n                # Not valid JSON → treat as no filters\n                return []\n\n        # Case A: already an explicit list/dict under \"filter\"\n        if \"filter\" in filter_obj:\n            raw = filter_obj[\"filter\"]\n            if isinstance(raw, dict):\n                raw = [raw]\n            clauses: List[dict] = []\n            for f in raw or []:\n                if (\n                    \"term\" in f\n                    and isinstance(f[\"term\"], dict)\n                    and not self._is_placeholder_term(f[\"term\"])\n                ):\n                    clauses.append(f)\n                elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n                    field, vals = next(iter(f[\"terms\"].items()))\n                    if isinstance(vals, list) and len(vals) > 0:\n                        clauses.append(f)\n            return clauses\n\n        # Case B: convert context-style maps into clauses\n        field_mapping = {\n            \"data_sources\": \"filename\",\n            \"document_types\": \"mimetype\",\n            \"owners\": \"owner\",\n        }\n        clauses: List[dict] = []\n        for k, values in filter_obj.items():\n            if not isinstance(values, list):\n                continue\n            field = field_mapping.get(k, k)\n            if len(values) == 0:\n                # Match-nothing placeholder (kept to mirror your tool semantics)\n                clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n            elif len(values) == 1:\n                if values[0] != \"__IMPOSSIBLE_VALUE__\":\n                    clauses.append({\"term\": {field: values[0]}})\n            else:\n                clauses.append({\"terms\": {field: values}})\n        return clauses\n\n    # ---------- search (single hybrid path matching your tool) ----------\n    def search(self, query: str | None = None) -> list[dict[str, Any]]:\n        logger.info(self.ingest_data)\n        client = self.build_client()\n        q = (query or \"\").strip()\n\n        # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n        filter_obj = None\n        if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n            try:\n                filter_obj = json.loads(self.filter_expression)\n            except json.JSONDecodeError as e:\n                raise ValueError(f\"Invalid filter_expression JSON: {e}\") from e\n\n        if not self.embedding:\n            raise ValueError(\n                \"Embedding is required to run hybrid search (KNN + keyword).\"\n            )\n\n        # Embed the query\n        vec = self.embedding.embed_query(q)\n\n        # Build filter clauses (accept both shapes)\n        clauses = self._coerce_filter_clauses(filter_obj)\n\n        # Respect the tool's limit/threshold defaults\n        limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n        score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n        # Build the same hybrid body as your SearchService\n        body = {\n            \"query\": {\n                \"bool\": {\n                    \"should\": [\n                        {\n                            \"knn\": {\n                                self.vector_field: {\n                                    \"vector\": vec,\n                                    \"k\": 10,  # fixed to match the tool\n                                    \"boost\": 0.7,\n                                }\n                            }\n                        },\n                        {\n                            \"multi_match\": {\n                                \"query\": q,\n                                \"fields\": [\"text^2\", \"filename^1.5\"],\n                                \"type\": \"best_fields\",\n                                \"fuzziness\": \"AUTO\",\n                                \"boost\": 0.3,\n                            }\n                        },\n                    ],\n                    \"minimum_should_match\": 1,\n                }\n            },\n            \"aggs\": {\n                \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n                \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n                \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n            },\n            \"_source\": [\n                \"filename\",\n                \"mimetype\",\n                \"page\",\n                \"text\",\n                \"source_url\",\n                \"owner\",\n                \"allowed_users\",\n                \"allowed_groups\",\n            ],\n            \"size\": limit,\n        }\n        if clauses:\n            body[\"query\"][\"bool\"][\"filter\"] = clauses\n\n        if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n            # top-level min_score (matches your tool)\n            body[\"min_score\"] = score_threshold\n\n        resp = client.search(index=self.index_name, body=body)\n        hits = resp.get(\"hits\", {}).get(\"hits\", [])\n        return [\n            {\n                \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n                \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n                \"score\": hit.get(\"_score\"),\n            }\n            for hit in hits\n        ]\n\n    def search_documents(self) -> list[Data]:\n        try:\n            raw = self.search(self.search_query or \"\")\n            return [Data(text=hit[\"page_content\"], **hit[\"metadata\"]) for hit in raw]\n            self.log(self.ingest_data)\n        except Exception as e:\n            self.log(f\"search_documents error: {e}\")\n            raise\n\n    # -------- dynamic UI handling (auth switch) --------\n    async def update_build_config(\n        self, build_config: dict, field_value: str, field_name: str | None = None\n    ) -> dict:\n        try:\n            if field_name == \"auth_mode\":\n                mode = (field_value or \"basic\").strip().lower()\n                is_basic = mode == \"basic\"\n                is_jwt = mode == \"jwt\"\n\n                build_config[\"username\"][\"show\"] = is_basic\n                build_config[\"password\"][\"show\"] = is_basic\n\n                build_config[\"jwt_token\"][\"show\"] = is_jwt\n                build_config[\"jwt_header\"][\"show\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n                build_config[\"username\"][\"required\"] = is_basic\n                build_config[\"password\"][\"required\"] = is_basic\n\n                build_config[\"jwt_token\"][\"required\"] = is_jwt\n                build_config[\"jwt_header\"][\"required\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"required\"] = False\n\n                if is_basic:\n                    build_config[\"jwt_token\"][\"value\"] = \"\"\n\n                return build_config\n\n            return build_config\n\n        except Exception as e:\n            self.log(f\"update_build_config error: {e}\")\n            return build_config\n"
+                "value": "from __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput, TableInput\nfrom lfx.log import logger\nfrom lfx.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchVectorStoreComponent(LCVectorStoreComponent):\n    \"\"\"OpenSearch Vector Store Component with Hybrid Search Capabilities.\n\n    This component provides vector storage and retrieval using OpenSearch, combining semantic\n    similarity search (KNN) with keyword-based search for optimal results. It supports document\n    ingestion, vector embeddings, and advanced filtering with authentication options.\n\n    Features:\n    - Vector storage with configurable engines (jvector, nmslib, faiss, lucene)\n    - Hybrid search combining KNN vector similarity and keyword matching\n    - Flexible authentication (Basic auth, JWT tokens)\n    - Advanced filtering and aggregations\n    - Metadata injection during document ingestion\n    \"\"\"\n\n    display_name: str = \"OpenSearch\"\n    icon: str = \"OpenSearch\"\n    description: str = (\n        \"Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.\"\n    )\n\n    # Keys we consider baseline\n    default_keys: list[str] = [\n        \"opensearch_url\",\n        \"index_name\",\n        *[i.name for i in LCVectorStoreComponent.inputs],  # search_query, add_documents, etc.\n        \"embedding\",\n        \"vector_field\",\n        \"number_of_results\",\n        \"auth_mode\",\n        \"username\",\n        \"password\",\n        \"jwt_token\",\n        \"jwt_header\",\n        \"bearer_prefix\",\n        \"use_ssl\",\n        \"verify_certs\",\n        \"filter_expression\",\n        \"engine\",\n        \"space_type\",\n        \"ef_construction\",\n        \"m\",\n        \"docs_metadata\",\n    ]\n\n    inputs = [\n        TableInput(\n            name=\"docs_metadata\",\n            display_name=\"Document Metadata\",\n            info=(\n                \"Additional metadata key-value pairs to be added to all ingested documents. \"\n                \"Useful for tagging documents with source information, categories, or other custom attributes.\"\n            ),\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Key\",\n                    \"type\": \"str\",\n                    \"description\": \"Key name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Value of the metadata\",\n                },\n            ],\n            value=[],\n            # advanced=True,\n            input_types=[\"Data\"]\n        ),\n        StrInput(\n            name=\"opensearch_url\",\n            display_name=\"OpenSearch URL\",\n            value=\"http://localhost:9200\",\n            info=(\n                \"The connection URL for your OpenSearch cluster \"\n                \"(e.g., http://localhost:9200 for local development or your cloud endpoint).\"\n            ),\n        ),\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow\",\n            info=(\n                \"The OpenSearch index name where documents will be stored and searched. \"\n                \"Will be created automatically if it doesn't exist.\"\n            ),\n        ),\n        DropdownInput(\n            name=\"engine\",\n            display_name=\"Vector Engine\",\n            options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n            value=\"jvector\",\n            info=(\n                \"Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. \"\n                \"Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.\"\n            ),\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"space_type\",\n            display_name=\"Distance Metric\",\n            options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n            value=\"l2\",\n            info=(\n                \"Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, \"\n                \"'cosinesimil' for cosine similarity, 'innerproduct' for dot product.\"\n            ),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"ef_construction\",\n            display_name=\"EF Construction\",\n            value=512,\n            info=(\n                \"Size of the dynamic candidate list during index construction. \"\n                \"Higher values improve recall but increase indexing time and memory usage.\"\n            ),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"m\",\n            display_name=\"M Parameter\",\n            value=16,\n            info=(\n                \"Number of bidirectional connections for each vector in the HNSW graph. \"\n                \"Higher values improve search quality but increase memory usage and indexing time.\"\n            ),\n            advanced=True,\n        ),\n        *LCVectorStoreComponent.inputs,  # includes search_query, add_documents, etc.\n        HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n        StrInput(\n            name=\"vector_field\",\n            display_name=\"Vector Field Name\",\n            value=\"chunk_embedding\",\n            advanced=True,\n            info=\"Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Default Result Limit\",\n            value=10,\n            advanced=True,\n            info=(\n                \"Default maximum number of search results to return when no limit is \"\n                \"specified in the filter expression.\"\n            ),\n        ),\n        MultilineInput(\n            name=\"filter_expression\",\n            display_name=\"Search Filters (JSON)\",\n            value=\"\",\n            info=(\n                \"Optional JSON configuration for search filtering, result limits, and score thresholds.\\n\\n\"\n                \"Format 1 - Explicit filters:\\n\"\n                '{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, '\n                '{\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\\n\\n'\n                \"Format 2 - Context-style mapping:\\n\"\n                '{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\\n\\n'\n                \"Use __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.\"\n            ),\n        ),\n        # ----- Auth controls (dynamic) -----\n        DropdownInput(\n            name=\"auth_mode\",\n            display_name=\"Authentication Mode\",\n            value=\"basic\",\n            options=[\"basic\", \"jwt\"],\n            info=(\n                \"Authentication method: 'basic' for username/password authentication, \"\n                \"or 'jwt' for JSON Web Token (Bearer) authentication.\"\n            ),\n            real_time_refresh=True,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"username\",\n            display_name=\"Username\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"password\",\n            display_name=\"OpenSearch Password\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"jwt_token\",\n            display_name=\"JWT Token\",\n            value=\"JWT\",\n            load_from_db=False,\n            show=True,\n            info=(\n                \"Valid JSON Web Token for authentication. \"\n                \"Will be sent in the Authorization header (with optional 'Bearer ' prefix).\"\n            ),\n        ),\n        StrInput(\n            name=\"jwt_header\",\n            display_name=\"JWT Header Name\",\n            value=\"Authorization\",\n            show=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"bearer_prefix\",\n            display_name=\"Prefix 'Bearer '\",\n            value=True,\n            show=False,\n            advanced=True,\n        ),\n        # ----- TLS -----\n        BoolInput(\n            name=\"use_ssl\",\n            display_name=\"Use SSL/TLS\",\n            value=True,\n            advanced=True,\n            info=\"Enable SSL/TLS encryption for secure connections to OpenSearch.\",\n        ),\n        BoolInput(\n            name=\"verify_certs\",\n            display_name=\"Verify SSL Certificates\",\n            value=False,\n            advanced=True,\n            info=(\n                \"Verify SSL certificates when connecting. \"\n                \"Disable for self-signed certificates in development environments.\"\n            ),\n        ),\n    ]\n\n    # ---------- helper functions for index management ----------\n    def _default_text_mapping(\n        self,\n        dim: int,\n        engine: str = \"jvector\",\n        space_type: str = \"l2\",\n        ef_search: int = 512,\n        ef_construction: int = 100,\n        m: int = 16,\n        vector_field: str = \"vector_field\",\n    ) -> dict[str, Any]:\n        \"\"\"Create the default OpenSearch index mapping for vector search.\n\n        This method generates the index configuration with k-NN settings optimized\n        for approximate nearest neighbor search using the specified vector engine.\n\n        Args:\n            dim: Dimensionality of the vector embeddings\n            engine: Vector search engine (jvector, nmslib, faiss, lucene)\n            space_type: Distance metric for similarity calculation\n            ef_search: Size of dynamic list used during search\n            ef_construction: Size of dynamic list used during index construction\n            m: Number of bidirectional links for each vector\n            vector_field: Name of the field storing vector embeddings\n\n        Returns:\n            Dictionary containing OpenSearch index mapping configuration\n        \"\"\"\n        return {\n            \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n            \"mappings\": {\n                \"properties\": {\n                    vector_field: {\n                        \"type\": \"knn_vector\",\n                        \"dimension\": dim,\n                        \"method\": {\n                            \"name\": \"disk_ann\",\n                            \"space_type\": space_type,\n                            \"engine\": engine,\n                            \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n                        },\n                    }\n                }\n            },\n        }\n\n    def _validate_aoss_with_engines(self, *, is_aoss: bool, engine: str) -> None:\n        \"\"\"Validate engine compatibility with Amazon OpenSearch Serverless (AOSS).\n\n        Amazon OpenSearch Serverless has restrictions on which vector engines\n        can be used. This method ensures the selected engine is compatible.\n\n        Args:\n            is_aoss: Whether the connection is to Amazon OpenSearch Serverless\n            engine: The selected vector search engine\n\n        Raises:\n            ValueError: If AOSS is used with an incompatible engine\n        \"\"\"\n        if is_aoss and engine not in {\"nmslib\", \"faiss\"}:\n            msg = \"Amazon OpenSearch Service Serverless only supports `nmslib` or `faiss` engines\"\n            raise ValueError(msg)\n\n    def _is_aoss_enabled(self, http_auth: Any) -> bool:\n        \"\"\"Determine if Amazon OpenSearch Serverless (AOSS) is being used.\n\n        Args:\n            http_auth: The HTTP authentication object\n\n        Returns:\n            True if AOSS is enabled, False otherwise\n        \"\"\"\n        return http_auth is not None and hasattr(http_auth, \"service\") and http_auth.service == \"aoss\"\n\n    def _bulk_ingest_embeddings(\n        self,\n        client: OpenSearch,\n        index_name: str,\n        embeddings: list[list[float]],\n        texts: list[str],\n        metadatas: list[dict] | None = None,\n        ids: list[str] | None = None,\n        vector_field: str = \"vector_field\",\n        text_field: str = \"text\",\n        mapping: dict | None = None,\n        max_chunk_bytes: int | None = 1 * 1024 * 1024,\n        *,\n        is_aoss: bool = False,\n    ) -> list[str]:\n        \"\"\"Efficiently ingest multiple documents with embeddings into OpenSearch.\n\n        This method uses bulk operations to insert documents with their vector\n        embeddings and metadata into the specified OpenSearch index.\n\n        Args:\n            client: OpenSearch client instance\n            index_name: Target index for document storage\n            embeddings: List of vector embeddings for each document\n            texts: List of document texts\n            metadatas: Optional metadata dictionaries for each document\n            ids: Optional document IDs (UUIDs generated if not provided)\n            vector_field: Field name for storing vector embeddings\n            text_field: Field name for storing document text\n            mapping: Optional index mapping configuration\n            max_chunk_bytes: Maximum size per bulk request chunk\n            is_aoss: Whether using Amazon OpenSearch Serverless\n\n        Returns:\n            List of document IDs that were successfully ingested\n        \"\"\"\n        if not mapping:\n            mapping = {}\n\n        requests = []\n        return_ids = []\n\n        for i, text in enumerate(texts):\n            metadata = metadatas[i] if metadatas else {}\n            _id = ids[i] if ids else str(uuid.uuid4())\n            request = {\n                \"_op_type\": \"index\",\n                \"_index\": index_name,\n                vector_field: embeddings[i],\n                text_field: text,\n                **metadata,\n            }\n            if is_aoss:\n                request[\"id\"] = _id\n            else:\n                request[\"_id\"] = _id\n            requests.append(request)\n            return_ids.append(_id)\n        if metadatas:\n            self.log(f\"Sample metadata: {metadatas[0] if metadatas else {}}\")\n        helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n        return return_ids\n\n    # ---------- auth / client ----------\n    def _build_auth_kwargs(self) -> dict[str, Any]:\n        \"\"\"Build authentication configuration for OpenSearch client.\n\n        Constructs the appropriate authentication parameters based on the\n        selected auth mode (basic username/password or JWT token).\n\n        Returns:\n            Dictionary containing authentication configuration\n\n        Raises:\n            ValueError: If required authentication parameters are missing\n        \"\"\"\n        mode = (self.auth_mode or \"basic\").strip().lower()\n        if mode == \"jwt\":\n            token = (self.jwt_token or \"\").strip()\n            if not token:\n                msg = \"Auth Mode is 'jwt' but no jwt_token was provided.\"\n                raise ValueError(msg)\n            header_name = (self.jwt_header or \"Authorization\").strip()\n            header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n            return {\"headers\": {header_name: header_value}}\n        user = (self.username or \"\").strip()\n        pwd = (self.password or \"\").strip()\n        if not user or not pwd:\n            msg = \"Auth Mode is 'basic' but username/password are missing.\"\n            raise ValueError(msg)\n        return {\"http_auth\": (user, pwd)}\n\n    def build_client(self) -> OpenSearch:\n        \"\"\"Create and configure an OpenSearch client instance.\n\n        Returns:\n            Configured OpenSearch client ready for operations\n        \"\"\"\n        auth_kwargs = self._build_auth_kwargs()\n        return OpenSearch(\n            hosts=[self.opensearch_url],\n            use_ssl=self.use_ssl,\n            verify_certs=self.verify_certs,\n            ssl_assert_hostname=False,\n            ssl_show_warn=False,\n            **auth_kwargs,\n        )\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> OpenSearch:\n        # Return raw OpenSearch client as our “vector store.”\n        self.log(self.ingest_data)\n        client = self.build_client()\n        self._add_documents_to_vector_store(client=client)\n        return client\n\n    # ---------- ingest ----------\n    def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n        \"\"\"Process and ingest documents into the OpenSearch vector store.\n\n        This method handles the complete document ingestion pipeline:\n        - Prepares document data and metadata\n        - Generates vector embeddings\n        - Creates appropriate index mappings\n        - Bulk inserts documents with vectors\n\n        Args:\n            client: OpenSearch client for performing operations\n        \"\"\"\n        # Convert DataFrame to Data if needed using parent's method\n        self.ingest_data = self._prepare_ingest_data()\n\n        docs = self.ingest_data or []\n        if not docs:\n            self.log(\"No documents to ingest.\")\n            return\n\n        # Extract texts and metadata from documents\n        texts = []\n        metadatas = []\n        # Process docs_metadata table input into a dict\n        additional_metadata = {}\n        if hasattr(self, \"docs_metadata\") and self.docs_metadata:\n            logger.info(f\"[LF] Docs metadata {self.docs_metadata}\")\n            if isinstance(self.docs_metadata[-1], Data):\n                logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n                self.docs_metadata = self.docs_metadata[-1].data\n                logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n                additional_metadata.update(self.docs_metadata)\n            else:\n                for item in self.docs_metadata:\n                    if isinstance(item, dict) and \"key\" in item and \"value\" in item:\n                        additional_metadata[item[\"key\"]] = item[\"value\"]\n        # Replace string \"None\" values with actual None\n        for key, value in additional_metadata.items():\n            if value == \"None\":\n                additional_metadata[key] = None\n        logger.info(f\"[LF] Additional metadata {additional_metadata}\")\n        for doc_obj in docs:\n            data_copy = json.loads(doc_obj.model_dump_json())\n            text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n            texts.append(text)\n\n            # Merge additional metadata from table input\n            data_copy.update(additional_metadata)\n\n            metadatas.append(data_copy)\n        self.log(metadatas)\n        if not self.embedding:\n            msg = \"Embedding handle is required to embed documents.\"\n            raise ValueError(msg)\n\n        # Generate embeddings\n        vectors = self.embedding.embed_documents(texts)\n\n        if not vectors:\n            self.log(\"No vectors generated from documents.\")\n            return\n\n        # Get vector dimension for mapping\n        dim = len(vectors[0]) if vectors else 768  # default fallback\n\n        # Check for AOSS\n        auth_kwargs = self._build_auth_kwargs()\n        is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n        # Validate engine with AOSS\n        engine = getattr(self, \"engine\", \"jvector\")\n        self._validate_aoss_with_engines(is_aoss=is_aoss, engine=engine)\n\n        # Create mapping with proper KNN settings\n        space_type = getattr(self, \"space_type\", \"l2\")\n        ef_construction = getattr(self, \"ef_construction\", 512)\n        m = getattr(self, \"m\", 16)\n\n        mapping = self._default_text_mapping(\n            dim=dim,\n            engine=engine,\n            space_type=space_type,\n            ef_construction=ef_construction,\n            m=m,\n            vector_field=self.vector_field,\n        )\n\n        self.log(f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\")\n\n        # Use the LangChain-style bulk ingestion\n        return_ids = self._bulk_ingest_embeddings(\n            client=client,\n            index_name=self.index_name,\n            embeddings=vectors,\n            texts=texts,\n            metadatas=metadatas,\n            vector_field=self.vector_field,\n            text_field=\"text\",\n            mapping=mapping,\n            is_aoss=is_aoss,\n        )\n        self.log(metadatas)\n\n        self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n    # ---------- helpers for filters ----------\n    def _is_placeholder_term(self, term_obj: dict) -> bool:\n        # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n        return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n    def _coerce_filter_clauses(self, filter_obj: dict | None) -> list[dict]:\n        \"\"\"Convert filter expressions into OpenSearch-compatible filter clauses.\n\n        This method accepts two filter formats and converts them to standardized\n        OpenSearch query clauses:\n\n        Format A - Explicit filters:\n        {\"filter\": [{\"term\": {\"field\": \"value\"}}, {\"terms\": {\"field\": [\"val1\", \"val2\"]}}],\n         \"limit\": 10, \"score_threshold\": 1.5}\n\n        Format B - Context-style mapping:\n        {\"data_sources\": [\"file1.pdf\"], \"document_types\": [\"pdf\"], \"owners\": [\"user1\"]}\n\n        Args:\n            filter_obj: Filter configuration dictionary or None\n\n        Returns:\n            List of OpenSearch filter clauses (term/terms objects)\n            Placeholder values with \"__IMPOSSIBLE_VALUE__\" are ignored\n        \"\"\"\n        if not filter_obj:\n            return []\n\n        # If it is a string, try to parse it once\n        if isinstance(filter_obj, str):\n            try:\n                filter_obj = json.loads(filter_obj)\n            except json.JSONDecodeError:\n                # Not valid JSON - treat as no filters\n                return []\n\n        # Case A: already an explicit list/dict under \"filter\"\n        if \"filter\" in filter_obj:\n            raw = filter_obj[\"filter\"]\n            if isinstance(raw, dict):\n                raw = [raw]\n            explicit_clauses: list[dict] = []\n            for f in raw or []:\n                if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n                    explicit_clauses.append(f)\n                elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n                    field, vals = next(iter(f[\"terms\"].items()))\n                    if isinstance(vals, list) and len(vals) > 0:\n                        explicit_clauses.append(f)\n            return explicit_clauses\n\n        # Case B: convert context-style maps into clauses\n        field_mapping = {\n            \"data_sources\": \"filename\",\n            \"document_types\": \"mimetype\",\n            \"owners\": \"owner\",\n        }\n        context_clauses: list[dict] = []\n        for k, values in filter_obj.items():\n            if not isinstance(values, list):\n                continue\n            field = field_mapping.get(k, k)\n            if len(values) == 0:\n                # Match-nothing placeholder (kept to mirror your tool semantics)\n                context_clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n            elif len(values) == 1:\n                if values[0] != \"__IMPOSSIBLE_VALUE__\":\n                    context_clauses.append({\"term\": {field: values[0]}})\n            else:\n                context_clauses.append({\"terms\": {field: values}})\n        return context_clauses\n\n    # ---------- search (single hybrid path matching your tool) ----------\n    def search(self, query: str | None = None) -> list[dict[str, Any]]:\n        \"\"\"Perform hybrid search combining vector similarity and keyword matching.\n\n        This method executes a sophisticated search that combines:\n        - K-nearest neighbor (KNN) vector similarity search (70% weight)\n        - Multi-field keyword search with fuzzy matching (30% weight)\n        - Optional filtering and score thresholds\n        - Aggregations for faceted search results\n\n        Args:\n            query: Search query string (used for both vector embedding and keyword search)\n\n        Returns:\n            List of search results with page_content, metadata, and relevance scores\n\n        Raises:\n            ValueError: If embedding component is not provided or filter JSON is invalid\n        \"\"\"\n        logger.info(self.ingest_data)\n        client = self.build_client()\n        q = (query or \"\").strip()\n\n        # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n        filter_obj = None\n        if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n            try:\n                filter_obj = json.loads(self.filter_expression)\n            except json.JSONDecodeError as e:\n                msg = f\"Invalid filter_expression JSON: {e}\"\n                raise ValueError(msg) from e\n\n        if not self.embedding:\n            msg = \"Embedding is required to run hybrid search (KNN + keyword).\"\n            raise ValueError(msg)\n\n        # Embed the query\n        vec = self.embedding.embed_query(q)\n\n        # Build filter clauses (accept both shapes)\n        filter_clauses = self._coerce_filter_clauses(filter_obj)\n\n        # Respect the tool's limit/threshold defaults\n        limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n        score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n        # Build the same hybrid body as your SearchService\n        body = {\n            \"query\": {\n                \"bool\": {\n                    \"should\": [\n                        {\n                            \"knn\": {\n                                self.vector_field: {\n                                    \"vector\": vec,\n                                    \"k\": 10,  # fixed to match the tool\n                                    \"boost\": 0.7,\n                                }\n                            }\n                        },\n                        {\n                            \"multi_match\": {\n                                \"query\": q,\n                                \"fields\": [\"text^2\", \"filename^1.5\"],\n                                \"type\": \"best_fields\",\n                                \"fuzziness\": \"AUTO\",\n                                \"boost\": 0.3,\n                            }\n                        },\n                    ],\n                    \"minimum_should_match\": 1,\n                }\n            },\n            \"aggs\": {\n                \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n                \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n                \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n            },\n            \"_source\": [\n                \"filename\",\n                \"mimetype\",\n                \"page\",\n                \"text\",\n                \"source_url\",\n                \"owner\",\n                \"allowed_users\",\n                \"allowed_groups\",\n            ],\n            \"size\": limit,\n        }\n        if filter_clauses:\n            body[\"query\"][\"bool\"][\"filter\"] = filter_clauses\n\n        if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n            # top-level min_score (matches your tool)\n            body[\"min_score\"] = score_threshold\n\n        resp = client.search(index=self.index_name, body=body)\n        hits = resp.get(\"hits\", {}).get(\"hits\", [])\n        return [\n            {\n                \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n                \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n                \"score\": hit.get(\"_score\"),\n            }\n            for hit in hits\n        ]\n\n    def search_documents(self) -> list[Data]:\n        \"\"\"Search documents and return results as Data objects.\n\n        This is the main interface method that performs the search using the\n        configured search_query and returns results in Langflow's Data format.\n\n        Returns:\n            List of Data objects containing search results with text and metadata\n\n        Raises:\n            Exception: If search operation fails\n        \"\"\"\n        try:\n            raw = self.search(self.search_query or \"\")\n            return [Data(text=hit[\"page_content\"], **hit[\"metadata\"]) for hit in raw]\n            self.log(self.ingest_data)\n        except Exception as e:\n            self.log(f\"search_documents error: {e}\")\n            raise\n\n    # -------- dynamic UI handling (auth switch) --------\n    async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n        \"\"\"Dynamically update component configuration based on field changes.\n\n        This method handles real-time UI updates, particularly for authentication\n        mode changes that show/hide relevant input fields.\n\n        Args:\n            build_config: Current component configuration\n            field_value: New value for the changed field\n            field_name: Name of the field that changed\n\n        Returns:\n            Updated build configuration with appropriate field visibility\n        \"\"\"\n        try:\n            if field_name == \"auth_mode\":\n                mode = (field_value or \"basic\").strip().lower()\n                is_basic = mode == \"basic\"\n                is_jwt = mode == \"jwt\"\n\n                build_config[\"username\"][\"show\"] = is_basic\n                build_config[\"password\"][\"show\"] = is_basic\n\n                build_config[\"jwt_token\"][\"show\"] = is_jwt\n                build_config[\"jwt_header\"][\"show\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n                build_config[\"username\"][\"required\"] = is_basic\n                build_config[\"password\"][\"required\"] = is_basic\n\n                build_config[\"jwt_token\"][\"required\"] = is_jwt\n                build_config[\"jwt_header\"][\"required\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"required\"] = False\n\n                if is_basic:\n                    build_config[\"jwt_token\"][\"value\"] = \"\"\n\n                return build_config\n\n        except (KeyError, ValueError) as e:\n            self.log(f\"update_build_config error: {e}\")\n\n        return build_config\n"
               },
               "docs_metadata": {
                 "_input_type": "TableInput",
-                "advanced": true,
-                "display_name": "Ingestion Metadata",
+                "advanced": false,
+                "display_name": "Document Metadata",
                 "dynamic": false,
-                "info": "Key value pairs to be inserted into each ingested document.",
+                "info": "Additional metadata key-value pairs to be added to all ingested documents. Useful for tagging documents with source information, categories, or other custom attributes.",
+                "input_types": [
+                  "Data"
+                ],
                 "is_list": true,
                 "list_add_label": "Add More",
                 "name": "docs_metadata",
@@ -554,36 +695,22 @@
                 "required": false,
                 "show": true,
                 "table_icon": "Table",
-                "table_schema": {
-                  "columns": [
-                    {
-                      "default": "None",
-                      "description": "Key name",
-                      "disable_edit": false,
-                      "display_name": "Key",
-                      "edit_mode": "popover",
-                      "filterable": true,
-                      "formatter": "text",
-                      "hidden": false,
-                      "name": "key",
-                      "sortable": true,
-                      "type": "str"
-                    },
-                    {
-                      "default": "None",
-                      "description": "Value of the metadata",
-                      "disable_edit": false,
-                      "display_name": "Value",
-                      "edit_mode": "popover",
-                      "filterable": true,
-                      "formatter": "text",
-                      "hidden": false,
-                      "name": "value",
-                      "sortable": true,
-                      "type": "str"
-                    }
-                  ]
-                },
+                "table_schema": [
+                  {
+                    "description": "Key name",
+                    "display_name": "Key",
+                    "formatter": "text",
+                    "name": "key",
+                    "type": "str"
+                  },
+                  {
+                    "description": "Value of the metadata",
+                    "display_name": "Value",
+                    "formatter": "text",
+                    "name": "value",
+                    "type": "str"
+                  }
+                ],
                 "title_case": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
@@ -597,7 +724,7 @@
                 "advanced": true,
                 "display_name": "EF Construction",
                 "dynamic": false,
-                "info": "Size of the dynamic list used during k-NN graph creation.",
+                "info": "Size of the dynamic candidate list during index construction. Higher values improve recall but increase indexing time and memory usage.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "ef_construction",
@@ -635,9 +762,10 @@
                 "advanced": true,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Engine",
+                "display_name": "Vector Engine",
                 "dynamic": false,
-                "info": "Vector search engine to use.",
+                "external_options": {},
+                "info": "Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.",
                 "load_from_db": false,
                 "name": "engine",
                 "options": [
@@ -661,9 +789,9 @@
                 "_input_type": "MultilineInput",
                 "advanced": false,
                 "copy_field": false,
-                "display_name": "Filter Expression (JSON)",
+                "display_name": "Search Filters (JSON)",
                 "dynamic": false,
-                "info": "Optional JSON to control filters/limit/score threshold.\nAccepted shapes:\n1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\n2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\nPlaceholders with __IMPOSSIBLE_VALUE__ are ignored.",
+                "info": "Optional JSON configuration for search filtering, result limits, and score thresholds.\n\nFormat 1 - Explicit filters:\n{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, {\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\n\nFormat 2 - Context-style mapping:\n{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\n\nUse __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.",
                 "input_types": [
                   "Message"
                 ],
@@ -687,7 +815,7 @@
                 "advanced": false,
                 "display_name": "Index Name",
                 "dynamic": false,
-                "info": "The index to search.",
+                "info": "The OpenSearch index name where documents will be stored and searched. Will be created automatically if it doesn't exist.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -746,9 +874,9 @@
                 "advanced": false,
                 "display_name": "JWT Token",
                 "dynamic": false,
-                "info": "Paste a valid JWT (sent as a header).",
+                "info": "Valid JSON Web Token for authentication. Will be sent in the Authorization header (with optional 'Bearer ' prefix).",
                 "input_types": [],
-                "load_from_db": true,
+                "load_from_db": false,
                 "name": "jwt_token",
                 "password": true,
                 "placeholder": "",
@@ -763,7 +891,7 @@
                 "advanced": true,
                 "display_name": "M Parameter",
                 "dynamic": false,
-                "info": "Number of bidirectional links created for each new element.",
+                "info": "Number of bidirectional connections for each vector in the HNSW graph. Higher values improve search quality but increase memory usage and indexing time.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "m",
@@ -779,9 +907,9 @@
               "number_of_results": {
                 "_input_type": "IntInput",
                 "advanced": true,
-                "display_name": "Default Size (limit)",
+                "display_name": "Default Result Limit",
                 "dynamic": false,
-                "info": "Default number of hits when no limit provided in filter_expression.",
+                "info": "Default maximum number of search results to return when no limit is specified in the filter expression.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -800,7 +928,7 @@
                 "advanced": false,
                 "display_name": "OpenSearch URL",
                 "dynamic": false,
-                "info": "URL for your OpenSearch cluster.",
+                "info": "The connection URL for your OpenSearch cluster (e.g., http://localhost:9200 for local development or your cloud endpoint).",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -817,7 +945,7 @@
               "password": {
                 "_input_type": "SecretStrInput",
                 "advanced": false,
-                "display_name": "Password",
+                "display_name": "OpenSearch Password",
                 "dynamic": false,
                 "info": "",
                 "input_types": [],
@@ -877,9 +1005,10 @@
                 "advanced": true,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Space Type",
+                "display_name": "Distance Metric",
                 "dynamic": false,
-                "info": "Distance metric for vector similarity.",
+                "external_options": {},
+                "info": "Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, 'cosinesimil' for cosine similarity, 'innerproduct' for dot product.",
                 "name": "space_type",
                 "options": [
                   "l2",
@@ -902,9 +1031,9 @@
               "use_ssl": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Use SSL",
+                "display_name": "Use SSL/TLS",
                 "dynamic": false,
-                "info": "",
+                "info": "Enable SSL/TLS encryption for secure connections to OpenSearch.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "use_ssl",
@@ -939,9 +1068,9 @@
               "vector_field": {
                 "_input_type": "StrInput",
                 "advanced": true,
-                "display_name": "Vector Field",
+                "display_name": "Vector Field Name",
                 "dynamic": false,
-                "info": "Vector field used for KNN.",
+                "info": "Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -958,9 +1087,9 @@
               "verify_certs": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Verify Certificates",
+                "display_name": "Verify SSL Certificates",
                 "dynamic": false,
-                "info": "",
+                "info": "Verify SSL certificates when connecting. Disable for self-signed certificates in development environments.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "verify_certs",
@@ -978,12 +1107,12 @@
           },
           "selected_output": "search_results",
           "showNode": true,
-          "type": "OpenSearchHybrid"
+          "type": "OpenSearchVectorStoreComponent"
         },
         "dragging": false,
         "id": "OpenSearchHybrid-XtKoA",
         "measured": {
-          "height": 760,
+          "height": 822,
           "width": 320
         },
         "position": {
@@ -1027,7 +1156,7 @@
             "icon": "Docling",
             "legacy": false,
             "metadata": {
-              "code_hash": "880538860431",
+              "code_hash": "930312ffe40c",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1043,7 +1172,7 @@
                     "version": "2.10.6"
                   },
                   {
-                    "name": "langflow",
+                    "name": "lfx",
                     "version": null
                   }
                 ],
@@ -1128,7 +1257,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import base64\nimport time\nfrom concurrent.futures import Future, ThreadPoolExecutor\nfrom pathlib import Path\nfrom typing import Any\n\nimport httpx\nfrom docling_core.types.doc import DoclingDocument\nfrom pydantic import ValidationError\n\nfrom langflow.base.data import BaseFileComponent\nfrom langflow.inputs import IntInput, NestedDictInput, StrInput\nfrom langflow.inputs.inputs import FloatInput\nfrom langflow.schema import Data\n\n\nclass DoclingRemoteComponent(BaseFileComponent):\n    display_name = \"Docling Serve\"\n    description = \"Uses Docling to process input documents connecting to your instance of Docling Serve.\"\n    documentation = \"https://docling-project.github.io/docling/\"\n    trace_type = \"tool\"\n    icon = \"Docling\"\n    name = \"DoclingRemote\"\n\n    MAX_500_RETRIES = 5\n\n    # https://docling-project.github.io/docling/usage/supported_formats/\n    VALID_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"csv\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"docx\",\n        \"htm\",\n        \"html\",\n        \"jpeg\",\n        \"json\",\n        \"md\",\n        \"pdf\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"txt\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"xml\",\n        \"webp\",\n    ]\n\n    inputs = [\n        *BaseFileComponent._base_inputs,\n        StrInput(\n            name=\"api_url\",\n            display_name=\"Server address\",\n            info=\"URL of the Docling Serve instance.\",\n            required=True,\n        ),\n        IntInput(\n            name=\"max_concurrency\",\n            display_name=\"Concurrency\",\n            info=\"Maximum number of concurrent requests for the server.\",\n            advanced=True,\n            value=2,\n        ),\n        FloatInput(\n            name=\"max_poll_timeout\",\n            display_name=\"Maximum poll time\",\n            info=\"Maximum waiting time for the document conversion to complete.\",\n            advanced=True,\n            value=3600,\n        ),\n        NestedDictInput(\n            name=\"api_headers\",\n            display_name=\"HTTP headers\",\n            advanced=True,\n            required=False,\n            info=(\"Optional dictionary of additional headers required for connecting to Docling Serve.\"),\n        ),\n        NestedDictInput(\n            name=\"docling_serve_opts\",\n            display_name=\"Docling options\",\n            advanced=True,\n            required=False,\n            info=(\n                \"Optional dictionary of additional options. \"\n                \"See https://github.com/docling-project/docling-serve/blob/main/docs/usage.md for more information.\"\n            ),\n        ),\n    ]\n\n    outputs = [\n        *BaseFileComponent._base_outputs,\n    ]\n\n    def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]:\n        base_url = f\"{self.api_url}/v1alpha\"\n\n        def _convert_document(client: httpx.Client, file_path: Path, options: dict[str, Any]) -> Data | None:\n            encoded_doc = base64.b64encode(file_path.read_bytes()).decode()\n            payload = {\n                \"options\": options,\n                \"file_sources\": [{\"base64_string\": encoded_doc, \"filename\": file_path.name}],\n            }\n\n            response = client.post(f\"{base_url}/convert/source/async\", json=payload)\n            response.raise_for_status()\n            task = response.json()\n\n            http_failures = 0\n            retry_status_start = 500\n            retry_status_end = 600\n            start_wait_time = time.monotonic()\n            while task[\"task_status\"] not in (\"success\", \"failure\"):\n                # Check if processing exceeds the maximum poll timeout\n                processing_time = time.monotonic() - start_wait_time\n                if processing_time >= self.max_poll_timeout:\n                    msg = (\n                        f\"Processing time {processing_time=} exceeds the maximum poll timeout {self.max_poll_timeout=}.\"\n                        \"Please increase the max_poll_timeout parameter or review why the processing \"\n                        \"takes long on the server.\"\n                    )\n                    self.log(msg)\n                    raise RuntimeError(msg)\n\n                # Call for a new status update\n                time.sleep(2)\n                response = client.get(f\"{base_url}/status/poll/{task['task_id']}\")\n\n                # Check if the status call gets into 5xx errors and retry\n                if retry_status_start <= response.status_code < retry_status_end:\n                    http_failures += 1\n                    if http_failures > self.MAX_500_RETRIES:\n                        self.log(f\"The status requests got a http response {response.status_code} too many times.\")\n                        return None\n                    continue\n\n                # Update task status\n                task = response.json()\n\n            result_resp = client.get(f\"{base_url}/result/{task['task_id']}\")\n            result_resp.raise_for_status()\n            result = result_resp.json()\n\n            if \"json_content\" not in result[\"document\"] or result[\"document\"][\"json_content\"] is None:\n                self.log(\"No JSON DoclingDocument found in the result.\")\n                return None\n\n            try:\n                doc = DoclingDocument.model_validate(result[\"document\"][\"json_content\"])\n                return Data(data={\"doc\": doc, \"file_path\": str(file_path)})\n            except ValidationError as e:\n                self.log(f\"Error validating the document. {e}\")\n                return None\n\n        docling_options = {\n            \"to_formats\": [\"json\"],\n            \"image_export_mode\": \"placeholder\",\n            \"return_as_file\": False,\n            **(self.docling_serve_opts or {}),\n        }\n\n        processed_data: list[Data | None] = []\n        with (\n            httpx.Client(headers=self.api_headers) as client,\n            ThreadPoolExecutor(max_workers=self.max_concurrency) as executor,\n        ):\n            futures: list[tuple[int, Future]] = []\n            for i, file in enumerate(file_list):\n                if file.path is None:\n                    processed_data.append(None)\n                    continue\n\n                futures.append((i, executor.submit(_convert_document, client, file.path, docling_options)))\n\n            for _index, future in futures:\n                try:\n                    result_data = future.result()\n                    processed_data.append(result_data)\n                except (httpx.HTTPStatusError, httpx.RequestError, KeyError, ValueError) as exc:\n                    self.log(f\"Docling remote processing failed: {exc}\")\n                    raise\n\n        return self.rollup_data(file_list, processed_data)\n"
+                "value": "import base64\nimport time\nfrom concurrent.futures import Future, ThreadPoolExecutor\nfrom pathlib import Path\nfrom typing import Any\n\nimport httpx\nfrom docling_core.types.doc import DoclingDocument\nfrom pydantic import ValidationError\n\nfrom lfx.base.data import BaseFileComponent\nfrom lfx.inputs import IntInput, NestedDictInput, StrInput\nfrom lfx.inputs.inputs import FloatInput\nfrom lfx.schema import Data\n\n\nclass DoclingRemoteComponent(BaseFileComponent):\n    display_name = \"Docling Serve\"\n    description = \"Uses Docling to process input documents connecting to your instance of Docling Serve.\"\n    documentation = \"https://docling-project.github.io/docling/\"\n    trace_type = \"tool\"\n    icon = \"Docling\"\n    name = \"DoclingRemote\"\n\n    MAX_500_RETRIES = 5\n\n    # https://docling-project.github.io/docling/usage/supported_formats/\n    VALID_EXTENSIONS = [\n        \"adoc\",\n        \"asciidoc\",\n        \"asc\",\n        \"bmp\",\n        \"csv\",\n        \"dotx\",\n        \"dotm\",\n        \"docm\",\n        \"docx\",\n        \"htm\",\n        \"html\",\n        \"jpeg\",\n        \"json\",\n        \"md\",\n        \"pdf\",\n        \"png\",\n        \"potx\",\n        \"ppsx\",\n        \"pptm\",\n        \"potm\",\n        \"ppsm\",\n        \"pptx\",\n        \"tiff\",\n        \"txt\",\n        \"xls\",\n        \"xlsx\",\n        \"xhtml\",\n        \"xml\",\n        \"webp\",\n    ]\n\n    inputs = [\n        *BaseFileComponent.get_base_inputs(),\n        StrInput(\n            name=\"api_url\",\n            display_name=\"Server address\",\n            info=\"URL of the Docling Serve instance.\",\n            required=True,\n        ),\n        IntInput(\n            name=\"max_concurrency\",\n            display_name=\"Concurrency\",\n            info=\"Maximum number of concurrent requests for the server.\",\n            advanced=True,\n            value=2,\n        ),\n        FloatInput(\n            name=\"max_poll_timeout\",\n            display_name=\"Maximum poll time\",\n            info=\"Maximum waiting time for the document conversion to complete.\",\n            advanced=True,\n            value=3600,\n        ),\n        NestedDictInput(\n            name=\"api_headers\",\n            display_name=\"HTTP headers\",\n            advanced=True,\n            required=False,\n            info=(\"Optional dictionary of additional headers required for connecting to Docling Serve.\"),\n        ),\n        NestedDictInput(\n            name=\"docling_serve_opts\",\n            display_name=\"Docling options\",\n            advanced=True,\n            required=False,\n            info=(\n                \"Optional dictionary of additional options. \"\n                \"See https://github.com/docling-project/docling-serve/blob/main/docs/usage.md for more information.\"\n            ),\n        ),\n    ]\n\n    outputs = [\n        *BaseFileComponent.get_base_outputs(),\n    ]\n\n    def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]:\n        base_url = f\"{self.api_url}/v1\"\n\n        def _convert_document(client: httpx.Client, file_path: Path, options: dict[str, Any]) -> Data | None:\n            encoded_doc = base64.b64encode(file_path.read_bytes()).decode()\n            payload = {\n                \"options\": options,\n                \"sources\": [{\"kind\": \"file\", \"base64_string\": encoded_doc, \"filename\": file_path.name}],\n            }\n\n            response = client.post(f\"{base_url}/convert/source/async\", json=payload)\n            response.raise_for_status()\n            task = response.json()\n\n            http_failures = 0\n            retry_status_start = 500\n            retry_status_end = 600\n            start_wait_time = time.monotonic()\n            while task[\"task_status\"] not in (\"success\", \"failure\"):\n                # Check if processing exceeds the maximum poll timeout\n                processing_time = time.monotonic() - start_wait_time\n                if processing_time >= self.max_poll_timeout:\n                    msg = (\n                        f\"Processing time {processing_time=} exceeds the maximum poll timeout {self.max_poll_timeout=}.\"\n                        \"Please increase the max_poll_timeout parameter or review why the processing \"\n                        \"takes long on the server.\"\n                    )\n                    self.log(msg)\n                    raise RuntimeError(msg)\n\n                # Call for a new status update\n                time.sleep(2)\n                response = client.get(f\"{base_url}/status/poll/{task['task_id']}\")\n\n                # Check if the status call gets into 5xx errors and retry\n                if retry_status_start <= response.status_code < retry_status_end:\n                    http_failures += 1\n                    if http_failures > self.MAX_500_RETRIES:\n                        self.log(f\"The status requests got a http response {response.status_code} too many times.\")\n                        return None\n                    continue\n\n                # Update task status\n                task = response.json()\n\n            result_resp = client.get(f\"{base_url}/result/{task['task_id']}\")\n            result_resp.raise_for_status()\n            result = result_resp.json()\n\n            if \"json_content\" not in result[\"document\"] or result[\"document\"][\"json_content\"] is None:\n                self.log(\"No JSON DoclingDocument found in the result.\")\n                return None\n\n            try:\n                doc = DoclingDocument.model_validate(result[\"document\"][\"json_content\"])\n                return Data(data={\"doc\": doc, \"file_path\": str(file_path)})\n            except ValidationError as e:\n                self.log(f\"Error validating the document. {e}\")\n                return None\n\n        docling_options = {\n            \"to_formats\": [\"json\"],\n            \"image_export_mode\": \"placeholder\",\n            **(self.docling_serve_opts or {}),\n        }\n\n        processed_data: list[Data | None] = []\n        with (\n            httpx.Client(headers=self.api_headers) as client,\n            ThreadPoolExecutor(max_workers=self.max_concurrency) as executor,\n        ):\n            futures: list[tuple[int, Future]] = []\n            for i, file in enumerate(file_list):\n                if file.path is None:\n                    processed_data.append(None)\n                    continue\n\n                futures.append((i, executor.submit(_convert_document, client, file.path, docling_options)))\n\n            for _index, future in futures:\n                try:\n                    result_data = future.result()\n                    processed_data.append(result_data)\n                except (httpx.HTTPStatusError, httpx.RequestError, KeyError, ValueError) as exc:\n                    self.log(f\"Docling remote processing failed: {exc}\")\n                    raise\n\n        return self.rollup_data(file_list, processed_data)\n"
               },
               "delete_server_file_after_processing": {
                 "_input_type": "BoolInput",
@@ -1364,7 +1493,7 @@
         "dragging": false,
         "id": "DoclingRemote-78KoX",
         "measured": {
-          "height": 472,
+          "height": 475,
           "width": 320
         },
         "position": {
@@ -1403,7 +1532,7 @@
             "icon": "Docling",
             "legacy": false,
             "metadata": {
-              "code_hash": "451c9673bd4c",
+              "code_hash": "4de16ddd37ac",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1411,7 +1540,7 @@
                     "version": "2.45.0"
                   },
                   {
-                    "name": "langflow",
+                    "name": "lfx",
                     "version": null
                   }
                 ],
@@ -1474,7 +1603,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom docling_core.types.doc import ImageRefMode\n\nfrom langflow.base.data.docling_utils import extract_docling_documents\nfrom langflow.custom import Component\nfrom langflow.io import DropdownInput, HandleInput, MessageTextInput, Output, StrInput\nfrom langflow.schema import Data, DataFrame\n\n\nclass ExportDoclingDocumentComponent(Component):\n    display_name: str = \"Export DoclingDocument\"\n    description: str = \"Export DoclingDocument to markdown, html or other formats.\"\n    documentation = \"https://docling-project.github.io/docling/\"\n    icon = \"Docling\"\n    name = \"ExportDoclingDocument\"\n\n    inputs = [\n        HandleInput(\n            name=\"data_inputs\",\n            display_name=\"Data or DataFrame\",\n            info=\"The data with documents to export.\",\n            input_types=[\"Data\", \"DataFrame\"],\n            required=True,\n        ),\n        DropdownInput(\n            name=\"export_format\",\n            display_name=\"Export format\",\n            options=[\"Markdown\", \"HTML\", \"Plaintext\", \"DocTags\"],\n            info=\"Select the export format to convert the input.\",\n            value=\"Markdown\",\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"image_mode\",\n            display_name=\"Image export mode\",\n            options=[\"placeholder\", \"embedded\"],\n            info=(\n                \"Specify how images are exported in the output. Placeholder will replace the images with a string, \"\n                \"whereas Embedded will include them as base64 encoded images.\"\n            ),\n            value=\"placeholder\",\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder betweek pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Exported data\", name=\"data\", method=\"export_document\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None) -> dict:\n        if field_name == \"export_format\" and field_value == \"Markdown\":\n            build_config[\"md_image_placeholder\"][\"show\"] = True\n            build_config[\"md_page_break_placeholder\"][\"show\"] = True\n            build_config[\"image_mode\"][\"show\"] = True\n        elif field_name == \"export_format\" and field_value == \"HTML\":\n            build_config[\"md_image_placeholder\"][\"show\"] = False\n            build_config[\"md_page_break_placeholder\"][\"show\"] = False\n            build_config[\"image_mode\"][\"show\"] = True\n        elif field_name == \"export_format\" and field_value in {\"Plaintext\", \"DocTags\"}:\n            build_config[\"md_image_placeholder\"][\"show\"] = False\n            build_config[\"md_page_break_placeholder\"][\"show\"] = False\n            build_config[\"image_mode\"][\"show\"] = False\n\n        return build_config\n\n    def export_document(self) -> list[Data]:\n        documents = extract_docling_documents(self.data_inputs, self.doc_key)\n\n        results: list[Data] = []\n        try:\n            image_mode = ImageRefMode(self.image_mode)\n            for doc in documents:\n                content = \"\"\n                if self.export_format == \"Markdown\":\n                    content = doc.export_to_markdown(\n                        image_mode=image_mode,\n                        image_placeholder=self.md_image_placeholder,\n                        page_break_placeholder=self.md_page_break_placeholder,\n                    )\n                elif self.export_format == \"HTML\":\n                    content = doc.export_to_html(image_mode=image_mode)\n                elif self.export_format == \"Plaintext\":\n                    content = doc.export_to_text()\n                elif self.export_format == \"DocTags\":\n                    content = doc.export_to_doctags()\n\n                results.append(Data(text=content))\n        except Exception as e:\n            msg = f\"Error splitting text: {e}\"\n            raise TypeError(msg) from e\n\n        return results\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.export_document())\n"
+                "value": "from typing import Any\n\nfrom docling_core.types.doc import ImageRefMode\n\nfrom lfx.base.data.docling_utils import extract_docling_documents\nfrom lfx.custom import Component\nfrom lfx.io import DropdownInput, HandleInput, MessageTextInput, Output, StrInput\nfrom lfx.schema import Data, DataFrame\n\n\nclass ExportDoclingDocumentComponent(Component):\n    display_name: str = \"Export DoclingDocument\"\n    description: str = \"Export DoclingDocument to markdown, html or other formats.\"\n    documentation = \"https://docling-project.github.io/docling/\"\n    icon = \"Docling\"\n    name = \"ExportDoclingDocument\"\n\n    inputs = [\n        HandleInput(\n            name=\"data_inputs\",\n            display_name=\"Data or DataFrame\",\n            info=\"The data with documents to export.\",\n            input_types=[\"Data\", \"DataFrame\"],\n            required=True,\n        ),\n        DropdownInput(\n            name=\"export_format\",\n            display_name=\"Export format\",\n            options=[\"Markdown\", \"HTML\", \"Plaintext\", \"DocTags\"],\n            info=\"Select the export format to convert the input.\",\n            value=\"Markdown\",\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"image_mode\",\n            display_name=\"Image export mode\",\n            options=[\"placeholder\", \"embedded\"],\n            info=(\n                \"Specify how images are exported in the output. Placeholder will replace the images with a string, \"\n                \"whereas Embedded will include them as base64 encoded images.\"\n            ),\n            value=\"placeholder\",\n        ),\n        StrInput(\n            name=\"md_image_placeholder\",\n            display_name=\"Image placeholder\",\n            info=\"Specify the image placeholder for markdown exports.\",\n            value=\"<!-- image -->\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"md_page_break_placeholder\",\n            display_name=\"Page break placeholder\",\n            info=\"Add this placeholder betweek pages in the markdown output.\",\n            value=\"\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"doc_key\",\n            display_name=\"Doc Key\",\n            info=\"The key to use for the DoclingDocument column.\",\n            value=\"doc\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Exported data\", name=\"data\", method=\"export_document\"),\n        Output(display_name=\"DataFrame\", name=\"dataframe\", method=\"as_dataframe\"),\n    ]\n\n    def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None) -> dict:\n        if field_name == \"export_format\" and field_value == \"Markdown\":\n            build_config[\"md_image_placeholder\"][\"show\"] = True\n            build_config[\"md_page_break_placeholder\"][\"show\"] = True\n            build_config[\"image_mode\"][\"show\"] = True\n        elif field_name == \"export_format\" and field_value == \"HTML\":\n            build_config[\"md_image_placeholder\"][\"show\"] = False\n            build_config[\"md_page_break_placeholder\"][\"show\"] = False\n            build_config[\"image_mode\"][\"show\"] = True\n        elif field_name == \"export_format\" and field_value in {\"Plaintext\", \"DocTags\"}:\n            build_config[\"md_image_placeholder\"][\"show\"] = False\n            build_config[\"md_page_break_placeholder\"][\"show\"] = False\n            build_config[\"image_mode\"][\"show\"] = False\n\n        return build_config\n\n    def export_document(self) -> list[Data]:\n        documents = extract_docling_documents(self.data_inputs, self.doc_key)\n\n        results: list[Data] = []\n        try:\n            image_mode = ImageRefMode(self.image_mode)\n            for doc in documents:\n                content = \"\"\n                if self.export_format == \"Markdown\":\n                    content = doc.export_to_markdown(\n                        image_mode=image_mode,\n                        image_placeholder=self.md_image_placeholder,\n                        page_break_placeholder=self.md_page_break_placeholder,\n                    )\n                elif self.export_format == \"HTML\":\n                    content = doc.export_to_html(image_mode=image_mode)\n                elif self.export_format == \"Plaintext\":\n                    content = doc.export_to_text()\n                elif self.export_format == \"DocTags\":\n                    content = doc.export_to_doctags()\n\n                results.append(Data(text=content))\n        except Exception as e:\n            msg = f\"Error splitting text: {e}\"\n            raise TypeError(msg) from e\n\n        return results\n\n    def as_dataframe(self) -> DataFrame:\n        return DataFrame(self.export_document())\n"
               },
               "data_inputs": {
                 "_input_type": "HandleInput",
@@ -1527,6 +1656,7 @@
                 "dialog_inputs": {},
                 "display_name": "Export format",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the export format to convert the input.",
                 "name": "export_format",
                 "options": [
@@ -1554,6 +1684,7 @@
                 "dialog_inputs": {},
                 "display_name": "Image export mode",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Specify how images are exported in the output. Placeholder will replace the images with a string, whereas Embedded will include them as base64 encoded images.",
                 "name": "image_mode",
                 "options": [
@@ -1619,7 +1750,7 @@
         "dragging": false,
         "id": "ExportDoclingDocument-xFoCI",
         "measured": {
-          "height": 344,
+          "height": 347,
           "width": 320
         },
         "position": {
@@ -1657,7 +1788,7 @@
             ],
             "frozen": false,
             "icon": "binary",
-            "last_updated": "2025-09-22T15:54:52.885Z",
+            "last_updated": "2025-09-29T18:46:06.711Z",
             "legacy": false,
             "metadata": {
               "code_hash": "93faf11517da",
@@ -1935,21 +2066,740 @@
           "x": 1726.6943524438122,
           "y": 1800.5330404375484
         },
-        "selected": true,
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "AdvancedDynamicFormBuilder-rDFKw",
+          "node": {
+            "base_classes": [
+              "Data",
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Creates dynamic input fields that can receive data from other components or manual input.",
+            "display_name": "Create Data",
+            "documentation": "",
+            "edited": true,
+            "field_order": [
+              "form_fields",
+              "include_metadata"
+            ],
+            "frozen": false,
+            "icon": "braces",
+            "last_updated": "2025-09-29T18:46:20.356Z",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Data",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "process_form",
+                "name": "form_data",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Data",
+                "tool_mode": true,
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Message",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "get_message",
+                "name": "message",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from typing import Any\r\n\r\nfrom langflow.custom import Component\r\nfrom langflow.io import (\r\n    BoolInput,\r\n    FloatInput,\r\n    HandleInput,\r\n    IntInput,\r\n    MultilineInput,\r\n    Output,\r\n    StrInput,\r\n    TableInput,\r\n)\r\nfrom langflow.schema.data import Data\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass CrateData(Component):\r\n    \"\"\"Dynamic Form Component\r\n\r\n    This component creates dynamic inputs that can receive data from other components\r\n    or be filled manually. It demonstrates advanced dynamic input functionality with\r\n    component connectivity.\r\n\r\n    ## Features\r\n    - **Dynamic Input Generation**: Create inputs based on table configuration\r\n    - **Component Connectivity**: Inputs can receive data from other components\r\n    - **Multiple Input Types**: Support for text, number, boolean, and handle inputs\r\n    - **Flexible Data Sources**: Manual input OR component connections\r\n    - **Real-time Updates**: Form fields update immediately when table changes\r\n         - **Multiple Output Formats**: Data and formatted Message outputs\r\n    - **JSON Output**: Collects all dynamic inputs into a structured JSON response\r\n\r\n    ## Use Cases\r\n    - Dynamic API parameter collection from multiple sources\r\n    - Variable data aggregation from different components\r\n    - Flexible pipeline configuration\r\n    - Multi-source data processing\r\n\r\n    ## Field Types Available\r\n    - **text**: Single-line text input (can connect to Text/String outputs)\r\n    - **multiline**: Multi-line text input (can connect to Text outputs)\r\n    - **number**: Integer input (can connect to Number outputs)\r\n    - **float**: Decimal number input (can connect to Number outputs)\r\n    - **boolean**: True/false checkbox (can connect to Boolean outputs)\r\n    - **handle**: Generic data input (can connect to any component output)\r\n    - **data**: Structured data input (can connect to Data outputs)\r\n\r\n    ## Input Types for Connections\r\n    - **Text**: Text/String data from components\r\n    - **Data**: Structured data objects\r\n    - **Message**: Message objects with text content\r\n    - **Number**: Numeric values\r\n    - **Boolean**: True/false values\r\n    - **Any**: Accepts any type of connection\r\n    - **Combinations**: Text,Message | Data,Text | Text,Data,Message | etc.\r\n    \"\"\"\r\n\r\n    display_name = \"Create Data\"\r\n    description = \"Creates dynamic input fields that can receive data from other components or manual input.\"\r\n    icon = \"braces\"\r\n    name = \"AdvancedDynamicFormBuilder\"\r\n\r\n    def __init__(self, **kwargs):\r\n        super().__init__(**kwargs)\r\n        self._dynamic_inputs = {}\r\n\r\n    inputs = [\r\n        TableInput(\r\n            name=\"form_fields\",\r\n            display_name=\"Input Configuration\",\r\n            info=\"Define the dynamic form fields. Each row creates a new input field that can connect to other components.\",\r\n            table_schema=[\r\n                {\r\n                    \"name\": \"field_name\",\r\n                    \"display_name\": \"Field Name\",\r\n                    \"type\": \"str\",\r\n                    \"description\": \"Name for the field (used as both internal name and display label)\",\r\n                },\r\n                {\r\n                    \"name\": \"field_type\",\r\n                    \"display_name\": \"Field Type\",\r\n                    \"type\": \"str\",\r\n                    \"description\": \"Type of input field to create\",\r\n                    \"options\": [\"Text\", \"Data\", \"Number\", \"Handle\", \"Boolean\"],\r\n                    \"value\": \"Text\",\r\n                },\r\n            ],\r\n            value=[{\"field_name\": \"field_name\", \"field_type\": \"Text\"}],\r\n            real_time_refresh=True,\r\n        ),\r\n        BoolInput(\r\n            name=\"include_metadata\",\r\n            display_name=\"Include Metadata\",\r\n            info=\"Include form configuration metadata in the output.\",\r\n            value=False,\r\n            advanced=True,\r\n        ),\r\n    ]\r\n\r\n    outputs = [\r\n        Output(display_name=\"Data\", name=\"form_data\", method=\"process_form\"),\r\n        Output(display_name=\"Message\", name=\"message\", method=\"get_message\"),\r\n    ]\r\n\r\n    def update_build_config(self, build_config: dict, field_value: Any, field_name: str = None) -> dict:\r\n        \"\"\"Update build configuration to add dynamic inputs that can connect to other components.\"\"\"\r\n        if field_name == \"form_fields\":\r\n            # Store current values before clearing dynamic inputs\r\n            current_values = {}\r\n            keys_to_remove = [key for key in build_config if key.startswith(\"dynamic_\")]\r\n            for key in keys_to_remove:\r\n                # Preserve the current value before deletion\r\n                if hasattr(self, key):\r\n                    current_values[key] = getattr(self, key)\r\n                del build_config[key]\r\n\r\n            # Add dynamic inputs based on table configuration\r\n            # Safety check to ensure field_value is not None and is iterable\r\n            if field_value is None:\r\n                field_value = []\r\n\r\n            for i, field_config in enumerate(field_value):\r\n                # Safety check to ensure field_config is not None\r\n                if field_config is None:\r\n                    continue\r\n\r\n                field_name = field_config.get(\"field_name\", f\"field_{i}\")\r\n                display_name = field_name  # Use field_name as display_name\r\n                field_type_option = field_config.get(\"field_type\", \"Text\")\r\n                default_value = \"\"  # All fields have empty default value\r\n                required = False  # All fields are optional by default\r\n                help_text = \"\"  # All fields have empty help text\r\n\r\n                # Map field type options to actual field types and input types\r\n                field_type_mapping = {\r\n                    \"Text\": {\"field_type\": \"multiline\", \"input_types\": [\"Text\", \"Message\"]},\r\n                    \"Data\": {\"field_type\": \"data\", \"input_types\": [\"Data\"]},\r\n                    \"Number\": {\"field_type\": \"number\", \"input_types\": [\"Text\", \"Message\"]},\r\n                    \"Handle\": {\"field_type\": \"handle\", \"input_types\": [\"Text\", \"Data\", \"Message\"]},\r\n                    \"Boolean\": {\"field_type\": \"boolean\", \"input_types\": None},\r\n                }\r\n\r\n                field_config_mapped = field_type_mapping.get(\r\n                    field_type_option, {\"field_type\": \"text\", \"input_types\": []}\r\n                )\r\n                field_type = field_config_mapped[\"field_type\"]\r\n                input_types_list = field_config_mapped[\"input_types\"]\r\n\r\n                # Create the appropriate input type based on field_type\r\n                dynamic_input_name = f\"dynamic_{field_name}\"\r\n\r\n                if field_type == \"text\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    if input_types_list:\r\n                        build_config[dynamic_input_name] = StrInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=f\"{help_text} (Can connect to: {', '.join(input_types_list)})\",\r\n                            value=current_value,\r\n                            required=required,\r\n                            input_types=input_types_list,\r\n                        )\r\n                    else:\r\n                        build_config[dynamic_input_name] = StrInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=help_text,\r\n                            value=current_value,\r\n                            required=required,\r\n                        )\r\n\r\n                elif field_type == \"multiline\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    if input_types_list:\r\n                        build_config[dynamic_input_name] = MultilineInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=f\"{help_text} (Can connect to: {', '.join(input_types_list)})\",\r\n                            value=current_value,\r\n                            required=required,\r\n                            input_types=input_types_list,\r\n                        )\r\n                    else:\r\n                        build_config[dynamic_input_name] = MultilineInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=help_text,\r\n                            value=current_value,\r\n                            required=required,\r\n                        )\r\n\r\n                elif field_type == \"number\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    try:\r\n                        if current_value:\r\n                            current_int = int(current_value)\r\n                        else:\r\n                            current_int = 0\r\n                    except (ValueError, TypeError):\r\n                        try:\r\n                            current_int = int(default_value) if default_value else 0\r\n                        except ValueError:\r\n                            current_int = 0\r\n\r\n                    if input_types_list:\r\n                        build_config[dynamic_input_name] = IntInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=f\"{help_text} (Can connect to: {', '.join(input_types_list)})\",\r\n                            value=current_int,\r\n                            required=required,\r\n                            input_types=input_types_list,\r\n                        )\r\n                    else:\r\n                        build_config[dynamic_input_name] = IntInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=help_text,\r\n                            value=current_int,\r\n                            required=required,\r\n                        )\r\n\r\n                elif field_type == \"float\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    try:\r\n                        if current_value:\r\n                            current_float = float(current_value)\r\n                        else:\r\n                            current_float = 0.0\r\n                    except (ValueError, TypeError):\r\n                        try:\r\n                            current_float = float(default_value) if default_value else 0.0\r\n                        except ValueError:\r\n                            current_float = 0.0\r\n\r\n                    if input_types_list:\r\n                        build_config[dynamic_input_name] = FloatInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=f\"{help_text} (Can connect to: {', '.join(input_types_list)})\",\r\n                            value=current_float,\r\n                            required=required,\r\n                            input_types=input_types_list,\r\n                        )\r\n                    else:\r\n                        build_config[dynamic_input_name] = FloatInput(\r\n                            name=dynamic_input_name,\r\n                            display_name=display_name,\r\n                            info=help_text,\r\n                            value=current_float,\r\n                            required=required,\r\n                        )\r\n\r\n                elif field_type == \"boolean\":\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    # Convert current value to boolean\r\n                    if isinstance(current_value, bool):\r\n                        current_bool = current_value\r\n                    else:\r\n                        current_bool = str(current_value).lower() in [\"true\", \"1\", \"yes\"] if current_value else False\r\n\r\n                    # Boolean fields don't use input_types parameter to avoid errors\r\n                    build_config[dynamic_input_name] = BoolInput(\r\n                        name=dynamic_input_name,\r\n                        display_name=display_name,\r\n                        info=help_text,\r\n                        value=current_bool,\r\n                        input_types=[],\r\n                        required=required,\r\n                    )\r\n\r\n                elif field_type == \"handle\":\r\n                    # HandleInput for generic data connections\r\n                    build_config[dynamic_input_name] = HandleInput(\r\n                        name=dynamic_input_name,\r\n                        display_name=display_name,\r\n                        info=f\"{help_text} (Accepts: {', '.join(input_types_list) if input_types_list else 'Any'})\",\r\n                        input_types=input_types_list if input_types_list else [\"Data\", \"Text\", \"Message\"],\r\n                        required=required,\r\n                    )\r\n\r\n                elif field_type == \"data\":\r\n                    # Specialized for Data type connections\r\n                    build_config[dynamic_input_name] = HandleInput(\r\n                        name=dynamic_input_name,\r\n                        display_name=display_name,\r\n                        info=f\"{help_text} (Data input)\",\r\n                        input_types=[\"Data\"] if not input_types_list else input_types_list,\r\n                        required=required,\r\n                    )\r\n\r\n                else:\r\n                    # Default to text input for unknown types\r\n                    # Use preserved value if available, otherwise use default\r\n                    current_value = current_values.get(dynamic_input_name, default_value)\r\n                    if current_value is None:\r\n                        current_value = default_value\r\n                    \r\n                    build_config[dynamic_input_name] = StrInput(\r\n                        name=dynamic_input_name,\r\n                        display_name=display_name,\r\n                        info=f\"{help_text} (Unknown type '{field_type}', defaulting to text)\",\r\n                        value=current_value,\r\n                        required=required,\r\n                    )\r\n\r\n        return build_config\r\n\r\n    def get_dynamic_values(self) -> dict[str, Any]:\r\n        \"\"\"Extract simple values from all dynamic inputs, handling both manual and connected inputs.\"\"\"\r\n        dynamic_values = {}\r\n        connection_info = {}\r\n        form_fields = getattr(self, \"form_fields\", [])\r\n\r\n        for field_config in form_fields:\r\n            # Safety check to ensure field_config is not None\r\n            if field_config is None:\r\n                continue\r\n\r\n            field_name = field_config.get(\"field_name\", \"\")\r\n            if field_name:\r\n                dynamic_input_name = f\"dynamic_{field_name}\"\r\n                value = getattr(self, dynamic_input_name, None)\r\n\r\n                # Extract simple values from connections or manual input\r\n                if value is not None:\r\n                    try:\r\n                        extracted_value = self._extract_simple_value(value)\r\n                        dynamic_values[field_name] = extracted_value\r\n\r\n                        # Determine connection type for status\r\n                        if hasattr(value, \"text\") and hasattr(value, \"timestamp\"):\r\n                            connection_info[field_name] = \"Connected (Message)\"\r\n                        elif hasattr(value, \"data\"):\r\n                            connection_info[field_name] = \"Connected (Data)\"\r\n                        elif isinstance(value, (str, int, float, bool, list, dict)):\r\n                            connection_info[field_name] = \"Manual input\"\r\n                        else:\r\n                            connection_info[field_name] = \"Connected (Object)\"\r\n\r\n                    except Exception:\r\n                        # Fallback to string representation if all else fails\r\n                        dynamic_values[field_name] = str(value)\r\n                        connection_info[field_name] = \"Error\"\r\n                else:\r\n                    # Use empty default value if nothing connected\r\n                    dynamic_values[field_name] = \"\"\r\n                    connection_info[field_name] = \"Empty default\"\r\n\r\n        # Store connection info for status output\r\n        self._connection_info = connection_info\r\n        return dynamic_values\r\n\r\n    def _extract_simple_value(self, value: Any) -> Any:\r\n        \"\"\"Extract the simplest, most useful value from any input type.\"\"\"\r\n        # Handle None\r\n        if value is None:\r\n            return None\r\n\r\n        # Handle simple types directly\r\n        if isinstance(value, (str, int, float, bool)):\r\n            return value\r\n\r\n        # Handle lists and tuples - keep simple\r\n        if isinstance(value, (list, tuple)):\r\n            return [self._extract_simple_value(item) for item in value]\r\n\r\n        # Handle dictionaries - keep simple\r\n        if isinstance(value, dict):\r\n            return {str(k): self._extract_simple_value(v) for k, v in value.items()}\r\n\r\n        # Handle Message objects - extract only the text\r\n        if hasattr(value, \"text\"):\r\n            return str(value.text) if value.text is not None else \"\"\r\n\r\n        # Handle Data objects - extract the data content\r\n        if hasattr(value, \"data\") and value.data is not None:\r\n            return self._extract_simple_value(value.data)\r\n\r\n        # For any other object, convert to string\r\n        return str(value)\r\n\r\n    def process_form(self) -> Data:\r\n        \"\"\"Process all dynamic form inputs and return clean data with just field values.\"\"\"\r\n        # Get all dynamic values (just the key:value pairs)\r\n        dynamic_values = self.get_dynamic_values()\r\n\r\n        # Update status with connection info\r\n        connected_fields = len([v for v in getattr(self, \"_connection_info\", {}).values() if \"Connected\" in v])\r\n        total_fields = len(dynamic_values)\r\n\r\n        self.status = f\"Form processed successfully. {connected_fields}/{total_fields} fields connected to components.\"\r\n\r\n        # Return clean Data object with just the field values\r\n        return Data(data=dynamic_values)\r\n\r\n    def get_message(self) -> Message:\r\n        \"\"\"Return form data as a formatted text message.\"\"\"\r\n        # Get all dynamic values\r\n        dynamic_values = self.get_dynamic_values()\r\n\r\n        if not dynamic_values:\r\n            return Message(text=\"No form data available\")\r\n\r\n        # Format as text message\r\n        message_lines = [\"📋 Form Data:\"]\r\n        message_lines.append(\"=\" * 40)\r\n\r\n        for field_name, value in dynamic_values.items():\r\n            # Use field_name as display_name\r\n            display_name = field_name\r\n\r\n            message_lines.append(f\"• {display_name}: {value}\")\r\n\r\n        message_lines.append(\"=\" * 40)\r\n        message_lines.append(f\"Total fields: {len(dynamic_values)}\")\r\n\r\n        message_text = \"\\n\".join(message_lines)\r\n        self.status = f\"Message formatted with {len(dynamic_values)} fields\"\r\n\r\n        return Message(text=message_text)"
+              },
+              "dynamic_connector_type": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "copy_field": false,
+                "display_name": "connector_type",
+                "dynamic": false,
+                "helper_text": null,
+                "info": " (Can connect to: Text, Message)",
+                "input_types": [
+                  "Text",
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "dynamic_connector_type",
+                "placeholder": "",
+                "real_time_refresh": null,
+                "refresh_button": null,
+                "refresh_button_text": null,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "dynamic_owner": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "copy_field": false,
+                "display_name": "owner",
+                "dynamic": false,
+                "helper_text": null,
+                "info": " (Can connect to: Text, Message)",
+                "input_types": [
+                  "Text",
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "dynamic_owner",
+                "placeholder": "",
+                "real_time_refresh": null,
+                "refresh_button": null,
+                "refresh_button_text": null,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "dynamic_owner_email": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "copy_field": false,
+                "display_name": "owner_email",
+                "dynamic": false,
+                "helper_text": null,
+                "info": " (Can connect to: Text, Message)",
+                "input_types": [
+                  "Text",
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "dynamic_owner_email",
+                "placeholder": "",
+                "real_time_refresh": null,
+                "refresh_button": null,
+                "refresh_button_text": null,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "dynamic_owner_name": {
+                "_input_type": "MultilineInput",
+                "advanced": false,
+                "copy_field": false,
+                "display_name": "owner_name",
+                "dynamic": false,
+                "helper_text": null,
+                "info": " (Can connect to: Text, Message)",
+                "input_types": [
+                  "Text",
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "multiline": true,
+                "name": "dynamic_owner_name",
+                "placeholder": "",
+                "real_time_refresh": null,
+                "refresh_button": null,
+                "refresh_button_text": null,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "form_fields": {
+                "_input_type": "TableInput",
+                "advanced": false,
+                "display_name": "Input Configuration",
+                "dynamic": false,
+                "info": "Define the dynamic form fields. Each row creates a new input field that can connect to other components.",
+                "is_list": true,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "form_fields",
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": false,
+                "show": true,
+                "table_icon": "Table",
+                "table_schema": {
+                  "columns": [
+                    {
+                      "default": "None",
+                      "description": "Name for the field (used as both internal name and display label)",
+                      "disable_edit": false,
+                      "display_name": "Field Name",
+                      "edit_mode": "popover",
+                      "filterable": true,
+                      "formatter": "text",
+                      "hidden": false,
+                      "name": "field_name",
+                      "sortable": true,
+                      "type": "str"
+                    },
+                    {
+                      "default": "None",
+                      "description": "Type of input field to create",
+                      "disable_edit": false,
+                      "display_name": "Field Type",
+                      "edit_mode": "popover",
+                      "filterable": true,
+                      "formatter": "text",
+                      "hidden": false,
+                      "name": "field_type",
+                      "options": [
+                        "Text",
+                        "Data",
+                        "Number",
+                        "Handle",
+                        "Boolean"
+                      ],
+                      "sortable": true,
+                      "type": "str"
+                    }
+                  ]
+                },
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "trigger_icon": "Table",
+                "trigger_text": "Open table",
+                "type": "table",
+                "value": [
+                  {
+                    "field_name": "owner",
+                    "field_type": "Text"
+                  },
+                  {
+                    "field_name": "owner_name",
+                    "field_type": "Text"
+                  },
+                  {
+                    "field_name": "owner_email",
+                    "field_type": "Text"
+                  },
+                  {
+                    "field_name": "connector_type",
+                    "field_type": "Text"
+                  }
+                ]
+              },
+              "include_metadata": {
+                "_input_type": "BoolInput",
+                "advanced": true,
+                "display_name": "Include Metadata",
+                "dynamic": false,
+                "info": "Include form configuration metadata in the output.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "include_metadata",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "bool",
+                "value": false
+              }
+            },
+            "tool_mode": false
+          },
+          "selected_output": "form_data",
+          "showNode": true,
+          "type": "AdvancedDynamicFormBuilder"
+        },
+        "dragging": false,
+        "id": "AdvancedDynamicFormBuilder-rDFKw",
+        "measured": {
+          "height": 552,
+          "width": 320
+        },
+        "position": {
+          "x": 1302.3946037119863,
+          "y": 2189.6234403785384
+        },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "SecretInput-GddHQ",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Allows the selection of a secret to be generated as output..",
+            "display_name": "Secret Input",
+            "documentation": "https://docs.langflow.org/components-io#text-input",
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
+            "frozen": false,
+            "icon": "type",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Output Text",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "text_response",
+                "name": "text",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.text import TextComponent\r\nfrom langflow.io import MultilineInput, Output, SecretStrInput\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass SecretInputComponent(TextComponent):\r\n    display_name = \"Secret Input\"\r\n    description = \"Allows the selection of a secret to be generated as output..\"\r\n    documentation: str = \"https://docs.langflow.org/components-io#text-input\"\r\n    icon = \"type\"\r\n    name = \"SecretInput\"\r\n\r\n    inputs = [\r\n        SecretStrInput(\r\n            name=\"input_value\",\r\n            display_name=\"Secret\",\r\n            info=\"Secret to be passed as input.\",\r\n        ),\r\n    ]\r\n    outputs = [\r\n        Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\r\n    ]\r\n\r\n    def text_response(self) -> Message:\r\n        return Message(\r\n            text=self.input_value,\r\n        )\r\n"
+              },
+              "input_value": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Secret",
+                "dynamic": false,
+                "info": "Secret to be passed as input.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "input_value",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "CONNECTOR_TYPE"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "SecretInput"
+        },
+        "dragging": false,
+        "id": "SecretInput-GddHQ",
+        "measured": {
+          "height": 220,
+          "width": 320
+        },
+        "position": {
+          "x": 577.3448254571293,
+          "y": 2047.4618586798306
+        },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "SecretInput-8QSeL",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Allows the selection of a secret to be generated as output..",
+            "display_name": "Secret Input",
+            "documentation": "https://docs.langflow.org/components-io#text-input",
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
+            "frozen": false,
+            "icon": "type",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Output Text",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "text_response",
+                "name": "text",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.text import TextComponent\r\nfrom langflow.io import MultilineInput, Output, SecretStrInput\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass SecretInputComponent(TextComponent):\r\n    display_name = \"Secret Input\"\r\n    description = \"Allows the selection of a secret to be generated as output..\"\r\n    documentation: str = \"https://docs.langflow.org/components-io#text-input\"\r\n    icon = \"type\"\r\n    name = \"SecretInput\"\r\n\r\n    inputs = [\r\n        SecretStrInput(\r\n            name=\"input_value\",\r\n            display_name=\"Secret\",\r\n            info=\"Secret to be passed as input.\",\r\n        ),\r\n    ]\r\n    outputs = [\r\n        Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\r\n    ]\r\n\r\n    def text_response(self) -> Message:\r\n        return Message(\r\n            text=self.input_value,\r\n        )\r\n"
+              },
+              "input_value": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Secret",
+                "dynamic": false,
+                "info": "Secret to be passed as input.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "input_value",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "OWNER"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "SecretInput"
+        },
+        "dragging": false,
+        "id": "SecretInput-8QSeL",
+        "measured": {
+          "height": 220,
+          "width": 320
+        },
+        "position": {
+          "x": 577.7412962636911,
+          "y": 2323.7696210844183
+        },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "SecretInput-qdu4S",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Allows the selection of a secret to be generated as output..",
+            "display_name": "Secret Input",
+            "documentation": "https://docs.langflow.org/components-io#text-input",
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
+            "frozen": false,
+            "icon": "type",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Output Text",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "text_response",
+                "name": "text",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.text import TextComponent\r\nfrom langflow.io import MultilineInput, Output, SecretStrInput\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass SecretInputComponent(TextComponent):\r\n    display_name = \"Secret Input\"\r\n    description = \"Allows the selection of a secret to be generated as output..\"\r\n    documentation: str = \"https://docs.langflow.org/components-io#text-input\"\r\n    icon = \"type\"\r\n    name = \"SecretInput\"\r\n\r\n    inputs = [\r\n        SecretStrInput(\r\n            name=\"input_value\",\r\n            display_name=\"Secret\",\r\n            info=\"Secret to be passed as input.\",\r\n        ),\r\n    ]\r\n    outputs = [\r\n        Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\r\n    ]\r\n\r\n    def text_response(self) -> Message:\r\n        return Message(\r\n            text=self.input_value,\r\n        )\r\n"
+              },
+              "input_value": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Secret",
+                "dynamic": false,
+                "info": "Secret to be passed as input.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "input_value",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "OWNER_EMAIL"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "SecretInput"
+        },
+        "dragging": false,
+        "id": "SecretInput-qdu4S",
+        "measured": {
+          "height": 220,
+          "width": 320
+        },
+        "position": {
+          "x": 573.1374442858917,
+          "y": 2610.3226902967785
+        },
+        "selected": false,
+        "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "SecretInput-p9iHD",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Allows the selection of a secret to be generated as output..",
+            "display_name": "Secret Input",
+            "documentation": "https://docs.langflow.org/components-io#text-input",
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
+            "frozen": false,
+            "icon": "type",
+            "legacy": false,
+            "lf_version": "1.6.0",
+            "metadata": {},
+            "minimized": false,
+            "output_types": [],
+            "outputs": [
+              {
+                "allows_loop": false,
+                "cache": true,
+                "display_name": "Output Text",
+                "group_outputs": false,
+                "hidden": null,
+                "method": "text_response",
+                "name": "text",
+                "options": null,
+                "required_inputs": null,
+                "selected": "Message",
+                "tool_mode": true,
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.text import TextComponent\r\nfrom langflow.io import MultilineInput, Output, SecretStrInput\r\nfrom langflow.schema.message import Message\r\n\r\n\r\nclass SecretInputComponent(TextComponent):\r\n    display_name = \"Secret Input\"\r\n    description = \"Allows the selection of a secret to be generated as output..\"\r\n    documentation: str = \"https://docs.langflow.org/components-io#text-input\"\r\n    icon = \"type\"\r\n    name = \"SecretInput\"\r\n\r\n    inputs = [\r\n        SecretStrInput(\r\n            name=\"input_value\",\r\n            display_name=\"Secret\",\r\n            info=\"Secret to be passed as input.\",\r\n        ),\r\n    ]\r\n    outputs = [\r\n        Output(display_name=\"Output Text\", name=\"text\", method=\"text_response\"),\r\n    ]\r\n\r\n    def text_response(self) -> Message:\r\n        return Message(\r\n            text=self.input_value,\r\n        )\r\n"
+              },
+              "input_value": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "Secret",
+                "dynamic": false,
+                "info": "Secret to be passed as input.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "input_value",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "OWNER_NAME"
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "SecretInput"
+        },
+        "dragging": false,
+        "id": "SecretInput-p9iHD",
+        "measured": {
+          "height": 220,
+          "width": 320
+        },
+        "position": {
+          "x": 591.2840506536836,
+          "y": 2932.4192840543737
+        },
+        "selected": false,
         "type": "genericNode"
       }
     ],
     "viewport": {
-      "x": -767.6929603556041,
-      "y": -1196.6455082358875,
-      "zoom": 0.9277466102702023
+      "x": 36.02462669663555,
+      "y": -593.1219298624992,
+      "zoom": 0.30094376899529046
     }
   },
   "description": "Load your data for chat context with Retrieval Augmented Generation.",
   "endpoint_name": null,
   "id": "1402618b-e6d1-4ff2-9a11-d6ce71186915",
   "is_component": false,
-  "last_tested_version": "1.5.0.post2",
+  "last_tested_version": "1.6.0",
   "name": "OpenSearch Ingestion Flow Docling Serve",
   "tags": [
     "openai",
diff --git a/flows/openrag_nudges.json b/flows/openrag_nudges.json
index 77839d0b..95ff9172 100644
--- a/flows/openrag_nudges.json
+++ b/flows/openrag_nudges.json
@@ -57,35 +57,6 @@
         "target": "Prompt Template-Wo6kR",
         "targetHandle": "{œfieldNameœ:œdocsœ,œidœ:œPrompt Template-Wo6kRœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"
       },
-      {
-        "animated": false,
-        "className": "",
-        "data": {
-          "sourceHandle": {
-            "dataType": "OpenSearchHybrid",
-            "id": "OpenSearch-iYfjf",
-            "name": "dataframe",
-            "output_types": [
-              "DataFrame"
-            ]
-          },
-          "targetHandle": {
-            "fieldName": "input_data",
-            "id": "ParserComponent-tZs7s",
-            "inputTypes": [
-              "DataFrame",
-              "Data"
-            ],
-            "type": "other"
-          }
-        },
-        "id": "xy-edge__OpenSearch-iYfjf{œdataTypeœ:œOpenSearchHybridœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œdataframeœ,œoutput_typesœ:[œDataFrameœ]}-ParserComponent-tZs7s{œfieldNameœ:œinput_dataœ,œidœ:œParserComponent-tZs7sœ,œinputTypesœ:[œDataFrameœ,œDataœ],œtypeœ:œotherœ}",
-        "selected": false,
-        "source": "OpenSearch-iYfjf",
-        "sourceHandle": "{œdataTypeœ:œOpenSearchHybridœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œdataframeœ,œoutput_typesœ:[œDataFrameœ]}",
-        "target": "ParserComponent-tZs7s",
-        "targetHandle": "{œfieldNameœ:œinput_dataœ,œidœ:œParserComponent-tZs7sœ,œinputTypesœ:[œDataFrameœ,œDataœ],œtypeœ:œotherœ}"
-      },
       {
         "animated": false,
         "className": "",
@@ -116,6 +87,7 @@
       },
       {
         "animated": false,
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "LanguageModelComponent",
@@ -145,6 +117,7 @@
       },
       {
         "animated": false,
+        "className": "",
         "data": {
           "sourceHandle": {
             "dataType": "Prompt Template",
@@ -169,23 +142,53 @@
         "sourceHandle": "{œdataTypeœ:œPrompt Templateœ,œidœ:œPrompt Template-Wo6kRœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}",
         "target": "LanguageModelComponent-NSTA6",
         "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œLanguageModelComponent-NSTA6œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "animated": false,
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "OpenSearchVectorStoreComponent",
+            "id": "OpenSearch-iYfjf",
+            "name": "dataframe",
+            "output_types": [
+              "DataFrame"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "input_data",
+            "id": "ParserComponent-tZs7s",
+            "inputTypes": [
+              "DataFrame",
+              "Data"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "xy-edge__OpenSearch-iYfjf{œdataTypeœ:œOpenSearchVectorStoreComponentœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œdataframeœ,œoutput_typesœ:[œDataFrameœ]}-ParserComponent-tZs7s{œfieldNameœ:œinput_dataœ,œidœ:œParserComponent-tZs7sœ,œinputTypesœ:[œDataFrameœ,œDataœ],œtypeœ:œotherœ}",
+        "selected": false,
+        "source": "OpenSearch-iYfjf",
+        "sourceHandle": "{œdataTypeœ:œOpenSearchVectorStoreComponentœ,œidœ:œOpenSearch-iYfjfœ,œnameœ:œdataframeœ,œoutput_typesœ:[œDataFrameœ]}",
+        "target": "ParserComponent-tZs7s",
+        "targetHandle": "{œfieldNameœ:œinput_dataœ,œidœ:œParserComponent-tZs7sœ,œinputTypesœ:[œDataFrameœ,œDataœ],œtypeœ:œotherœ}"
       }
     ],
     "nodes": [
       {
         "data": {
+          "description": "Get chat inputs from the Playground.",
+          "display_name": "Chat Input",
           "id": "ChatInput-bqH7H",
           "node": {
             "base_classes": [
               "Message"
             ],
             "beta": false,
-            "category": "inputs",
             "conditional_paths": [],
             "custom_fields": {},
             "description": "Get chat inputs from the Playground.",
             "display_name": "Chat Input",
-            "documentation": "",
+            "documentation": "https://docs.langflow.org/components-io#chat-input",
             "edited": false,
             "field_order": [
               "input_value",
@@ -193,17 +196,24 @@
               "sender",
               "sender_name",
               "session_id",
-              "files",
-              "background_color",
-              "chat_icon",
-              "text_color"
+              "files"
             ],
             "frozen": false,
             "icon": "MessagesSquare",
-            "key": "ChatInput",
             "legacy": false,
-            "lf_version": "1.5.0.post1",
-            "metadata": {},
+            "metadata": {
+              "code_hash": "f701f686b325",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "lfx",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 1
+              },
+              "module": "custom_components.chat_input"
+            },
             "minimized": true,
             "output_types": [],
             "outputs": [
@@ -214,6 +224,8 @@
                 "group_outputs": false,
                 "method": "message_response",
                 "name": "message",
+                "options": null,
+                "required_inputs": null,
                 "selected": "Message",
                 "tool_mode": true,
                 "types": [
@@ -223,55 +235,8 @@
               }
             ],
             "pinned": false,
-            "score": 0.0020353564437605998,
             "template": {
               "_type": "Component",
-              "background_color": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Background Color",
-                "dynamic": false,
-                "info": "The background color of the icon.",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "background_color",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "chat_icon": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Icon",
-                "dynamic": false,
-                "info": "The icon of the message.",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "chat_icon",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -288,7 +253,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs.inputs import BoolInput\nfrom langflow.io import (\n    DropdownInput,\n    FileInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n)\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_USER,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n    minimized = True\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n            input_types=[],\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n            temp_file=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\n                \"background_color\": background_color,\n                \"text_color\": text_color,\n                \"icon\": icon,\n            },\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from lfx.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import (\n    DropdownInput,\n    FileInput,\n    MessageTextInput,\n    MultilineInput,\n    Output,\n)\nfrom lfx.schema.message import Message\nfrom lfx.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_USER,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n    minimized = True\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Input Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n            input_types=[],\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n            temp_file=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        # Ensure files is a list and filter out empty/None values\n        files = self.files if self.files else []\n        if files and not isinstance(files, list):\n            files = [files]\n        # Filter out None/empty values\n        files = [f for f in files if f is not None and f != \"\"]\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=files,\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -296,17 +261,17 @@
                 "display_name": "Files",
                 "dynamic": false,
                 "fileTypes": [
+                  "csv",
+                  "json",
+                  "pdf",
                   "txt",
                   "md",
                   "mdx",
-                  "csv",
-                  "json",
                   "yaml",
                   "yml",
                   "xml",
                   "html",
                   "htm",
-                  "pdf",
                   "docx",
                   "py",
                   "sh",
@@ -364,6 +329,7 @@
                 "dialog_inputs": {},
                 "display_name": "Sender Type",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Type of sender.",
                 "name": "sender",
                 "options": [
@@ -375,6 +341,7 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
+                "toggle": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
                 "type": "str",
@@ -443,29 +410,6 @@
                 "trace_as_metadata": true,
                 "type": "bool",
                 "value": true
-              },
-              "text_color": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Text Color",
-                "dynamic": false,
-                "info": "The text color of the name",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "text_color",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
               }
             },
             "tool_mode": false
@@ -489,18 +433,19 @@
       },
       {
         "data": {
+          "description": "Display a chat message in the Playground.",
+          "display_name": "Chat Output",
           "id": "ChatOutput-BMVN5",
           "node": {
             "base_classes": [
               "Message"
             ],
             "beta": false,
-            "category": "outputs",
             "conditional_paths": [],
             "custom_fields": {},
             "description": "Display a chat message in the Playground.",
             "display_name": "Chat Output",
-            "documentation": "",
+            "documentation": "https://docs.langflow.org/components-io#chat-output",
             "edited": false,
             "field_order": [
               "input_value",
@@ -509,17 +454,32 @@
               "sender_name",
               "session_id",
               "data_template",
-              "background_color",
-              "chat_icon",
-              "text_color",
               "clean_data"
             ],
             "frozen": false,
             "icon": "MessagesSquare",
-            "key": "ChatOutput",
             "legacy": false,
-            "lf_version": "1.5.0.post1",
-            "metadata": {},
+            "metadata": {
+              "code_hash": "9647f4d2f4b4",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "orjson",
+                    "version": "3.10.15"
+                  },
+                  {
+                    "name": "fastapi",
+                    "version": "0.117.1"
+                  },
+                  {
+                    "name": "lfx",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 3
+              },
+              "module": "custom_components.chat_output"
+            },
             "minimized": true,
             "output_types": [],
             "outputs": [
@@ -530,6 +490,8 @@
                 "group_outputs": false,
                 "method": "message_response",
                 "name": "message",
+                "options": null,
+                "required_inputs": null,
                 "selected": "Message",
                 "tool_mode": true,
                 "types": [
@@ -539,61 +501,14 @@
               }
             ],
             "pinned": false,
-            "score": 0.003169567463043492,
             "template": {
               "_type": "Component",
-              "background_color": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Background Color",
-                "dynamic": false,
-                "info": "The background color of the icon.",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "background_color",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "chat_icon": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Icon",
-                "dynamic": false,
-                "info": "The icon of the message.",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "chat_icon",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
               "clean_data": {
                 "_input_type": "BoolInput",
                 "advanced": true,
                 "display_name": "Basic Clean Data",
                 "dynamic": false,
-                "info": "Whether to clean the data",
+                "info": "Whether to clean data before converting to string.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "clean_data",
@@ -622,7 +537,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.helpers.data import safe_convert\nfrom langflow.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.template.field.base import Output\nfrom langflow.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Inputs\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            info=\"Whether to clean the data\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Output Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n\n        # Get source properties\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return safe_convert(self.input_value)\n"
+                "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n    MESSAGE_SENDER_AI,\n    MESSAGE_SENDER_NAME_AI,\n    MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n    minimized = True\n\n    inputs = [\n        HandleInput(\n            name=\"input_value\",\n            display_name=\"Inputs\",\n            info=\"Message to be passed as output.\",\n            input_types=[\"Data\", \"DataFrame\", \"Message\"],\n            required=True,\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        BoolInput(\n            name=\"clean_data\",\n            display_name=\"Basic Clean Data\",\n            value=True,\n            advanced=True,\n            info=\"Whether to clean data before converting to string.\",\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Output Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            # Handle case where source is a ChatOpenAI object\n            if hasattr(source, \"model_name\"):\n                source_dict[\"source\"] = source.model_name\n            elif hasattr(source, \"model\"):\n                source_dict[\"source\"] = str(source.model)\n            else:\n                source_dict[\"source\"] = str(source)\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        # First convert the input to string if needed\n        text = self.convert_to_string()\n\n        # Get source properties\n        source, _, display_name, source_id = self.get_properties_from_source_component()\n\n        # Create or use existing Message object\n        if isinstance(self.input_value, Message):\n            message = self.input_value\n            # Update message properties\n            message.text = text\n        else:\n            message = Message(text=text)\n\n        # Set message properties\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n\n        # Store message if needed\n        if self.session_id and self.should_store_message:\n            stored_message = await self.send_message(message)\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n\n    def _serialize_data(self, data: Data) -> str:\n        \"\"\"Serialize Data object to JSON string.\"\"\"\n        # Convert data.data to JSON-serializable format\n        serializable_data = jsonable_encoder(data.data)\n        # Serialize with orjson, enabling pretty printing with indentation\n        json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n        # Convert bytes to string and wrap in Markdown code blocks\n        return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n    def _validate_input(self) -> None:\n        \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n        if self.input_value is None:\n            msg = \"Input data cannot be None\"\n            raise ValueError(msg)\n        if isinstance(self.input_value, list) and not all(\n            isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n        ):\n            invalid_types = [\n                type(item).__name__\n                for item in self.input_value\n                if not isinstance(item, Message | Data | DataFrame | str)\n            ]\n            msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n            raise TypeError(msg)\n        if not isinstance(\n            self.input_value,\n            Message | Data | DataFrame | str | list | Generator | type(None),\n        ):\n            type_name = type(self.input_value).__name__\n            msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n            raise TypeError(msg)\n\n    def convert_to_string(self) -> str | Generator[Any, None, None]:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        self._validate_input()\n        if isinstance(self.input_value, list):\n            clean_data: bool = getattr(self, \"clean_data\", False)\n            return \"\\n\".join([safe_convert(item, clean_data=clean_data) for item in self.input_value])\n        if isinstance(self.input_value, Generator):\n            return self.input_value\n        return safe_convert(self.input_value)\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -676,6 +591,7 @@
                 "dialog_inputs": {},
                 "display_name": "Sender Type",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Type of sender.",
                 "name": "sender",
                 "options": [
@@ -687,6 +603,7 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
+                "toggle": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
                 "type": "str",
@@ -755,29 +672,6 @@
                 "trace_as_metadata": true,
                 "type": "bool",
                 "value": true
-              },
-              "text_color": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Text Color",
-                "dynamic": false,
-                "info": "The text color of the name",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "text_color",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
               }
             },
             "tool_mode": false
@@ -810,13 +704,18 @@
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
-            "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
-            "display_name": "OpenSearch (Hybrid)",
+            "description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
+            "display_name": "OpenSearch",
             "documentation": "",
             "edited": true,
             "field_order": [
+              "docs_metadata",
               "opensearch_url",
               "index_name",
+              "engine",
+              "space_type",
+              "ef_construction",
+              "m",
               "ingest_data",
               "search_query",
               "should_cache_vector_store",
@@ -835,10 +734,24 @@
             ],
             "frozen": false,
             "icon": "OpenSearch",
-            "last_updated": "2025-09-04T19:50:05.460Z",
             "legacy": false,
-            "lf_version": "1.5.0.post2",
-            "metadata": {},
+            "metadata": {
+              "code_hash": "c81b23acb81a",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "opensearchpy",
+                    "version": "2.8.0"
+                  },
+                  {
+                    "name": "lfx",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 2
+              },
+              "module": "custom_components.opensearch"
+            },
             "minimized": false,
             "output_types": [],
             "outputs": [
@@ -852,6 +765,7 @@
                 "name": "search_results",
                 "options": null,
                 "required_inputs": null,
+                "selected": "Data",
                 "tool_mode": true,
                 "types": [
                   "Data"
@@ -880,11 +794,12 @@
                 "cache": true,
                 "display_name": "Vector Store Connection",
                 "group_outputs": false,
-                "hidden": true,
+                "hidden": false,
                 "method": "as_vector_store",
                 "name": "vectorstoreconnection",
                 "options": null,
                 "required_inputs": null,
+                "selected": "VectorStore",
                 "tool_mode": true,
                 "types": [
                   "VectorStore"
@@ -900,9 +815,10 @@
                 "advanced": false,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Auth Mode",
+                "display_name": "Authentication Mode",
                 "dynamic": false,
-                "info": "Choose Basic (username/password) or JWT (Bearer token).",
+                "external_options": {},
+                "info": "Authentication method: 'basic' for username/password authentication, or 'jwt' for JSON Web Token (Bearer) authentication.",
                 "load_from_db": false,
                 "name": "auth_mode",
                 "options": [
@@ -932,7 +848,7 @@
                 "name": "bearer_prefix",
                 "placeholder": "",
                 "required": false,
-                "show": true,
+                "show": false,
                 "title_case": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
@@ -955,7 +871,65 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from __future__ import annotations\n\nimport json\nfrom typing import Any, Dict, List\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom langflow.io import (\n    BoolInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n    DropdownInput,\n)\nfrom langflow.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchHybridComponent(LCVectorStoreComponent):\n    \"\"\"OpenSearch hybrid search: KNN (k=10, boost=0.7) + multi_match (boost=0.3) with optional filters & min_score.\"\"\"\n    display_name: str = \"OpenSearch (Hybrid)\"\n    name: str = \"OpenSearchHybrid\"\n    icon: str = \"OpenSearch\"\n    description: str = \"Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.\"\n\n    # Keys we consider baseline\n    default_keys: list[str] = [\n        \"opensearch_url\",\n        \"index_name\",\n        *[i.name for i in LCVectorStoreComponent.inputs],  # search_query, add_documents, etc.\n        \"embedding\",\n        \"vector_field\",\n        \"number_of_results\",\n        \"auth_mode\",\n        \"username\",\n        \"password\",\n        \"jwt_token\",\n        \"jwt_header\",\n        \"bearer_prefix\",\n        \"use_ssl\",\n        \"verify_certs\",\n        \"filter_expression\",\n    ]\n\n    inputs = [\n        StrInput(\n            name=\"opensearch_url\",\n            display_name=\"OpenSearch URL\",\n            value=\"http://localhost:9200\",\n            info=\"URL for your OpenSearch cluster.\"\n        ),\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow\",\n            info=\"The index to search.\"\n        ),\n        *LCVectorStoreComponent.inputs,  # includes search_query, add_documents, etc.\n        HandleInput(\n            name=\"embedding\",\n            display_name=\"Embedding\",\n            input_types=[\"Embeddings\"]\n        ),\n        StrInput(\n            name=\"vector_field\",\n            display_name=\"Vector Field\",\n            value=\"chunk_embedding\",\n            advanced=True,\n            info=\"Vector field used for KNN.\"\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Default Size (limit)\",\n            value=10,\n            advanced=True,\n            info=\"Default number of hits when no limit provided in filter_expression.\"\n        ),\n        MultilineInput(\n            name=\"filter_expression\",\n            display_name=\"Filter Expression (JSON)\",\n            value=\"\",\n            info=(\n                \"Optional JSON to control filters/limit/score threshold.\\n\"\n                \"Accepted shapes:\\n\"\n                '1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\\n'\n                '2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\\n'\n                \"Placeholders with __IMPOSSIBLE_VALUE__ are ignored.\"\n            )\n        ),\n\n        # ----- Auth controls (dynamic) -----\n        DropdownInput(\n            name=\"auth_mode\",\n            display_name=\"Auth Mode\",\n            value=\"basic\",\n            options=[\"basic\", \"jwt\"],\n            info=\"Choose Basic (username/password) or JWT (Bearer token).\",\n            real_time_refresh=True,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"username\",\n            display_name=\"Username\",\n            value=\"admin\",\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"password\",\n            display_name=\"Password\",\n            value=\"admin\",\n            show=True,\n        ),\n        SecretStrInput(\n            name=\"jwt_token\",\n            display_name=\"JWT Token\",\n            value=\"\",\n            show=False,\n            info=\"Paste a valid JWT (sent as a header).\",\n        ),\n        StrInput(\n            name=\"jwt_header\",\n            display_name=\"JWT Header Name\",\n            value=\"Authorization\",\n            show=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"bearer_prefix\",\n            display_name=\"Prefix 'Bearer '\",\n            value=True,\n            show=False,\n            advanced=True,\n        ),\n\n        # ----- TLS -----\n        BoolInput(\n            name=\"use_ssl\",\n            display_name=\"Use SSL\",\n            value=True,\n            advanced=True\n        ),\n        BoolInput(\n            name=\"verify_certs\",\n            display_name=\"Verify Certificates\",\n            value=False,\n            advanced=True\n        ),\n    ]\n\n    # ---------- auth / client ----------\n    def _build_auth_kwargs(self) -> Dict[str, Any]:\n        mode = (self.auth_mode or \"basic\").strip().lower()\n        if mode == \"jwt\":\n            token = (self.jwt_token or \"\").strip()\n            if not token:\n                raise ValueError(\"Auth Mode is 'jwt' but no jwt_token was provided.\")\n            header_name = (self.jwt_header or \"Authorization\").strip()\n            header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n            return {\"headers\": {header_name: header_value}}\n        user = (self.username or \"\").strip()\n        pwd = (self.password or \"\").strip()\n        if not user or not pwd:\n            raise ValueError(\"Auth Mode is 'basic' but username/password are missing.\")\n        return {\"http_auth\": (user, pwd)}\n\n    def build_client(self) -> OpenSearch:\n        auth_kwargs = self._build_auth_kwargs()\n        return OpenSearch(\n            hosts=[self.opensearch_url],\n            use_ssl=self.use_ssl,\n            verify_certs=self.verify_certs,\n            ssl_assert_hostname=False,\n            ssl_show_warn=False,\n            **auth_kwargs,\n        )\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> OpenSearch:\n        # Return raw OpenSearch client as our “vector store.”\n        return self.build_client()\n\n    # ---------- ingest ----------\n    def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n        docs = self._prepare_ingest_data() or []\n        if not docs:\n            self.log(\"No documents to ingest.\")\n            return\n\n        texts = [d.to_lc_document().page_content for d in docs]\n        if not self.embedding:\n            raise ValueError(\"Embedding handle is required to embed documents.\")\n        vectors = self.embedding.embed_documents(texts)\n\n        actions = []\n        for doc_obj, vec in zip(docs, vectors):\n            lc_doc = doc_obj.to_lc_document()\n            body = {\n                **lc_doc.metadata,\n                \"text\": lc_doc.page_content,\n                self.vector_field: vec,\n            }\n            actions.append({\n                \"_op_type\": \"index\",\n                \"_index\": self.index_name,\n                \"_source\": body,\n            })\n\n        self.log(f\"Indexing {len(actions)} docs into '{self.index_name}'…\")\n        helpers.bulk(client, actions)\n\n    # ---------- helpers for filters ----------\n    def _is_placeholder_term(self, term_obj: dict) -> bool:\n        # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n        return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n    def _coerce_filter_clauses(self, filter_obj: dict | None) -> List[dict]:\n        \"\"\"\n        Accepts either:\n          A) {\"filter\":[ ...term/terms objects... ], \"limit\":..., \"score_threshold\":...}\n          B) Context-style: {\"data_sources\":[...], \"document_types\":[...], \"owners\":[...]}\n        Returns a list of OS filter clauses (term/terms), skipping placeholders and empty terms.\n        \"\"\"\n        \n        if not filter_obj:\n            return []\n\n        # If it’s a string, try to parse it once\n        if isinstance(filter_obj, str):\n            try:\n                filter_obj = json.loads(filter_obj)\n            except Exception:\n                # Not valid JSON → treat as no filters\n                return []\n            \n        # Case A: already an explicit list/dict under \"filter\"\n        if \"filter\" in filter_obj:\n            raw = filter_obj[\"filter\"]\n            if isinstance(raw, dict):\n                raw = [raw]\n            clauses: List[dict] = []\n            for f in (raw or []):\n                if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n                    clauses.append(f)\n                elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n                    field, vals = next(iter(f[\"terms\"].items()))\n                    if isinstance(vals, list) and len(vals) > 0:\n                        clauses.append(f)\n            return clauses\n\n        # Case B: convert context-style maps into clauses\n        field_mapping = {\"data_sources\": \"filename\", \"document_types\": \"mimetype\", \"owners\": \"owner\"}\n        print(f\"filter_obj {filter_obj}\")\n        clauses: List[dict] = []\n        for k, values in filter_obj.items():\n            if not isinstance(values, list):\n                continue\n            field = field_mapping.get(k, k)\n            if len(values) == 0:\n                # Match-nothing placeholder (kept to mirror your tool semantics)\n                clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n            elif len(values) == 1:\n                if values[0] != \"__IMPOSSIBLE_VALUE__\":\n                    clauses.append({\"term\": {field: values[0]}})\n            else:\n                clauses.append({\"terms\": {field: values}})\n        return clauses\n\n    # ---------- search (single hybrid path matching your tool) ----------\n    def search(self, query: str | None = None) -> list[dict[str, Any]]:\n        print(\"search method\")\n        client = self.build_client()\n        q = (query or \"\").strip()\n\n        # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n        filter_obj = None\n        print(f\"DEBUG q {q}\")\n        if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n            try:\n                self.log(f\"DEBUG FILTER EXPRESSION {self.filter_expression}\")\n            except json.JSONDecodeError as e:\n                raise ValueError(f\"Invalid filter_expression JSON: {e}\") from e\n\n        if not self.embedding:\n            raise ValueError(\"Embedding is required to run hybrid search (KNN + keyword).\")\n\n        # Embed the query\n        vec = self.embedding.embed_query(q)\n\n        # Build filter clauses (accept both shapes)\n        clauses = self._coerce_filter_clauses(filter_obj)\n\n        # Respect the tool's limit/threshold defaults\n        limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n        score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n        print(f\"DEBUG clauses {clauses}\")\n        # Build the same hybrid body as your SearchService\n        body = {\n            \"query\": {\n                \"bool\": {\n                    \"should\": [\n                        {\n                            \"knn\": {\n                                self.vector_field: {\n                                    \"vector\": vec,\n                                    \"k\": 10,          # fixed to match the tool\n                                    \"boost\": 0.7\n                                }\n                            }\n                        },\n                        {\n                            \"multi_match\": {\n                                \"query\": q,\n                                \"fields\": [\"text^2\", \"filename^1.5\"],\n                                \"type\": \"best_fields\",\n                                \"fuzziness\": \"AUTO\",\n                                \"boost\": 0.3\n                            }\n                        }\n                    ],\n                    \"minimum_should_match\": 1\n                }\n            },\n            \"aggs\": {\n                \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n                \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n                \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}}\n            },\n            \"_source\": [\n                \"filename\", \"mimetype\", \"page\", \"text\", \"source_url\",\n                \"owner\", \"allowed_users\", \"allowed_groups\"\n            ],\n            \"size\": limit\n        }\n        print(f\"DEBUG BODY {body}\")\n        if clauses:\n            body[\"query\"][\"bool\"][\"filter\"] = clauses\n\n        if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n            # top-level min_score (matches your tool)\n            body[\"min_score\"] = score_threshold\n\n        print(f\"DEBUG HYBRID BODY {body}\")\n        resp = client.search(index=self.index_name, body=body)\n        hits = resp.get(\"hits\", {}).get(\"hits\", [])\n        return [\n            {\n                \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n                \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n                \"score\": hit.get(\"_score\"),\n            }\n            for hit in hits\n        ]\n\n    def search_documents(self) -> list[Data]:\n        try:\n            raw = self.search(self.search_query or \"\")\n            return [\n                Data(file_path=hit[\"metadata\"].get(\"file_path\", \"\"), text=hit[\"page_content\"])\n                for hit in raw\n            ]\n        except Exception as e:\n            print(f\"ERROR search_documents: {e}\")\n            self.log(f\"search_documents error: {e}\")\n            raise\n\n    # -------- dynamic UI handling (auth switch) --------\n    async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n        try:\n            if field_name == \"auth_mode\":\n                mode = (field_value or \"basic\").strip().lower()\n                is_basic = mode == \"basic\"\n                is_jwt = mode == \"jwt\"\n\n                build_config[\"username\"][\"show\"] = is_basic\n                build_config[\"password\"][\"show\"] = is_basic\n\n                build_config[\"jwt_token\"][\"show\"] = is_jwt\n                build_config[\"jwt_header\"][\"show\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n                build_config[\"username\"][\"required\"] = is_basic\n                build_config[\"password\"][\"required\"] = is_basic\n\n                build_config[\"jwt_token\"][\"required\"] = is_jwt\n                build_config[\"jwt_header\"][\"required\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"required\"] = False\n\n                if is_basic:\n                    build_config[\"jwt_token\"][\"value\"] = \"\"\n\n                return build_config\n\n            return build_config\n\n        except Exception as e:\n            self.log(f\"update_build_config error: {e}\")\n            return build_config\n"
+                "value": "from __future__ import annotations\n\nimport json\nimport uuid\nfrom typing import Any\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom lfx.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput, TableInput\nfrom lfx.log import logger\nfrom lfx.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchVectorStoreComponent(LCVectorStoreComponent):\n    \"\"\"OpenSearch Vector Store Component with Hybrid Search Capabilities.\n\n    This component provides vector storage and retrieval using OpenSearch, combining semantic\n    similarity search (KNN) with keyword-based search for optimal results. It supports document\n    ingestion, vector embeddings, and advanced filtering with authentication options.\n\n    Features:\n    - Vector storage with configurable engines (jvector, nmslib, faiss, lucene)\n    - Hybrid search combining KNN vector similarity and keyword matching\n    - Flexible authentication (Basic auth, JWT tokens)\n    - Advanced filtering and aggregations\n    - Metadata injection during document ingestion\n    \"\"\"\n\n    display_name: str = \"OpenSearch\"\n    icon: str = \"OpenSearch\"\n    description: str = (\n        \"Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.\"\n    )\n\n    # Keys we consider baseline\n    default_keys: list[str] = [\n        \"opensearch_url\",\n        \"index_name\",\n        *[i.name for i in LCVectorStoreComponent.inputs],  # search_query, add_documents, etc.\n        \"embedding\",\n        \"vector_field\",\n        \"number_of_results\",\n        \"auth_mode\",\n        \"username\",\n        \"password\",\n        \"jwt_token\",\n        \"jwt_header\",\n        \"bearer_prefix\",\n        \"use_ssl\",\n        \"verify_certs\",\n        \"filter_expression\",\n        \"engine\",\n        \"space_type\",\n        \"ef_construction\",\n        \"m\",\n        \"docs_metadata\",\n    ]\n\n    inputs = [\n        TableInput(\n            name=\"docs_metadata\",\n            display_name=\"Document Metadata\",\n            info=(\n                \"Additional metadata key-value pairs to be added to all ingested documents. \"\n                \"Useful for tagging documents with source information, categories, or other custom attributes.\"\n            ),\n            table_schema=[\n                {\n                    \"name\": \"key\",\n                    \"display_name\": \"Key\",\n                    \"type\": \"str\",\n                    \"description\": \"Key name\",\n                },\n                {\n                    \"name\": \"value\",\n                    \"display_name\": \"Value\",\n                    \"type\": \"str\",\n                    \"description\": \"Value of the metadata\",\n                },\n            ],\n            value=[],\n            # advanced=True,\n            input_types=[\"Data\"]\n        ),\n        StrInput(\n            name=\"opensearch_url\",\n            display_name=\"OpenSearch URL\",\n            value=\"http://localhost:9200\",\n            info=(\n                \"The connection URL for your OpenSearch cluster \"\n                \"(e.g., http://localhost:9200 for local development or your cloud endpoint).\"\n            ),\n        ),\n        StrInput(\n            name=\"index_name\",\n            display_name=\"Index Name\",\n            value=\"langflow\",\n            info=(\n                \"The OpenSearch index name where documents will be stored and searched. \"\n                \"Will be created automatically if it doesn't exist.\"\n            ),\n        ),\n        DropdownInput(\n            name=\"engine\",\n            display_name=\"Vector Engine\",\n            options=[\"jvector\", \"nmslib\", \"faiss\", \"lucene\"],\n            value=\"jvector\",\n            info=(\n                \"Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. \"\n                \"Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.\"\n            ),\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"space_type\",\n            display_name=\"Distance Metric\",\n            options=[\"l2\", \"l1\", \"cosinesimil\", \"linf\", \"innerproduct\"],\n            value=\"l2\",\n            info=(\n                \"Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, \"\n                \"'cosinesimil' for cosine similarity, 'innerproduct' for dot product.\"\n            ),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"ef_construction\",\n            display_name=\"EF Construction\",\n            value=512,\n            info=(\n                \"Size of the dynamic candidate list during index construction. \"\n                \"Higher values improve recall but increase indexing time and memory usage.\"\n            ),\n            advanced=True,\n        ),\n        IntInput(\n            name=\"m\",\n            display_name=\"M Parameter\",\n            value=16,\n            info=(\n                \"Number of bidirectional connections for each vector in the HNSW graph. \"\n                \"Higher values improve search quality but increase memory usage and indexing time.\"\n            ),\n            advanced=True,\n        ),\n        *LCVectorStoreComponent.inputs,  # includes search_query, add_documents, etc.\n        HandleInput(name=\"embedding\", display_name=\"Embedding\", input_types=[\"Embeddings\"]),\n        StrInput(\n            name=\"vector_field\",\n            display_name=\"Vector Field Name\",\n            value=\"chunk_embedding\",\n            advanced=True,\n            info=\"Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Default Result Limit\",\n            value=10,\n            advanced=True,\n            info=(\n                \"Default maximum number of search results to return when no limit is \"\n                \"specified in the filter expression.\"\n            ),\n        ),\n        MultilineInput(\n            name=\"filter_expression\",\n            display_name=\"Search Filters (JSON)\",\n            value=\"\",\n            info=(\n                \"Optional JSON configuration for search filtering, result limits, and score thresholds.\\n\\n\"\n                \"Format 1 - Explicit filters:\\n\"\n                '{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, '\n                '{\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\\n\\n'\n                \"Format 2 - Context-style mapping:\\n\"\n                '{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\\n\\n'\n                \"Use __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.\"\n            ),\n        ),\n        # ----- Auth controls (dynamic) -----\n        DropdownInput(\n            name=\"auth_mode\",\n            display_name=\"Authentication Mode\",\n            value=\"basic\",\n            options=[\"basic\", \"jwt\"],\n            info=(\n                \"Authentication method: 'basic' for username/password authentication, \"\n                \"or 'jwt' for JSON Web Token (Bearer) authentication.\"\n            ),\n            real_time_refresh=True,\n            advanced=False,\n        ),\n        StrInput(\n            name=\"username\",\n            display_name=\"Username\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"password\",\n            display_name=\"OpenSearch Password\",\n            value=\"admin\",\n            show=False,\n        ),\n        SecretStrInput(\n            name=\"jwt_token\",\n            display_name=\"JWT Token\",\n            value=\"JWT\",\n            load_from_db=False,\n            show=True,\n            info=(\n                \"Valid JSON Web Token for authentication. \"\n                \"Will be sent in the Authorization header (with optional 'Bearer ' prefix).\"\n            ),\n        ),\n        StrInput(\n            name=\"jwt_header\",\n            display_name=\"JWT Header Name\",\n            value=\"Authorization\",\n            show=False,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"bearer_prefix\",\n            display_name=\"Prefix 'Bearer '\",\n            value=True,\n            show=False,\n            advanced=True,\n        ),\n        # ----- TLS -----\n        BoolInput(\n            name=\"use_ssl\",\n            display_name=\"Use SSL/TLS\",\n            value=True,\n            advanced=True,\n            info=\"Enable SSL/TLS encryption for secure connections to OpenSearch.\",\n        ),\n        BoolInput(\n            name=\"verify_certs\",\n            display_name=\"Verify SSL Certificates\",\n            value=False,\n            advanced=True,\n            info=(\n                \"Verify SSL certificates when connecting. \"\n                \"Disable for self-signed certificates in development environments.\"\n            ),\n        ),\n    ]\n\n    # ---------- helper functions for index management ----------\n    def _default_text_mapping(\n        self,\n        dim: int,\n        engine: str = \"jvector\",\n        space_type: str = \"l2\",\n        ef_search: int = 512,\n        ef_construction: int = 100,\n        m: int = 16,\n        vector_field: str = \"vector_field\",\n    ) -> dict[str, Any]:\n        \"\"\"Create the default OpenSearch index mapping for vector search.\n\n        This method generates the index configuration with k-NN settings optimized\n        for approximate nearest neighbor search using the specified vector engine.\n\n        Args:\n            dim: Dimensionality of the vector embeddings\n            engine: Vector search engine (jvector, nmslib, faiss, lucene)\n            space_type: Distance metric for similarity calculation\n            ef_search: Size of dynamic list used during search\n            ef_construction: Size of dynamic list used during index construction\n            m: Number of bidirectional links for each vector\n            vector_field: Name of the field storing vector embeddings\n\n        Returns:\n            Dictionary containing OpenSearch index mapping configuration\n        \"\"\"\n        return {\n            \"settings\": {\"index\": {\"knn\": True, \"knn.algo_param.ef_search\": ef_search}},\n            \"mappings\": {\n                \"properties\": {\n                    vector_field: {\n                        \"type\": \"knn_vector\",\n                        \"dimension\": dim,\n                        \"method\": {\n                            \"name\": \"disk_ann\",\n                            \"space_type\": space_type,\n                            \"engine\": engine,\n                            \"parameters\": {\"ef_construction\": ef_construction, \"m\": m},\n                        },\n                    }\n                }\n            },\n        }\n\n    def _validate_aoss_with_engines(self, *, is_aoss: bool, engine: str) -> None:\n        \"\"\"Validate engine compatibility with Amazon OpenSearch Serverless (AOSS).\n\n        Amazon OpenSearch Serverless has restrictions on which vector engines\n        can be used. This method ensures the selected engine is compatible.\n\n        Args:\n            is_aoss: Whether the connection is to Amazon OpenSearch Serverless\n            engine: The selected vector search engine\n\n        Raises:\n            ValueError: If AOSS is used with an incompatible engine\n        \"\"\"\n        if is_aoss and engine not in {\"nmslib\", \"faiss\"}:\n            msg = \"Amazon OpenSearch Service Serverless only supports `nmslib` or `faiss` engines\"\n            raise ValueError(msg)\n\n    def _is_aoss_enabled(self, http_auth: Any) -> bool:\n        \"\"\"Determine if Amazon OpenSearch Serverless (AOSS) is being used.\n\n        Args:\n            http_auth: The HTTP authentication object\n\n        Returns:\n            True if AOSS is enabled, False otherwise\n        \"\"\"\n        return http_auth is not None and hasattr(http_auth, \"service\") and http_auth.service == \"aoss\"\n\n    def _bulk_ingest_embeddings(\n        self,\n        client: OpenSearch,\n        index_name: str,\n        embeddings: list[list[float]],\n        texts: list[str],\n        metadatas: list[dict] | None = None,\n        ids: list[str] | None = None,\n        vector_field: str = \"vector_field\",\n        text_field: str = \"text\",\n        mapping: dict | None = None,\n        max_chunk_bytes: int | None = 1 * 1024 * 1024,\n        *,\n        is_aoss: bool = False,\n    ) -> list[str]:\n        \"\"\"Efficiently ingest multiple documents with embeddings into OpenSearch.\n\n        This method uses bulk operations to insert documents with their vector\n        embeddings and metadata into the specified OpenSearch index.\n\n        Args:\n            client: OpenSearch client instance\n            index_name: Target index for document storage\n            embeddings: List of vector embeddings for each document\n            texts: List of document texts\n            metadatas: Optional metadata dictionaries for each document\n            ids: Optional document IDs (UUIDs generated if not provided)\n            vector_field: Field name for storing vector embeddings\n            text_field: Field name for storing document text\n            mapping: Optional index mapping configuration\n            max_chunk_bytes: Maximum size per bulk request chunk\n            is_aoss: Whether using Amazon OpenSearch Serverless\n\n        Returns:\n            List of document IDs that were successfully ingested\n        \"\"\"\n        if not mapping:\n            mapping = {}\n\n        requests = []\n        return_ids = []\n\n        for i, text in enumerate(texts):\n            metadata = metadatas[i] if metadatas else {}\n            _id = ids[i] if ids else str(uuid.uuid4())\n            request = {\n                \"_op_type\": \"index\",\n                \"_index\": index_name,\n                vector_field: embeddings[i],\n                text_field: text,\n                **metadata,\n            }\n            if is_aoss:\n                request[\"id\"] = _id\n            else:\n                request[\"_id\"] = _id\n            requests.append(request)\n            return_ids.append(_id)\n        if metadatas:\n            self.log(f\"Sample metadata: {metadatas[0] if metadatas else {}}\")\n        helpers.bulk(client, requests, max_chunk_bytes=max_chunk_bytes)\n        return return_ids\n\n    # ---------- auth / client ----------\n    def _build_auth_kwargs(self) -> dict[str, Any]:\n        \"\"\"Build authentication configuration for OpenSearch client.\n\n        Constructs the appropriate authentication parameters based on the\n        selected auth mode (basic username/password or JWT token).\n\n        Returns:\n            Dictionary containing authentication configuration\n\n        Raises:\n            ValueError: If required authentication parameters are missing\n        \"\"\"\n        mode = (self.auth_mode or \"basic\").strip().lower()\n        if mode == \"jwt\":\n            token = (self.jwt_token or \"\").strip()\n            if not token:\n                msg = \"Auth Mode is 'jwt' but no jwt_token was provided.\"\n                raise ValueError(msg)\n            header_name = (self.jwt_header or \"Authorization\").strip()\n            header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n            return {\"headers\": {header_name: header_value}}\n        user = (self.username or \"\").strip()\n        pwd = (self.password or \"\").strip()\n        if not user or not pwd:\n            msg = \"Auth Mode is 'basic' but username/password are missing.\"\n            raise ValueError(msg)\n        return {\"http_auth\": (user, pwd)}\n\n    def build_client(self) -> OpenSearch:\n        \"\"\"Create and configure an OpenSearch client instance.\n\n        Returns:\n            Configured OpenSearch client ready for operations\n        \"\"\"\n        auth_kwargs = self._build_auth_kwargs()\n        return OpenSearch(\n            hosts=[self.opensearch_url],\n            use_ssl=self.use_ssl,\n            verify_certs=self.verify_certs,\n            ssl_assert_hostname=False,\n            ssl_show_warn=False,\n            **auth_kwargs,\n        )\n\n    @check_cached_vector_store\n    def build_vector_store(self) -> OpenSearch:\n        # Return raw OpenSearch client as our “vector store.”\n        self.log(self.ingest_data)\n        client = self.build_client()\n        self._add_documents_to_vector_store(client=client)\n        return client\n\n    # ---------- ingest ----------\n    def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n        \"\"\"Process and ingest documents into the OpenSearch vector store.\n\n        This method handles the complete document ingestion pipeline:\n        - Prepares document data and metadata\n        - Generates vector embeddings\n        - Creates appropriate index mappings\n        - Bulk inserts documents with vectors\n\n        Args:\n            client: OpenSearch client for performing operations\n        \"\"\"\n        # Convert DataFrame to Data if needed using parent's method\n        self.ingest_data = self._prepare_ingest_data()\n\n        docs = self.ingest_data or []\n        if not docs:\n            self.log(\"No documents to ingest.\")\n            return\n\n        # Extract texts and metadata from documents\n        texts = []\n        metadatas = []\n        # Process docs_metadata table input into a dict\n        additional_metadata = {}\n        if hasattr(self, \"docs_metadata\") and self.docs_metadata:\n            logger.info(f\"[LF] Docs metadata {self.docs_metadata}\")\n            if isinstance(self.docs_metadata[-1], Data):\n                logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n                self.docs_metadata = self.docs_metadata[-1].data\n                logger.info(f\"[LF] Docs metadata is a Data object {self.docs_metadata}\")\n                additional_metadata.update(self.docs_metadata)\n            else:\n                for item in self.docs_metadata:\n                    if isinstance(item, dict) and \"key\" in item and \"value\" in item:\n                        additional_metadata[item[\"key\"]] = item[\"value\"]\n        # Replace string \"None\" values with actual None\n        for key, value in additional_metadata.items():\n            if value == \"None\":\n                additional_metadata[key] = None\n        logger.info(f\"[LF] Additional metadata {additional_metadata}\")\n        for doc_obj in docs:\n            data_copy = json.loads(doc_obj.model_dump_json())\n            text = data_copy.pop(doc_obj.text_key, doc_obj.default_value)\n            texts.append(text)\n\n            # Merge additional metadata from table input\n            data_copy.update(additional_metadata)\n\n            metadatas.append(data_copy)\n        self.log(metadatas)\n        if not self.embedding:\n            msg = \"Embedding handle is required to embed documents.\"\n            raise ValueError(msg)\n\n        # Generate embeddings\n        vectors = self.embedding.embed_documents(texts)\n\n        if not vectors:\n            self.log(\"No vectors generated from documents.\")\n            return\n\n        # Get vector dimension for mapping\n        dim = len(vectors[0]) if vectors else 768  # default fallback\n\n        # Check for AOSS\n        auth_kwargs = self._build_auth_kwargs()\n        is_aoss = self._is_aoss_enabled(auth_kwargs.get(\"http_auth\"))\n\n        # Validate engine with AOSS\n        engine = getattr(self, \"engine\", \"jvector\")\n        self._validate_aoss_with_engines(is_aoss=is_aoss, engine=engine)\n\n        # Create mapping with proper KNN settings\n        space_type = getattr(self, \"space_type\", \"l2\")\n        ef_construction = getattr(self, \"ef_construction\", 512)\n        m = getattr(self, \"m\", 16)\n\n        mapping = self._default_text_mapping(\n            dim=dim,\n            engine=engine,\n            space_type=space_type,\n            ef_construction=ef_construction,\n            m=m,\n            vector_field=self.vector_field,\n        )\n\n        self.log(f\"Indexing {len(texts)} documents into '{self.index_name}' with proper KNN mapping...\")\n\n        # Use the LangChain-style bulk ingestion\n        return_ids = self._bulk_ingest_embeddings(\n            client=client,\n            index_name=self.index_name,\n            embeddings=vectors,\n            texts=texts,\n            metadatas=metadatas,\n            vector_field=self.vector_field,\n            text_field=\"text\",\n            mapping=mapping,\n            is_aoss=is_aoss,\n        )\n        self.log(metadatas)\n\n        self.log(f\"Successfully indexed {len(return_ids)} documents.\")\n\n    # ---------- helpers for filters ----------\n    def _is_placeholder_term(self, term_obj: dict) -> bool:\n        # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n        return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n    def _coerce_filter_clauses(self, filter_obj: dict | None) -> list[dict]:\n        \"\"\"Convert filter expressions into OpenSearch-compatible filter clauses.\n\n        This method accepts two filter formats and converts them to standardized\n        OpenSearch query clauses:\n\n        Format A - Explicit filters:\n        {\"filter\": [{\"term\": {\"field\": \"value\"}}, {\"terms\": {\"field\": [\"val1\", \"val2\"]}}],\n         \"limit\": 10, \"score_threshold\": 1.5}\n\n        Format B - Context-style mapping:\n        {\"data_sources\": [\"file1.pdf\"], \"document_types\": [\"pdf\"], \"owners\": [\"user1\"]}\n\n        Args:\n            filter_obj: Filter configuration dictionary or None\n\n        Returns:\n            List of OpenSearch filter clauses (term/terms objects)\n            Placeholder values with \"__IMPOSSIBLE_VALUE__\" are ignored\n        \"\"\"\n        if not filter_obj:\n            return []\n\n        # If it is a string, try to parse it once\n        if isinstance(filter_obj, str):\n            try:\n                filter_obj = json.loads(filter_obj)\n            except json.JSONDecodeError:\n                # Not valid JSON - treat as no filters\n                return []\n\n        # Case A: already an explicit list/dict under \"filter\"\n        if \"filter\" in filter_obj:\n            raw = filter_obj[\"filter\"]\n            if isinstance(raw, dict):\n                raw = [raw]\n            explicit_clauses: list[dict] = []\n            for f in raw or []:\n                if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n                    explicit_clauses.append(f)\n                elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n                    field, vals = next(iter(f[\"terms\"].items()))\n                    if isinstance(vals, list) and len(vals) > 0:\n                        explicit_clauses.append(f)\n            return explicit_clauses\n\n        # Case B: convert context-style maps into clauses\n        field_mapping = {\n            \"data_sources\": \"filename\",\n            \"document_types\": \"mimetype\",\n            \"owners\": \"owner\",\n        }\n        context_clauses: list[dict] = []\n        for k, values in filter_obj.items():\n            if not isinstance(values, list):\n                continue\n            field = field_mapping.get(k, k)\n            if len(values) == 0:\n                # Match-nothing placeholder (kept to mirror your tool semantics)\n                context_clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n            elif len(values) == 1:\n                if values[0] != \"__IMPOSSIBLE_VALUE__\":\n                    context_clauses.append({\"term\": {field: values[0]}})\n            else:\n                context_clauses.append({\"terms\": {field: values}})\n        return context_clauses\n\n    # ---------- search (single hybrid path matching your tool) ----------\n    def search(self, query: str | None = None) -> list[dict[str, Any]]:\n        \"\"\"Perform hybrid search combining vector similarity and keyword matching.\n\n        This method executes a sophisticated search that combines:\n        - K-nearest neighbor (KNN) vector similarity search (70% weight)\n        - Multi-field keyword search with fuzzy matching (30% weight)\n        - Optional filtering and score thresholds\n        - Aggregations for faceted search results\n\n        Args:\n            query: Search query string (used for both vector embedding and keyword search)\n\n        Returns:\n            List of search results with page_content, metadata, and relevance scores\n\n        Raises:\n            ValueError: If embedding component is not provided or filter JSON is invalid\n        \"\"\"\n        logger.info(self.ingest_data)\n        client = self.build_client()\n        q = (query or \"\").strip()\n\n        # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n        filter_obj = None\n        if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n            try:\n                filter_obj = json.loads(self.filter_expression)\n            except json.JSONDecodeError as e:\n                msg = f\"Invalid filter_expression JSON: {e}\"\n                raise ValueError(msg) from e\n\n        if not self.embedding:\n            msg = \"Embedding is required to run hybrid search (KNN + keyword).\"\n            raise ValueError(msg)\n\n        # Embed the query\n        vec = self.embedding.embed_query(q)\n\n        # Build filter clauses (accept both shapes)\n        filter_clauses = self._coerce_filter_clauses(filter_obj)\n\n        # Respect the tool's limit/threshold defaults\n        limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n        score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n        # Build the same hybrid body as your SearchService\n        body = {\n            \"query\": {\n                \"bool\": {\n                    \"should\": [\n                        {\n                            \"knn\": {\n                                self.vector_field: {\n                                    \"vector\": vec,\n                                    \"k\": 10,  # fixed to match the tool\n                                    \"boost\": 0.7,\n                                }\n                            }\n                        },\n                        {\n                            \"multi_match\": {\n                                \"query\": q,\n                                \"fields\": [\"text^2\", \"filename^1.5\"],\n                                \"type\": \"best_fields\",\n                                \"fuzziness\": \"AUTO\",\n                                \"boost\": 0.3,\n                            }\n                        },\n                    ],\n                    \"minimum_should_match\": 1,\n                }\n            },\n            \"aggs\": {\n                \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n                \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n                \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}},\n            },\n            \"_source\": [\n                \"filename\",\n                \"mimetype\",\n                \"page\",\n                \"text\",\n                \"source_url\",\n                \"owner\",\n                \"allowed_users\",\n                \"allowed_groups\",\n            ],\n            \"size\": limit,\n        }\n        if filter_clauses:\n            body[\"query\"][\"bool\"][\"filter\"] = filter_clauses\n\n        if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n            # top-level min_score (matches your tool)\n            body[\"min_score\"] = score_threshold\n\n        resp = client.search(index=self.index_name, body=body)\n        hits = resp.get(\"hits\", {}).get(\"hits\", [])\n        return [\n            {\n                \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n                \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n                \"score\": hit.get(\"_score\"),\n            }\n            for hit in hits\n        ]\n\n    def search_documents(self) -> list[Data]:\n        \"\"\"Search documents and return results as Data objects.\n\n        This is the main interface method that performs the search using the\n        configured search_query and returns results in Langflow's Data format.\n\n        Returns:\n            List of Data objects containing search results with text and metadata\n\n        Raises:\n            Exception: If search operation fails\n        \"\"\"\n        try:\n            raw = self.search(self.search_query or \"\")\n            return [Data(text=hit[\"page_content\"], **hit[\"metadata\"]) for hit in raw]\n            self.log(self.ingest_data)\n        except Exception as e:\n            self.log(f\"search_documents error: {e}\")\n            raise\n\n    # -------- dynamic UI handling (auth switch) --------\n    async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n        \"\"\"Dynamically update component configuration based on field changes.\n\n        This method handles real-time UI updates, particularly for authentication\n        mode changes that show/hide relevant input fields.\n\n        Args:\n            build_config: Current component configuration\n            field_value: New value for the changed field\n            field_name: Name of the field that changed\n\n        Returns:\n            Updated build configuration with appropriate field visibility\n        \"\"\"\n        try:\n            if field_name == \"auth_mode\":\n                mode = (field_value or \"basic\").strip().lower()\n                is_basic = mode == \"basic\"\n                is_jwt = mode == \"jwt\"\n\n                build_config[\"username\"][\"show\"] = is_basic\n                build_config[\"password\"][\"show\"] = is_basic\n\n                build_config[\"jwt_token\"][\"show\"] = is_jwt\n                build_config[\"jwt_header\"][\"show\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n                build_config[\"username\"][\"required\"] = is_basic\n                build_config[\"password\"][\"required\"] = is_basic\n\n                build_config[\"jwt_token\"][\"required\"] = is_jwt\n                build_config[\"jwt_header\"][\"required\"] = is_jwt\n                build_config[\"bearer_prefix\"][\"required\"] = False\n\n                if is_basic:\n                    build_config[\"jwt_token\"][\"value\"] = \"\"\n\n                return build_config\n\n        except (KeyError, ValueError) as e:\n            self.log(f\"update_build_config error: {e}\")\n\n        return build_config\n"
+              },
+              "docs_metadata": {
+                "_input_type": "TableInput",
+                "advanced": false,
+                "display_name": "Document Metadata",
+                "dynamic": false,
+                "info": "Additional metadata key-value pairs to be added to all ingested documents. Useful for tagging documents with source information, categories, or other custom attributes.",
+                "input_types": [
+                  "Data"
+                ],
+                "is_list": true,
+                "list_add_label": "Add More",
+                "name": "docs_metadata",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "table_icon": "Table",
+                "table_schema": [
+                  {
+                    "description": "Key name",
+                    "display_name": "Key",
+                    "formatter": "text",
+                    "name": "key",
+                    "type": "str"
+                  },
+                  {
+                    "description": "Value of the metadata",
+                    "display_name": "Value",
+                    "formatter": "text",
+                    "name": "value",
+                    "type": "str"
+                  }
+                ],
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "trigger_icon": "Table",
+                "trigger_text": "Open table",
+                "type": "table",
+                "value": []
+              },
+              "ef_construction": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "EF Construction",
+                "dynamic": false,
+                "info": "Size of the dynamic candidate list during index construction. Higher values improve recall but increase indexing time and memory usage.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "ef_construction",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "int",
+                "value": 512
               },
               "embedding": {
                 "_input_type": "HandleInput",
@@ -977,13 +951,40 @@
                 "type": "other",
                 "value": ""
               },
+              "engine": {
+                "_input_type": "DropdownInput",
+                "advanced": true,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Vector Engine",
+                "dynamic": false,
+                "external_options": {},
+                "info": "Vector search engine for similarity calculations. 'jvector' is recommended for most use cases. Note: Amazon OpenSearch Serverless only supports 'nmslib' or 'faiss'.",
+                "name": "engine",
+                "options": [
+                  "jvector",
+                  "nmslib",
+                  "faiss",
+                  "lucene"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "toggle": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "jvector"
+              },
               "filter_expression": {
                 "_input_type": "MultilineInput",
                 "advanced": false,
                 "copy_field": false,
-                "display_name": "Filter Expression (JSON)",
+                "display_name": "Search Filters (JSON)",
                 "dynamic": false,
-                "info": "Optional JSON to control filters/limit/score threshold.\nAccepted shapes:\n1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\n2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\nPlaceholders with __IMPOSSIBLE_VALUE__ are ignored.",
+                "info": "Optional JSON configuration for search filtering, result limits, and score thresholds.\n\nFormat 1 - Explicit filters:\n{\"filter\": [{\"term\": {\"filename\":\"doc.pdf\"}}, {\"terms\":{\"owner\":[\"user1\",\"user2\"]}}], \"limit\": 10, \"score_threshold\": 1.6}\n\nFormat 2 - Context-style mapping:\n{\"data_sources\":[\"file.pdf\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"user123\"]}\n\nUse __IMPOSSIBLE_VALUE__ as placeholder to ignore specific filters.",
                 "input_types": [
                   "Message"
                 ],
@@ -1007,7 +1008,7 @@
                 "advanced": false,
                 "display_name": "Index Name",
                 "dynamic": false,
-                "info": "The index to search.",
+                "info": "The OpenSearch index name where documents will be stored and searched. Will be created automatically if it doesn't exist.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1053,8 +1054,8 @@
                 "load_from_db": false,
                 "name": "jwt_header",
                 "placeholder": "",
-                "required": true,
-                "show": true,
+                "required": false,
+                "show": false,
                 "title_case": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
@@ -1066,24 +1067,42 @@
                 "advanced": false,
                 "display_name": "JWT Token",
                 "dynamic": false,
-                "info": "Paste a valid JWT (sent as a header).",
+                "info": "Valid JSON Web Token for authentication. Will be sent in the Authorization header (with optional 'Bearer ' prefix).",
                 "input_types": [],
                 "load_from_db": true,
                 "name": "jwt_token",
                 "password": true,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
                 "value": "JWT"
               },
+              "m": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "M Parameter",
+                "dynamic": false,
+                "info": "Number of bidirectional connections for each vector in the HNSW graph. Higher values improve search quality but increase memory usage and indexing time.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "m",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "int",
+                "value": 16
+              },
               "number_of_results": {
                 "_input_type": "IntInput",
                 "advanced": true,
-                "display_name": "Default Size (limit)",
+                "display_name": "Default Result Limit",
                 "dynamic": false,
-                "info": "Default number of hits when no limit provided in filter_expression.",
+                "info": "Default maximum number of search results to return when no limit is specified in the filter expression.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1102,7 +1121,7 @@
                 "advanced": false,
                 "display_name": "OpenSearch URL",
                 "dynamic": false,
-                "info": "URL for your OpenSearch cluster.",
+                "info": "The connection URL for your OpenSearch cluster (e.g., http://localhost:9200 for local development or your cloud endpoint).",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1119,7 +1138,7 @@
               "password": {
                 "_input_type": "SecretStrInput",
                 "advanced": false,
-                "display_name": "Password",
+                "display_name": "OpenSearch Password",
                 "dynamic": false,
                 "info": "",
                 "input_types": [],
@@ -1174,12 +1193,40 @@
                 "type": "bool",
                 "value": true
               },
+              "space_type": {
+                "_input_type": "DropdownInput",
+                "advanced": true,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Distance Metric",
+                "dynamic": false,
+                "external_options": {},
+                "info": "Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, 'cosinesimil' for cosine similarity, 'innerproduct' for dot product.",
+                "name": "space_type",
+                "options": [
+                  "l2",
+                  "l1",
+                  "cosinesimil",
+                  "linf",
+                  "innerproduct"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "toggle": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "l2"
+              },
               "use_ssl": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Use SSL",
+                "display_name": "Use SSL/TLS",
                 "dynamic": false,
-                "info": "",
+                "info": "Enable SSL/TLS encryption for secure connections to OpenSearch.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "use_ssl",
@@ -1214,9 +1261,9 @@
               "vector_field": {
                 "_input_type": "StrInput",
                 "advanced": true,
-                "display_name": "Vector Field",
+                "display_name": "Vector Field Name",
                 "dynamic": false,
-                "info": "Vector field used for KNN.",
+                "info": "Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1233,9 +1280,9 @@
               "verify_certs": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Verify Certificates",
+                "display_name": "Verify SSL Certificates",
                 "dynamic": false,
-                "info": "",
+                "info": "Verify SSL certificates when connecting. Disable for self-signed certificates in development environments.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "verify_certs",
@@ -1253,12 +1300,12 @@
           },
           "selected_output": "dataframe",
           "showNode": true,
-          "type": "OpenSearchHybrid"
+          "type": "OpenSearchVectorStoreComponent"
         },
         "dragging": false,
         "id": "OpenSearch-iYfjf",
         "measured": {
-          "height": 765,
+          "height": 822,
           "width": 320
         },
         "position": {
@@ -1276,7 +1323,6 @@
               "Embeddings"
             ],
             "beta": false,
-            "category": "models",
             "conditional_paths": [],
             "custom_fields": {},
             "description": "Generate embeddings using a specified provider.",
@@ -1297,11 +1343,25 @@
             ],
             "frozen": false,
             "icon": "binary",
-            "key": "EmbeddingModel",
-            "last_updated": "2025-09-22T20:02:23.156Z",
+            "last_updated": "2025-10-01T20:14:43.010Z",
             "legacy": false,
-            "lf_version": "1.5.0.post2",
-            "metadata": {},
+            "metadata": {
+              "code_hash": "8607e963fdef",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "langchain_openai",
+                    "version": "0.3.23"
+                  },
+                  {
+                    "name": "lfx",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 2
+              },
+              "module": "custom_components.embedding_model"
+            },
             "minimized": false,
             "output_types": [],
             "outputs": [
@@ -1323,7 +1383,6 @@
               }
             ],
             "pinned": false,
-            "score": 0.002833550413469189,
             "template": {
               "_type": "Component",
               "api_base": {
@@ -1401,7 +1460,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageTextInput,\n    SecretStrInput,\n)\nfrom langflow.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n    display_name = \"Embedding Model\"\n    description = \"Generate embeddings using a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n    icon = \"binary\"\n    name = \"EmbeddingModel\"\n    category = \"models\"\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\"],\n            value=\"OpenAI\",\n            info=\"Select the embedding model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}],\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model Name\",\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n            info=\"Select the embedding model to use\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=True,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"api_base\",\n            display_name=\"API Base URL\",\n            info=\"Base URL for the API. Leave empty for default.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"dimensions\",\n            display_name=\"Dimensions\",\n            info=\"The number of dimensions the resulting output embeddings should have. \"\n            \"Only supported by certain models.\",\n            advanced=True,\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        provider = self.provider\n        model = self.model\n        api_key = self.api_key\n        api_base = self.api_base\n        dimensions = self.dimensions\n        chunk_size = self.chunk_size\n        request_timeout = self.request_timeout\n        max_retries = self.max_retries\n        show_progress_bar = self.show_progress_bar\n        model_kwargs = self.model_kwargs or {}\n\n        if provider == \"OpenAI\":\n            if not api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n            return OpenAIEmbeddings(\n                model=model,\n                dimensions=dimensions or None,\n                base_url=api_base or None,\n                api_key=api_key,\n                chunk_size=chunk_size,\n                max_retries=max_retries,\n                timeout=request_timeout or None,\n                show_progress_bar=show_progress_bar,\n                model_kwargs=model_kwargs,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\" and field_value == \"OpenAI\":\n            build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n            build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n            build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n        return build_config\n"
+                "value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom lfx.base.embeddings.model import LCEmbeddingsModel\nfrom lfx.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom lfx.field_typing import Embeddings\nfrom lfx.io import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageTextInput,\n    SecretStrInput,\n)\nfrom lfx.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n    display_name = \"Embedding Model\"\n    description = \"Generate embeddings using a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n    icon = \"binary\"\n    name = \"EmbeddingModel\"\n    category = \"models\"\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\"],\n            value=\"OpenAI\",\n            info=\"Select the embedding model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}],\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model Name\",\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n            info=\"Select the embedding model to use\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=True,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"api_base\",\n            display_name=\"API Base URL\",\n            info=\"Base URL for the API. Leave empty for default.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"dimensions\",\n            display_name=\"Dimensions\",\n            info=\"The number of dimensions the resulting output embeddings should have. \"\n            \"Only supported by certain models.\",\n            advanced=True,\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        provider = self.provider\n        model = self.model\n        api_key = self.api_key\n        api_base = self.api_base\n        dimensions = self.dimensions\n        chunk_size = self.chunk_size\n        request_timeout = self.request_timeout\n        max_retries = self.max_retries\n        show_progress_bar = self.show_progress_bar\n        model_kwargs = self.model_kwargs or {}\n\n        if provider == \"OpenAI\":\n            if not api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n            return OpenAIEmbeddings(\n                model=model,\n                dimensions=dimensions or None,\n                base_url=api_base or None,\n                api_key=api_key,\n                chunk_size=chunk_size,\n                max_retries=max_retries,\n                timeout=request_timeout or None,\n                show_progress_bar=show_progress_bar,\n                model_kwargs=model_kwargs,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\" and field_value == \"OpenAI\":\n            build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n            build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n            build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n        return build_config\n"
               },
               "dimensions": {
                 "_input_type": "IntInput",
@@ -1446,6 +1505,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Name",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the embedding model to use",
                 "name": "model",
                 "options": [
@@ -1489,6 +1549,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Provider",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the embedding model provider",
                 "name": "provider",
                 "options": [
@@ -1584,33 +1645,27 @@
             "display_name": "Prompt Template",
             "documentation": "https://docs.langflow.org/components-prompts",
             "edited": false,
-            "error": null,
             "field_order": [
               "template",
               "tool_placeholder"
             ],
             "frozen": false,
-            "full_path": null,
             "icon": "braces",
-            "is_composition": null,
-            "is_input": null,
-            "is_output": null,
             "legacy": false,
             "metadata": {
-              "code_hash": "c478a572ccd3",
+              "code_hash": "7382d03ce412",
               "dependencies": {
                 "dependencies": [
                   {
-                    "name": "langflow",
-                    "version": "0.5.0.post2"
+                    "name": "lfx",
+                    "version": null
                   }
                 ],
                 "total_dependencies": 1
               },
-              "module": "langflow.components.processing.prompt.PromptComponent"
+              "module": "custom_components.prompt_template"
             },
             "minimized": false,
-            "name": "",
             "output_types": [],
             "outputs": [
               {
@@ -1651,7 +1706,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt Template\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    documentation: str = \"https://docs.langflow.org/components-prompts\"\n    icon = \"braces\"\n    trace_type = \"prompt\"\n    name = \"Prompt Template\"\n    priority = 0  # Set priority to 0 to make it appear first\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from lfx.base.prompts.api_utils import process_prompt_template\nfrom lfx.custom.custom_component.component import Component\nfrom lfx.inputs.inputs import DefaultPromptField\nfrom lfx.io import MessageTextInput, Output, PromptInput\nfrom lfx.schema.message import Message\nfrom lfx.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt Template\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    documentation: str = \"https://docs.langflow.org/components-prompts\"\n    icon = \"braces\"\n    trace_type = \"prompt\"\n    name = \"Prompt Template\"\n    priority = 0  # Set priority to 0 to make it appear first\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "docs": {
                 "advanced": false,
@@ -1705,6 +1760,7 @@
                 "info": "",
                 "list": false,
                 "list_add_label": "Add More",
+                "load_from_db": false,
                 "name": "template",
                 "placeholder": "",
                 "required": false,
@@ -1781,17 +1837,17 @@
             "icon": "braces",
             "legacy": false,
             "metadata": {
-              "code_hash": "556209520650",
+              "code_hash": "bf19ee6feee3",
               "dependencies": {
                 "dependencies": [
                   {
-                    "name": "langflow",
-                    "version": "0.5.0.post2"
+                    "name": "lfx",
+                    "version": null
                   }
                 ],
                 "total_dependencies": 1
               },
-              "module": "langflow.components.processing.parser.ParserComponent"
+              "module": "custom_components.parser"
             },
             "minimized": false,
             "output_types": [],
@@ -1803,6 +1859,8 @@
                 "group_outputs": false,
                 "method": "parse_combined_text",
                 "name": "parsed_text",
+                "options": null,
+                "required_inputs": null,
                 "selected": "Message",
                 "tool_mode": true,
                 "types": [
@@ -1830,7 +1888,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom.custom_component.component import Component\nfrom langflow.helpers.data import safe_convert\nfrom langflow.inputs.inputs import BoolInput, HandleInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\n\n\nclass ParserComponent(Component):\n    display_name = \"Parser\"\n    description = \"Extracts text using a template.\"\n    documentation: str = \"https://docs.langflow.org/components-processing#parser\"\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"input_data\",\n            display_name=\"Data or DataFrame\",\n            input_types=[\"DataFrame\", \"Data\"],\n            info=\"Accepts either a DataFrame or a Data object.\",\n            required=True,\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Parser\", \"Stringify\"],\n            value=\"Parser\",\n            info=\"Convert into raw string instead of using a template.\",\n            real_time_refresh=True,\n        ),\n        MultilineInput(\n            name=\"pattern\",\n            display_name=\"Template\",\n            info=(\n                \"Use variables within curly brackets to extract column values for DataFrames \"\n                \"or key values for Data.\"\n                \"For example: `Name: {Name}, Age: {Age}, Country: {Country}`\"\n            ),\n            value=\"Text: {text}\",  # Example default\n            dynamic=True,\n            show=True,\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"sep\",\n            display_name=\"Separator\",\n            advanced=True,\n            value=\"\\n\",\n            info=\"String used to separate rows/items.\",\n        ),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Parsed Text\",\n            name=\"parsed_text\",\n            info=\"Formatted text output.\",\n            method=\"parse_combined_text\",\n        ),\n    ]\n\n    def update_build_config(self, build_config, field_value, field_name=None):\n        \"\"\"Dynamically hide/show `template` and enforce requirement based on `stringify`.\"\"\"\n        if field_name == \"mode\":\n            build_config[\"pattern\"][\"show\"] = self.mode == \"Parser\"\n            build_config[\"pattern\"][\"required\"] = self.mode == \"Parser\"\n            if field_value:\n                clean_data = BoolInput(\n                    name=\"clean_data\",\n                    display_name=\"Clean Data\",\n                    info=(\n                        \"Enable to clean the data by removing empty rows and lines \"\n                        \"in each cell of the DataFrame/ Data object.\"\n                    ),\n                    value=True,\n                    advanced=True,\n                    required=False,\n                )\n                build_config[\"clean_data\"] = clean_data.to_dict()\n            else:\n                build_config.pop(\"clean_data\", None)\n\n        return build_config\n\n    def _clean_args(self):\n        \"\"\"Prepare arguments based on input type.\"\"\"\n        input_data = self.input_data\n\n        match input_data:\n            case list() if all(isinstance(item, Data) for item in input_data):\n                msg = \"List of Data objects is not supported.\"\n                raise ValueError(msg)\n            case DataFrame():\n                return input_data, None\n            case Data():\n                return None, input_data\n            case dict() if \"data\" in input_data:\n                try:\n                    if \"columns\" in input_data:  # Likely a DataFrame\n                        return DataFrame.from_dict(input_data), None\n                    # Likely a Data object\n                    return None, Data(**input_data)\n                except (TypeError, ValueError, KeyError) as e:\n                    msg = f\"Invalid structured input provided: {e!s}\"\n                    raise ValueError(msg) from e\n            case _:\n                msg = f\"Unsupported input type: {type(input_data)}. Expected DataFrame or Data.\"\n                raise ValueError(msg)\n\n    def parse_combined_text(self) -> Message:\n        \"\"\"Parse all rows/items into a single text or convert input to string if `stringify` is enabled.\"\"\"\n        # Early return for stringify option\n        if self.mode == \"Stringify\":\n            return self.convert_to_string()\n\n        df, data = self._clean_args()\n\n        lines = []\n        if df is not None:\n            for _, row in df.iterrows():\n                formatted_text = self.pattern.format(**row.to_dict())\n                lines.append(formatted_text)\n        elif data is not None:\n            formatted_text = self.pattern.format(**data.data)\n            lines.append(formatted_text)\n\n        combined_text = self.sep.join(lines)\n        self.status = combined_text\n        return Message(text=combined_text)\n\n    def convert_to_string(self) -> Message:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        result = \"\"\n        if isinstance(self.input_data, list):\n            result = \"\\n\".join([safe_convert(item, clean_data=self.clean_data or False) for item in self.input_data])\n        else:\n            result = safe_convert(self.input_data or False)\n        self.log(f\"Converted to string with length: {len(result)}\")\n\n        message = Message(text=result)\n        self.status = message\n        return message\n"
+                "value": "from lfx.custom.custom_component.component import Component\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, HandleInput, MessageTextInput, MultilineInput, TabInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.template.field.base import Output\n\n\nclass ParserComponent(Component):\n    display_name = \"Parser\"\n    description = \"Extracts text using a template.\"\n    documentation: str = \"https://docs.langflow.org/components-processing#parser\"\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"input_data\",\n            display_name=\"Data or DataFrame\",\n            input_types=[\"DataFrame\", \"Data\"],\n            info=\"Accepts either a DataFrame or a Data object.\",\n            required=True,\n        ),\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Parser\", \"Stringify\"],\n            value=\"Parser\",\n            info=\"Convert into raw string instead of using a template.\",\n            real_time_refresh=True,\n        ),\n        MultilineInput(\n            name=\"pattern\",\n            display_name=\"Template\",\n            info=(\n                \"Use variables within curly brackets to extract column values for DataFrames \"\n                \"or key values for Data.\"\n                \"For example: `Name: {Name}, Age: {Age}, Country: {Country}`\"\n            ),\n            value=\"Text: {text}\",  # Example default\n            dynamic=True,\n            show=True,\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"sep\",\n            display_name=\"Separator\",\n            advanced=True,\n            value=\"\\n\",\n            info=\"String used to separate rows/items.\",\n        ),\n    ]\n\n    outputs = [\n        Output(\n            display_name=\"Parsed Text\",\n            name=\"parsed_text\",\n            info=\"Formatted text output.\",\n            method=\"parse_combined_text\",\n        ),\n    ]\n\n    def update_build_config(self, build_config, field_value, field_name=None):\n        \"\"\"Dynamically hide/show `template` and enforce requirement based on `stringify`.\"\"\"\n        if field_name == \"mode\":\n            build_config[\"pattern\"][\"show\"] = self.mode == \"Parser\"\n            build_config[\"pattern\"][\"required\"] = self.mode == \"Parser\"\n            if field_value:\n                clean_data = BoolInput(\n                    name=\"clean_data\",\n                    display_name=\"Clean Data\",\n                    info=(\n                        \"Enable to clean the data by removing empty rows and lines \"\n                        \"in each cell of the DataFrame/ Data object.\"\n                    ),\n                    value=True,\n                    advanced=True,\n                    required=False,\n                )\n                build_config[\"clean_data\"] = clean_data.to_dict()\n            else:\n                build_config.pop(\"clean_data\", None)\n\n        return build_config\n\n    def _clean_args(self):\n        \"\"\"Prepare arguments based on input type.\"\"\"\n        input_data = self.input_data\n\n        match input_data:\n            case list() if all(isinstance(item, Data) for item in input_data):\n                msg = \"List of Data objects is not supported.\"\n                raise ValueError(msg)\n            case DataFrame():\n                return input_data, None\n            case Data():\n                return None, input_data\n            case dict() if \"data\" in input_data:\n                try:\n                    if \"columns\" in input_data:  # Likely a DataFrame\n                        return DataFrame.from_dict(input_data), None\n                    # Likely a Data object\n                    return None, Data(**input_data)\n                except (TypeError, ValueError, KeyError) as e:\n                    msg = f\"Invalid structured input provided: {e!s}\"\n                    raise ValueError(msg) from e\n            case _:\n                msg = f\"Unsupported input type: {type(input_data)}. Expected DataFrame or Data.\"\n                raise ValueError(msg)\n\n    def parse_combined_text(self) -> Message:\n        \"\"\"Parse all rows/items into a single text or convert input to string if `stringify` is enabled.\"\"\"\n        # Early return for stringify option\n        if self.mode == \"Stringify\":\n            return self.convert_to_string()\n\n        df, data = self._clean_args()\n\n        lines = []\n        if df is not None:\n            for _, row in df.iterrows():\n                formatted_text = self.pattern.format(**row.to_dict())\n                lines.append(formatted_text)\n        elif data is not None:\n            formatted_text = self.pattern.format(**data.data)\n            lines.append(formatted_text)\n\n        combined_text = self.sep.join(lines)\n        self.status = combined_text\n        return Message(text=combined_text)\n\n    def convert_to_string(self) -> Message:\n        \"\"\"Convert input data to string with proper error handling.\"\"\"\n        result = \"\"\n        if isinstance(self.input_data, list):\n            result = \"\\n\".join([safe_convert(item, clean_data=self.clean_data or False) for item in self.input_data])\n        else:\n            result = safe_convert(self.input_data or False)\n        self.log(f\"Converted to string with length: {len(result)}\")\n\n        message = Message(text=result)\n        self.status = message\n        return message\n"
               },
               "input_data": {
                 "_input_type": "HandleInput",
@@ -1955,7 +2013,7 @@
             "description": "Runs a language model given a specified provider.",
             "display_name": "Language Model",
             "documentation": "https://docs.langflow.org/components-models",
-            "edited": false,
+            "edited": true,
             "field_order": [
               "provider",
               "model_name",
@@ -1967,10 +2025,10 @@
             ],
             "frozen": false,
             "icon": "brain-circuit",
-            "last_updated": "2025-09-22T20:02:40.428Z",
+            "last_updated": "2025-10-01T20:14:43.010Z",
             "legacy": false,
             "metadata": {
-              "code_hash": "6ac42a7167a4",
+              "code_hash": "bb5f8714781b",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1986,7 +2044,7 @@
                     "version": "0.3.23"
                   },
                   {
-                    "name": "langflow",
+                    "name": "lfx",
                     "version": null
                   }
                 ],
@@ -1998,7 +2056,7 @@
                 "language model",
                 "large language model"
               ],
-              "module": "langflow.components.models.language_model.LanguageModelComponent"
+              "module": "custom_components.language_model"
             },
             "minimized": false,
             "output_types": [],
@@ -2008,6 +2066,7 @@
                 "cache": true,
                 "display_name": "Model Response",
                 "group_outputs": false,
+                "hidden": null,
                 "method": "text_response",
                 "name": "text_output",
                 "options": null,
@@ -2024,6 +2083,7 @@
                 "cache": true,
                 "display_name": "Language Model",
                 "group_outputs": false,
+                "hidden": null,
                 "method": "build_model",
                 "name": "model_output",
                 "options": null,
@@ -2074,7 +2134,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_CHAT_MODEL_NAMES, OPENAI_REASONING_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass LanguageModelComponent(LCModelComponent):\n    display_name = \"Language Model\"\n    description = \"Runs a language model given a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-models\"\n    icon = \"brain-circuit\"\n    category = \"models\"\n    priority = 0  # Set priority to 0 to make it appear first\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\", \"Anthropic\", \"Google\"],\n            value=\"OpenAI\",\n            info=\"Select the model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Anthropic\"}, {\"icon\": \"GoogleGenerativeAI\"}],\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n            value=OPENAI_CHAT_MODEL_NAMES[0],\n            info=\"Select the model to use\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=False,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            info=\"The input text to send to the model\",\n        ),\n        MultilineInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"A system message that helps set the behavior of the assistant\",\n            advanced=False,\n        ),\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=\"Whether to stream the response\",\n            value=False,\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            info=\"Controls randomness in responses\",\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        provider = self.provider\n        model_name = self.model_name\n        temperature = self.temperature\n        stream = self.stream\n\n        if provider == \"OpenAI\":\n            if not self.api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n\n            if model_name in OPENAI_REASONING_MODEL_NAMES:\n                # reasoning models do not support temperature (yet)\n                temperature = None\n\n            return ChatOpenAI(\n                model_name=model_name,\n                temperature=temperature,\n                streaming=stream,\n                openai_api_key=self.api_key,\n            )\n        if provider == \"Anthropic\":\n            if not self.api_key:\n                msg = \"Anthropic API key is required when using Anthropic provider\"\n                raise ValueError(msg)\n            return ChatAnthropic(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                anthropic_api_key=self.api_key,\n            )\n        if provider == \"Google\":\n            if not self.api_key:\n                msg = \"Google API key is required when using Google provider\"\n                raise ValueError(msg)\n            return ChatGoogleGenerativeAI(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                google_api_key=self.api_key,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\":\n            if field_value == \"OpenAI\":\n                build_config[\"model_name\"][\"options\"] = OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES\n                build_config[\"model_name\"][\"value\"] = OPENAI_CHAT_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            elif field_value == \"Anthropic\":\n                build_config[\"model_name\"][\"options\"] = ANTHROPIC_MODELS\n                build_config[\"model_name\"][\"value\"] = ANTHROPIC_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Anthropic API Key\"\n            elif field_value == \"Google\":\n                build_config[\"model_name\"][\"options\"] = GOOGLE_GENERATIVE_AI_MODELS\n                build_config[\"model_name\"][\"value\"] = GOOGLE_GENERATIVE_AI_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Google API Key\"\n        elif field_name == \"model_name\" and field_value.startswith(\"o1\") and self.provider == \"OpenAI\":\n            # Hide system_message for o1 models - currently unsupported\n            if \"system_message\" in build_config:\n                build_config[\"system_message\"][\"show\"] = False\n        elif field_name == \"model_name\" and not field_value.startswith(\"o1\") and \"system_message\" in build_config:\n            build_config[\"system_message\"][\"show\"] = True\n        return build_config\n"
+                "value": "from typing import Any\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom langchain_openai import ChatOpenAI\n\nfrom lfx.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom lfx.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS\nfrom lfx.base.models.model import LCModelComponent\nfrom lfx.base.models.openai_constants import OPENAI_CHAT_MODEL_NAMES, OPENAI_REASONING_MODEL_NAMES\nfrom lfx.field_typing import LanguageModel\nfrom lfx.field_typing.range_spec import RangeSpec\nfrom lfx.inputs.inputs import BoolInput\nfrom lfx.io import DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput\nfrom lfx.schema.dotdict import dotdict\n\n\nclass LanguageModelComponent(LCModelComponent):\n    display_name = \"Language Model\"\n    description = \"Runs a language model given a specified provider.\"\n    documentation: str = \"https://docs.langflow.org/components-models\"\n    icon = \"brain-circuit\"\n    category = \"models\"\n    priority = 0  # Set priority to 0 to make it appear first\n\n    inputs = [\n        DropdownInput(\n            name=\"provider\",\n            display_name=\"Model Provider\",\n            options=[\"OpenAI\", \"Anthropic\", \"Google\"],\n            value=\"OpenAI\",\n            info=\"Select the model provider\",\n            real_time_refresh=True,\n            options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Anthropic\"}, {\"icon\": \"GoogleGenerativeAI\"}],\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            options=OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n            value=OPENAI_CHAT_MODEL_NAMES[0],\n            info=\"Select the model to use\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"Model Provider API key\",\n            required=False,\n            show=True,\n            real_time_refresh=True,\n        ),\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Input\",\n            info=\"The input text to send to the model\",\n        ),\n        MultilineInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"A system message that helps set the behavior of the assistant\",\n            advanced=False,\n        ),\n        BoolInput(\n            name=\"stream\",\n            display_name=\"Stream\",\n            info=\"Whether to stream the response\",\n            value=False,\n            advanced=True,\n        ),\n        SliderInput(\n            name=\"temperature\",\n            display_name=\"Temperature\",\n            value=0.1,\n            info=\"Controls randomness in responses\",\n            range_spec=RangeSpec(min=0, max=1, step=0.01),\n            advanced=True,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        provider = self.provider\n        model_name = self.model_name\n        temperature = self.temperature\n        stream = self.stream\n\n        if provider == \"OpenAI\":\n            if not self.api_key:\n                msg = \"OpenAI API key is required when using OpenAI provider\"\n                raise ValueError(msg)\n\n            if model_name in OPENAI_REASONING_MODEL_NAMES:\n                # reasoning models do not support temperature (yet)\n                temperature = None\n\n            return ChatOpenAI(\n                model_name=model_name,\n                temperature=temperature,\n                streaming=stream,\n                openai_api_key=self.api_key,\n            )\n        if provider == \"Anthropic\":\n            if not self.api_key:\n                msg = \"Anthropic API key is required when using Anthropic provider\"\n                raise ValueError(msg)\n            return ChatAnthropic(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                anthropic_api_key=self.api_key,\n            )\n        if provider == \"Google\":\n            if not self.api_key:\n                msg = \"Google API key is required when using Google provider\"\n                raise ValueError(msg)\n            return ChatGoogleGenerativeAI(\n                model=model_name,\n                temperature=temperature,\n                streaming=stream,\n                google_api_key=self.api_key,\n            )\n        msg = f\"Unknown provider: {provider}\"\n        raise ValueError(msg)\n\n    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n        if field_name == \"provider\":\n            if field_value == \"OpenAI\":\n                build_config[\"model_name\"][\"options\"] = OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES\n                build_config[\"model_name\"][\"value\"] = OPENAI_CHAT_MODEL_NAMES[0]\n                build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n            elif field_value == \"Anthropic\":\n                build_config[\"model_name\"][\"options\"] = ANTHROPIC_MODELS\n                build_config[\"model_name\"][\"value\"] = ANTHROPIC_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Anthropic API Key\"\n            elif field_value == \"Google\":\n                build_config[\"model_name\"][\"options\"] = GOOGLE_GENERATIVE_AI_MODELS\n                build_config[\"model_name\"][\"value\"] = GOOGLE_GENERATIVE_AI_MODELS[0]\n                build_config[\"api_key\"][\"display_name\"] = \"Google API Key\"\n        elif field_name == \"model_name\" and field_value.startswith(\"o1\") and self.provider == \"OpenAI\":\n            # Hide system_message for o1 models - currently unsupported\n            if \"system_message\" in build_config:\n                build_config[\"system_message\"][\"show\"] = False\n        elif field_name == \"model_name\" and not field_value.startswith(\"o1\") and \"system_message\" in build_config:\n            build_config[\"system_message\"][\"show\"] = True\n        return build_config\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -2106,6 +2166,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Name",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the model to use",
                 "name": "model_name",
                 "options": [
@@ -2148,6 +2209,7 @@
                 "dialog_inputs": {},
                 "display_name": "Model Provider",
                 "dynamic": false,
+                "external_options": {},
                 "info": "Select the model provider",
                 "name": "provider",
                 "options": [
@@ -2270,16 +2332,16 @@
       }
     ],
     "viewport": {
-      "x": -194.9599233786812,
-      "y": 65.54639038496208,
-      "zoom": 0.5547428131761893
+      "x": -196.5855347896957,
+      "y": 78.13813811398654,
+      "zoom": 0.5380793988167256
     }
   },
   "description": "OpenRAG Open Search Nudges generator, based on the Open Search documents and the chat history.",
   "endpoint_name": null,
   "id": "ebc01d31-1976-46ce-a385-b0240327226c",
   "is_component": false,
-  "last_tested_version": "1.5.0.post2",
+  "last_tested_version": "1.6.0",
   "name": "OpenRAG Open Search Nudges",
   "tags": [
     "assistants",
diff --git a/frontend/components/ui/button.tsx b/frontend/components/ui/button.tsx
index 381ce3f7..c901d8ad 100644
--- a/frontend/components/ui/button.tsx
+++ b/frontend/components/ui/button.tsx
@@ -11,7 +11,7 @@ const buttonVariants = cva(
         destructive:
           "bg-destructive text-destructive-foreground hover:bg-destructive/90",
         outline:
-          "border border-input hover:bg-muted hover:text-accent-foreground disabled:bg-muted disabled:!border-none",
+          "border border-border hover:bg-muted hover:text-accent-foreground disabled:bg-muted disabled:!border-none",
         primary:
           "border bg-background text-secondary-foreground hover:bg-muted hover:shadow-sm",
         warning: "bg-warning text-warning-foreground hover:bg-warning/90",
diff --git a/frontend/components/ui/popover.tsx b/frontend/components/ui/popover.tsx
index ec42c030..ce79b6ae 100644
--- a/frontend/components/ui/popover.tsx
+++ b/frontend/components/ui/popover.tsx
@@ -1,31 +1,33 @@
-"use client"
+"use client";
 
-import * as React from "react"
-import * as PopoverPrimitive from "@radix-ui/react-popover"
+import * as PopoverPrimitive from "@radix-ui/react-popover";
+import * as React from "react";
 
-import { cn } from "@/lib/utils"
+import { cn } from "@/lib/utils";
 
-const Popover = PopoverPrimitive.Root
+const Popover = PopoverPrimitive.Root;
 
-const PopoverTrigger = PopoverPrimitive.Trigger
+const PopoverTrigger = PopoverPrimitive.Trigger;
+
+const PopoverAnchor = PopoverPrimitive.Anchor;
 
 const PopoverContent = React.forwardRef<
-  React.ElementRef<typeof PopoverPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof PopoverPrimitive.Content>
+	React.ElementRef<typeof PopoverPrimitive.Content>,
+	React.ComponentPropsWithoutRef<typeof PopoverPrimitive.Content>
 >(({ className, align = "center", sideOffset = 4, ...props }, ref) => (
-  <PopoverPrimitive.Portal>
-    <PopoverPrimitive.Content
-      ref={ref}
-      align={align}
-      sideOffset={sideOffset}
-      className={cn(
-        "z-50 w-72 rounded-md border bg-popover p-4 text-popover-foreground shadow-md outline-none data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
-        className
-      )}
-      {...props}
-    />
-  </PopoverPrimitive.Portal>
-))
-PopoverContent.displayName = PopoverPrimitive.Content.displayName
+	<PopoverPrimitive.Portal>
+		<PopoverPrimitive.Content
+			ref={ref}
+			align={align}
+			sideOffset={sideOffset}
+			className={cn(
+				"z-50 w-72 rounded-md border bg-popover p-4 text-popover-foreground shadow-md outline-none data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
+				className,
+			)}
+			{...props}
+		/>
+	</PopoverPrimitive.Portal>
+));
+PopoverContent.displayName = PopoverPrimitive.Content.displayName;
 
-export { Popover, PopoverTrigger, PopoverContent }
\ No newline at end of file
+export { Popover, PopoverTrigger, PopoverAnchor, PopoverContent };
diff --git a/frontend/components/ui/switch.tsx b/frontend/components/ui/switch.tsx
index 9d78fcfd..def77130 100644
--- a/frontend/components/ui/switch.tsx
+++ b/frontend/components/ui/switch.tsx
@@ -1,29 +1,29 @@
-"use client"
+"use client";
 
-import * as React from "react"
-import * as SwitchPrimitives from "@radix-ui/react-switch"
+import * as SwitchPrimitives from "@radix-ui/react-switch";
+import * as React from "react";
 
-import { cn } from "@/lib/utils"
+import { cn } from "@/lib/utils";
 
 const Switch = React.forwardRef<
-  React.ElementRef<typeof SwitchPrimitives.Root>,
-  React.ComponentPropsWithoutRef<typeof SwitchPrimitives.Root>
+	React.ElementRef<typeof SwitchPrimitives.Root>,
+	React.ComponentPropsWithoutRef<typeof SwitchPrimitives.Root>
 >(({ className, ...props }, ref) => (
-  <SwitchPrimitives.Root
-    className={cn(
-      "peer inline-flex h-6 w-11 shrink-0 cursor-pointer items-center rounded-full border-2 border-transparent transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 focus-visible:ring-offset-background disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-primary data-[state=unchecked]:bg-muted",
-      className
-    )}
-    {...props}
-    ref={ref}
-  >
-    <SwitchPrimitives.Thumb
-      className={cn(
-        "pointer-events-none block h-5 w-5 rounded-full bg-background shadow-lg ring-0 transition-transform data-[state=checked]:translate-x-5 data-[state=unchecked]:translate-x-0 data-[state=unchecked]:bg-primary"
-      )}
-    />
-  </SwitchPrimitives.Root>
-))
-Switch.displayName = SwitchPrimitives.Root.displayName
+	<SwitchPrimitives.Root
+		className={cn(
+			"peer inline-flex h-6 w-11 shrink-0 cursor-pointer items-center rounded-full border-2 border-transparent transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 focus-visible:ring-offset-background disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-primary data-[state=unchecked]:bg-muted",
+			className,
+		)}
+		{...props}
+		ref={ref}
+	>
+		<SwitchPrimitives.Thumb
+			className={cn(
+				"pointer-events-none block h-5 w-5 rounded-full bg-background shadow-lg ring-0 transition-transform data-[state=checked]:translate-x-5 data-[state=unchecked]:translate-x-0 data-[state=unchecked]:bg-primary",
+			)}
+		/>
+	</SwitchPrimitives.Root>
+));
+Switch.displayName = SwitchPrimitives.Root.displayName;
 
-export { Switch } 
\ No newline at end of file
+export { Switch };
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
index 58d78031..33300bd4 100644
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
@@ -44,6 +44,7 @@
         "react-icons": "^5.5.0",
         "react-markdown": "^10.1.0",
         "react-syntax-highlighter": "^15.6.1",
+        "react-textarea-autosize": "^8.5.9",
         "rehype-mathjax": "^7.1.0",
         "rehype-raw": "^7.0.0",
         "remark-gfm": "^4.0.1",
@@ -8473,6 +8474,23 @@
         "react": ">= 0.14.0"
       }
     },
+    "node_modules/react-textarea-autosize": {
+      "version": "8.5.9",
+      "resolved": "https://registry.npmjs.org/react-textarea-autosize/-/react-textarea-autosize-8.5.9.tgz",
+      "integrity": "sha512-U1DGlIQN5AwgjTyOEnI1oCcMuEr1pv1qOtklB2l4nyMGbHzWrI0eFsYK0zos2YWqAolJyG0IWJaqWmWj5ETh0A==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.20.13",
+        "use-composed-ref": "^1.3.0",
+        "use-latest": "^1.2.1"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "peerDependencies": {
+        "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      }
+    },
     "node_modules/read-cache": {
       "version": "1.0.0",
       "resolved": "https://registry.npmjs.org/read-cache/-/read-cache-1.0.0.tgz",
@@ -10126,6 +10144,51 @@
         }
       }
     },
+    "node_modules/use-composed-ref": {
+      "version": "1.4.0",
+      "resolved": "https://registry.npmjs.org/use-composed-ref/-/use-composed-ref-1.4.0.tgz",
+      "integrity": "sha512-djviaxuOOh7wkj0paeO1Q/4wMZ8Zrnag5H6yBvzN7AKKe8beOaED9SF5/ByLqsku8NP4zQqsvM2u3ew/tJK8/w==",
+      "license": "MIT",
+      "peerDependencies": {
+        "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/use-isomorphic-layout-effect": {
+      "version": "1.2.1",
+      "resolved": "https://registry.npmjs.org/use-isomorphic-layout-effect/-/use-isomorphic-layout-effect-1.2.1.tgz",
+      "integrity": "sha512-tpZZ+EX0gaghDAiFR37hj5MgY6ZN55kLiPkJsKxBMZ6GZdOSPJXiOzPM984oPYZ5AnehYx5WQp1+ME8I/P/pRA==",
+      "license": "MIT",
+      "peerDependencies": {
+        "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/use-latest": {
+      "version": "1.3.0",
+      "resolved": "https://registry.npmjs.org/use-latest/-/use-latest-1.3.0.tgz",
+      "integrity": "sha512-mhg3xdm9NaM8q+gLT8KryJPnRFOz1/5XPBhmDEVZK1webPzDjrPk7f/mbpeLqTgB9msytYWANxgALOCJKnLvcQ==",
+      "license": "MIT",
+      "dependencies": {
+        "use-isomorphic-layout-effect": "^1.1.1"
+      },
+      "peerDependencies": {
+        "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/use-sidecar": {
       "version": "1.1.3",
       "resolved": "https://registry.npmjs.org/use-sidecar/-/use-sidecar-1.1.3.tgz",
diff --git a/frontend/package.json b/frontend/package.json
index bc9eb72c..fd996c33 100644
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -45,6 +45,7 @@
     "react-icons": "^5.5.0",
     "react-markdown": "^10.1.0",
     "react-syntax-highlighter": "^15.6.1",
+    "react-textarea-autosize": "^8.5.9",
     "rehype-mathjax": "^7.1.0",
     "rehype-raw": "^7.0.0",
     "remark-gfm": "^4.0.1",
diff --git a/frontend/src/app/chat/page.tsx b/frontend/src/app/chat/page.tsx
index 240bb4d2..01ee43c7 100644
--- a/frontend/src/app/chat/page.tsx
+++ b/frontend/src/app/chat/page.tsx
@@ -3,8 +3,10 @@
 import {
   AtSign,
   Bot,
+  Check,
   ChevronDown,
   ChevronRight,
+  Funnel,
   GitBranch,
   Loader2,
   Plus,
@@ -15,10 +17,17 @@ import {
   Zap,
 } from "lucide-react";
 import { useEffect, useRef, useState } from "react";
+import TextareaAutosize from "react-textarea-autosize";
+import { filterAccentClasses } from "@/components/knowledge-filter-panel";
 import { MarkdownRenderer } from "@/components/markdown-renderer";
 import { ProtectedRoute } from "@/components/protected-route";
 import { Avatar, AvatarFallback, AvatarImage } from "@/components/ui/avatar";
 import { Button } from "@/components/ui/button";
+import {
+  Popover,
+  PopoverAnchor,
+  PopoverContent,
+} from "@/components/ui/popover";
 import { useAuth } from "@/contexts/auth-context";
 import { type EndpointType, useChat } from "@/contexts/chat-context";
 import { useKnowledgeFilter } from "@/contexts/knowledge-filter-context";
@@ -26,7 +35,6 @@ import { useTask } from "@/contexts/task-context";
 import { useLoadingStore } from "@/stores/loadingStore";
 import { useGetNudgesQuery } from "../api/queries/useGetNudgesQuery";
 import Nudges from "./nudges";
-import { filterAccentClasses } from "@/components/knowledge-filter-panel";
 
 interface Message {
   role: "user" | "assistant";
@@ -125,16 +133,20 @@ function ChatPage() {
   const [availableFilters, setAvailableFilters] = useState<
     KnowledgeFilterData[]
   >([]);
+  const [textareaHeight, setTextareaHeight] = useState(40);
   const [filterSearchTerm, setFilterSearchTerm] = useState("");
   const [selectedFilterIndex, setSelectedFilterIndex] = useState(0);
   const [isFilterHighlighted, setIsFilterHighlighted] = useState(false);
   const [dropdownDismissed, setDropdownDismissed] = useState(false);
   const [isUserInteracting, setIsUserInteracting] = useState(false);
   const [isForkingInProgress, setIsForkingInProgress] = useState(false);
+  const [anchorPosition, setAnchorPosition] = useState<{
+    x: number;
+    y: number;
+  } | null>(null);
   const messagesEndRef = useRef<HTMLDivElement>(null);
   const inputRef = useRef<HTMLTextAreaElement>(null);
   const fileInputRef = useRef<HTMLInputElement>(null);
-  const dropdownRef = useRef<HTMLDivElement>(null);
   const streamAbortRef = useRef<AbortController | null>(null);
   const streamIdRef = useRef(0);
   const lastLoadedConversationRef = useRef<string | null>(null);
@@ -146,6 +158,59 @@ function ChatPage() {
     messagesEndRef.current?.scrollIntoView({ behavior: "smooth" });
   };
 
+  const getCursorPosition = (textarea: HTMLTextAreaElement) => {
+    // Create a hidden div with the same styles as the textarea
+    const div = document.createElement("div");
+    const computedStyle = getComputedStyle(textarea);
+
+    // Copy all computed styles to the hidden div
+    for (const style of computedStyle) {
+      (div.style as any)[style] = computedStyle.getPropertyValue(style);
+    }
+
+    // Set the div to be hidden but not un-rendered
+    div.style.position = "absolute";
+    div.style.visibility = "hidden";
+    div.style.whiteSpace = "pre-wrap";
+    div.style.wordWrap = "break-word";
+    div.style.overflow = "hidden";
+    div.style.height = "auto";
+    div.style.width = `${textarea.getBoundingClientRect().width}px`;
+
+    // Get the text up to the cursor position
+    const cursorPos = textarea.selectionStart || 0;
+    const textBeforeCursor = textarea.value.substring(0, cursorPos);
+
+    // Add the text before cursor
+    div.textContent = textBeforeCursor;
+
+    // Create a span to mark the end position
+    const span = document.createElement("span");
+    span.textContent = "|"; // Cursor marker
+    div.appendChild(span);
+
+    // Add the text after cursor to handle word wrapping
+    const textAfterCursor = textarea.value.substring(cursorPos);
+    div.appendChild(document.createTextNode(textAfterCursor));
+
+    // Add the div to the document temporarily
+    document.body.appendChild(div);
+
+    // Get positions
+    const inputRect = textarea.getBoundingClientRect();
+    const divRect = div.getBoundingClientRect();
+    const spanRect = span.getBoundingClientRect();
+
+    // Calculate the cursor position relative to the input
+    const x = inputRect.left + (spanRect.left - divRect.left);
+    const y = inputRect.top + (spanRect.top - divRect.top);
+
+    // Clean up
+    document.body.removeChild(div);
+
+    return { x, y };
+  };
+
   const handleEndpointChange = (newEndpoint: EndpointType) => {
     setEndpoint(newEndpoint);
     // Clear the conversation when switching endpoints to avoid response ID conflicts
@@ -193,7 +258,7 @@ function ChatPage() {
           "Upload failed with status:",
           response.status,
           "Response:",
-          errorText
+          errorText,
         );
         throw new Error("Failed to process document");
       }
@@ -314,13 +379,6 @@ function ChatPage() {
     }
   };
 
-  const handleFilterDropdownToggle = () => {
-    if (!isFilterDropdownOpen) {
-      loadAvailableFilters();
-    }
-    setIsFilterDropdownOpen(!isFilterDropdownOpen);
-  };
-
   const handleFilterSelect = (filter: KnowledgeFilterData | null) => {
     setSelectedFilter(filter);
     setIsFilterDropdownOpen(false);
@@ -410,7 +468,7 @@ function ChatPage() {
       console.log(
         "Loading conversation with",
         conversationData.messages.length,
-        "messages"
+        "messages",
       );
       // Convert backend message format to frontend Message interface
       const convertedMessages: Message[] = conversationData.messages.map(
@@ -538,7 +596,7 @@ function ChatPage() {
                       ) === "string"
                         ? toolCall.function?.arguments || toolCall.arguments
                         : JSON.stringify(
-                            toolCall.function?.arguments || toolCall.arguments
+                            toolCall.function?.arguments || toolCall.arguments,
                           ),
                     result: toolCall.result,
                     status: "completed",
@@ -557,7 +615,7 @@ function ChatPage() {
           }
 
           return message;
-        }
+        },
       );
 
       setMessages(convertedMessages);
@@ -646,7 +704,7 @@ function ChatPage() {
       console.log(
         "Chat page received file upload error event:",
         filename,
-        error
+        error,
       );
 
       // Replace the last message with error message
@@ -660,64 +718,43 @@ function ChatPage() {
 
     window.addEventListener(
       "fileUploadStart",
-      handleFileUploadStart as EventListener
+      handleFileUploadStart as EventListener,
     );
     window.addEventListener(
       "fileUploaded",
-      handleFileUploaded as EventListener
+      handleFileUploaded as EventListener,
     );
     window.addEventListener(
       "fileUploadComplete",
-      handleFileUploadComplete as EventListener
+      handleFileUploadComplete as EventListener,
     );
     window.addEventListener(
       "fileUploadError",
-      handleFileUploadError as EventListener
+      handleFileUploadError as EventListener,
     );
 
     return () => {
       window.removeEventListener(
         "fileUploadStart",
-        handleFileUploadStart as EventListener
+        handleFileUploadStart as EventListener,
       );
       window.removeEventListener(
         "fileUploaded",
-        handleFileUploaded as EventListener
+        handleFileUploaded as EventListener,
       );
       window.removeEventListener(
         "fileUploadComplete",
-        handleFileUploadComplete as EventListener
+        handleFileUploadComplete as EventListener,
       );
       window.removeEventListener(
         "fileUploadError",
-        handleFileUploadError as EventListener
+        handleFileUploadError as EventListener,
       );
     };
   }, [endpoint, setPreviousResponseIds]);
 
-  // Handle click outside to close dropdown
-  useEffect(() => {
-    const handleClickOutside = (event: MouseEvent) => {
-      if (
-        isFilterDropdownOpen &&
-        dropdownRef.current &&
-        !dropdownRef.current.contains(event.target as Node) &&
-        !inputRef.current?.contains(event.target as Node)
-      ) {
-        setIsFilterDropdownOpen(false);
-        setFilterSearchTerm("");
-        setSelectedFilterIndex(0);
-      }
-    };
-
-    document.addEventListener("mousedown", handleClickOutside);
-    return () => {
-      document.removeEventListener("mousedown", handleClickOutside);
-    };
-  }, [isFilterDropdownOpen]);
-
   const { data: nudges = [], cancel: cancelNudges } = useGetNudgesQuery(
-    previousResponseIds[endpoint]
+    previousResponseIds[endpoint],
   );
 
   const handleSSEStream = async (userMessage: Message) => {
@@ -822,7 +859,7 @@ function ChatPage() {
                 console.log(
                   "Received chunk:",
                   chunk.type || chunk.object,
-                  chunk
+                  chunk,
                 );
 
                 // Extract response ID if present
@@ -838,14 +875,14 @@ function ChatPage() {
                   if (chunk.delta.function_call) {
                     console.log(
                       "Function call in delta:",
-                      chunk.delta.function_call
+                      chunk.delta.function_call,
                     );
 
                     // Check if this is a new function call
                     if (chunk.delta.function_call.name) {
                       console.log(
                         "New function call:",
-                        chunk.delta.function_call.name
+                        chunk.delta.function_call.name,
                       );
                       const functionCall: FunctionCall = {
                         name: chunk.delta.function_call.name,
@@ -861,7 +898,7 @@ function ChatPage() {
                     else if (chunk.delta.function_call.arguments) {
                       console.log(
                         "Function call arguments delta:",
-                        chunk.delta.function_call.arguments
+                        chunk.delta.function_call.arguments,
                       );
                       const lastFunctionCall =
                         currentFunctionCalls[currentFunctionCalls.length - 1];
@@ -873,14 +910,14 @@ function ChatPage() {
                           chunk.delta.function_call.arguments;
                         console.log(
                           "Accumulated arguments:",
-                          lastFunctionCall.argumentsString
+                          lastFunctionCall.argumentsString,
                         );
 
                         // Try to parse arguments if they look complete
                         if (lastFunctionCall.argumentsString.includes("}")) {
                           try {
                             const parsed = JSON.parse(
-                              lastFunctionCall.argumentsString
+                              lastFunctionCall.argumentsString,
                             );
                             lastFunctionCall.arguments = parsed;
                             lastFunctionCall.status = "completed";
@@ -888,7 +925,7 @@ function ChatPage() {
                           } catch (e) {
                             console.log(
                               "Arguments not yet complete or invalid JSON:",
-                              e
+                              e,
                             );
                           }
                         }
@@ -921,7 +958,7 @@ function ChatPage() {
                         else if (toolCall.function.arguments) {
                           console.log(
                             "Tool call arguments delta:",
-                            toolCall.function.arguments
+                            toolCall.function.arguments,
                           );
                           const lastFunctionCall =
                             currentFunctionCalls[
@@ -935,7 +972,7 @@ function ChatPage() {
                               toolCall.function.arguments;
                             console.log(
                               "Accumulated tool arguments:",
-                              lastFunctionCall.argumentsString
+                              lastFunctionCall.argumentsString,
                             );
 
                             // Try to parse arguments if they look complete
@@ -944,7 +981,7 @@ function ChatPage() {
                             ) {
                               try {
                                 const parsed = JSON.parse(
-                                  lastFunctionCall.argumentsString
+                                  lastFunctionCall.argumentsString,
                                 );
                                 lastFunctionCall.arguments = parsed;
                                 lastFunctionCall.status = "completed";
@@ -952,7 +989,7 @@ function ChatPage() {
                               } catch (e) {
                                 console.log(
                                   "Tool arguments not yet complete or invalid JSON:",
-                                  e
+                                  e,
                                 );
                               }
                             }
@@ -984,7 +1021,7 @@ function ChatPage() {
                           console.log(
                             "Error parsing function call on finish:",
                             fc,
-                            e
+                            e,
                           );
                         }
                       }
@@ -1000,12 +1037,12 @@ function ChatPage() {
                   console.log(
                     "🟢 CREATING function call (added):",
                     chunk.item.id,
-                    chunk.item.tool_name || chunk.item.name
+                    chunk.item.tool_name || chunk.item.name,
                   );
 
                   // Try to find an existing pending call to update (created by earlier deltas)
                   let existing = currentFunctionCalls.find(
-                    (fc) => fc.id === chunk.item.id
+                    (fc) => fc.id === chunk.item.id,
                   );
                   if (!existing) {
                     existing = [...currentFunctionCalls]
@@ -1014,7 +1051,7 @@ function ChatPage() {
                         (fc) =>
                           fc.status === "pending" &&
                           !fc.id &&
-                          fc.name === (chunk.item.tool_name || chunk.item.name)
+                          fc.name === (chunk.item.tool_name || chunk.item.name),
                       );
                   }
 
@@ -1027,7 +1064,7 @@ function ChatPage() {
                       chunk.item.inputs || existing.arguments;
                     console.log(
                       "🟢 UPDATED existing pending function call with id:",
-                      existing.id
+                      existing.id,
                     );
                   } else {
                     const functionCall: FunctionCall = {
@@ -1045,7 +1082,7 @@ function ChatPage() {
                       currentFunctionCalls.map((fc) => ({
                         id: fc.id,
                         name: fc.name,
-                      }))
+                      })),
                     );
                   }
                 }
@@ -1056,7 +1093,7 @@ function ChatPage() {
                 ) {
                   console.log(
                     "Function args delta (Realtime API):",
-                    chunk.delta
+                    chunk.delta,
                   );
                   const lastFunctionCall =
                     currentFunctionCalls[currentFunctionCalls.length - 1];
@@ -1067,7 +1104,7 @@ function ChatPage() {
                     lastFunctionCall.argumentsString += chunk.delta || "";
                     console.log(
                       "Accumulated arguments (Realtime API):",
-                      lastFunctionCall.argumentsString
+                      lastFunctionCall.argumentsString,
                     );
                   }
                 }
@@ -1078,26 +1115,26 @@ function ChatPage() {
                 ) {
                   console.log(
                     "Function args done (Realtime API):",
-                    chunk.arguments
+                    chunk.arguments,
                   );
                   const lastFunctionCall =
                     currentFunctionCalls[currentFunctionCalls.length - 1];
                   if (lastFunctionCall) {
                     try {
                       lastFunctionCall.arguments = JSON.parse(
-                        chunk.arguments || "{}"
+                        chunk.arguments || "{}",
                       );
                       lastFunctionCall.status = "completed";
                       console.log(
                         "Parsed function arguments (Realtime API):",
-                        lastFunctionCall.arguments
+                        lastFunctionCall.arguments,
                       );
                     } catch (e) {
                       lastFunctionCall.arguments = { raw: chunk.arguments };
                       lastFunctionCall.status = "error";
                       console.log(
                         "Error parsing function arguments (Realtime API):",
-                        e
+                        e,
                       );
                     }
                   }
@@ -1111,14 +1148,14 @@ function ChatPage() {
                   console.log(
                     "🔵 UPDATING function call (done):",
                     chunk.item.id,
-                    chunk.item.tool_name || chunk.item.name
+                    chunk.item.tool_name || chunk.item.name,
                   );
                   console.log(
                     "🔵 Looking for existing function calls:",
                     currentFunctionCalls.map((fc) => ({
                       id: fc.id,
                       name: fc.name,
-                    }))
+                    })),
                   );
 
                   // Find existing function call by ID or name
@@ -1126,14 +1163,14 @@ function ChatPage() {
                     (fc) =>
                       fc.id === chunk.item.id ||
                       fc.name === chunk.item.tool_name ||
-                      fc.name === chunk.item.name
+                      fc.name === chunk.item.name,
                   );
 
                   if (functionCall) {
                     console.log(
                       "🔵 FOUND existing function call, updating:",
                       functionCall.id,
-                      functionCall.name
+                      functionCall.name,
                     );
                     // Update existing function call with completion data
                     functionCall.status =
@@ -1156,7 +1193,7 @@ function ChatPage() {
                       "🔴 WARNING: Could not find existing function call to update:",
                       chunk.item.id,
                       chunk.item.tool_name,
-                      chunk.item.name
+                      chunk.item.name,
                     );
                   }
                 }
@@ -1177,7 +1214,7 @@ function ChatPage() {
                       fc.name === chunk.item.name ||
                       fc.name === chunk.item.type ||
                       fc.name.includes(chunk.item.type.replace("_call", "")) ||
-                      chunk.item.type.includes(fc.name)
+                      chunk.item.type.includes(fc.name),
                   );
 
                   if (functionCall) {
@@ -1221,12 +1258,12 @@ function ChatPage() {
                     "🟡 CREATING tool call (added):",
                     chunk.item.id,
                     chunk.item.tool_name || chunk.item.name,
-                    chunk.item.type
+                    chunk.item.type,
                   );
 
                   // Dedupe by id or pending with same name
                   let existing = currentFunctionCalls.find(
-                    (fc) => fc.id === chunk.item.id
+                    (fc) => fc.id === chunk.item.id,
                   );
                   if (!existing) {
                     existing = [...currentFunctionCalls]
@@ -1238,7 +1275,7 @@ function ChatPage() {
                           fc.name ===
                             (chunk.item.tool_name ||
                               chunk.item.name ||
-                              chunk.item.type)
+                              chunk.item.type),
                       );
                   }
 
@@ -1254,7 +1291,7 @@ function ChatPage() {
                       chunk.item.inputs || existing.arguments;
                     console.log(
                       "🟡 UPDATED existing pending tool call with id:",
-                      existing.id
+                      existing.id,
                     );
                   } else {
                     const functionCall = {
@@ -1275,7 +1312,7 @@ function ChatPage() {
                         id: fc.id,
                         name: fc.name,
                         type: fc.type,
-                      }))
+                      })),
                     );
                   }
                 }
@@ -1553,7 +1590,7 @@ function ChatPage() {
 
   const handleForkConversation = (
     messageIndex: number,
-    event?: React.MouseEvent
+    event?: React.MouseEvent,
   ) => {
     // Prevent any default behavior and stop event propagation
     if (event) {
@@ -1618,7 +1655,7 @@ function ChatPage() {
 
   const renderFunctionCalls = (
     functionCalls: FunctionCall[],
-    messageIndex?: number
+    messageIndex?: number,
   ) => {
     if (!functionCalls || functionCalls.length === 0) return null;
 
@@ -1851,6 +1888,162 @@ function ChatPage() {
     handleSendMessage(suggestion);
   };
 
+  const handleKeyDown = (e: React.KeyboardEvent<HTMLTextAreaElement>) => {
+    // Handle backspace for filter clearing
+    if (e.key === "Backspace" && selectedFilter && input.trim() === "") {
+      e.preventDefault();
+
+      if (isFilterHighlighted) {
+        // Second backspace - remove the filter
+        setSelectedFilter(null);
+        setIsFilterHighlighted(false);
+      } else {
+        // First backspace - highlight the filter
+        setIsFilterHighlighted(true);
+      }
+      return;
+    }
+
+    if (isFilterDropdownOpen) {
+      const filteredFilters = availableFilters.filter((filter) =>
+        filter.name.toLowerCase().includes(filterSearchTerm.toLowerCase()),
+      );
+
+      if (e.key === "Escape") {
+        e.preventDefault();
+        setIsFilterDropdownOpen(false);
+        setFilterSearchTerm("");
+        setSelectedFilterIndex(0);
+        setDropdownDismissed(true);
+
+        // Keep focus on the textarea so user can continue typing normally
+        inputRef.current?.focus();
+        return;
+      }
+
+      if (e.key === "ArrowDown") {
+        e.preventDefault();
+        setSelectedFilterIndex((prev) =>
+          prev < filteredFilters.length - 1 ? prev + 1 : 0,
+        );
+        return;
+      }
+
+      if (e.key === "ArrowUp") {
+        e.preventDefault();
+        setSelectedFilterIndex((prev) =>
+          prev > 0 ? prev - 1 : filteredFilters.length - 1,
+        );
+        return;
+      }
+
+      if (e.key === "Enter") {
+        // Check if we're at the end of an @ mention (space before cursor or end of input)
+        const cursorPos = e.currentTarget.selectionStart || 0;
+        const textBeforeCursor = input.slice(0, cursorPos);
+        const words = textBeforeCursor.split(" ");
+        const lastWord = words[words.length - 1];
+
+        if (lastWord.startsWith("@") && filteredFilters[selectedFilterIndex]) {
+          e.preventDefault();
+          handleFilterSelect(filteredFilters[selectedFilterIndex]);
+          return;
+        }
+      }
+
+      if (e.key === " ") {
+        // Select filter on space if we're typing an @ mention
+        const cursorPos = e.currentTarget.selectionStart || 0;
+        const textBeforeCursor = input.slice(0, cursorPos);
+        const words = textBeforeCursor.split(" ");
+        const lastWord = words[words.length - 1];
+
+        if (lastWord.startsWith("@") && filteredFilters[selectedFilterIndex]) {
+          e.preventDefault();
+          handleFilterSelect(filteredFilters[selectedFilterIndex]);
+          return;
+        }
+      }
+    }
+
+    if (e.key === "Enter" && !e.shiftKey && !isFilterDropdownOpen) {
+      e.preventDefault();
+      if (input.trim() && !loading) {
+        // Trigger form submission by finding the form and calling submit
+        const form = e.currentTarget.closest("form");
+        if (form) {
+          form.requestSubmit();
+        }
+      }
+    }
+  };
+
+  const onChange = (e: React.ChangeEvent<HTMLTextAreaElement>) => {
+    const newValue = e.target.value;
+    setInput(newValue);
+
+    // Clear filter highlight when user starts typing
+    if (isFilterHighlighted) {
+      setIsFilterHighlighted(false);
+    }
+
+    // Find if there's an @ at the start of the last word
+    const words = newValue.split(" ");
+    const lastWord = words[words.length - 1];
+
+    if (lastWord.startsWith("@") && !dropdownDismissed) {
+      const searchTerm = lastWord.slice(1); // Remove the @
+      console.log("Setting search term:", searchTerm);
+      setFilterSearchTerm(searchTerm);
+      setSelectedFilterIndex(0);
+
+      // Only set anchor position when @ is first detected (search term is empty)
+      if (searchTerm === "") {
+        const pos = getCursorPosition(e.target);
+        setAnchorPosition(pos);
+      }
+
+      if (!isFilterDropdownOpen) {
+        loadAvailableFilters();
+        setIsFilterDropdownOpen(true);
+      }
+    } else if (isFilterDropdownOpen) {
+      // Close dropdown if @ is no longer present
+      console.log("Closing dropdown - no @ found");
+      setIsFilterDropdownOpen(false);
+      setFilterSearchTerm("");
+    }
+
+    // Reset dismissed flag when user moves to a different word
+    if (dropdownDismissed && !lastWord.startsWith("@")) {
+      setDropdownDismissed(false);
+    }
+  };
+
+  const onAtClick = () => {
+    if (!isFilterDropdownOpen) {
+      loadAvailableFilters();
+      setIsFilterDropdownOpen(true);
+      setFilterSearchTerm("");
+      setSelectedFilterIndex(0);
+
+      // Get button position for popover anchoring
+      const button = document.querySelector(
+        "[data-filter-button]",
+      ) as HTMLElement;
+      if (button) {
+        const rect = button.getBoundingClientRect();
+        setAnchorPosition({
+          x: rect.left + rect.width / 2,
+          y: rect.top + rect.height / 2 - 12,
+        });
+      }
+    } else {
+      setIsFilterDropdownOpen(false);
+      setAnchorPosition(null);
+    }
+  };
+
   return (
     <div
       className={`fixed inset-0 md:left-72 top-[53px] flex flex-col transition-all duration-300 ${
@@ -1942,7 +2135,11 @@ function ChatPage() {
                     {message.role === "user" && (
                       <div className="flex gap-3">
                         <Avatar className="w-8 h-8 flex-shrink-0 select-none">
-                          <AvatarImage draggable={false} src={user?.picture} alt={user?.name} />
+                          <AvatarImage
+                            draggable={false}
+                            src={user?.picture}
+                            alt={user?.name}
+                          />
                           <AvatarFallback className="text-sm bg-primary/20 text-primary">
                             {user?.name ? (
                               user.name.charAt(0).toUpperCase()
@@ -1967,7 +2164,7 @@ function ChatPage() {
                         <div className="flex-1 min-w-0">
                           {renderFunctionCalls(
                             message.functionCalls || [],
-                            index
+                            index,
                           )}
                           <MarkdownRenderer chatMessage={message.content} />
                         </div>
@@ -1996,7 +2193,7 @@ function ChatPage() {
                     <div className="flex-1">
                       {renderFunctionCalls(
                         streamingMessage.functionCalls,
-                        messages.length
+                        messages.length,
                       )}
                       <MarkdownRenderer
                         chatMessage={streamingMessage.content}
@@ -2063,160 +2260,29 @@ function ChatPage() {
                   </span>
                 </div>
               )}
-              <textarea
+              <div className="relative" style={{height: `${textareaHeight + 60}px`}}>
+              <TextareaAutosize
                 ref={inputRef}
                 value={input}
-                onChange={(e) => {
-                  const newValue = e.target.value;
-                  setInput(newValue);
-
-                  // Clear filter highlight when user starts typing
-                  if (isFilterHighlighted) {
-                    setIsFilterHighlighted(false);
-                  }
-
-                  // Find if there's an @ at the start of the last word
-                  const words = newValue.split(" ");
-                  const lastWord = words[words.length - 1];
-
-                  if (lastWord.startsWith("@") && !dropdownDismissed) {
-                    const searchTerm = lastWord.slice(1); // Remove the @
-                    console.log("Setting search term:", searchTerm);
-                    setFilterSearchTerm(searchTerm);
-                    setSelectedFilterIndex(0);
-
-                    if (!isFilterDropdownOpen) {
-                      loadAvailableFilters();
-                      setIsFilterDropdownOpen(true);
-                    }
-                  } else if (isFilterDropdownOpen) {
-                    // Close dropdown if @ is no longer present
-                    console.log("Closing dropdown - no @ found");
-                    setIsFilterDropdownOpen(false);
-                    setFilterSearchTerm("");
-                  }
-
-                  // Reset dismissed flag when user moves to a different word
-                  if (dropdownDismissed && !lastWord.startsWith("@")) {
-                    setDropdownDismissed(false);
-                  }
-                }}
-                onKeyDown={(e) => {
-                  // Handle backspace for filter clearing
-                  if (
-                    e.key === "Backspace" &&
-                    selectedFilter &&
-                    input.trim() === ""
-                  ) {
-                    e.preventDefault();
-
-                    if (isFilterHighlighted) {
-                      // Second backspace - remove the filter
-                      setSelectedFilter(null);
-                      setIsFilterHighlighted(false);
-                    } else {
-                      // First backspace - highlight the filter
-                      setIsFilterHighlighted(true);
-                    }
-                    return;
-                  }
-
-                  if (isFilterDropdownOpen) {
-                    const filteredFilters = availableFilters.filter((filter) =>
-                      filter.name
-                        .toLowerCase()
-                        .includes(filterSearchTerm.toLowerCase())
-                    );
-
-                    if (e.key === "Escape") {
-                      e.preventDefault();
-                      setIsFilterDropdownOpen(false);
-                      setFilterSearchTerm("");
-                      setSelectedFilterIndex(0);
-                      setDropdownDismissed(true);
-
-                      // Keep focus on the textarea so user can continue typing normally
-                      inputRef.current?.focus();
-                      return;
-                    }
-
-                    if (e.key === "ArrowDown") {
-                      e.preventDefault();
-                      setSelectedFilterIndex((prev) =>
-                        prev < filteredFilters.length - 1 ? prev + 1 : 0
-                      );
-                      return;
-                    }
-
-                    if (e.key === "ArrowUp") {
-                      e.preventDefault();
-                      setSelectedFilterIndex((prev) =>
-                        prev > 0 ? prev - 1 : filteredFilters.length - 1
-                      );
-                      return;
-                    }
-
-                    if (e.key === "Enter") {
-                      // Check if we're at the end of an @ mention (space before cursor or end of input)
-                      const cursorPos = e.currentTarget.selectionStart || 0;
-                      const textBeforeCursor = input.slice(0, cursorPos);
-                      const words = textBeforeCursor.split(" ");
-                      const lastWord = words[words.length - 1];
-
-                      if (
-                        lastWord.startsWith("@") &&
-                        filteredFilters[selectedFilterIndex]
-                      ) {
-                        e.preventDefault();
-                        handleFilterSelect(
-                          filteredFilters[selectedFilterIndex]
-                        );
-                        return;
-                      }
-                    }
-
-                    if (e.key === " ") {
-                      // Select filter on space if we're typing an @ mention
-                      const cursorPos = e.currentTarget.selectionStart || 0;
-                      const textBeforeCursor = input.slice(0, cursorPos);
-                      const words = textBeforeCursor.split(" ");
-                      const lastWord = words[words.length - 1];
-
-                      if (
-                        lastWord.startsWith("@") &&
-                        filteredFilters[selectedFilterIndex]
-                      ) {
-                        e.preventDefault();
-                        handleFilterSelect(
-                          filteredFilters[selectedFilterIndex]
-                        );
-                        return;
-                      }
-                    }
-                  }
-
-                  if (
-                    e.key === "Enter" &&
-                    !e.shiftKey &&
-                    !isFilterDropdownOpen
-                  ) {
-                    e.preventDefault();
-                    if (input.trim() && !loading) {
-                      // Trigger form submission by finding the form and calling submit
-                      const form = e.currentTarget.closest("form");
-                      if (form) {
-                        form.requestSubmit();
-                      }
-                    }
-                  }
-                }}
+                onChange={onChange}
+                onKeyDown={handleKeyDown}
+                onHeightChange={(height) => setTextareaHeight(height)}
+                maxRows={7}
+                minRows={2}
                 placeholder="Type to ask a question..."
                 disabled={loading}
                 className={`w-full bg-transparent px-4 ${
-                  selectedFilter ? "py-2 pb-4" : "py-4"
-                } min-h-[100px] focus-visible:outline-none resize-none`}
-                rows={1}
+                  selectedFilter ? "pt-2" : "pt-4"
+                } focus-visible:outline-none resize-none`}
+                rows={2}
               />
+                {/* Safe area at bottom for buttons */}
+                <div 
+                  className="absolute bottom-0 left-0 right-0 bg-transparent pointer-events-none"
+                  style={{ height: '60px' }}
+                />
+              </div>
+              
             </div>
             <input
               ref={fileInputRef}
@@ -2227,17 +2293,49 @@ function ChatPage() {
             />
             <Button
               type="button"
-              variant="ghost"
-              size="sm"
-              onClick={handleFilterDropdownToggle}
+              variant="outline"
+              size="iconSm"
               className="absolute bottom-3 left-3 h-8 w-8 p-0 rounded-full hover:bg-muted/50"
+              onMouseDown={(e) => {
+                e.preventDefault();
+              }}
+              onClick={onAtClick}
+              data-filter-button
             >
-              <AtSign className="h-4 w-4" />
+              <Funnel className="h-4 w-4" />
             </Button>
-            {isFilterDropdownOpen && (
-              <div
-                ref={dropdownRef}
-                className="absolute bottom-14 left-0 w-64 bg-popover border border-border rounded-md shadow-md z-50 p-2"
+            <Popover
+              open={isFilterDropdownOpen}
+              onOpenChange={(open) => {
+                setIsFilterDropdownOpen(open);
+              }}
+            >
+              {anchorPosition && (
+                <PopoverAnchor
+                  asChild
+                  style={{
+                    position: "fixed",
+                    left: anchorPosition.x,
+                    top: anchorPosition.y,
+                    width: 1,
+                    height: 1,
+                    pointerEvents: "none",
+                  }}
+                >
+                  <div />
+                </PopoverAnchor>
+              )}
+              <PopoverContent
+                className="w-64 p-2"
+                side="top"
+                align="start"
+                sideOffset={6}
+                alignOffset={-18}
+                onOpenAutoFocus={(e) => {
+                  // Prevent auto focus on the popover content
+                  e.preventDefault();
+                  // Keep focus on the input
+                }}
               >
                 <div className="space-y-1">
                   {filterSearchTerm && (
@@ -2253,14 +2351,15 @@ function ChatPage() {
                     <>
                       {!filterSearchTerm && (
                         <button
+                          type="button"
                           onClick={() => handleFilterSelect(null)}
                           className={`w-full text-left px-2 py-2 text-sm rounded hover:bg-muted/50 flex items-center justify-between ${
                             selectedFilterIndex === -1 ? "bg-muted/50" : ""
                           }`}
                         >
-                          <span>No filter</span>
+                          <span>No knowledge filter</span>
                           {!selectedFilter && (
-                            <div className="w-2 h-2 rounded-full bg-blue-500" />
+                            <Check className="h-4 w-4 shrink-0" />
                           )}
                         </button>
                       )}
@@ -2268,11 +2367,12 @@ function ChatPage() {
                         .filter((filter) =>
                           filter.name
                             .toLowerCase()
-                            .includes(filterSearchTerm.toLowerCase())
+                            .includes(filterSearchTerm.toLowerCase()),
                         )
                         .map((filter, index) => (
                           <button
                             key={filter.id}
+                            type="button"
                             onClick={() => handleFilterSelect(filter)}
                             className={`w-full overflow-hidden text-left px-2 py-2 gap-2 text-sm rounded hover:bg-muted/50 flex items-center justify-between ${
                               index === selectedFilterIndex ? "bg-muted/50" : ""
@@ -2289,14 +2389,14 @@ function ChatPage() {
                               )}
                             </div>
                             {selectedFilter?.id === filter.id && (
-                              <div className="w-2 h-2 shrink-0 rounded-full bg-blue-500" />
+                              <Check className="h-4 w-4 shrink-0" />
                             )}
                           </button>
                         ))}
                       {availableFilters.filter((filter) =>
                         filter.name
                           .toLowerCase()
-                          .includes(filterSearchTerm.toLowerCase())
+                          .includes(filterSearchTerm.toLowerCase()),
                       ).length === 0 &&
                         filterSearchTerm && (
                           <div className="px-2 py-3 text-sm text-muted-foreground">
@@ -2306,12 +2406,12 @@ function ChatPage() {
                     </>
                   )}
                 </div>
-              </div>
-            )}
+              </PopoverContent>
+            </Popover>
             <Button
               type="button"
-              variant="ghost"
-              size="sm"
+              variant="outline"
+              size="iconSm"
               onClick={handleFilePickerClick}
               disabled={isUploading}
               className="absolute bottom-3 left-12 h-8 w-8 p-0 rounded-full hover:bg-muted/50"
diff --git a/frontend/src/app/settings/helpers/model-helpers.tsx b/frontend/src/app/settings/helpers/model-helpers.tsx
index 093b8862..829053fe 100644
--- a/frontend/src/app/settings/helpers/model-helpers.tsx
+++ b/frontend/src/app/settings/helpers/model-helpers.tsx
@@ -1,87 +1,97 @@
-import OpenAILogo from "@/components/logo/openai-logo";
-import OllamaLogo from "@/components/logo/ollama-logo";
 import IBMLogo from "@/components/logo/ibm-logo";
+import OllamaLogo from "@/components/logo/ollama-logo";
+import OpenAILogo from "@/components/logo/openai-logo";
 
-export type ModelProvider = 'openai' | 'ollama' | 'ibm';
+export type ModelProvider = "openai" | "ollama" | "watsonx";
 
 export interface ModelOption {
-  value: string;
-  label: string;
+	value: string;
+	label: string;
 }
 
 // Helper function to get model logo based on provider or model name
 export function getModelLogo(modelValue: string, provider?: ModelProvider) {
-  // First check by provider
-  if (provider === 'openai') {
-    return <OpenAILogo className="w-4 h-4" />;
-  } else if (provider === 'ollama') {
-    return <OllamaLogo className="w-4 h-4" />;
-  } else if (provider === 'ibm') {
-    return <IBMLogo className="w-4 h-4" />;
-  }
+	// First check by provider
+	if (provider === "openai") {
+		return <OpenAILogo className="w-4 h-4" />;
+	} else if (provider === "ollama") {
+		return <OllamaLogo className="w-4 h-4" />;
+	} else if (provider === "watsonx") {
+		return <IBMLogo className="w-4 h-4" />;
+	}
 
-  // Fallback to model name analysis
-  if (modelValue.includes('gpt') || modelValue.includes('text-embedding')) {
-    return <OpenAILogo className="w-4 h-4" />;
-  } else if (modelValue.includes('llama') || modelValue.includes('ollama')) {
-    return <OllamaLogo className="w-4 h-4" />;
-  } else if (modelValue.includes('granite') || modelValue.includes('slate') || modelValue.includes('ibm')) {
-    return <IBMLogo className="w-4 h-4" />;
-  }
+	// Fallback to model name analysis
+	if (modelValue.includes("gpt") || modelValue.includes("text-embedding")) {
+		return <OpenAILogo className="w-4 h-4" />;
+	} else if (modelValue.includes("llama") || modelValue.includes("ollama")) {
+		return <OllamaLogo className="w-4 h-4" />;
+	} else if (
+		modelValue.includes("granite") ||
+		modelValue.includes("slate") ||
+		modelValue.includes("ibm")
+	) {
+		return <IBMLogo className="w-4 h-4" />;
+	}
 
-  return <OpenAILogo className="w-4 h-4" />; // Default to OpenAI logo
+	return <OpenAILogo className="w-4 h-4" />; // Default to OpenAI logo
 }
 
 // Helper function to get fallback models by provider
 export function getFallbackModels(provider: ModelProvider) {
-  switch (provider) {
-    case 'openai':
-      return {
-        language: [
-          { value: 'gpt-4', label: 'GPT-4' },
-          { value: 'gpt-4-turbo', label: 'GPT-4 Turbo' },
-          { value: 'gpt-3.5-turbo', label: 'GPT-3.5 Turbo' },
-        ],
-        embedding: [
-          { value: 'text-embedding-ada-002', label: 'text-embedding-ada-002' },
-          { value: 'text-embedding-3-small', label: 'text-embedding-3-small' },
-          { value: 'text-embedding-3-large', label: 'text-embedding-3-large' },
-        ],
-      };
-    case 'ollama':
-      return {
-        language: [
-          { value: 'llama2', label: 'Llama 2' },
-          { value: 'llama2:13b', label: 'Llama 2 13B' },
-          { value: 'codellama', label: 'Code Llama' },
-        ],
-        embedding: [
-          { value: 'mxbai-embed-large', label: 'MxBai Embed Large' },
-          { value: 'nomic-embed-text', label: 'Nomic Embed Text' },
-        ],
-      };
-    case 'ibm':
-      return {
-        language: [
-          { value: 'meta-llama/llama-3-1-70b-instruct', label: 'Llama 3.1 70B Instruct' },
-          { value: 'ibm/granite-13b-chat-v2', label: 'Granite 13B Chat v2' },
-        ],
-        embedding: [
-          { value: 'ibm/slate-125m-english-rtrvr', label: 'Slate 125M English Retriever' },
-        ],
-      };
-    default:
-      return {
-        language: [
-          { value: 'gpt-4', label: 'GPT-4' },
-          { value: 'gpt-4-turbo', label: 'GPT-4 Turbo' },
-          { value: 'gpt-3.5-turbo', label: 'GPT-3.5 Turbo' },
-        ],
-        embedding: [
-          { value: 'text-embedding-ada-002', label: 'text-embedding-ada-002' },
-          { value: 'text-embedding-3-small', label: 'text-embedding-3-small' },
-          { value: 'text-embedding-3-large', label: 'text-embedding-3-large' },
-        ],
-      };
-  }
-}
\ No newline at end of file
+	switch (provider) {
+		case "openai":
+			return {
+				language: [
+					{ value: "gpt-4", label: "GPT-4" },
+					{ value: "gpt-4-turbo", label: "GPT-4 Turbo" },
+					{ value: "gpt-3.5-turbo", label: "GPT-3.5 Turbo" },
+				],
+				embedding: [
+					{ value: "text-embedding-ada-002", label: "text-embedding-ada-002" },
+					{ value: "text-embedding-3-small", label: "text-embedding-3-small" },
+					{ value: "text-embedding-3-large", label: "text-embedding-3-large" },
+				],
+			};
+		case "ollama":
+			return {
+				language: [
+					{ value: "llama2", label: "Llama 2" },
+					{ value: "llama2:13b", label: "Llama 2 13B" },
+					{ value: "codellama", label: "Code Llama" },
+				],
+				embedding: [
+					{ value: "mxbai-embed-large", label: "MxBai Embed Large" },
+					{ value: "nomic-embed-text", label: "Nomic Embed Text" },
+				],
+			};
+		case "watsonx":
+			return {
+				language: [
+					{
+						value: "meta-llama/llama-3-1-70b-instruct",
+						label: "Llama 3.1 70B Instruct",
+					},
+					{ value: "ibm/granite-13b-chat-v2", label: "Granite 13B Chat v2" },
+				],
+				embedding: [
+					{
+						value: "ibm/slate-125m-english-rtrvr",
+						label: "Slate 125M English Retriever",
+					},
+				],
+			};
+		default:
+			return {
+				language: [
+					{ value: "gpt-4", label: "GPT-4" },
+					{ value: "gpt-4-turbo", label: "GPT-4 Turbo" },
+					{ value: "gpt-3.5-turbo", label: "GPT-3.5 Turbo" },
+				],
+				embedding: [
+					{ value: "text-embedding-ada-002", label: "text-embedding-ada-002" },
+					{ value: "text-embedding-3-small", label: "text-embedding-3-small" },
+					{ value: "text-embedding-3-large", label: "text-embedding-3-large" },
+				],
+			};
+	}
+}
diff --git a/frontend/src/app/settings/page.tsx b/frontend/src/app/settings/page.tsx
index 6017ab5b..cf9e0497 100644
--- a/frontend/src/app/settings/page.tsx
+++ b/frontend/src/app/settings/page.tsx
@@ -1,47 +1,47 @@
 "use client";
 
-import { ArrowUpRight, Loader2, Plus, Minus } from "lucide-react";
-import { useRouter, useSearchParams } from "next/navigation";
+import { ArrowUpRight, Loader2, Minus, Plus } from "lucide-react";
 import Link from "next/link";
+import { useRouter, useSearchParams } from "next/navigation";
 import { Suspense, useCallback, useEffect, useState } from "react";
 import { useUpdateFlowSettingMutation } from "@/app/api/mutations/useUpdateFlowSettingMutation";
 import {
-  useGetIBMModelsQuery,
-  useGetOllamaModelsQuery,
-  useGetOpenAIModelsQuery,
+	useGetIBMModelsQuery,
+	useGetOllamaModelsQuery,
+	useGetOpenAIModelsQuery,
 } from "@/app/api/queries/useGetModelsQuery";
 import { useGetSettingsQuery } from "@/app/api/queries/useGetSettingsQuery";
 import { ConfirmationDialog } from "@/components/confirmation-dialog";
+import { LabelWrapper } from "@/components/label-wrapper";
 import { ProtectedRoute } from "@/components/protected-route";
 import { Button } from "@/components/ui/button";
 import {
-  Card,
-  CardContent,
-  CardDescription,
-  CardHeader,
-  CardTitle,
+	Card,
+	CardContent,
+	CardDescription,
+	CardHeader,
+	CardTitle,
 } from "@/components/ui/card";
-import { Switch } from "@/components/ui/switch";
 import { Input } from "@/components/ui/input";
 import { Label } from "@/components/ui/label";
 import {
-  Select,
-  SelectContent,
-  SelectTrigger,
-  SelectValue,
+	Select,
+	SelectContent,
+	SelectTrigger,
+	SelectValue,
 } from "@/components/ui/select";
+import { Switch } from "@/components/ui/switch";
 import { Textarea } from "@/components/ui/textarea";
 import { useAuth } from "@/contexts/auth-context";
 import { useTask } from "@/contexts/task-context";
-import { useDebounce } from "@/lib/debounce";
 import {
-  DEFAULT_AGENT_SETTINGS,
-  DEFAULT_KNOWLEDGE_SETTINGS,
-  UI_CONSTANTS,
+	DEFAULT_AGENT_SETTINGS,
+	DEFAULT_KNOWLEDGE_SETTINGS,
+	UI_CONSTANTS,
 } from "@/lib/constants";
+import { useDebounce } from "@/lib/debounce";
 import { getFallbackModels, type ModelProvider } from "./helpers/model-helpers";
 import { ModelSelectItems } from "./helpers/model-select-item";
-import { LabelWrapper } from "@/components/label-wrapper";
 
 import GoogleDriveIcon from "./icons/google-drive-icon";
 import OneDriveIcon from "./icons/one-drive-icon";
@@ -50,1181 +50,1180 @@ import SharePointIcon from "./icons/share-point-icon";
 const { MAX_SYSTEM_PROMPT_CHARS } = UI_CONSTANTS;
 
 interface GoogleDriveFile {
-  id: string;
-  name: string;
-  mimeType: string;
-  webViewLink?: string;
-  iconLink?: string;
+	id: string;
+	name: string;
+	mimeType: string;
+	webViewLink?: string;
+	iconLink?: string;
 }
 
 interface OneDriveFile {
-  id: string;
-  name: string;
-  mimeType?: string;
-  webUrl?: string;
-  driveItem?: {
-    file?: { mimeType: string };
-    folder?: unknown;
-  };
+	id: string;
+	name: string;
+	mimeType?: string;
+	webUrl?: string;
+	driveItem?: {
+		file?: { mimeType: string };
+		folder?: unknown;
+	};
 }
 
 interface Connector {
-  id: string;
-  name: string;
-  description: string;
-  icon: React.ReactNode;
-  status: "not_connected" | "connecting" | "connected" | "error";
-  type: string;
-  connectionId?: string;
-  access_token?: string;
-  selectedFiles?: GoogleDriveFile[] | OneDriveFile[];
+	id: string;
+	name: string;
+	description: string;
+	icon: React.ReactNode;
+	status: "not_connected" | "connecting" | "connected" | "error";
+	type: string;
+	connectionId?: string;
+	access_token?: string;
+	selectedFiles?: GoogleDriveFile[] | OneDriveFile[];
 }
 
 interface SyncResult {
-  processed?: number;
-  added?: number;
-  errors?: number;
-  skipped?: number;
-  total?: number;
+	processed?: number;
+	added?: number;
+	errors?: number;
+	skipped?: number;
+	total?: number;
 }
 
 interface Connection {
-  connection_id: string;
-  is_active: boolean;
-  created_at: string;
-  last_sync?: string;
+	connection_id: string;
+	is_active: boolean;
+	created_at: string;
+	last_sync?: string;
 }
 
 const DEFAULT_CONNECTORS: Connector[] = [
-  {
-    id: "google_drive",
-    name: "Google Drive",
-    description: "Google Drive is not configured.",
-    icon: <GoogleDriveIcon />,
-    status: "not_connected",
-    type: "google_drive",
-  },
-  {
-    id: "one_drive",
-    name: "OneDrive",
-    description: "OneDrive is not configured.",
-    icon: <OneDriveIcon />,
-    status: "not_connected",
-    type: "one_drive",
-  },
-  {
-    id: "amazon_s3",
-    name: "SharePoint",
-    description: "SharePoint is not configured.",
-    icon: <SharePointIcon />,
-    status: "not_connected",
-    type: "sharepoint",
-  },
+	{
+		id: "google_drive",
+		name: "Google Drive",
+		description: "Google Drive is not configured.",
+		icon: <GoogleDriveIcon />,
+		status: "not_connected",
+		type: "google_drive",
+	},
+	{
+		id: "one_drive",
+		name: "OneDrive",
+		description: "OneDrive is not configured.",
+		icon: <OneDriveIcon />,
+		status: "not_connected",
+		type: "one_drive",
+	},
+	{
+		id: "amazon_s3",
+		name: "SharePoint",
+		description: "SharePoint is not configured.",
+		icon: <SharePointIcon />,
+		status: "not_connected",
+		type: "sharepoint",
+	},
 ];
 
 function KnowledgeSourcesPage() {
-  const { isAuthenticated, isNoAuthMode } = useAuth();
-  const { addTask, tasks } = useTask();
-  const searchParams = useSearchParams();
-  const router = useRouter();
-
-  // Connectors state
-  const [connectors, setConnectors] = useState<Connector[]>([]);
-  const [isConnecting, setIsConnecting] = useState<string | null>(null);
-  const [isSyncing, setIsSyncing] = useState<string | null>(null);
-  const [syncResults, setSyncResults] = useState<{
-    [key: string]: SyncResult | null;
-  }>({});
-  const [maxFiles, setMaxFiles] = useState<number>(10);
-  const [syncAllFiles, setSyncAllFiles] = useState<boolean>(false);
-
-  // Only keep systemPrompt state since it needs manual save button
-  const [systemPrompt, setSystemPrompt] = useState<string>("");
-  const [chunkSize, setChunkSize] = useState<number>(1024);
-  const [chunkOverlap, setChunkOverlap] = useState<number>(50);
-  const [tableStructure, setTableStructure] = useState<boolean>(false);
-  const [ocr, setOcr] = useState<boolean>(false);
-  const [pictureDescriptions, setPictureDescriptions] =
-    useState<boolean>(false);
-
-  // Fetch settings using React Query
-  const { data: settings = {} } = useGetSettingsQuery({
-    enabled: isAuthenticated || isNoAuthMode,
-  });
-
-  // Get the current provider from settings
-  const currentProvider = (settings.provider?.model_provider ||
-    "openai") as ModelProvider;
-
-  // Fetch available models based on provider
-  const { data: openaiModelsData } = useGetOpenAIModelsQuery(
-    undefined, // Let backend use stored API key from configuration
-    {
-      enabled:
-        (isAuthenticated || isNoAuthMode) && currentProvider === "openai",
-    }
-  );
-
-  const { data: ollamaModelsData } = useGetOllamaModelsQuery(
-    undefined, // No params for now, could be extended later
-    {
-      enabled:
-        (isAuthenticated || isNoAuthMode) && currentProvider === "ollama",
-    }
-  );
-
-  const { data: ibmModelsData } = useGetIBMModelsQuery(
-    undefined, // No params for now, could be extended later
-    {
-      enabled: (isAuthenticated || isNoAuthMode) && currentProvider === "ibm",
-    }
-  );
-
-  // Select the appropriate models data based on provider
-  const modelsData =
-    currentProvider === "openai"
-      ? openaiModelsData
-      : currentProvider === "ollama"
-      ? ollamaModelsData
-      : currentProvider === "ibm"
-      ? ibmModelsData
-      : openaiModelsData; // fallback to openai
-
-  // Mutations
-  const updateFlowSettingMutation = useUpdateFlowSettingMutation({
-    onSuccess: () => {
-      console.log("Setting updated successfully");
-    },
-    onError: (error) => {
-      console.error("Failed to update setting:", error.message);
-    },
-  });
-
-  // Debounced update function
-  const debouncedUpdate = useDebounce(
-    (variables: Parameters<typeof updateFlowSettingMutation.mutate>[0]) => {
-      updateFlowSettingMutation.mutate(variables);
-    },
-    500
-  );
-
-  // Sync system prompt state with settings data
-  useEffect(() => {
-    if (settings.agent?.system_prompt) {
-      setSystemPrompt(settings.agent.system_prompt);
-    }
-  }, [settings.agent?.system_prompt]);
-
-  // Sync chunk size and overlap state with settings data
-  useEffect(() => {
-    if (settings.knowledge?.chunk_size) {
-      setChunkSize(settings.knowledge.chunk_size);
-    }
-  }, [settings.knowledge?.chunk_size]);
-
-  useEffect(() => {
-    if (settings.knowledge?.chunk_overlap) {
-      setChunkOverlap(settings.knowledge.chunk_overlap);
-    }
-  }, [settings.knowledge?.chunk_overlap]);
-
-  // Sync docling settings with settings data
-  useEffect(() => {
-    if (settings.knowledge?.table_structure !== undefined) {
-      setTableStructure(settings.knowledge.table_structure);
-    }
-  }, [settings.knowledge?.table_structure]);
-
-  useEffect(() => {
-    if (settings.knowledge?.ocr !== undefined) {
-      setOcr(settings.knowledge.ocr);
-    }
-  }, [settings.knowledge?.ocr]);
-
-  useEffect(() => {
-    if (settings.knowledge?.picture_descriptions !== undefined) {
-      setPictureDescriptions(settings.knowledge.picture_descriptions);
-    }
-  }, [settings.knowledge?.picture_descriptions]);
-
-  // Update model selection immediately
-  const handleModelChange = (newModel: string) => {
-    updateFlowSettingMutation.mutate({ llm_model: newModel });
-  };
-
-  // Update system prompt with save button
-  const handleSystemPromptSave = () => {
-    updateFlowSettingMutation.mutate({ system_prompt: systemPrompt });
-  };
-
-  // Update embedding model selection immediately
-  const handleEmbeddingModelChange = (newModel: string) => {
-    updateFlowSettingMutation.mutate({ embedding_model: newModel });
-  };
-
-  // Update chunk size setting with debounce
-  const handleChunkSizeChange = (value: string) => {
-    const numValue = Math.max(0, parseInt(value) || 0);
-    setChunkSize(numValue);
-    debouncedUpdate({ chunk_size: numValue });
-  };
-
-  // Update chunk overlap setting with debounce
-  const handleChunkOverlapChange = (value: string) => {
-    const numValue = Math.max(0, parseInt(value) || 0);
-    setChunkOverlap(numValue);
-    debouncedUpdate({ chunk_overlap: numValue });
-  };
-
-  // Update docling settings
-  const handleTableStructureChange = (checked: boolean) => {
-    setTableStructure(checked);
-    updateFlowSettingMutation.mutate({ table_structure: checked });
-  };
-
-  const handleOcrChange = (checked: boolean) => {
-    setOcr(checked);
-    updateFlowSettingMutation.mutate({ ocr: checked });
-  };
-
-  const handlePictureDescriptionsChange = (checked: boolean) => {
-    setPictureDescriptions(checked);
-    updateFlowSettingMutation.mutate({ picture_descriptions: checked });
-  };
-
-  console.log({ connectors });
-
-  // Helper function to get connector icon
-  const getConnectorIcon = useCallback((iconName: string) => {
-    const iconMap: { [key: string]: React.ReactElement } = {
-      "google-drive": <GoogleDriveIcon />,
-      sharepoint: (
-        <div className="w-8 h-8 bg-blue-700 rounded flex items-center justify-center text-white font-bold leading-none shrink-0">
-          SP
-        </div>
-      ),
-      onedrive: (
-        <div className="w-8 h-8 bg-white border border-gray-300 rounded flex items-center justify-center">
-          <OneDriveIcon />
-        </div>
-      ),
-    };
-    return (
-      iconMap[iconName] || (
-        <div className="w-8 h-8 bg-gray-500 rounded flex items-center justify-center text-white font-bold leading-none shrink-0">
-          ?
-        </div>
-      )
-    );
-  }, []);
-
-  // Connector functions
-  const checkConnectorStatuses = useCallback(async () => {
-    try {
-      // Fetch available connectors from backend
-      const connectorsResponse = await fetch("/api/connectors");
-      if (!connectorsResponse.ok) {
-        throw new Error("Failed to load connectors");
-      }
-
-      const connectorsResult = await connectorsResponse.json();
-      const connectorTypes = Object.keys(connectorsResult.connectors);
-
-      // Initialize connectors list with metadata from backend
-      const initialConnectors = connectorTypes
-        .filter((type) => connectorsResult.connectors[type].available) // Only show available connectors
-        .map((type) => ({
-          id: type,
-          name: connectorsResult.connectors[type].name,
-          description: connectorsResult.connectors[type].description,
-          icon: getConnectorIcon(connectorsResult.connectors[type].icon),
-          status: "not_connected" as const,
-          type: type,
-        }));
-      console.log({ initialConnectors });
-      setConnectors(initialConnectors);
-
-      // Check status for each connector type
-
-      for (const connectorType of connectorTypes) {
-        const response = await fetch(`/api/connectors/${connectorType}/status`);
-        if (response.ok) {
-          const data = await response.json();
-          const connections = data.connections || [];
-          const activeConnection = connections.find(
-            (conn: Connection) => conn.is_active
-          );
-          const isConnected = activeConnection !== undefined;
-
-          setConnectors((prev) =>
-            prev.map((c) =>
-              c.type === connectorType
-                ? {
-                    ...c,
-                    status: isConnected ? "connected" : "not_connected",
-                    connectionId: activeConnection?.connection_id,
-                  }
-                : c
-            )
-          );
-        }
-      }
-    } catch (error) {
-      console.error("Failed to check connector statuses:", error);
-    }
-  }, [getConnectorIcon]);
-
-  const handleConnect = async (connector: Connector) => {
-    setIsConnecting(connector.id);
-    setSyncResults((prev) => ({ ...prev, [connector.id]: null }));
-
-    try {
-      // Use the shared auth callback URL, same as connectors page
-      const redirectUri = `${window.location.origin}/auth/callback`;
-
-      const response = await fetch("/api/auth/init", {
-        method: "POST",
-        headers: {
-          "Content-Type": "application/json",
-        },
-        body: JSON.stringify({
-          connector_type: connector.type,
-          purpose: "data_source",
-          name: `${connector.name} Connection`,
-          redirect_uri: redirectUri,
-        }),
-      });
-
-      if (response.ok) {
-        const result = await response.json();
-
-        if (result.oauth_config) {
-          localStorage.setItem("connecting_connector_id", result.connection_id);
-          localStorage.setItem("connecting_connector_type", connector.type);
-
-          const authUrl =
-            `${result.oauth_config.authorization_endpoint}?` +
-            `client_id=${result.oauth_config.client_id}&` +
-            `response_type=code&` +
-            `scope=${result.oauth_config.scopes.join(" ")}&` +
-            `redirect_uri=${encodeURIComponent(
-              result.oauth_config.redirect_uri
-            )}&` +
-            `access_type=offline&` +
-            `prompt=consent&` +
-            `state=${result.connection_id}`;
-
-          window.location.href = authUrl;
-        }
-      } else {
-        console.error("Failed to initiate connection");
-        setIsConnecting(null);
-      }
-    } catch (error) {
-      console.error("Connection error:", error);
-      setIsConnecting(null);
-    }
-  };
-
-  // const handleSync = async (connector: Connector) => {
-  //   if (!connector.connectionId) return;
-
-  //   setIsSyncing(connector.id);
-  //   setSyncResults(prev => ({ ...prev, [connector.id]: null }));
-
-  //   try {
-  //     const syncBody: {
-  //       connection_id: string;
-  //       max_files?: number;
-  //       selected_files?: string[];
-  //     } = {
-  //       connection_id: connector.connectionId,
-  //       max_files: syncAllFiles ? 0 : maxFiles || undefined,
-  //     };
-
-  //     // Note: File selection is now handled via the cloud connectors dialog
-
-  //     const response = await fetch(`/api/connectors/${connector.type}/sync`, {
-  //       method: "POST",
-  //       headers: {
-  //         "Content-Type": "application/json",
-  //       },
-  //       body: JSON.stringify(syncBody),
-  //     });
-
-  //     const result = await response.json();
-
-  //     if (response.status === 201) {
-  //       const taskId = result.task_id;
-  //       if (taskId) {
-  //         addTask(taskId);
-  //         setSyncResults(prev => ({
-  //           ...prev,
-  //           [connector.id]: {
-  //             processed: 0,
-  //             total: result.total_files || 0,
-  //           },
-  //         }));
-  //       }
-  //     } else if (response.ok) {
-  //       setSyncResults(prev => ({ ...prev, [connector.id]: result }));
-  //       // Note: Stats will auto-refresh via task completion watcher for async syncs
-  //     } else {
-  //       console.error("Sync failed:", result.error);
-  //     }
-  //   } catch (error) {
-  //     console.error("Sync error:", error);
-  //   } finally {
-  //     setIsSyncing(null);
-  //   }
-  // };
-
-  const getStatusBadge = (status: Connector["status"]) => {
-    switch (status) {
-      case "connected":
-        return <div className="h-2 w-2 bg-green-500 rounded-full" />;
-      case "connecting":
-        return <div className="h-2 w-2 bg-yellow-500 rounded-full" />;
-      case "error":
-        return <div className="h-2 w-2 bg-red-500 rounded-full" />;
-      default:
-        return <div className="h-2 w-2 bg-muted rounded-full" />;
-    }
-  };
-
-  const navigateToKnowledgePage = (connector: Connector) => {
-    const provider = connector.type.replace(/-/g, "_");
-    router.push(`/upload/${provider}`);
-  };
-
-  // Check connector status on mount and when returning from OAuth
-  useEffect(() => {
-    if (isAuthenticated) {
-      checkConnectorStatuses();
-    }
-
-    if (searchParams.get("oauth_success") === "true") {
-      const url = new URL(window.location.href);
-      url.searchParams.delete("oauth_success");
-      window.history.replaceState({}, "", url.toString());
-    }
-  }, [searchParams, isAuthenticated, checkConnectorStatuses]);
-
-  // Track previous tasks to detect new completions
-  const [prevTasks, setPrevTasks] = useState<typeof tasks>([]);
-
-  // Watch for task completions and refresh stats
-  useEffect(() => {
-    // Find newly completed tasks by comparing with previous state
-    const newlyCompletedTasks = tasks.filter((task) => {
-      const wasCompleted =
-        prevTasks.find((prev) => prev.task_id === task.task_id)?.status ===
-        "completed";
-      return task.status === "completed" && !wasCompleted;
-    });
-
-    if (newlyCompletedTasks.length > 0) {
-      // Task completed - could refresh data here if needed
-      const timeoutId = setTimeout(() => {
-        // Stats refresh removed
-      }, 1000);
-
-      // Update previous tasks state
-      setPrevTasks(tasks);
-
-      return () => clearTimeout(timeoutId);
-    } else {
-      // Always update previous tasks state
-      setPrevTasks(tasks);
-    }
-  }, [tasks, prevTasks]);
-
-  const handleEditInLangflow = (
-    flowType: "chat" | "ingest",
-    closeDialog: () => void
-  ) => {
-    // Select the appropriate flow ID and edit URL based on flow type
-    const targetFlowId =
-      flowType === "ingest" ? settings.ingest_flow_id : settings.flow_id;
-    const editUrl =
-      flowType === "ingest"
-        ? settings.langflow_ingest_edit_url
-        : settings.langflow_edit_url;
-
-    const derivedFromWindow =
-      typeof window !== "undefined"
-        ? `${window.location.protocol}//${window.location.hostname}:7860`
-        : "";
-    const base = (
-      settings.langflow_public_url ||
-      derivedFromWindow ||
-      "http://localhost:7860"
-    ).replace(/\/$/, "");
-    const computed = targetFlowId ? `${base}/flow/${targetFlowId}` : base;
-
-    const url = editUrl || computed;
-
-    window.open(url, "_blank");
-    closeDialog(); // Close immediately after opening Langflow
-  };
-
-  const handleRestoreRetrievalFlow = (closeDialog: () => void) => {
-    fetch(`/api/reset-flow/retrieval`, {
-      method: "POST",
-    })
-      .then((response) => {
-        if (response.ok) {
-          return response.json();
-        }
-        throw new Error(`HTTP ${response.status}: ${response.statusText}`);
-      })
-      .then(() => {
-        // Only reset form values if the API call was successful
-        setSystemPrompt(DEFAULT_AGENT_SETTINGS.system_prompt);
-        // Trigger model update to default model
-        handleModelChange(DEFAULT_AGENT_SETTINGS.llm_model);
-        closeDialog(); // Close after successful completion
-      })
-      .catch((error) => {
-        console.error("Error restoring retrieval flow:", error);
-        closeDialog(); // Close even on error (could show error toast instead)
-      });
-  };
-
-  const handleRestoreIngestFlow = (closeDialog: () => void) => {
-    fetch(`/api/reset-flow/ingest`, {
-      method: "POST",
-    })
-      .then((response) => {
-        if (response.ok) {
-          return response.json();
-        }
-        throw new Error(`HTTP ${response.status}: ${response.statusText}`);
-      })
-      .then(() => {
-        // Only reset form values if the API call was successful
-        setChunkSize(DEFAULT_KNOWLEDGE_SETTINGS.chunk_size);
-        setChunkOverlap(DEFAULT_KNOWLEDGE_SETTINGS.chunk_overlap);
-        setTableStructure(false);
-        setOcr(false);
-        setPictureDescriptions(false);
-        closeDialog(); // Close after successful completion
-      })
-      .catch((error) => {
-        console.error("Error restoring ingest flow:", error);
-        closeDialog(); // Close even on error (could show error toast instead)
-      });
-  };
-
-  return (
-    <div className="space-y-8">
-      {/* Connectors Section */}
-      <div className="space-y-6">
-        <div>
-          <h2 className="text-lg font-semibold tracking-tight mb-2">
-            Cloud Connectors
-          </h2>
-        </div>
-
-        {/* Conditional Sync Settings or No-Auth Message */}
-        {
-          isNoAuthMode ? (
-            <Card className="border-yellow-500/50 bg-yellow-500/5">
-              <CardHeader>
-                <CardTitle className="text-lg text-yellow-600">
-                  Cloud connectors are only available with auth mode enabled
-                </CardTitle>
-                <CardDescription className="text-sm">
-                  Please provide the following environment variables and
-                  restart:
-                </CardDescription>
-              </CardHeader>
-              <CardContent>
-                <div className="bg-muted rounded-md p-4 font-mono text-sm">
-                  <div className="text-muted-foreground mb-2">
-                    # make here
-                    https://console.cloud.google.com/apis/credentials
-                  </div>
-                  <div>GOOGLE_OAUTH_CLIENT_ID=</div>
-                  <div>GOOGLE_OAUTH_CLIENT_SECRET=</div>
-                </div>
-              </CardContent>
-            </Card>
-          ) : null
-          // <div className="flex items-center justify-between py-4">
-          //   <div>
-          //     <h3 className="text-lg font-medium">Sync Settings</h3>
-          //     <p className="text-sm text-muted-foreground">
-          //       Configure how many files to sync when manually triggering a sync
-          //     </p>
-          //   </div>
-          //   <div className="flex items-center gap-4">
-          //     <div className="flex items-center space-x-2">
-          //       <Checkbox
-          //         id="syncAllFiles"
-          //         checked={syncAllFiles}
-          //         onCheckedChange={checked => {
-          //           setSyncAllFiles(!!checked);
-          //           if (checked) {
-          //             setMaxFiles(0);
-          //           } else {
-          //             setMaxFiles(10);
-          //           }
-          //         }}
-          //       />
-          //       <Label
-          //         htmlFor="syncAllFiles"
-          //         className="font-medium whitespace-nowrap"
-          //       >
-          //         Sync all files
-          //       </Label>
-          //     </div>
-          //     <Label
-          //       htmlFor="maxFiles"
-          //       className="font-medium whitespace-nowrap"
-          //     >
-          //       Max files per sync:
-          //     </Label>
-          //     <div className="relative">
-          //       <Input
-          //         id="maxFiles"
-          //         type="number"
-          //         value={syncAllFiles ? 0 : maxFiles}
-          //         onChange={e => setMaxFiles(parseInt(e.target.value) || 10)}
-          //         disabled={syncAllFiles}
-          //         className="w-16 min-w-16 max-w-16 flex-shrink-0 disabled:opacity-50 disabled:cursor-not-allowed"
-          //         min="1"
-          //         max="100"
-          //         title={
-          //           syncAllFiles
-          //             ? "Disabled when 'Sync all files' is checked"
-          //             : "Leave blank or set to 0 for unlimited"
-          //         }
-          //       />
-          //     </div>
-          //   </div>
-          // </div>
-        }
-
-        {/* Connectors Grid */}
-        <div className="grid gap-6 md:grid-cols-2 lg:grid-cols-3">
-          {DEFAULT_CONNECTORS.map((connector) => {
-            const actualConnector = connectors.find(
-              (c) => c.id === connector.id
-            );
-            return (
-              <Card key={connector.id} className="relative flex flex-col">
-                <CardHeader>
-                  <div className="flex flex-col items-start justify-between">
-                    <div className="flex flex-col gap-3">
-                      <div className="mb-1">
-                        <div
-                          className={`w-8 h-8 ${
-                            actualConnector ? "bg-white" : "bg-muted grayscale"
-                          } rounded flex items-center justify-center`}
-                        >
-                          {connector.icon}
-                        </div>
-                      </div>
-                      <CardTitle className="flex flex-row items-center gap-2">
-                        {connector.name}
-                        {actualConnector &&
-                          getStatusBadge(actualConnector.status)}
-                      </CardTitle>
-                      <CardDescription className="text-[13px]">
-                        {actualConnector?.description
-                          ? `${actualConnector.name} is configured.`
-                          : connector.description}
-                      </CardDescription>
-                    </div>
-                  </div>
-                </CardHeader>
-                <CardContent className="flex-1 flex flex-col justify-end space-y-4">
-                  {actualConnector?.status === "connected" ? (
-                    <div className="space-y-3">
-                      <Button
-                        onClick={() => navigateToKnowledgePage(connector)}
-                        disabled={isSyncing === connector.id}
-                        className="w-full cursor-pointer"
-                        size="sm"
-                      >
-                        <Plus className="h-4 w-4" />
-                        Add Knowledge
-                      </Button>
-
-                      {syncResults[connector.id] && (
-                        <div className="text-xs text-muted-foreground bg-muted/50 p-2 rounded">
-                          <div>
-                            Processed:{" "}
-                            {syncResults[connector.id]?.processed || 0}
-                          </div>
-                          <div>
-                            Added: {syncResults[connector.id]?.added || 0}
-                          </div>
-                          {syncResults[connector.id]?.errors && (
-                            <div>
-                              Errors: {syncResults[connector.id]?.errors}
-                            </div>
-                          )}
-                        </div>
-                      )}
-                    </div>
-                  ) : (
-                    <div className="text-[13px] text-muted-foreground">
-                      <p>
-                        See our{" "}
-                        <Link
-                          className="text-accent-pink-foreground"
-                          href="https://github.com/langflow-ai/openrag/pull/96/files#diff-06889aa94ccf8dac64e70c8cc30a2ceed32cc3c0c2c14a6ff0336fe882a9c2ccR41"
-                        >
-                          Cloud Connectors installation guide
-                        </Link>{" "}
-                        for more detail.
-                      </p>
-                    </div>
-                  )}
-                </CardContent>
-              </Card>
-            );
-          })}
-        </div>
-      </div>
-      {/* Agent Behavior Section */}
-      <Card>
-        <CardHeader>
-          <div className="flex items-center justify-between">
-            <div>
-              <CardTitle className="text-lg mb-4">Agent</CardTitle>
-              <CardDescription>
-                Quick Agent settings. Edit in Langflow for full control.
-              </CardDescription>
-            </div>
-            <div className="flex gap-2">
-              <ConfirmationDialog
-                trigger={
-                  <Button ignoreTitleCase={true} variant="outline">
-                    Restore flow
-                  </Button>
-                }
-                title="Restore default Retrieval flow"
-                description="This restores defaults and discards all custom settings and overrides. This can’t be undone."
-                confirmText="Restore"
-                variant="destructive"
-                onConfirm={handleRestoreRetrievalFlow}
-              />
-              <ConfirmationDialog
-                trigger={
-                  <Button>
-                    <svg
-                      xmlns="http://www.w3.org/2000/svg"
-                      width="24"
-                      height="22"
-                      viewBox="0 0 24 22"
-                      className="h-4 w-4 mr-2"
-                      aria-label="Langflow icon"
-                    >
-                      <title>Langflow icon</title>
-                      <path
-                        fill="currentColor"
-                        d="M13.0486 0.462158H9.75399C9.44371 0.462158 9.14614 0.586082 8.92674 0.806667L4.03751 5.72232C3.81811 5.9429 3.52054 6.06682 3.21026 6.06682H1.16992C0.511975 6.06682 -0.0165756 6.61212 0.000397655 7.2734L0.0515933 9.26798C0.0679586 9.90556 0.586745 10.4139 1.22111 10.4139H3.59097C3.90124 10.4139 4.19881 10.2899 4.41821 10.0694L9.34823 5.11269C9.56763 4.89211 9.8652 4.76818 10.1755 4.76818H13.0486C13.6947 4.76818 14.2185 4.24157 14.2185 3.59195V1.63839C14.2185 0.988773 13.6947 0.462158 13.0486 0.462158Z"
-                      ></path>
-                      <path
-                        fill="currentColor"
-                        d="M19.5355 11.5862H22.8301C23.4762 11.5862 24 12.1128 24 12.7624V14.716C24 15.3656 23.4762 15.8922 22.8301 15.8922H19.957C19.6467 15.8922 19.3491 16.0161 19.1297 16.2367L14.1997 21.1934C13.9803 21.414 13.6827 21.5379 13.3725 21.5379H11.0026C10.3682 21.5379 9.84945 21.0296 9.83309 20.392L9.78189 18.3974C9.76492 17.7361 10.2935 17.1908 10.9514 17.1908H12.9918C13.302 17.1908 13.5996 17.0669 13.819 16.8463L18.7082 11.9307C18.9276 11.7101 19.2252 11.5862 19.5355 11.5862Z"
-                      ></path>
-                      <path
-                        fill="currentColor"
-                        d="M19.5355 2.9796L22.8301 2.9796C23.4762 2.9796 24 3.50622 24 4.15583V6.1094C24 6.75901 23.4762 7.28563 22.8301 7.28563H19.957C19.6467 7.28563 19.3491 7.40955 19.1297 7.63014L14.1997 12.5868C13.9803 12.8074 13.6827 12.9313 13.3725 12.9313H10.493C10.1913 12.9313 9.90126 13.0485 9.68346 13.2583L4.14867 18.5917C3.93087 18.8016 3.64085 18.9187 3.33917 18.9187H1.32174C0.675616 18.9187 0.151832 18.3921 0.151832 17.7425V15.7343C0.151832 15.0846 0.675616 14.558 1.32174 14.558H3.32468C3.63496 14.558 3.93253 14.4341 4.15193 14.2135L9.40827 8.92878C9.62767 8.70819 9.92524 8.58427 10.2355 8.58427H12.9918C13.302 8.58427 13.5996 8.46034 13.819 8.23976L18.7082 3.32411C18.9276 3.10353 19.2252 2.9796 19.5355 2.9796Z"
-                      ></path>
-                    </svg>
-                    Edit in Langflow
-                  </Button>
-                }
-                title="Edit Retrieval flow in Langflow"
-                description={
-                  <>
-                    <p className="mb-2">
-                      You're entering Langflow. You can edit the{" "}
-                      <b>Retrieval flow</b> and other underlying flows. Manual
-                      changes to components, wiring, or I/O can break this
-                      experience.
-                    </p>
-                    <p>You can restore this flow from Settings.</p>
-                  </>
-                }
-                confirmText="Proceed"
-                confirmIcon={<ArrowUpRight />}
-                onConfirm={(closeDialog) =>
-                  handleEditInLangflow("chat", closeDialog)
-                }
-                variant="warning"
-              />
-            </div>
-          </div>
-        </CardHeader>
-        <CardContent>
-          <div className="space-y-6">
-            <div className="space-y-2">
-              <LabelWrapper
-                helperText="Model used for chat"
-                id="model-select"
-                label="Language model"
-                required
-              >
-                <Select
-                  value={
-                    settings.agent?.llm_model ||
-                    modelsData?.language_models?.find((m) => m.default)
-                      ?.value ||
-                    "gpt-4"
-                  }
-                  onValueChange={handleModelChange}
-                >
-                  <SelectTrigger id="model-select">
-                    <SelectValue placeholder="Select a model" />
-                  </SelectTrigger>
-                  <SelectContent>
-                    <ModelSelectItems
-                      models={modelsData?.language_models}
-                      fallbackModels={
-                        getFallbackModels(currentProvider).language
-                      }
-                      provider={currentProvider}
-                    />
-                  </SelectContent>
-                </Select>
-              </LabelWrapper>
-            </div>
-            <div className="space-y-2">
-              <LabelWrapper label="Agent Instructions" id="system-prompt">
-                <Textarea
-                  id="system-prompt"
-                  placeholder="Enter your agent instructions here..."
-                  value={systemPrompt}
-                  onChange={(e) => setSystemPrompt(e.target.value)}
-                  rows={6}
-                  className={`resize-none ${
-                    systemPrompt.length > MAX_SYSTEM_PROMPT_CHARS
-                      ? "border-red-500 focus:border-red-500"
-                      : ""
-                  }`}
-                />
-                <div className="flex justify-start">
-                  <span
-                    className={`text-xs ${
-                      systemPrompt.length > MAX_SYSTEM_PROMPT_CHARS
-                        ? "text-red-500"
-                        : "text-muted-foreground"
-                    }`}
-                  >
-                    {systemPrompt.length}/{MAX_SYSTEM_PROMPT_CHARS} characters
-                  </span>
-                </div>
-              </LabelWrapper>
-            </div>
-            <div className="flex justify-end pt-2">
-              <Button
-                onClick={handleSystemPromptSave}
-                disabled={
-                  updateFlowSettingMutation.isPending ||
-                  systemPrompt.length > MAX_SYSTEM_PROMPT_CHARS
-                }
-                className="min-w-[120px]"
-                size="sm"
-                variant="outline"
-              >
-                {updateFlowSettingMutation.isPending ? (
-                  <>
-                    <Loader2 className="mr-2 h-4 w-4 animate-spin" />
-                    Saving...
-                  </>
-                ) : (
-                  "Save Agent Instructions"
-                )}
-              </Button>
-            </div>
-          </div>
-        </CardContent>
-      </Card>
-
-      {/* Knowledge Ingest Section */}
-      <Card>
-        <CardHeader>
-          <div className="flex items-center justify-between">
-            <div>
-              <CardTitle className="text-lg mb-4">
-                Knowledge ingestion and retrieval
-              </CardTitle>
-              <CardDescription>
-                Quick knowledge settings. Edit in Langflow for full control.
-              </CardDescription>
-            </div>
-            <div className="flex gap-2">
-              <ConfirmationDialog
-                trigger={
-                  <Button ignoreTitleCase={true} variant="outline">
-                    Restore flow
-                  </Button>
-                }
-                title="Restore default Ingest flow"
-                description="This restores defaults and discards all custom settings and overrides. This can't be undone."
-                confirmText="Restore"
-                variant="destructive"
-                onConfirm={handleRestoreIngestFlow}
-              />
-              <ConfirmationDialog
-                trigger={
-                  <Button>
-                    <svg
-                      xmlns="http://www.w3.org/2000/svg"
-                      width="24"
-                      height="22"
-                      viewBox="0 0 24 22"
-                      className="h-4 w-4 mr-2"
-                      aria-label="Langflow icon"
-                    >
-                      <title>Langflow icon</title>
-                      <path
-                        fill="currentColor"
-                        d="M13.0486 0.462158H9.75399C9.44371 0.462158 9.14614 0.586082 8.92674 0.806667L4.03751 5.72232C3.81811 5.9429 3.52054 6.06682 3.21026 6.06682H1.16992C0.511975 6.06682 -0.0165756 6.61212 0.000397655 7.2734L0.0515933 9.26798C0.0679586 9.90556 0.586745 10.4139 1.22111 10.4139H3.59097C3.90124 10.4139 4.19881 10.2899 4.41821 10.0694L9.34823 5.11269C9.56763 4.89211 9.8652 4.76818 10.1755 4.76818H13.0486C13.6947 4.76818 14.2185 4.24157 14.2185 3.59195V1.63839C14.2185 0.988773 13.6947 0.462158 13.0486 0.462158Z"
-                      ></path>
-                      <path
-                        fill="currentColor"
-                        d="M19.5355 11.5862H22.8301C23.4762 11.5862 24 12.1128 24 12.7624V14.716C24 15.3656 23.4762 15.8922 22.8301 15.8922H19.957C19.6467 15.8922 19.3491 16.0161 19.1297 16.2367L14.1997 21.1934C13.9803 21.414 13.6827 21.5379 13.3725 21.5379H11.0026C10.3682 21.5379 9.84945 21.0296 9.83309 20.392L9.78189 18.3974C9.76492 17.7361 10.2935 17.1908 10.9514 17.1908H12.9918C13.302 17.1908 13.5996 17.0669 13.819 16.8463L18.7082 11.9307C18.9276 11.7101 19.2252 11.5862 19.5355 11.5862Z"
-                      ></path>
-                      <path
-                        fill="currentColor"
-                        d="M19.5355 2.9796L22.8301 2.9796C23.4762 2.9796 24 3.50622 24 4.15583V6.1094C24 6.75901 23.4762 7.28563 22.8301 7.28563H19.957C19.6467 7.28563 19.3491 7.40955 19.1297 7.63014L14.1997 12.5868C13.9803 12.8074 13.6827 12.9313 13.3725 12.9313H10.493C10.1913 12.9313 9.90126 13.0485 9.68346 13.2583L4.14867 18.5917C3.93087 18.8016 3.64085 18.9187 3.33917 18.9187H1.32174C0.675616 18.9187 0.151832 18.3921 0.151832 17.7425V15.7343C0.151832 15.0846 0.675616 14.558 1.32174 14.558H3.32468C3.63496 14.558 3.93253 14.4341 4.15193 14.2135L9.40827 8.92878C9.62767 8.70819 9.92524 8.58427 10.2355 8.58427H12.9918C13.302 8.58427 13.5996 8.46034 13.819 8.23976L18.7082 3.32411C18.9276 3.10353 19.2252 2.9796 19.5355 2.9796Z"
-                      ></path>
-                    </svg>
-                    Edit in Langflow
-                  </Button>
-                }
-                title="Edit Ingest flow in Langflow"
-                description={
-                  <>
-                    <p className="mb-2">
-                      You're entering Langflow. You can edit the{" "}
-                      <b>Ingest flow</b> and other underlying flows. Manual
-                      changes to components, wiring, or I/O can break this
-                      experience.
-                    </p>
-                    <p>You can restore this flow from Settings.</p>
-                  </>
-                }
-                confirmText="Proceed"
-                confirmIcon={<ArrowUpRight />}
-                variant="warning"
-                onConfirm={(closeDialog) =>
-                  handleEditInLangflow("ingest", closeDialog)
-                }
-              />
-            </div>
-          </div>
-        </CardHeader>
-        <CardContent>
-          <div className="space-y-6">
-            <div className="space-y-2">
-              <LabelWrapper
-                helperText="Model used for knowledge ingest and retrieval"
-                id="embedding-model-select"
-                label="Embedding model"
-              >
-                <Select
-                  // Disabled until API supports multiple embedding models
-                  disabled={true}
-                  value={
-                    settings.knowledge?.embedding_model ||
-                    modelsData?.embedding_models?.find((m) => m.default)
-                      ?.value ||
-                    "text-embedding-ada-002"
-                  }
-                  onValueChange={handleEmbeddingModelChange}
-                >
-                  {/* <Tooltip>
+	const { isAuthenticated, isNoAuthMode } = useAuth();
+	const { addTask, tasks } = useTask();
+	const searchParams = useSearchParams();
+	const router = useRouter();
+
+	// Connectors state
+	const [connectors, setConnectors] = useState<Connector[]>([]);
+	const [isConnecting, setIsConnecting] = useState<string | null>(null);
+	const [isSyncing, setIsSyncing] = useState<string | null>(null);
+	const [syncResults, setSyncResults] = useState<{
+		[key: string]: SyncResult | null;
+	}>({});
+	const [maxFiles, setMaxFiles] = useState<number>(10);
+	const [syncAllFiles, setSyncAllFiles] = useState<boolean>(false);
+
+	// Only keep systemPrompt state since it needs manual save button
+	const [systemPrompt, setSystemPrompt] = useState<string>("");
+	const [chunkSize, setChunkSize] = useState<number>(1024);
+	const [chunkOverlap, setChunkOverlap] = useState<number>(50);
+	const [tableStructure, setTableStructure] = useState<boolean>(false);
+	const [ocr, setOcr] = useState<boolean>(false);
+	const [pictureDescriptions, setPictureDescriptions] =
+		useState<boolean>(false);
+
+	// Fetch settings using React Query
+	const { data: settings = {} } = useGetSettingsQuery({
+		enabled: isAuthenticated || isNoAuthMode,
+	});
+
+	// Get the current provider from settings
+	const currentProvider = (settings.provider?.model_provider ||
+		"openai") as ModelProvider;
+
+	// Fetch available models based on provider
+	const { data: openaiModelsData } = useGetOpenAIModelsQuery(
+		undefined, // Let backend use stored API key from configuration
+		{
+			enabled:
+				(isAuthenticated || isNoAuthMode) && currentProvider === "openai",
+		},
+	);
+
+	const { data: ollamaModelsData } = useGetOllamaModelsQuery(
+		undefined, // No params for now, could be extended later
+		{
+			enabled:
+				(isAuthenticated || isNoAuthMode) && currentProvider === "ollama",
+		},
+	);
+
+	const { data: ibmModelsData } = useGetIBMModelsQuery(
+		undefined, // No params for now, could be extended later
+		{
+			enabled:
+				(isAuthenticated || isNoAuthMode) && currentProvider === "watsonx",
+		},
+	);
+
+	// Select the appropriate models data based on provider
+	const modelsData =
+		currentProvider === "openai"
+			? openaiModelsData
+			: currentProvider === "ollama"
+				? ollamaModelsData
+				: currentProvider === "watsonx"
+					? ibmModelsData
+					: openaiModelsData; // fallback to openai
+
+	// Mutations
+	const updateFlowSettingMutation = useUpdateFlowSettingMutation({
+		onSuccess: () => {
+			console.log("Setting updated successfully");
+		},
+		onError: (error) => {
+			console.error("Failed to update setting:", error.message);
+		},
+	});
+
+	// Debounced update function
+	const debouncedUpdate = useDebounce(
+		(variables: Parameters<typeof updateFlowSettingMutation.mutate>[0]) => {
+			updateFlowSettingMutation.mutate(variables);
+		},
+		500,
+	);
+
+	// Sync system prompt state with settings data
+	useEffect(() => {
+		if (settings.agent?.system_prompt) {
+			setSystemPrompt(settings.agent.system_prompt);
+		}
+	}, [settings.agent?.system_prompt]);
+
+	// Sync chunk size and overlap state with settings data
+	useEffect(() => {
+		if (settings.knowledge?.chunk_size) {
+			setChunkSize(settings.knowledge.chunk_size);
+		}
+	}, [settings.knowledge?.chunk_size]);
+
+	useEffect(() => {
+		if (settings.knowledge?.chunk_overlap) {
+			setChunkOverlap(settings.knowledge.chunk_overlap);
+		}
+	}, [settings.knowledge?.chunk_overlap]);
+
+	// Sync docling settings with settings data
+	useEffect(() => {
+		if (settings.knowledge?.table_structure !== undefined) {
+			setTableStructure(settings.knowledge.table_structure);
+		}
+	}, [settings.knowledge?.table_structure]);
+
+	useEffect(() => {
+		if (settings.knowledge?.ocr !== undefined) {
+			setOcr(settings.knowledge.ocr);
+		}
+	}, [settings.knowledge?.ocr]);
+
+	useEffect(() => {
+		if (settings.knowledge?.picture_descriptions !== undefined) {
+			setPictureDescriptions(settings.knowledge.picture_descriptions);
+		}
+	}, [settings.knowledge?.picture_descriptions]);
+
+	// Update model selection immediately
+	const handleModelChange = (newModel: string) => {
+		updateFlowSettingMutation.mutate({ llm_model: newModel });
+	};
+
+	// Update system prompt with save button
+	const handleSystemPromptSave = () => {
+		updateFlowSettingMutation.mutate({ system_prompt: systemPrompt });
+	};
+
+	// Update embedding model selection immediately
+	const handleEmbeddingModelChange = (newModel: string) => {
+		updateFlowSettingMutation.mutate({ embedding_model: newModel });
+	};
+
+	// Update chunk size setting with debounce
+	const handleChunkSizeChange = (value: string) => {
+		const numValue = Math.max(0, parseInt(value) || 0);
+		setChunkSize(numValue);
+		debouncedUpdate({ chunk_size: numValue });
+	};
+
+	// Update chunk overlap setting with debounce
+	const handleChunkOverlapChange = (value: string) => {
+		const numValue = Math.max(0, parseInt(value) || 0);
+		setChunkOverlap(numValue);
+		debouncedUpdate({ chunk_overlap: numValue });
+	};
+
+	// Update docling settings
+	const handleTableStructureChange = (checked: boolean) => {
+		setTableStructure(checked);
+		updateFlowSettingMutation.mutate({ table_structure: checked });
+	};
+
+	const handleOcrChange = (checked: boolean) => {
+		setOcr(checked);
+		updateFlowSettingMutation.mutate({ ocr: checked });
+	};
+
+	const handlePictureDescriptionsChange = (checked: boolean) => {
+		setPictureDescriptions(checked);
+		updateFlowSettingMutation.mutate({ picture_descriptions: checked });
+	};
+
+	// Helper function to get connector icon
+	const getConnectorIcon = useCallback((iconName: string) => {
+		const iconMap: { [key: string]: React.ReactElement } = {
+			"google-drive": <GoogleDriveIcon />,
+			sharepoint: (
+				<div className="w-8 h-8 bg-blue-700 rounded flex items-center justify-center text-white font-bold leading-none shrink-0">
+					SP
+				</div>
+			),
+			onedrive: (
+				<div className="w-8 h-8 bg-white border border-gray-300 rounded flex items-center justify-center">
+					<OneDriveIcon />
+				</div>
+			),
+		};
+		return (
+			iconMap[iconName] || (
+				<div className="w-8 h-8 bg-gray-500 rounded flex items-center justify-center text-white font-bold leading-none shrink-0">
+					?
+				</div>
+			)
+		);
+	}, []);
+
+	// Connector functions
+	const checkConnectorStatuses = useCallback(async () => {
+		try {
+			// Fetch available connectors from backend
+			const connectorsResponse = await fetch("/api/connectors");
+			if (!connectorsResponse.ok) {
+				throw new Error("Failed to load connectors");
+			}
+
+			const connectorsResult = await connectorsResponse.json();
+			const connectorTypes = Object.keys(connectorsResult.connectors);
+
+			// Initialize connectors list with metadata from backend
+			const initialConnectors = connectorTypes
+				.filter((type) => connectorsResult.connectors[type].available) // Only show available connectors
+				.map((type) => ({
+					id: type,
+					name: connectorsResult.connectors[type].name,
+					description: connectorsResult.connectors[type].description,
+					icon: getConnectorIcon(connectorsResult.connectors[type].icon),
+					status: "not_connected" as const,
+					type: type,
+				}));
+
+			setConnectors(initialConnectors);
+
+			// Check status for each connector type
+
+			for (const connectorType of connectorTypes) {
+				const response = await fetch(`/api/connectors/${connectorType}/status`);
+				if (response.ok) {
+					const data = await response.json();
+					const connections = data.connections || [];
+					const activeConnection = connections.find(
+						(conn: Connection) => conn.is_active,
+					);
+					const isConnected = activeConnection !== undefined;
+
+					setConnectors((prev) =>
+						prev.map((c) =>
+							c.type === connectorType
+								? {
+										...c,
+										status: isConnected ? "connected" : "not_connected",
+										connectionId: activeConnection?.connection_id,
+									}
+								: c,
+						),
+					);
+				}
+			}
+		} catch (error) {
+			console.error("Failed to check connector statuses:", error);
+		}
+	}, [getConnectorIcon]);
+
+	const handleConnect = async (connector: Connector) => {
+		setIsConnecting(connector.id);
+		setSyncResults((prev) => ({ ...prev, [connector.id]: null }));
+
+		try {
+			// Use the shared auth callback URL, same as connectors page
+			const redirectUri = `${window.location.origin}/auth/callback`;
+
+			const response = await fetch("/api/auth/init", {
+				method: "POST",
+				headers: {
+					"Content-Type": "application/json",
+				},
+				body: JSON.stringify({
+					connector_type: connector.type,
+					purpose: "data_source",
+					name: `${connector.name} Connection`,
+					redirect_uri: redirectUri,
+				}),
+			});
+
+			if (response.ok) {
+				const result = await response.json();
+
+				if (result.oauth_config) {
+					localStorage.setItem("connecting_connector_id", result.connection_id);
+					localStorage.setItem("connecting_connector_type", connector.type);
+
+					const authUrl =
+						`${result.oauth_config.authorization_endpoint}?` +
+						`client_id=${result.oauth_config.client_id}&` +
+						`response_type=code&` +
+						`scope=${result.oauth_config.scopes.join(" ")}&` +
+						`redirect_uri=${encodeURIComponent(
+							result.oauth_config.redirect_uri,
+						)}&` +
+						`access_type=offline&` +
+						`prompt=consent&` +
+						`state=${result.connection_id}`;
+
+					window.location.href = authUrl;
+				}
+			} else {
+				console.error("Failed to initiate connection");
+				setIsConnecting(null);
+			}
+		} catch (error) {
+			console.error("Connection error:", error);
+			setIsConnecting(null);
+		}
+	};
+
+	// const handleSync = async (connector: Connector) => {
+	//   if (!connector.connectionId) return;
+
+	//   setIsSyncing(connector.id);
+	//   setSyncResults(prev => ({ ...prev, [connector.id]: null }));
+
+	//   try {
+	//     const syncBody: {
+	//       connection_id: string;
+	//       max_files?: number;
+	//       selected_files?: string[];
+	//     } = {
+	//       connection_id: connector.connectionId,
+	//       max_files: syncAllFiles ? 0 : maxFiles || undefined,
+	//     };
+
+	//     // Note: File selection is now handled via the cloud connectors dialog
+
+	//     const response = await fetch(`/api/connectors/${connector.type}/sync`, {
+	//       method: "POST",
+	//       headers: {
+	//         "Content-Type": "application/json",
+	//       },
+	//       body: JSON.stringify(syncBody),
+	//     });
+
+	//     const result = await response.json();
+
+	//     if (response.status === 201) {
+	//       const taskId = result.task_id;
+	//       if (taskId) {
+	//         addTask(taskId);
+	//         setSyncResults(prev => ({
+	//           ...prev,
+	//           [connector.id]: {
+	//             processed: 0,
+	//             total: result.total_files || 0,
+	//           },
+	//         }));
+	//       }
+	//     } else if (response.ok) {
+	//       setSyncResults(prev => ({ ...prev, [connector.id]: result }));
+	//       // Note: Stats will auto-refresh via task completion watcher for async syncs
+	//     } else {
+	//       console.error("Sync failed:", result.error);
+	//     }
+	//   } catch (error) {
+	//     console.error("Sync error:", error);
+	//   } finally {
+	//     setIsSyncing(null);
+	//   }
+	// };
+
+	const getStatusBadge = (status: Connector["status"]) => {
+		switch (status) {
+			case "connected":
+				return <div className="h-2 w-2 bg-green-500 rounded-full" />;
+			case "connecting":
+				return <div className="h-2 w-2 bg-yellow-500 rounded-full" />;
+			case "error":
+				return <div className="h-2 w-2 bg-red-500 rounded-full" />;
+			default:
+				return <div className="h-2 w-2 bg-muted rounded-full" />;
+		}
+	};
+
+	const navigateToKnowledgePage = (connector: Connector) => {
+		const provider = connector.type.replace(/-/g, "_");
+		router.push(`/upload/${provider}`);
+	};
+
+	// Check connector status on mount and when returning from OAuth
+	useEffect(() => {
+		if (isAuthenticated) {
+			checkConnectorStatuses();
+		}
+
+		if (searchParams.get("oauth_success") === "true") {
+			const url = new URL(window.location.href);
+			url.searchParams.delete("oauth_success");
+			window.history.replaceState({}, "", url.toString());
+		}
+	}, [searchParams, isAuthenticated, checkConnectorStatuses]);
+
+	// Track previous tasks to detect new completions
+	const [prevTasks, setPrevTasks] = useState<typeof tasks>([]);
+
+	// Watch for task completions and refresh stats
+	useEffect(() => {
+		// Find newly completed tasks by comparing with previous state
+		const newlyCompletedTasks = tasks.filter((task) => {
+			const wasCompleted =
+				prevTasks.find((prev) => prev.task_id === task.task_id)?.status ===
+				"completed";
+			return task.status === "completed" && !wasCompleted;
+		});
+
+		if (newlyCompletedTasks.length > 0) {
+			// Task completed - could refresh data here if needed
+			const timeoutId = setTimeout(() => {
+				// Stats refresh removed
+			}, 1000);
+
+			// Update previous tasks state
+			setPrevTasks(tasks);
+
+			return () => clearTimeout(timeoutId);
+		} else {
+			// Always update previous tasks state
+			setPrevTasks(tasks);
+		}
+	}, [tasks, prevTasks]);
+
+	const handleEditInLangflow = (
+		flowType: "chat" | "ingest",
+		closeDialog: () => void,
+	) => {
+		// Select the appropriate flow ID and edit URL based on flow type
+		const targetFlowId =
+			flowType === "ingest" ? settings.ingest_flow_id : settings.flow_id;
+		const editUrl =
+			flowType === "ingest"
+				? settings.langflow_ingest_edit_url
+				: settings.langflow_edit_url;
+
+		const derivedFromWindow =
+			typeof window !== "undefined"
+				? `${window.location.protocol}//${window.location.hostname}:7860`
+				: "";
+		const base = (
+			settings.langflow_public_url ||
+			derivedFromWindow ||
+			"http://localhost:7860"
+		).replace(/\/$/, "");
+		const computed = targetFlowId ? `${base}/flow/${targetFlowId}` : base;
+
+		const url = editUrl || computed;
+
+		window.open(url, "_blank");
+		closeDialog(); // Close immediately after opening Langflow
+	};
+
+	const handleRestoreRetrievalFlow = (closeDialog: () => void) => {
+		fetch(`/api/reset-flow/retrieval`, {
+			method: "POST",
+		})
+			.then((response) => {
+				if (response.ok) {
+					return response.json();
+				}
+				throw new Error(`HTTP ${response.status}: ${response.statusText}`);
+			})
+			.then(() => {
+				// Only reset form values if the API call was successful
+				setSystemPrompt(DEFAULT_AGENT_SETTINGS.system_prompt);
+				// Trigger model update to default model
+				handleModelChange(DEFAULT_AGENT_SETTINGS.llm_model);
+				closeDialog(); // Close after successful completion
+			})
+			.catch((error) => {
+				console.error("Error restoring retrieval flow:", error);
+				closeDialog(); // Close even on error (could show error toast instead)
+			});
+	};
+
+	const handleRestoreIngestFlow = (closeDialog: () => void) => {
+		fetch(`/api/reset-flow/ingest`, {
+			method: "POST",
+		})
+			.then((response) => {
+				if (response.ok) {
+					return response.json();
+				}
+				throw new Error(`HTTP ${response.status}: ${response.statusText}`);
+			})
+			.then(() => {
+				// Only reset form values if the API call was successful
+				setChunkSize(DEFAULT_KNOWLEDGE_SETTINGS.chunk_size);
+				setChunkOverlap(DEFAULT_KNOWLEDGE_SETTINGS.chunk_overlap);
+				setTableStructure(false);
+				setOcr(false);
+				setPictureDescriptions(false);
+				closeDialog(); // Close after successful completion
+			})
+			.catch((error) => {
+				console.error("Error restoring ingest flow:", error);
+				closeDialog(); // Close even on error (could show error toast instead)
+			});
+	};
+
+	return (
+		<div className="space-y-8">
+			{/* Connectors Section */}
+			<div className="space-y-6">
+				<div>
+					<h2 className="text-lg font-semibold tracking-tight mb-2">
+						Cloud Connectors
+					</h2>
+				</div>
+
+				{/* Conditional Sync Settings or No-Auth Message */}
+				{
+					isNoAuthMode ? (
+						<Card className="border-yellow-500/50 bg-yellow-500/5">
+							<CardHeader>
+								<CardTitle className="text-lg text-yellow-600">
+									Cloud connectors are only available with auth mode enabled
+								</CardTitle>
+								<CardDescription className="text-sm">
+									Please provide the following environment variables and
+									restart:
+								</CardDescription>
+							</CardHeader>
+							<CardContent>
+								<div className="bg-muted rounded-md p-4 font-mono text-sm">
+									<div className="text-muted-foreground mb-2">
+										# make here
+										https://console.cloud.google.com/apis/credentials
+									</div>
+									<div>GOOGLE_OAUTH_CLIENT_ID=</div>
+									<div>GOOGLE_OAUTH_CLIENT_SECRET=</div>
+								</div>
+							</CardContent>
+						</Card>
+					) : null
+					// <div className="flex items-center justify-between py-4">
+					//   <div>
+					//     <h3 className="text-lg font-medium">Sync Settings</h3>
+					//     <p className="text-sm text-muted-foreground">
+					//       Configure how many files to sync when manually triggering a sync
+					//     </p>
+					//   </div>
+					//   <div className="flex items-center gap-4">
+					//     <div className="flex items-center space-x-2">
+					//       <Checkbox
+					//         id="syncAllFiles"
+					//         checked={syncAllFiles}
+					//         onCheckedChange={checked => {
+					//           setSyncAllFiles(!!checked);
+					//           if (checked) {
+					//             setMaxFiles(0);
+					//           } else {
+					//             setMaxFiles(10);
+					//           }
+					//         }}
+					//       />
+					//       <Label
+					//         htmlFor="syncAllFiles"
+					//         className="font-medium whitespace-nowrap"
+					//       >
+					//         Sync all files
+					//       </Label>
+					//     </div>
+					//     <Label
+					//       htmlFor="maxFiles"
+					//       className="font-medium whitespace-nowrap"
+					//     >
+					//       Max files per sync:
+					//     </Label>
+					//     <div className="relative">
+					//       <Input
+					//         id="maxFiles"
+					//         type="number"
+					//         value={syncAllFiles ? 0 : maxFiles}
+					//         onChange={e => setMaxFiles(parseInt(e.target.value) || 10)}
+					//         disabled={syncAllFiles}
+					//         className="w-16 min-w-16 max-w-16 flex-shrink-0 disabled:opacity-50 disabled:cursor-not-allowed"
+					//         min="1"
+					//         max="100"
+					//         title={
+					//           syncAllFiles
+					//             ? "Disabled when 'Sync all files' is checked"
+					//             : "Leave blank or set to 0 for unlimited"
+					//         }
+					//       />
+					//     </div>
+					//   </div>
+					// </div>
+				}
+
+				{/* Connectors Grid */}
+				<div className="grid gap-6 md:grid-cols-2 lg:grid-cols-3">
+					{DEFAULT_CONNECTORS.map((connector) => {
+						const actualConnector = connectors.find(
+							(c) => c.id === connector.id,
+						);
+						return (
+							<Card key={connector.id} className="relative flex flex-col">
+								<CardHeader>
+									<div className="flex flex-col items-start justify-between">
+										<div className="flex flex-col gap-3">
+											<div className="mb-1">
+												<div
+													className={`w-8 h-8 ${
+														actualConnector ? "bg-white" : "bg-muted grayscale"
+													} rounded flex items-center justify-center`}
+												>
+													{connector.icon}
+												</div>
+											</div>
+											<CardTitle className="flex flex-row items-center gap-2">
+												{connector.name}
+												{actualConnector &&
+													getStatusBadge(actualConnector.status)}
+											</CardTitle>
+											<CardDescription className="text-[13px]">
+												{actualConnector?.description
+													? `${actualConnector.name} is configured.`
+													: connector.description}
+											</CardDescription>
+										</div>
+									</div>
+								</CardHeader>
+								<CardContent className="flex-1 flex flex-col justify-end space-y-4">
+									{actualConnector?.status === "connected" ? (
+										<div className="space-y-3">
+											<Button
+												onClick={() => navigateToKnowledgePage(connector)}
+												disabled={isSyncing === connector.id}
+												className="w-full cursor-pointer"
+												size="sm"
+											>
+												<Plus className="h-4 w-4" />
+												Add Knowledge
+											</Button>
+
+											{syncResults[connector.id] && (
+												<div className="text-xs text-muted-foreground bg-muted/50 p-2 rounded">
+													<div>
+														Processed:{" "}
+														{syncResults[connector.id]?.processed || 0}
+													</div>
+													<div>
+														Added: {syncResults[connector.id]?.added || 0}
+													</div>
+													{syncResults[connector.id]?.errors && (
+														<div>
+															Errors: {syncResults[connector.id]?.errors}
+														</div>
+													)}
+												</div>
+											)}
+										</div>
+									) : (
+										<div className="text-[13px] text-muted-foreground">
+											<p>
+												See our{" "}
+												<Link
+													className="text-accent-pink-foreground"
+													href="https://github.com/langflow-ai/openrag/pull/96/files#diff-06889aa94ccf8dac64e70c8cc30a2ceed32cc3c0c2c14a6ff0336fe882a9c2ccR41"
+												>
+													Cloud Connectors installation guide
+												</Link>{" "}
+												for more detail.
+											</p>
+										</div>
+									)}
+								</CardContent>
+							</Card>
+						);
+					})}
+				</div>
+			</div>
+			{/* Agent Behavior Section */}
+			<Card>
+				<CardHeader>
+					<div className="flex items-center justify-between">
+						<div>
+							<CardTitle className="text-lg mb-4">Agent</CardTitle>
+							<CardDescription>
+								Quick Agent settings. Edit in Langflow for full control.
+							</CardDescription>
+						</div>
+						<div className="flex gap-2">
+							<ConfirmationDialog
+								trigger={
+									<Button ignoreTitleCase={true} variant="outline">
+										Restore flow
+									</Button>
+								}
+								title="Restore default Retrieval flow"
+								description="This restores defaults and discards all custom settings and overrides. This can’t be undone."
+								confirmText="Restore"
+								variant="destructive"
+								onConfirm={handleRestoreRetrievalFlow}
+							/>
+							<ConfirmationDialog
+								trigger={
+									<Button>
+										<svg
+											xmlns="http://www.w3.org/2000/svg"
+											width="24"
+											height="22"
+											viewBox="0 0 24 22"
+											className="h-4 w-4 mr-2"
+											aria-label="Langflow icon"
+										>
+											<title>Langflow icon</title>
+											<path
+												fill="currentColor"
+												d="M13.0486 0.462158H9.75399C9.44371 0.462158 9.14614 0.586082 8.92674 0.806667L4.03751 5.72232C3.81811 5.9429 3.52054 6.06682 3.21026 6.06682H1.16992C0.511975 6.06682 -0.0165756 6.61212 0.000397655 7.2734L0.0515933 9.26798C0.0679586 9.90556 0.586745 10.4139 1.22111 10.4139H3.59097C3.90124 10.4139 4.19881 10.2899 4.41821 10.0694L9.34823 5.11269C9.56763 4.89211 9.8652 4.76818 10.1755 4.76818H13.0486C13.6947 4.76818 14.2185 4.24157 14.2185 3.59195V1.63839C14.2185 0.988773 13.6947 0.462158 13.0486 0.462158Z"
+											></path>
+											<path
+												fill="currentColor"
+												d="M19.5355 11.5862H22.8301C23.4762 11.5862 24 12.1128 24 12.7624V14.716C24 15.3656 23.4762 15.8922 22.8301 15.8922H19.957C19.6467 15.8922 19.3491 16.0161 19.1297 16.2367L14.1997 21.1934C13.9803 21.414 13.6827 21.5379 13.3725 21.5379H11.0026C10.3682 21.5379 9.84945 21.0296 9.83309 20.392L9.78189 18.3974C9.76492 17.7361 10.2935 17.1908 10.9514 17.1908H12.9918C13.302 17.1908 13.5996 17.0669 13.819 16.8463L18.7082 11.9307C18.9276 11.7101 19.2252 11.5862 19.5355 11.5862Z"
+											></path>
+											<path
+												fill="currentColor"
+												d="M19.5355 2.9796L22.8301 2.9796C23.4762 2.9796 24 3.50622 24 4.15583V6.1094C24 6.75901 23.4762 7.28563 22.8301 7.28563H19.957C19.6467 7.28563 19.3491 7.40955 19.1297 7.63014L14.1997 12.5868C13.9803 12.8074 13.6827 12.9313 13.3725 12.9313H10.493C10.1913 12.9313 9.90126 13.0485 9.68346 13.2583L4.14867 18.5917C3.93087 18.8016 3.64085 18.9187 3.33917 18.9187H1.32174C0.675616 18.9187 0.151832 18.3921 0.151832 17.7425V15.7343C0.151832 15.0846 0.675616 14.558 1.32174 14.558H3.32468C3.63496 14.558 3.93253 14.4341 4.15193 14.2135L9.40827 8.92878C9.62767 8.70819 9.92524 8.58427 10.2355 8.58427H12.9918C13.302 8.58427 13.5996 8.46034 13.819 8.23976L18.7082 3.32411C18.9276 3.10353 19.2252 2.9796 19.5355 2.9796Z"
+											></path>
+										</svg>
+										Edit in Langflow
+									</Button>
+								}
+								title="Edit Retrieval flow in Langflow"
+								description={
+									<>
+										<p className="mb-2">
+											You're entering Langflow. You can edit the{" "}
+											<b>Retrieval flow</b> and other underlying flows. Manual
+											changes to components, wiring, or I/O can break this
+											experience.
+										</p>
+										<p>You can restore this flow from Settings.</p>
+									</>
+								}
+								confirmText="Proceed"
+								confirmIcon={<ArrowUpRight />}
+								onConfirm={(closeDialog) =>
+									handleEditInLangflow("chat", closeDialog)
+								}
+								variant="warning"
+							/>
+						</div>
+					</div>
+				</CardHeader>
+				<CardContent>
+					<div className="space-y-6">
+						<div className="space-y-2">
+							<LabelWrapper
+								helperText="Model used for chat"
+								id="model-select"
+								label="Language model"
+								required
+							>
+								<Select
+									value={
+										settings.agent?.llm_model ||
+										modelsData?.language_models?.find((m) => m.default)
+											?.value ||
+										"gpt-4"
+									}
+									onValueChange={handleModelChange}
+								>
+									<SelectTrigger id="model-select">
+										<SelectValue placeholder="Select a model" />
+									</SelectTrigger>
+									<SelectContent>
+										<ModelSelectItems
+											models={modelsData?.language_models}
+											fallbackModels={
+												getFallbackModels(currentProvider).language
+											}
+											provider={currentProvider}
+										/>
+									</SelectContent>
+								</Select>
+							</LabelWrapper>
+						</div>
+						<div className="space-y-2">
+							<LabelWrapper label="Agent Instructions" id="system-prompt">
+								<Textarea
+									id="system-prompt"
+									placeholder="Enter your agent instructions here..."
+									value={systemPrompt}
+									onChange={(e) => setSystemPrompt(e.target.value)}
+									rows={6}
+									className={`resize-none ${
+										systemPrompt.length > MAX_SYSTEM_PROMPT_CHARS
+											? "border-red-500 focus:border-red-500"
+											: ""
+									}`}
+								/>
+								<div className="flex justify-start">
+									<span
+										className={`text-xs ${
+											systemPrompt.length > MAX_SYSTEM_PROMPT_CHARS
+												? "text-red-500"
+												: "text-muted-foreground"
+										}`}
+									>
+										{systemPrompt.length}/{MAX_SYSTEM_PROMPT_CHARS} characters
+									</span>
+								</div>
+							</LabelWrapper>
+						</div>
+						<div className="flex justify-end pt-2">
+							<Button
+								onClick={handleSystemPromptSave}
+								disabled={
+									updateFlowSettingMutation.isPending ||
+									systemPrompt.length > MAX_SYSTEM_PROMPT_CHARS
+								}
+								className="min-w-[120px]"
+								size="sm"
+								variant="outline"
+							>
+								{updateFlowSettingMutation.isPending ? (
+									<>
+										<Loader2 className="mr-2 h-4 w-4 animate-spin" />
+										Saving...
+									</>
+								) : (
+									"Save Agent Instructions"
+								)}
+							</Button>
+						</div>
+					</div>
+				</CardContent>
+			</Card>
+
+			{/* Knowledge Ingest Section */}
+			<Card>
+				<CardHeader>
+					<div className="flex items-center justify-between">
+						<div>
+							<CardTitle className="text-lg mb-4">
+								Knowledge ingestion and retrieval
+							</CardTitle>
+							<CardDescription>
+								Quick knowledge settings. Edit in Langflow for full control.
+							</CardDescription>
+						</div>
+						<div className="flex gap-2">
+							<ConfirmationDialog
+								trigger={
+									<Button ignoreTitleCase={true} variant="outline">
+										Restore flow
+									</Button>
+								}
+								title="Restore default Ingest flow"
+								description="This restores defaults and discards all custom settings and overrides. This can't be undone."
+								confirmText="Restore"
+								variant="destructive"
+								onConfirm={handleRestoreIngestFlow}
+							/>
+							<ConfirmationDialog
+								trigger={
+									<Button>
+										<svg
+											xmlns="http://www.w3.org/2000/svg"
+											width="24"
+											height="22"
+											viewBox="0 0 24 22"
+											className="h-4 w-4 mr-2"
+											aria-label="Langflow icon"
+										>
+											<title>Langflow icon</title>
+											<path
+												fill="currentColor"
+												d="M13.0486 0.462158H9.75399C9.44371 0.462158 9.14614 0.586082 8.92674 0.806667L4.03751 5.72232C3.81811 5.9429 3.52054 6.06682 3.21026 6.06682H1.16992C0.511975 6.06682 -0.0165756 6.61212 0.000397655 7.2734L0.0515933 9.26798C0.0679586 9.90556 0.586745 10.4139 1.22111 10.4139H3.59097C3.90124 10.4139 4.19881 10.2899 4.41821 10.0694L9.34823 5.11269C9.56763 4.89211 9.8652 4.76818 10.1755 4.76818H13.0486C13.6947 4.76818 14.2185 4.24157 14.2185 3.59195V1.63839C14.2185 0.988773 13.6947 0.462158 13.0486 0.462158Z"
+											></path>
+											<path
+												fill="currentColor"
+												d="M19.5355 11.5862H22.8301C23.4762 11.5862 24 12.1128 24 12.7624V14.716C24 15.3656 23.4762 15.8922 22.8301 15.8922H19.957C19.6467 15.8922 19.3491 16.0161 19.1297 16.2367L14.1997 21.1934C13.9803 21.414 13.6827 21.5379 13.3725 21.5379H11.0026C10.3682 21.5379 9.84945 21.0296 9.83309 20.392L9.78189 18.3974C9.76492 17.7361 10.2935 17.1908 10.9514 17.1908H12.9918C13.302 17.1908 13.5996 17.0669 13.819 16.8463L18.7082 11.9307C18.9276 11.7101 19.2252 11.5862 19.5355 11.5862Z"
+											></path>
+											<path
+												fill="currentColor"
+												d="M19.5355 2.9796L22.8301 2.9796C23.4762 2.9796 24 3.50622 24 4.15583V6.1094C24 6.75901 23.4762 7.28563 22.8301 7.28563H19.957C19.6467 7.28563 19.3491 7.40955 19.1297 7.63014L14.1997 12.5868C13.9803 12.8074 13.6827 12.9313 13.3725 12.9313H10.493C10.1913 12.9313 9.90126 13.0485 9.68346 13.2583L4.14867 18.5917C3.93087 18.8016 3.64085 18.9187 3.33917 18.9187H1.32174C0.675616 18.9187 0.151832 18.3921 0.151832 17.7425V15.7343C0.151832 15.0846 0.675616 14.558 1.32174 14.558H3.32468C3.63496 14.558 3.93253 14.4341 4.15193 14.2135L9.40827 8.92878C9.62767 8.70819 9.92524 8.58427 10.2355 8.58427H12.9918C13.302 8.58427 13.5996 8.46034 13.819 8.23976L18.7082 3.32411C18.9276 3.10353 19.2252 2.9796 19.5355 2.9796Z"
+											></path>
+										</svg>
+										Edit in Langflow
+									</Button>
+								}
+								title="Edit Ingest flow in Langflow"
+								description={
+									<>
+										<p className="mb-2">
+											You're entering Langflow. You can edit the{" "}
+											<b>Ingest flow</b> and other underlying flows. Manual
+											changes to components, wiring, or I/O can break this
+											experience.
+										</p>
+										<p>You can restore this flow from Settings.</p>
+									</>
+								}
+								confirmText="Proceed"
+								confirmIcon={<ArrowUpRight />}
+								variant="warning"
+								onConfirm={(closeDialog) =>
+									handleEditInLangflow("ingest", closeDialog)
+								}
+							/>
+						</div>
+					</div>
+				</CardHeader>
+				<CardContent>
+					<div className="space-y-6">
+						<div className="space-y-2">
+							<LabelWrapper
+								helperText="Model used for knowledge ingest and retrieval"
+								id="embedding-model-select"
+								label="Embedding model"
+							>
+								<Select
+									// Disabled until API supports multiple embedding models
+									disabled={true}
+									value={
+										settings.knowledge?.embedding_model ||
+										modelsData?.embedding_models?.find((m) => m.default)
+											?.value ||
+										"text-embedding-ada-002"
+									}
+									onValueChange={handleEmbeddingModelChange}
+								>
+									{/* <Tooltip>
                     <TooltipTrigger> */}
-                  <SelectTrigger disabled id="embedding-model-select">
-                    <SelectValue placeholder="Select an embedding model" />
-                  </SelectTrigger>
-                  {/* <TooltipContent>
+									<SelectTrigger disabled id="embedding-model-select">
+										<SelectValue placeholder="Select an embedding model" />
+									</SelectTrigger>
+									{/* <TooltipContent>
                         Locked to keep embeddings consistent
                       </TooltipContent>
                     </TooltipTrigger>
                   </Tooltip> */}
-                  <SelectContent>
-                    <ModelSelectItems
-                      models={modelsData?.embedding_models}
-                      fallbackModels={
-                        getFallbackModels(currentProvider).embedding
-                      }
-                      provider={currentProvider}
-                    />
-                  </SelectContent>
-                </Select>
-              </LabelWrapper>
-            </div>
-            <div className="grid grid-cols-2 gap-4">
-              <div className="space-y-2">
-                <LabelWrapper id="chunk-size" label="Chunk size">
-                  <div className="relative">
-                    <Input
-                      id="chunk-size"
-                      type="number"
-                      min="1"
-                      value={chunkSize}
-                      onChange={(e) => handleChunkSizeChange(e.target.value)}
-                      className="w-full pr-20 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none"
-                    />
-                    <div className="absolute inset-y-0 right-0 flex items-center">
-                      <span className="text-sm text-placeholder-foreground mr-4 pointer-events-none">
-                        characters
-                      </span>
-                      <div className="flex flex-col">
-                        <Button
-                          aria-label="Increase value"
-                          className="h-5 rounded-l-none rounded-br-none border-input border-b-[0.5px] focus-visible:relative"
-                          variant="outline"
-                          size="iconSm"
-                          onClick={() =>
-                            handleChunkSizeChange((chunkSize + 1).toString())
-                          }
-                        >
-                          <Plus className="text-muted-foreground" size={8} />
-                        </Button>
-                        <Button
-                          aria-label="Decrease value"
-                          className="h-5 rounded-l-none rounded-tr-none border-input border-t-[0.5px] focus-visible:relative"
-                          variant="outline"
-                          size="iconSm"
-                          onClick={() =>
-                            handleChunkSizeChange((chunkSize - 1).toString())
-                          }
-                        >
-                          <Minus className="text-muted-foreground" size={8} />
-                        </Button>
-                      </div>
-                    </div>
-                  </div>
-                </LabelWrapper>
-              </div>
-              <div className="space-y-2">
-                <LabelWrapper id="chunk-overlap" label="Chunk overlap">
-                  <div className="relative">
-                    <Input
-                      id="chunk-overlap"
-                      type="number"
-                      min="0"
-                      value={chunkOverlap}
-                      onChange={(e) => handleChunkOverlapChange(e.target.value)}
-                      className="w-full pr-20 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none"
-                    />
-                    <div className="absolute inset-y-0 right-0 flex items-center">
-                      <span className="text-sm text-placeholder-foreground mr-4 pointer-events-none">
-                        characters
-                      </span>
-                      <div className="flex flex-col">
-                        <Button
-                          aria-label="Increase value"
-                          className="h-5 rounded-l-none rounded-br-none border-input border-b-[0.5px] focus-visible:relative"
-                          variant="outline"
-                          size="iconSm"
-                          onClick={() =>
-                            handleChunkOverlapChange(
-                              (chunkOverlap + 1).toString()
-                            )
-                          }
-                        >
-                          <Plus className="text-muted-foreground" size={8} />
-                        </Button>
-                        <Button
-                          aria-label="Decrease value"
-                          className="h-5 rounded-l-none rounded-tr-none border-input border-t-[0.5px] focus-visible:relative"
-                          variant="outline"
-                          size="iconSm"
-                          onClick={() =>
-                            handleChunkOverlapChange(
-                              (chunkOverlap - 1).toString()
-                            )
-                          }
-                        >
-                          <Minus className="text-muted-foreground" size={8} />
-                        </Button>
-                      </div>
-                    </div>
-                  </div>
-                </LabelWrapper>
-              </div>
-            </div>
-            <div className="">
-              <div className="flex items-center justify-between py-3 border-b border-border">
-                <div className="flex-1">
-                  <Label
-                    htmlFor="table-structure"
-                    className="text-base font-medium cursor-pointer pb-3"
-                  >
-                    Table Structure
-                  </Label>
-                  <div className="text-sm text-muted-foreground">
-                    Capture table structure during ingest.
-                  </div>
-                </div>
-                <Switch
-                  id="table-structure"
-                  checked={tableStructure}
-                  onCheckedChange={handleTableStructureChange}
-                />
-              </div>
-              <div className="flex items-center justify-between py-3 border-b border-border">
-                <div className="flex-1">
-                  <Label
-                    htmlFor="ocr"
-                    className="text-base font-medium cursor-pointer pb-3"
-                  >
-                    OCR
-                  </Label>
-                  <div className="text-sm text-muted-foreground">
-                    Extracts text from images/PDFs. Ingest is slower when
-                    enabled.
-                  </div>
-                </div>
-                <Switch
-                  id="ocr"
-                  checked={ocr}
-                  onCheckedChange={handleOcrChange}
-                />
-              </div>
-              <div className="flex items-center justify-between py-3">
-                <div className="flex-1">
-                  <Label
-                    htmlFor="picture-descriptions"
-                    className="text-base font-medium cursor-pointer pb-3"
-                  >
-                    Picture Descriptions
-                  </Label>
-                  <div className="text-sm text-muted-foreground">
-                    Adds captions for images. Ingest is slower when enabled.
-                  </div>
-                </div>
-                <Switch
-                  id="picture-descriptions"
-                  checked={pictureDescriptions}
-                  onCheckedChange={handlePictureDescriptionsChange}
-                />
-              </div>
-            </div>
-          </div>
-        </CardContent>
-      </Card>
-    </div>
-  );
+									<SelectContent>
+										<ModelSelectItems
+											models={modelsData?.embedding_models}
+											fallbackModels={
+												getFallbackModels(currentProvider).embedding
+											}
+											provider={currentProvider}
+										/>
+									</SelectContent>
+								</Select>
+							</LabelWrapper>
+						</div>
+						<div className="grid grid-cols-2 gap-4">
+							<div className="space-y-2">
+								<LabelWrapper id="chunk-size" label="Chunk size">
+									<div className="relative">
+										<Input
+											id="chunk-size"
+											type="number"
+											min="1"
+											value={chunkSize}
+											onChange={(e) => handleChunkSizeChange(e.target.value)}
+											className="w-full pr-20 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none"
+										/>
+										<div className="absolute inset-y-0 right-0 flex items-center">
+											<span className="text-sm text-placeholder-foreground mr-4 pointer-events-none">
+												characters
+											</span>
+											<div className="flex flex-col">
+												<Button
+													aria-label="Increase value"
+													className="h-5 rounded-l-none rounded-br-none border-input border-b-[0.5px] focus-visible:relative"
+													variant="outline"
+													size="iconSm"
+													onClick={() =>
+														handleChunkSizeChange((chunkSize + 1).toString())
+													}
+												>
+													<Plus className="text-muted-foreground" size={8} />
+												</Button>
+												<Button
+													aria-label="Decrease value"
+													className="h-5 rounded-l-none rounded-tr-none border-input border-t-[0.5px] focus-visible:relative"
+													variant="outline"
+													size="iconSm"
+													onClick={() =>
+														handleChunkSizeChange((chunkSize - 1).toString())
+													}
+												>
+													<Minus className="text-muted-foreground" size={8} />
+												</Button>
+											</div>
+										</div>
+									</div>
+								</LabelWrapper>
+							</div>
+							<div className="space-y-2">
+								<LabelWrapper id="chunk-overlap" label="Chunk overlap">
+									<div className="relative">
+										<Input
+											id="chunk-overlap"
+											type="number"
+											min="0"
+											value={chunkOverlap}
+											onChange={(e) => handleChunkOverlapChange(e.target.value)}
+											className="w-full pr-20 [appearance:textfield] [&::-webkit-outer-spin-button]:appearance-none [&::-webkit-inner-spin-button]:appearance-none"
+										/>
+										<div className="absolute inset-y-0 right-0 flex items-center">
+											<span className="text-sm text-placeholder-foreground mr-4 pointer-events-none">
+												characters
+											</span>
+											<div className="flex flex-col">
+												<Button
+													aria-label="Increase value"
+													className="h-5 rounded-l-none rounded-br-none border-input border-b-[0.5px] focus-visible:relative"
+													variant="outline"
+													size="iconSm"
+													onClick={() =>
+														handleChunkOverlapChange(
+															(chunkOverlap + 1).toString(),
+														)
+													}
+												>
+													<Plus className="text-muted-foreground" size={8} />
+												</Button>
+												<Button
+													aria-label="Decrease value"
+													className="h-5 rounded-l-none rounded-tr-none border-input border-t-[0.5px] focus-visible:relative"
+													variant="outline"
+													size="iconSm"
+													onClick={() =>
+														handleChunkOverlapChange(
+															(chunkOverlap - 1).toString(),
+														)
+													}
+												>
+													<Minus className="text-muted-foreground" size={8} />
+												</Button>
+											</div>
+										</div>
+									</div>
+								</LabelWrapper>
+							</div>
+						</div>
+						<div className="">
+							<div className="flex items-center justify-between py-3 border-b border-border">
+								<div className="flex-1">
+									<Label
+										htmlFor="table-structure"
+										className="text-base font-medium cursor-pointer pb-3"
+									>
+										Table Structure
+									</Label>
+									<div className="text-sm text-muted-foreground">
+										Capture table structure during ingest.
+									</div>
+								</div>
+								<Switch
+									id="table-structure"
+									checked={tableStructure}
+									onCheckedChange={handleTableStructureChange}
+								/>
+							</div>
+							<div className="flex items-center justify-between py-3 border-b border-border">
+								<div className="flex-1">
+									<Label
+										htmlFor="ocr"
+										className="text-base font-medium cursor-pointer pb-3"
+									>
+										OCR
+									</Label>
+									<div className="text-sm text-muted-foreground">
+										Extracts text from images/PDFs. Ingest is slower when
+										enabled.
+									</div>
+								</div>
+								<Switch
+									id="ocr"
+									checked={ocr}
+									onCheckedChange={handleOcrChange}
+								/>
+							</div>
+							<div className="flex items-center justify-between py-3">
+								<div className="flex-1">
+									<Label
+										htmlFor="picture-descriptions"
+										className="text-base font-medium cursor-pointer pb-3"
+									>
+										Picture Descriptions
+									</Label>
+									<div className="text-sm text-muted-foreground">
+										Adds captions for images. Ingest is slower when enabled.
+									</div>
+								</div>
+								<Switch
+									id="picture-descriptions"
+									checked={pictureDescriptions}
+									onCheckedChange={handlePictureDescriptionsChange}
+								/>
+							</div>
+						</div>
+					</div>
+				</CardContent>
+			</Card>
+		</div>
+	);
 }
 
 export default function ProtectedKnowledgeSourcesPage() {
-  return (
-    <ProtectedRoute>
-      <Suspense fallback={<div>Loading knowledge sources...</div>}>
-        <KnowledgeSourcesPage />
-      </Suspense>
-    </ProtectedRoute>
-  );
+	return (
+		<ProtectedRoute>
+			<Suspense fallback={<div>Loading knowledge sources...</div>}>
+				<KnowledgeSourcesPage />
+			</Suspense>
+		</ProtectedRoute>
+	);
 }
diff --git a/src/api/langflow_files.py b/src/api/langflow_files.py
index ae36bf04..0226d4d5 100644
--- a/src/api/langflow_files.py
+++ b/src/api/langflow_files.py
@@ -70,22 +70,22 @@ async def run_ingestion(
         if settings:
             logger.debug("Applying ingestion settings", settings=settings)
 
-            # Split Text component tweaks (SplitText-QIKhg)
+            # Split Text component tweaks (SplitText-PC36h)
             if (
                 settings.get("chunkSize")
                 or settings.get("chunkOverlap")
                 or settings.get("separator")
             ):
-                if "SplitText-QIKhg" not in tweaks:
-                    tweaks["SplitText-QIKhg"] = {}
+                if "SplitText-PC36h" not in tweaks:
+                    tweaks["SplitText-PC36h"] = {}
                 if settings.get("chunkSize"):
-                    tweaks["SplitText-QIKhg"]["chunk_size"] = settings["chunkSize"]
+                    tweaks["SplitText-PC36h"]["chunk_size"] = settings["chunkSize"]
                 if settings.get("chunkOverlap"):
-                    tweaks["SplitText-QIKhg"]["chunk_overlap"] = settings[
+                    tweaks["SplitText-PC36h"]["chunk_overlap"] = settings[
                         "chunkOverlap"
                     ]
                 if settings.get("separator"):
-                    tweaks["SplitText-QIKhg"]["separator"] = settings["separator"]
+                    tweaks["SplitText-PC36h"]["separator"] = settings["separator"]
 
             # OpenAI Embeddings component tweaks (OpenAIEmbeddings-joRJ6)
             if settings.get("embeddingModel"):
@@ -276,4 +276,4 @@ async def delete_user_files(
             status_code=status,
         )
     except Exception as e:
-        return JSONResponse({"error": str(e)}, status_code=500)
+        return JSONResponse({"error": str(e)}, status_code=500)
\ No newline at end of file
diff --git a/src/api/router.py b/src/api/router.py
index 857914c0..23ce5bdf 100644
--- a/src/api/router.py
+++ b/src/api/router.py
@@ -142,6 +142,14 @@ async def langflow_upload_ingest_task(
             )
 
             # Create langflow upload task
+            print(f"tweaks: {tweaks}")
+            print(f"settings: {settings}")
+            print(f"jwt_token: {jwt_token}")
+            print(f"user_name: {user_name}")
+            print(f"user_email: {user_email}")
+            print(f"session_id: {session_id}")
+            print(f"delete_after_ingest: {delete_after_ingest}")
+            print(f"temp_file_paths: {temp_file_paths}")
             task_id = await task_service.create_langflow_upload_task(
                 user_id=user_id,
                 file_paths=temp_file_paths,
diff --git a/src/api/settings.py b/src/api/settings.py
index 686912b6..846c0fda 100644
--- a/src/api/settings.py
+++ b/src/api/settings.py
@@ -9,7 +9,6 @@ from config.settings import (
     LANGFLOW_CHAT_FLOW_ID,
     LANGFLOW_INGEST_FLOW_ID,
     LANGFLOW_PUBLIC_URL,
-    DOCLING_COMPONENT_ID,
     LOCALHOST_URL,
     clients,
     get_openrag_config,
@@ -206,7 +205,7 @@ async def update_settings(request, session_manager):
             # Also update the chat flow with the new model
             try:
                 flows_service = _get_flows_service()
-                await flows_service.update_chat_flow_model(body["llm_model"])
+                await flows_service.update_chat_flow_model(body["llm_model"], current_config.provider.model_provider.lower())
                 logger.info(
                     f"Successfully updated chat flow model to '{body['llm_model']}'"
                 )
@@ -223,7 +222,8 @@ async def update_settings(request, session_manager):
             try:
                 flows_service = _get_flows_service()
                 await flows_service.update_chat_flow_system_prompt(
-                    body["system_prompt"]
+                    body["system_prompt"],
+                    current_config.provider.model_provider.lower()
                 )
                 logger.info(f"Successfully updated chat flow system prompt")
             except Exception as e:
@@ -248,7 +248,8 @@ async def update_settings(request, session_manager):
             try:
                 flows_service = _get_flows_service()
                 await flows_service.update_ingest_flow_embedding_model(
-                    body["embedding_model"].strip()
+                    body["embedding_model"].strip(),
+                    current_config.provider.model_provider.lower()
                 )
                 logger.info(
                     f"Successfully updated ingest flow embedding model to '{body['embedding_model'].strip()}'"
diff --git a/src/config/settings.py b/src/config/settings.py
index fc6a9ec9..27ea9502 100644
--- a/src/config/settings.py
+++ b/src/config/settings.py
@@ -543,38 +543,28 @@ OLLAMA_EMBEDDING_COMPONENT_PATH = os.getenv(
 
 # Component IDs in flows
 
-OPENAI_EMBEDDING_COMPONENT_ID = os.getenv(
-    "OPENAI_EMBEDDING_COMPONENT_ID", "EmbeddingModel-eZ6bT"
+OPENAI_EMBEDDING_COMPONENT_DISPLAY_NAME = os.getenv(
+    "OPENAI_EMBEDDING_COMPONENT_DISPLAY_NAME", "Embedding Model"
 )
-OPENAI_LLM_COMPONENT_ID = os.getenv(
-    "OPENAI_LLM_COMPONENT_ID", "LanguageModelComponent-0YME7"
-)
-OPENAI_LLM_TEXT_COMPONENT_ID = os.getenv(
-    "OPENAI_LLM_TEXT_COMPONENT_ID", "LanguageModelComponent-NSTA6"
+OPENAI_LLM_COMPONENT_DISPLAY_NAME = os.getenv(
+    "OPENAI_LLM_COMPONENT_DISPLAY_NAME", "Language Model"
 )
 
 # Provider-specific component IDs
-WATSONX_EMBEDDING_COMPONENT_ID = os.getenv(
-    "WATSONX_EMBEDDING_COMPONENT_ID", "WatsonxEmbeddingsComponent-pJfXI"
+WATSONX_EMBEDDING_COMPONENT_DISPLAY_NAME = os.getenv(
+    "WATSONX_EMBEDDING_COMPONENT_DISPLAY_NAME", "IBM watsonx.ai Embeddings"
 )
-WATSONX_LLM_COMPONENT_ID = os.getenv(
-    "WATSONX_LLM_COMPONENT_ID", "IBMwatsonxModel-jA4Nw"
-)
-WATSONX_LLM_TEXT_COMPONENT_ID = os.getenv(
-    "WATSONX_LLM_TEXT_COMPONENT_ID", "IBMwatsonxModel-18kmA"
+WATSONX_LLM_COMPONENT_DISPLAY_NAME = os.getenv(
+    "WATSONX_LLM_COMPONENT_DISPLAY_NAME", "IBM watsonx.ai"
 )
 
-
-OLLAMA_EMBEDDING_COMPONENT_ID = os.getenv(
-    "OLLAMA_EMBEDDING_COMPONENT_ID", "OllamaEmbeddings-4ah5Q"
-)
-OLLAMA_LLM_COMPONENT_ID = os.getenv("OLLAMA_LLM_COMPONENT_ID", "OllamaModel-eCsJx")
-OLLAMA_LLM_TEXT_COMPONENT_ID = os.getenv(
-    "OLLAMA_LLM_TEXT_COMPONENT_ID", "OllamaModel-XDGqZ"
+OLLAMA_EMBEDDING_COMPONENT_DISPLAY_NAME = os.getenv(
+    "OLLAMA_EMBEDDING_COMPONENT_DISPLAY_NAME", "Ollama Model"
 )
+OLLAMA_LLM_COMPONENT_DISPLAY_NAME = os.getenv("OLLAMA_LLM_COMPONENT_DISPLAY_NAME", "Ollama")
 
 # Docling component ID for ingest flow
-DOCLING_COMPONENT_ID = os.getenv("DOCLING_COMPONENT_ID", "DoclingRemote-78KoX")
+DOCLING_COMPONENT_DISPLAY_NAME = os.getenv("DOCLING_COMPONENT_DISPLAY_NAME", "Docling Serve")
 
 LOCALHOST_URL = get_container_host() or "localhost"
 
diff --git a/src/main.py b/src/main.py
index 4ed57b75..230ded79 100644
--- a/src/main.py
+++ b/src/main.py
@@ -382,6 +382,10 @@ async def _ingest_default_documents_langflow(services, file_paths):
                 settings=None,  # Use default ingestion settings
                 jwt_token=effective_jwt,  # Use JWT token (anonymous if needed)
                 delete_after_ingest=True,  # Clean up after ingestion
+                owner=None,
+                owner_name=anonymous_user.name,
+                owner_email=anonymous_user.email,
+                connector_type="system_default",
             )
 
             logger.info(
@@ -1189,4 +1193,4 @@ if __name__ == "__main__":
         host="0.0.0.0",
         port=8000,
         reload=False,  # Disable reload since we're running from main
-    )
+    )
\ No newline at end of file
diff --git a/src/models/processors.py b/src/models/processors.py
index 0ce8e33d..a1d72777 100644
--- a/src/models/processors.py
+++ b/src/models/processors.py
@@ -621,7 +621,12 @@ class LangflowFileProcessor(TaskProcessor):
                 tweaks=final_tweaks,
                 settings=self.settings,
                 jwt_token=effective_jwt,
-                delete_after_ingest=self.delete_after_ingest
+                delete_after_ingest=self.delete_after_ingest,
+                owner=self.owner_user_id,
+                owner_name=self.owner_name,
+                owner_email=self.owner_email,
+                connector_type="local",
+
             )
 
             # Update task with success
@@ -636,4 +641,4 @@ class LangflowFileProcessor(TaskProcessor):
             file_task.error_message = str(e)
             file_task.updated_at = time.time()
             upload_task.failed_files += 1
-            raise
+            raise
\ No newline at end of file
diff --git a/src/services/chat_service.py b/src/services/chat_service.py
index 32536f4b..26b436ff 100644
--- a/src/services/chat_service.py
+++ b/src/services/chat_service.py
@@ -110,15 +110,14 @@ class ChatService:
             filter_expression["score_threshold"] = score_threshold
 
         # Pass the complete filter expression as a single header to Langflow (only if we have something to send)
-        if filter_expression:
-            logger.info(
-                "Sending OpenRAG query filter to Langflow",
-                filter_expression=filter_expression,
-            )
-            extra_headers["X-LANGFLOW-GLOBAL-VAR-OPENRAG-QUERY-FILTER"] = json.dumps(
-                filter_expression
-            )
-
+        logger.info(
+            "Sending OpenRAG query filter to Langflow",
+            filter_expression=filter_expression,
+        )
+        extra_headers["X-LANGFLOW-GLOBAL-VAR-OPENRAG-QUERY-FILTER"] = json.dumps(
+            filter_expression
+        )
+        logger.info(f"[LF] Extra headers {extra_headers}")
         # Ensure the Langflow client exists; try lazy init if needed
         langflow_client = await clients.ensure_langflow_client()
         if not langflow_client:
diff --git a/src/services/flows_service.py b/src/services/flows_service.py
index 13b75a82..999d9930 100644
--- a/src/services/flows_service.py
+++ b/src/services/flows_service.py
@@ -1,26 +1,22 @@
-import asyncio
 from config.settings import (
     DISABLE_INGEST_WITH_LANGFLOW,
     NUDGES_FLOW_ID,
     LANGFLOW_URL,
     LANGFLOW_CHAT_FLOW_ID,
     LANGFLOW_INGEST_FLOW_ID,
-    OLLAMA_LLM_TEXT_COMPONENT_ID,
     OLLAMA_LLM_TEXT_COMPONENT_PATH,
-    OPENAI_EMBEDDING_COMPONENT_ID,
-    OPENAI_LLM_COMPONENT_ID,
-    OPENAI_LLM_TEXT_COMPONENT_ID,
-    WATSONX_LLM_TEXT_COMPONENT_ID,
+    OPENAI_EMBEDDING_COMPONENT_DISPLAY_NAME,
+    OPENAI_LLM_COMPONENT_DISPLAY_NAME,
     WATSONX_LLM_TEXT_COMPONENT_PATH,
     clients,
     WATSONX_LLM_COMPONENT_PATH,
     WATSONX_EMBEDDING_COMPONENT_PATH,
     OLLAMA_LLM_COMPONENT_PATH,
     OLLAMA_EMBEDDING_COMPONENT_PATH,
-    WATSONX_EMBEDDING_COMPONENT_ID,
-    WATSONX_LLM_COMPONENT_ID,
-    OLLAMA_EMBEDDING_COMPONENT_ID,
-    OLLAMA_LLM_COMPONENT_ID,
+    WATSONX_EMBEDDING_COMPONENT_DISPLAY_NAME,
+    WATSONX_LLM_COMPONENT_DISPLAY_NAME,
+    OLLAMA_EMBEDDING_COMPONENT_DISPLAY_NAME,
+    OLLAMA_LLM_COMPONENT_DISPLAY_NAME,
     get_openrag_config,
 )
 import json
@@ -277,23 +273,23 @@ class FlowsService:
                 {
                     "name": "nudges",
                     "flow_id": NUDGES_FLOW_ID,
-                    "embedding_id": OPENAI_EMBEDDING_COMPONENT_ID,
-                    "llm_id": OPENAI_LLM_COMPONENT_ID,
-                    "llm_text_id": OPENAI_LLM_TEXT_COMPONENT_ID,
+                    "embedding_name": OPENAI_EMBEDDING_COMPONENT_DISPLAY_NAME,
+                    "llm_text_name": OPENAI_LLM_COMPONENT_DISPLAY_NAME,
+                    "llm_name": None,
                 },
                 {
                     "name": "retrieval",
                     "flow_id": LANGFLOW_CHAT_FLOW_ID,
-                    "embedding_id": OPENAI_EMBEDDING_COMPONENT_ID,
-                    "llm_id": OPENAI_LLM_COMPONENT_ID,
-                    "llm_text_id": None,
+                    "embedding_name": OPENAI_EMBEDDING_COMPONENT_DISPLAY_NAME,
+                    "llm_name": OPENAI_LLM_COMPONENT_DISPLAY_NAME,
+                    "llm_text_name": None,
                 },
                 {
                     "name": "ingest",
                     "flow_id": LANGFLOW_INGEST_FLOW_ID,
-                    "embedding_id": OPENAI_EMBEDDING_COMPONENT_ID,
-                    "llm_id": None,  # Ingestion flow might not have LLM
-                    "llm_text_id": None,  # Ingestion flow might not have LLM Text
+                    "embedding_name": OPENAI_EMBEDDING_COMPONENT_DISPLAY_NAME,
+                    "llm_name": None,  # Ingestion flow might not have LLM
+                    "llm_text_name": None,
                 },
             ]
 
@@ -389,9 +385,9 @@ class FlowsService:
         """Update components in a specific flow"""
         flow_name = config["name"]
         flow_id = config["flow_id"]
-        old_embedding_id = config["embedding_id"]
-        old_llm_id = config["llm_id"]
-        old_llm_text_id = config["llm_text_id"]
+        old_embedding_name = config["embedding_name"]
+        old_llm_name = config["llm_name"]
+        old_llm_text_name = config["llm_text_name"]
         # Extract IDs from templates
         new_llm_id = llm_template["data"]["id"]
         new_embedding_id = embedding_template["data"]["id"]
@@ -411,7 +407,7 @@ class FlowsService:
 
         # Replace embedding component
         if not DISABLE_INGEST_WITH_LANGFLOW:
-            embedding_node = self._find_node_by_id(flow_data, old_embedding_id)
+            embedding_node, _ = self._find_node_in_flow(flow_data, display_name=old_embedding_name)
             if embedding_node:
                 # Preserve position
                 original_position = embedding_node.get("position", {})
@@ -421,16 +417,14 @@ class FlowsService:
                 new_embedding_node["position"] = original_position
 
                 # Replace in flow
-                self._replace_node_in_flow(
-                    flow_data, old_embedding_id, new_embedding_node
-                )
+                self._replace_node_in_flow(flow_data, old_embedding_name, new_embedding_node)
                 components_updated.append(
-                    f"embedding: {old_embedding_id} -> {new_embedding_id}"
+                    f"embedding: {old_embedding_name} -> {new_embedding_id}"
                 )
 
         # Replace LLM component (if exists in this flow)
-        if old_llm_id:
-            llm_node = self._find_node_by_id(flow_data, old_llm_id)
+        if old_llm_name:
+            llm_node, _ = self._find_node_in_flow(flow_data, display_name=old_llm_name)
             if llm_node:
                 # Preserve position
                 original_position = llm_node.get("position", {})
@@ -440,12 +434,12 @@ class FlowsService:
                 new_llm_node["position"] = original_position
 
                 # Replace in flow
-                self._replace_node_in_flow(flow_data, old_llm_id, new_llm_node)
-                components_updated.append(f"llm: {old_llm_id} -> {new_llm_id}")
+                self._replace_node_in_flow(flow_data, old_llm_name, new_llm_node)
+                components_updated.append(f"llm: {old_llm_name} -> {new_llm_id}")
 
         # Replace LLM component (if exists in this flow)
-        if old_llm_text_id:
-            llm_text_node = self._find_node_by_id(flow_data, old_llm_text_id)
+        if old_llm_text_name:
+            llm_text_node, _ = self._find_node_in_flow(flow_data, display_name=old_llm_text_name)
             if llm_text_node:
                 # Preserve position
                 original_position = llm_text_node.get("position", {})
@@ -455,12 +449,18 @@ class FlowsService:
                 new_llm_text_node["position"] = original_position
 
                 # Replace in flow
-                self._replace_node_in_flow(
-                    flow_data, old_llm_text_id, new_llm_text_node
-                )
-                components_updated.append(
-                    f"llm: {old_llm_text_id} -> {new_llm_text_id}"
-                )
+                self._replace_node_in_flow(flow_data, old_llm_text_name, new_llm_text_node)
+                components_updated.append(f"llm: {old_llm_text_name} -> {new_llm_text_id}")
+
+        old_embedding_id = None
+        old_llm_id = None
+        old_llm_text_id = None
+        if embedding_node:
+            old_embedding_id = embedding_node.get("data", {}).get("id")
+        if old_llm_name and llm_node:
+            old_llm_id = llm_node.get("data", {}).get("id")
+        if old_llm_text_name and llm_text_node:
+            old_llm_text_id = llm_text_node.get("data", {}).get("id")
 
         # Update all edge references using regex replacement
         flow_json_str = json.dumps(flow_data)
@@ -478,17 +478,27 @@ class FlowsService:
 
         # Replace LLM ID references (if applicable)
         if old_llm_id:
-            flow_json_str = re.sub(re.escape(old_llm_id), new_llm_id, flow_json_str)
-            if old_llm_text_id:
-                flow_json_str = re.sub(
-                    re.escape(old_llm_text_id), new_llm_text_id, flow_json_str
-                )
+            flow_json_str = re.sub(
+                re.escape(old_llm_id), new_llm_id, flow_json_str
+            )
 
             flow_json_str = re.sub(
                 re.escape(old_llm_id.split("-")[0]),
                 new_llm_id.split("-")[0],
                 flow_json_str,
             )
+        
+        # Replace text LLM ID references (if applicable)
+        if old_llm_text_id:
+            flow_json_str = re.sub(
+                re.escape(old_llm_text_id), new_llm_text_id, flow_json_str
+            )
+
+            flow_json_str = re.sub(
+                re.escape(old_llm_text_id.split("-")[0]),
+                new_llm_text_id.split("-")[0],
+                flow_json_str,
+            )
 
         # Convert back to JSON
         flow_data = json.loads(flow_json_str)
@@ -510,14 +520,6 @@ class FlowsService:
             "flow_id": flow_id,
         }
 
-    def _find_node_by_id(self, flow_data, node_id):
-        """Find a node by ID in the flow data"""
-        nodes = flow_data.get("data", {}).get("nodes", [])
-        for node in nodes:
-            if node.get("id") == node_id:
-                return node
-        return None
-
     def _find_node_in_flow(self, flow_data, node_id=None, display_name=None):
         """
         Helper function to find a node in flow data by ID or display name.
@@ -539,14 +541,7 @@ class FlowsService:
 
         return None, None
 
-    async def _update_flow_field(
-        self,
-        flow_id: str,
-        field_name: str,
-        field_value: str,
-        node_display_name: str = None,
-        node_id: str = None,
-    ):
+    async def _update_flow_field(self, flow_id: str, field_name: str, field_value: str, node_display_name: str = None):
         """
         Generic helper function to update any field in any Langflow component.
 
@@ -577,23 +572,18 @@ class FlowsService:
                 flow_data, display_name=node_display_name
             )
 
-        if target_node is None and node_id:
-            target_node, target_node_index = self._find_node_in_flow(
-                flow_data, node_id=node_id
-            )
-
         if target_node is None:
-            identifier = node_display_name or node_id
+            identifier = node_display_name
             raise Exception(f"Component '{identifier}' not found in flow {flow_id}")
 
         # Update the field value directly in the existing node
         template = target_node.get("data", {}).get("node", {}).get("template", {})
         if template.get(field_name):
-            flow_data["data"]["nodes"][target_node_index]["data"]["node"]["template"][
-                field_name
-            ]["value"] = field_value
+            flow_data["data"]["nodes"][target_node_index]["data"]["node"]["template"][field_name]["value"] = field_value
+            if "options" in flow_data["data"]["nodes"][target_node_index]["data"]["node"]["template"][field_name] and field_value not in flow_data["data"]["nodes"][target_node_index]["data"]["node"]["template"][field_name]["options"]:
+                flow_data["data"]["nodes"][target_node_index]["data"]["node"]["template"][field_name]["options"].append(field_value)
         else:
-            identifier = node_display_name or node_id
+            identifier = node_display_name
             raise Exception(f"{field_name} field not found in {identifier} component")
 
         # Update the flow via PATCH request
@@ -606,41 +596,36 @@ class FlowsService:
                 f"Failed to update flow: HTTP {patch_response.status_code} - {patch_response.text}"
             )
 
-    async def update_chat_flow_model(self, model_name: str):
+    async def update_chat_flow_model(self, model_name: str, provider: str):
         """Helper function to update the model in the chat flow"""
         if not LANGFLOW_CHAT_FLOW_ID:
             raise ValueError("LANGFLOW_CHAT_FLOW_ID is not configured")
-        await self._update_flow_field(
-            LANGFLOW_CHAT_FLOW_ID,
-            "model_name",
-            model_name,
-            node_display_name="Language Model",
-        )
 
-    async def update_chat_flow_system_prompt(self, system_prompt: str):
+        # Determine target component IDs based on provider
+        target_llm_id = self._get_provider_component_ids(provider)[1]
+
+        await self._update_flow_field(LANGFLOW_CHAT_FLOW_ID, "model_name", model_name,
+                                node_display_name=target_llm_id)
+
+    async def update_chat_flow_system_prompt(self, system_prompt: str, provider: str):
         """Helper function to update the system prompt in the chat flow"""
         if not LANGFLOW_CHAT_FLOW_ID:
             raise ValueError("LANGFLOW_CHAT_FLOW_ID is not configured")
-        await self._update_flow_field(
-            LANGFLOW_CHAT_FLOW_ID,
-            "system_prompt",
-            system_prompt,
-            node_display_name="Agent",
-        )
+
+        # Determine target component IDs based on provider
+        target_agent_id = self._get_provider_component_ids(provider)[1]
+
+        await self._update_flow_field(LANGFLOW_CHAT_FLOW_ID, "system_prompt", system_prompt,
+                                node_display_name=target_agent_id)
 
     async def update_flow_docling_preset(self, preset: str, preset_config: dict):
         """Helper function to update docling preset in the ingest flow"""
         if not LANGFLOW_INGEST_FLOW_ID:
             raise ValueError("LANGFLOW_INGEST_FLOW_ID is not configured")
 
-        from config.settings import DOCLING_COMPONENT_ID
-
-        await self._update_flow_field(
-            LANGFLOW_INGEST_FLOW_ID,
-            "docling_serve_opts",
-            preset_config,
-            node_id=DOCLING_COMPONENT_ID,
-        )
+        from config.settings import DOCLING_COMPONENT_DISPLAY_NAME
+        await self._update_flow_field(LANGFLOW_INGEST_FLOW_ID, "docling_serve_opts", preset_config,
+                                node_display_name=DOCLING_COMPONENT_DISPLAY_NAME)
 
     async def update_ingest_flow_chunk_size(self, chunk_size: int):
         """Helper function to update chunk size in the ingest flow"""
@@ -664,22 +649,22 @@ class FlowsService:
             node_display_name="Split Text",
         )
 
-    async def update_ingest_flow_embedding_model(self, embedding_model: str):
+    async def update_ingest_flow_embedding_model(self, embedding_model: str, provider: str):
         """Helper function to update embedding model in the ingest flow"""
         if not LANGFLOW_INGEST_FLOW_ID:
             raise ValueError("LANGFLOW_INGEST_FLOW_ID is not configured")
-        await self._update_flow_field(
-            LANGFLOW_INGEST_FLOW_ID,
-            "model",
-            embedding_model,
-            node_display_name="Embedding Model",
-        )
 
-    def _replace_node_in_flow(self, flow_data, old_id, new_node):
+        # Determine target component IDs based on provider
+        target_embedding_id = self._get_provider_component_ids(provider)[0]
+
+        await self._update_flow_field(LANGFLOW_INGEST_FLOW_ID, "model", embedding_model,
+                                node_display_name=target_embedding_id)
+
+    def _replace_node_in_flow(self, flow_data, old_display_name, new_node):
         """Replace a node in the flow data"""
         nodes = flow_data.get("data", {}).get("nodes", [])
         for i, node in enumerate(nodes):
-            if node.get("id") == old_id:
+            if node.get("data", {}).get("node", {}).get("display_name") == old_display_name:
                 nodes[i] = new_node
                 return True
         return False
@@ -734,8 +719,8 @@ class FlowsService:
                 ]
 
             # Determine target component IDs based on provider
-            target_embedding_id, target_llm_id, target_llm_text_id = (
-                self._get_provider_component_ids(provider)
+            target_embedding_name, target_llm_name = self._get_provider_component_ids(
+                provider
             )
 
             results = []
@@ -746,9 +731,8 @@ class FlowsService:
                     result = await self._update_provider_components(
                         config,
                         provider,
-                        target_embedding_id,
-                        target_llm_id,
-                        target_llm_text_id,
+                        target_embedding_name,
+                        target_llm_name,
                         embedding_model,
                         llm_model,
                         endpoint,
@@ -791,24 +775,12 @@ class FlowsService:
     def _get_provider_component_ids(self, provider: str):
         """Get the component IDs for a specific provider"""
         if provider == "watsonx":
-            return (
-                WATSONX_EMBEDDING_COMPONENT_ID,
-                WATSONX_LLM_COMPONENT_ID,
-                WATSONX_LLM_TEXT_COMPONENT_ID,
-            )
+            return WATSONX_EMBEDDING_COMPONENT_DISPLAY_NAME, WATSONX_LLM_COMPONENT_DISPLAY_NAME
         elif provider == "ollama":
-            return (
-                OLLAMA_EMBEDDING_COMPONENT_ID,
-                OLLAMA_LLM_COMPONENT_ID,
-                OLLAMA_LLM_TEXT_COMPONENT_ID,
-            )
+            return OLLAMA_EMBEDDING_COMPONENT_DISPLAY_NAME, OLLAMA_LLM_COMPONENT_DISPLAY_NAME
         elif provider == "openai":
             # OpenAI components are the default ones
-            return (
-                OPENAI_EMBEDDING_COMPONENT_ID,
-                OPENAI_LLM_COMPONENT_ID,
-                OPENAI_LLM_TEXT_COMPONENT_ID,
-            )
+            return OPENAI_EMBEDDING_COMPONENT_DISPLAY_NAME, OPENAI_LLM_COMPONENT_DISPLAY_NAME
         else:
             raise ValueError(f"Unsupported provider: {provider}")
 
@@ -816,9 +788,8 @@ class FlowsService:
         self,
         config,
         provider: str,
-        target_embedding_id: str,
-        target_llm_id: str,
-        target_llm_text_id: str,
+        target_embedding_name: str,
+        target_llm_name: str,
         embedding_model: str,
         llm_model: str,
         endpoint: str = None,
@@ -841,7 +812,7 @@ class FlowsService:
 
         # Update embedding component
         if not DISABLE_INGEST_WITH_LANGFLOW:
-            embedding_node = self._find_node_by_id(flow_data, target_embedding_id)
+            embedding_node, _ = self._find_node_in_flow(flow_data, display_name=target_embedding_name)
             if embedding_node:
                 if self._update_component_fields(
                     embedding_node, provider, embedding_model, endpoint
@@ -849,22 +820,14 @@ class FlowsService:
                     updates_made.append(f"embedding model: {embedding_model}")
 
         # Update LLM component (if exists in this flow)
-        if target_llm_id:
-            llm_node = self._find_node_by_id(flow_data, target_llm_id)
+        if target_llm_name:
+            llm_node, _ = self._find_node_in_flow(flow_data, display_name=target_llm_name)
             if llm_node:
                 if self._update_component_fields(
                     llm_node, provider, llm_model, endpoint
                 ):
                     updates_made.append(f"llm model: {llm_model}")
 
-        if target_llm_text_id:
-            llm_text_node = self._find_node_by_id(flow_data, target_llm_text_id)
-            if llm_text_node:
-                if self._update_component_fields(
-                    llm_text_node, provider, llm_model, endpoint
-                ):
-                    updates_made.append(f"llm model: {llm_model}")
-
         # If no updates were made, return skip message
         if not updates_made:
             return {
diff --git a/src/services/langflow_file_service.py b/src/services/langflow_file_service.py
index 39c5eac7..7ffdd3aa 100644
--- a/src/services/langflow_file_service.py
+++ b/src/services/langflow_file_service.py
@@ -98,7 +98,7 @@ class LangflowFileService:
 
         # Pass metadata via tweaks to OpenSearch component
         metadata_tweaks = []
-        if owner:
+        if owner or owner is None:
             metadata_tweaks.append({"key": "owner", "value": owner})
         if owner_name:
             metadata_tweaks.append({"key": "owner_name", "value": owner_name})
@@ -106,17 +106,18 @@ class LangflowFileService:
             metadata_tweaks.append({"key": "owner_email", "value": owner_email})
         if connector_type:
             metadata_tweaks.append({"key": "connector_type", "value": connector_type})
-
-        if metadata_tweaks:
-            # Initialize the OpenSearch component tweaks if not already present
-            if "OpenSearchHybrid-Ve6bS" not in tweaks:
-                tweaks["OpenSearchHybrid-Ve6bS"] = {}
-            tweaks["OpenSearchHybrid-Ve6bS"]["docs_metadata"] = metadata_tweaks
-            logger.debug(
-                "[LF] Added metadata to tweaks", metadata_count=len(metadata_tweaks)
-            )
+        logger.info(f"[LF] Metadata tweaks {metadata_tweaks}")
+        # if metadata_tweaks:
+        #     # Initialize the OpenSearch component tweaks if not already present
+        #     if "OpenSearchHybrid-Ve6bS" not in tweaks:
+        #         tweaks["OpenSearchHybrid-Ve6bS"] = {}
+        #     tweaks["OpenSearchHybrid-Ve6bS"]["docs_metadata"] = metadata_tweaks
+        #     logger.debug(
+        #         "[LF] Added metadata to tweaks", metadata_count=len(metadata_tweaks)
+        #     )
         if tweaks:
             payload["tweaks"] = tweaks
+            logger.debug(f"[LF] Tweaks {tweaks}")
         if session_id:
             payload["session_id"] = session_id
 
@@ -137,9 +138,13 @@ class LangflowFileService:
                 "X-Langflow-Global-Var-OWNER_EMAIL": str(owner_email),
                 "X-Langflow-Global-Var-CONNECTOR_TYPE": str(connector_type),
             }
-
+        logger.info(f"[LF] Headers {headers}")
+        logger.info(f"[LF] Payload {payload}")
         resp = await clients.langflow_request(
-            "POST", f"/api/v1/run/{self.flow_id_ingest}", json=payload, headers=headers
+            "POST",
+            f"/api/v1/run/{self.flow_id_ingest}",
+            json=payload,
+            headers=headers,
         )
         logger.debug(
             "[LF] Run response", status_code=resp.status_code, reason=resp.reason_phrase
@@ -160,6 +165,7 @@ class LangflowFileService:
                 body=resp.text[:1000],
                 error=str(e),
             )
+
             raise
         return resp_json
 
@@ -171,6 +177,10 @@ class LangflowFileService:
         settings: Optional[Dict[str, Any]] = None,
         jwt_token: Optional[str] = None,
         delete_after_ingest: bool = True,
+        owner: Optional[str] = None,
+        owner_name: Optional[str] = None,
+        owner_email: Optional[str] = None,
+        connector_type: Optional[str] = None,   
     ) -> Dict[str, Any]:
         """
         Combined upload, ingest, and delete operation.
@@ -257,6 +267,10 @@ class LangflowFileService:
                 session_id=session_id,
                 tweaks=final_tweaks,
                 jwt_token=jwt_token,
+                owner=owner,
+                owner_name=owner_name,
+                owner_email=owner_email,
+                connector_type=connector_type,
             )
             logger.debug("[LF] Ingestion completed successfully")
         except Exception as e: