Merge remote-tracking branch 'origin/main' into fix/tasks_ingest

2025-10-03 09:39:58 -03:00 · 2025-10-03 09:39:58 -03:00 · efb74f5ab0
commit efb74f5ab0
parent 179a7403cc 1628f84ef5
40 changed files with 7286 additions and 5377 deletions
--- a/.DS_Store
+++ b/.DS_Store
--- 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

--- 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
--- 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
--- 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>
--- a/docs/docs/configure/configuration.mdx
+++ b/docs/docs/configure/configuration.mdx
@ -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.
--- 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.
--- 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.

--- 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
+```
--- 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**.

--- a/docs/docs/reference/configuration.mdx
+++ 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).
--- 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.
--- 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;
--- 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"
 }
--- a/flows/components/ollama_llm.json
+++ b/flows/components/ollama_llm.json
--- a/flows/components/ollama_llm_text.json
+++ b/flows/components/ollama_llm_text.json
--- a/flows/components/watsonx_embedding.json
+++ b/flows/components/watsonx_embedding.json
--- a/flows/components/watsonx_llm.json
+++ b/flows/components/watsonx_llm.json
--- a/flows/components/watsonx_llm_text.json
+++ b/flows/components/watsonx_llm_text.json
--- a/flows/ingestion_flow.json
+++ b/flows/ingestion_flow.json
--- a/flows/openrag_agent.json
+++ b/flows/openrag_agent.json
--- a/flows/openrag_ingest_docling.json
+++ b/flows/openrag_ingest_docling.json
--- a/flows/openrag_nudges.json
+++ b/flows/openrag_nudges.json
--- 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",
--- 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 }
+export { Popover, PopoverTrigger, PopoverAnchor, PopoverContent };
--- 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 } 
+export { Switch };
--- 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",
--- 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",
--- a/frontend/src/app/chat/page.tsx
+++ b/frontend/src/app/chat/page.tsx
--- 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' },
-        ],
-      };
-  }
-}
+	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" },
+				],
+			};
+	}
+}
--- a/frontend/src/app/settings/page.tsx
+++ b/frontend/src/app/settings/page.tsx
--- 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)
--- 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,
--- 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()}'"
--- 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"

--- 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
-    )
+    )
--- 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
--- 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:
--- 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 {
--- 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: