diff --git a/.env.example b/.env.example
index fe908795..ee2a838c 100644
--- a/.env.example
+++ b/.env.example
@@ -9,7 +9,7 @@ LANGFLOW_SECRET_KEY=
 LANGFLOW_CHAT_FLOW_ID=1098eea1-6649-4e1d-aed1-b77249fb8dd0
 LANGFLOW_INGEST_FLOW_ID=5488df7c-b93f-4f87-a446-b67028bc0813
 # Ingest flow using docling
-LANGFLOW_INGEST_FLOW_ID=1402618b-e6d1-4ff2-9a11-d6ce71186915
+# LANGFLOW_INGEST_FLOW_ID=1402618b-e6d1-4ff2-9a11-d6ce71186915
 NUDGES_FLOW_ID=ebc01d31-1976-46ce-a385-b0240327226c
 
 # Set a strong admin password for OpenSearch; a bcrypt hash is generated at
diff --git a/.github/workflows/build-multiarch.yml b/.github/workflows/build-multiarch.yml
index c9d4b40d..620bcf3b 100644
--- a/.github/workflows/build-multiarch.yml
+++ b/.github/workflows/build-multiarch.yml
@@ -2,6 +2,12 @@ name: Build Multi-Architecture Docker Images
 
 on:
   workflow_dispatch:
+    inputs:
+      update_latest:
+        description: 'Update latest tags (production release)'
+        required: false
+        default: false
+        type: boolean
 
 jobs:
   build:
@@ -123,30 +129,41 @@ jobs:
         run: |
           VERSION=${{ steps.version.outputs.version }}
 
+          # Create versioned tags
           docker buildx imagetools create -t phact/openrag-backend:$VERSION \
             phact/openrag-backend:$VERSION-amd64 \
             phact/openrag-backend:$VERSION-arm64
-          docker buildx imagetools create -t phact/openrag-backend:latest \
-            phact/openrag-backend:$VERSION-amd64 \
-            phact/openrag-backend:$VERSION-arm64
 
           docker buildx imagetools create -t phact/openrag-frontend:$VERSION \
             phact/openrag-frontend:$VERSION-amd64 \
             phact/openrag-frontend:$VERSION-arm64
-          docker buildx imagetools create -t phact/openrag-frontend:latest \
-            phact/openrag-frontend:$VERSION-amd64 \
-            phact/openrag-frontend:$VERSION-arm64
 
           docker buildx imagetools create -t phact/openrag-langflow:$VERSION \
             phact/openrag-langflow:$VERSION-amd64 \
             phact/openrag-langflow:$VERSION-arm64
-          docker buildx imagetools create -t phact/openrag-langflow:latest \
-            phact/openrag-langflow:$VERSION-amd64 \
-            phact/openrag-langflow:$VERSION-arm64
 
           docker buildx imagetools create -t phact/openrag-opensearch:$VERSION \
             phact/openrag-opensearch:$VERSION-amd64 \
             phact/openrag-opensearch:$VERSION-arm64
-          docker buildx imagetools create -t phact/openrag-opensearch:latest \
-            phact/openrag-opensearch:$VERSION-amd64 \
-            phact/openrag-opensearch:$VERSION-arm64
+
+          # Only update latest tags if version is numeric AND checkbox is checked
+          if [[ "$VERSION" =~ ^[0-9.-]+$ ]] && [[ "${{ github.event.inputs.update_latest }}" == "true" ]]; then
+            echo "Updating latest tags for production release: $VERSION"
+            docker buildx imagetools create -t phact/openrag-backend:latest \
+              phact/openrag-backend:$VERSION-amd64 \
+              phact/openrag-backend:$VERSION-arm64
+
+            docker buildx imagetools create -t phact/openrag-frontend:latest \
+              phact/openrag-frontend:$VERSION-amd64 \
+              phact/openrag-frontend:$VERSION-arm64
+
+            docker buildx imagetools create -t phact/openrag-langflow:latest \
+              phact/openrag-langflow:$VERSION-amd64 \
+              phact/openrag-langflow:$VERSION-arm64
+
+            docker buildx imagetools create -t phact/openrag-opensearch:latest \
+              phact/openrag-opensearch:$VERSION-amd64 \
+              phact/openrag-opensearch:$VERSION-arm64
+          else
+            echo "Skipping latest tags - version: $VERSION, update_latest: ${{ github.event.inputs.update_latest }}"
+          fi
diff --git a/docs/docs/get-started/install.mdx b/docs/docs/get-started/install.mdx
new file mode 100644
index 00000000..67f4ae89
--- /dev/null
+++ b/docs/docs/get-started/install.mdx
@@ -0,0 +1,179 @@
+---
+title: Install OpenRAG
+slug: /install
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+OpenRAG can be installed in multiple ways:
+
+* [**Python wheel**](#install-python-wheel): Install the OpenRAG Python wheel and use the [OpenRAG Terminal User Interface (TUI)](/get-started/tui) to install, run, and configure your OpenRAG deployment without running Docker commands.
+
+* [**Docker Compose**](#install-and-run-docker): Clone the OpenRAG repository and deploy OpenRAG with Docker Compose, including all services and dependencies.
+
+## Prerequisites 
+
+- [Python Version 3.10 to 3.13](https://www.python.org/downloads/release/python-3100/)
+- [uv](https://docs.astral.sh/uv/getting-started/installation/)
+- [Docker](https://docs.docker.com/get-docker/) or [Podman](https://podman.io/docs/installation) installed
+- [Docker Compose](https://docs.docker.com/compose/install/) installed. If using Podman, use [podman-compose](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or alias Docker compose commands to Podman commands.
+- For GPU support: (TBD)
+
+## Python wheel {#install-python-wheel}
+
+The Python wheel is currently available internally, but will be available on PyPI at launch.
+The wheel installs the OpenRAG wheel, which includes the TUI for installing, running, and managing OpenRAG.
+For more information on virtual environments, see [uv](https://docs.astral.sh/uv/pip/environments).
+
+1. Create a new project with a virtual environment using [uv](https://docs.astral.sh/uv/pip/environments).
+
+   ```bash
+   uv init YOUR_PROJECT_NAME
+   cd YOUR_PROJECT_NAME
+   ```
+2. Add the OpenRAG wheel to your project and install it in the virtual environment.
+   Replace `PATH/TO/` and `VERSION` with your OpenRAG wheel location and version.
+   ```bash
+   uv add PATH/TO/openrag-VERSION-py3-none-any.whl
+   ```
+3. Ensure all dependencies are installed and updated in your virtual environment.
+   ```bash
+   uv sync
+   ```
+
+4. Start the OpenRAG TUI.
+   ```bash
+   uv run openrag
+   ```
+
+   The OpenRAG TUI opens.
+
+5. To install OpenRAG with Basic Setup, click **Basic Setup** or press <kbd>1</kbd>. Basic Setup does not set up OAuth connections for ingestion from Google Drive, OneDrive, or AWS. For OAuth setup, see [Advanced Setup](#advanced-setup).
+   The TUI prompts you for the required startup values.
+   Click **Generate Passwords** to autocomplete fields that contain **Auto-generated Secure Password**, or bring your own passwords.
+    <details closed>
+    <summary>Where do I find the required startup values?</summary>
+    
+    | Variable | Where to Find | Description |
+    |----------|---------------|-------------|
+    | `OPENSEARCH_PASSWORD` | Auto-generated secure password | The password for OpenSearch database access. Must be at least 8 characters and must contain at least one uppercase letter, one lowercase letter, one digit, and one special character. |
+    | `OPENAI_API_KEY` | [OpenAI Platform](https://platform.openai.com/api-keys) | API key from your OpenAI account. |
+    | `LANGFLOW_SUPERUSER` | User generated | Username for Langflow admin access. For more, see [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-superuser). |
+    | `LANGFLOW_SUPERUSER_PASSWORD` | Auto-generated secure password | Password for Langflow admin access. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-superuser). |
+    | `LANGFLOW_SECRET_KEY` | Auto-generated secure key | Secret key for Langflow security. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key). |
+    | `LANGFLOW_AUTO_LOGIN` | Auto-generated or manual | Auto-login configuration. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login). |
+    | `LANGFLOW_NEW_USER_IS_ACTIVE` | Langflow | New user activation setting. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-new-user-is-active). |
+    | `LANGFLOW_ENABLE_SUPERUSER_CLI` | Langflow server | Superuser CLI access setting. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-enable-superuser-cli). |
+    | `DOCUMENTS_PATH` | Set your local path | Path to your document storage directory. |
+    
+    </details>
+   
+   To complete credentials, click **Save Configuration**.
+
+6. To start OpenRAG with your credentials, click **Start Container Services**.
+   Startup pulls container images and starts them, so it can take some time.
+   The operation has completed when the **Close** button is available and the terminal displays:
+   ```bash
+   Services started successfully
+   Command completed successfully
+   ```
+
+7. To open the OpenRAG application, click **Open App** or press <kbd>6</kbd>.
+8. Continue with the Quickstart.
+
+### Advanced Setup {#advanced-setup}
+
+**Advanced Setup** includes the required values from **Basic Setup**, with additional settings for OAuth credentials.
+If the OpenRAG TUI detects OAuth credentials, it enforces the Advanced Setup path.
+1. Add your client and secret values for Google, Azure, or AWS OAuth.
+These values can be found in your OAuth provider.
+2. The OpenRAG TUI presents redirect URIs for your OAuth app.
+These are the URLs your OAuth provider will redirect back to after user sign-in.
+Register these redirect values with your OAuth provider as they are presented in the TUI.
+3. To open the OpenRAG application, click **Open App** or press <kbd>6</kbd>.
+You will be presented with your provider's OAuth sign-in screen, and be redirected to the redirect URI after sign-in.
+
+Two additional variables are available for Advanced Setup:
+
+The `LANGFLOW_PUBLIC_URL` controls where the Langflow web interface can be accessed. This is where users interact with their flows in a browser.
+
+The `WEBHOOK_BASE_URL` controls where the endpoint for `/connectors/CONNECTOR_TYPE/webhook` will be available.
+This connection enables real-time document synchronization with external services.
+For example, for Google Drive file synchronization the webhook URL is `/connectors/google_drive/webhook`.
+
+## Docker {#install-and-run-docker}
+
+There are two different Docker Compose files.
+They deploy the same applications and containers, but to different environments.
+
+- [`docker-compose.yml`](https://github.com/langflow-ai/openrag/blob/main/docker-compose.yml) is an OpenRAG deployment with GPU support for accelerated AI processing.
+
+- [`docker-compose-cpu.yml`](https://github.com/langflow-ai/openrag/blob/main/docker-compose-cpu.yml) is a CPU-only version of OpenRAG for systems without GPU support. Use this Docker compose file for environments where GPU drivers aren't available.
+
+To install OpenRAG with Docker Compose:
+
+1. Clone the OpenRAG repository.
+   ```bash
+   git clone https://github.com/langflow-ai/openrag.git
+   cd openrag
+   ```
+
+2. Copy the example `.env` file that is included in the repository root.
+  The example file includes all environment variables with comments to guide you in finding and setting their values.
+   ```bash
+   cp .env.example .env
+   ```
+
+   Alternatively, create a new `.env` file in the repository root.
+   ```
+   touch .env
+   ```
+
+3. Set environment variables. The Docker Compose files are populated with values from your `.env`, so 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 additional configuration values, including `config.yaml`, see [Configuration](/configure/configuration).
+
+4. Deploy OpenRAG with Docker Compose based on your deployment type.
+
+   For GPU-enabled systems, run the following command:
+   ```bash
+   docker compose up -d
+   ```
+
+   For CPU-only systems, run the following command:
+   ```bash
+   docker compose -f docker-compose-cpu.yml up -d
+   ```
+
+   The OpenRAG Docker Compose file starts five containers:
+   | Container Name | Default Address | Purpose |
+   |---|---|---|
+   | OpenRAG Backend | http://localhost:8000 | FastAPI server and core functionality. |
+   | OpenRAG Frontend | http://localhost:3000 | React web interface for users. |
+   | Langflow | http://localhost:7860 | AI workflow engine and flow management. |
+   | OpenSearch | http://localhost:9200 | Vector database for document storage. |
+   | OpenSearch Dashboards | http://localhost:5601 | Database administration interface. |
+
+5. Verify installation by confirming all services are running.
+
+   ```bash
+   docker compose ps
+   ```
+
+   You can now access the application at:
+
+   - **Frontend**: http://localhost:3000
+   - **Backend API**: http://localhost:8000
+   - **Langflow**: http://localhost:7860
+
+Continue with the Quickstart.
\ No newline at end of file
diff --git a/docs/docs/get-started/quickstart.mdx b/docs/docs/get-started/quickstart.mdx
new file mode 100644
index 00000000..993d9739
--- /dev/null
+++ b/docs/docs/get-started/quickstart.mdx
@@ -0,0 +1,390 @@
+---
+title: Quickstart
+slug: /quickstart
+---
+
+import Icon from "@site/src/components/icon/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the OpenRAG API.
+
+## Prerequisites
+
+- Install and start OpenRAG
+
+## Find your way around 
+
+1. In OpenRAG, click <Icon name="MessageSquare" aria-hidden="true"/> **Chat**.
+2. Ask `What documents are available to you?`
+   The agent responds with a message summarizing the documents that OpenRAG loads by default, which are PDFs about evaluating data quality when using LLMs in health care.
+3. To confirm the agent is correct, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
+   The **Knowledge** page lists the documents OpenRAG has ingested into the OpenSearch vector database. Click on a document to display the chunks derived from splitting the default documents into the vector database.
+
+## Add your own knowledge
+
+1. To add documents to your knowledge base, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**.
+   * Select **Add File** to add a single file from your local machine (mapped with the Docker volume mount).
+   * Select **Process Folder** to process an entire folder of documents from your local machine (mapped with the Docker volume mount).
+2. Return to the Chat window and ask a question about your loaded data.
+   For example, with a manual about a PC tablet loaded, ask `How do I connect this device to WiFI?`
+   The agent responds with a message indicating it now has your knowledge as context for answering questions.
+3. Click the <Icon name="Gear" aria-hidden="true"/> **Function Call: search_documents (tool_call)** that is printed in the Playground.
+These events log the agent's request to the tool and the tool's response, so you have direct visibility into your agent's functionality.
+If you aren't getting the results you need, you can further tune the knowledge ingestion and agent behavior in the next section.
+
+## Swap out the language model to modify agent behavior
+
+To modify the knowledge ingestion or Agent behavior, click <Icon name="Settings" aria-hidden="true"/> **Settings**.
+
+In this example, you'll try a different LLM to demonstrate how the Agent's response changes.
+
+1. To edit the Agent's behavior, click **Edit in Langflow**.
+2. OpenRAG warns you that you're entering Langflow. Click **Proceed**.
+3. The OpenRAG Open Search Agent flow appears. 
+
+![OpenRAG Open Search Agent Flow](/img/opensearch-agent-flow.png)
+
+4. In the **Language Model** component, under **Model Provider**, select **Anthropic**.
+   :::note
+   This guide uses an Anthropic model for demonstration purposes. If you want to use a different provider, change the **Model Provider** and **Model Name** fields, and then provide credentials for your selected provider.
+   :::
+5. Save your flow with <kbd>Command+S</kbd>.
+6. In OpenRAG, start a new conversation by clicking the <Icon name="Plus" aria-hidden="true"/> in the **Conversations** tab.
+7. Ask the same question as before to demonstrate how a different language model changes the results.
+
+## Integrate OpenRAG into your application
+
+:::tip
+Ensure the `openrag-backend` container has port 8000 exposed in your `docker-compose.yml`:
+
+```yaml
+openrag-backend:
+  ports:
+    - "8000:8000"
+```
+:::
+
+OpenRAG provides a REST API that you can call from Python, TypeScript, or any HTTP client to chat with your documents.
+
+These example requests are run assuming OpenRAG is in "no-auth" mode.
+For complete API documentation, including authentication, request and response parameters, and example requests, see the API documentation.
+
+### Chat with your documents
+
+Prompt OpenRAG at the `/chat` API endpoint.
+
+<Tabs>
+<TabItem value="python" label="Python">
+
+```python
+import requests
+
+url = "http://localhost:8000/chat"
+payload = {
+    "prompt": "What documents are available to you?",
+    "previous_response_id": None
+}
+
+response = requests.post(url, json=payload)
+print("OpenRAG Response:", response.json())
+```
+
+</TabItem>
+<TabItem value="typescript" label="TypeScript">
+
+```typescript
+import fetch from 'node-fetch';
+
+const response = await fetch("http://localhost:8000/chat", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({
+        prompt: "What documents are available to you?",
+        previous_response_id: null
+    })
+});
+
+const data = await response.json();
+console.log("OpenRAG Response:", data);
+```
+
+</TabItem>
+<TabItem value="curl" label="curl">
+
+```bash
+curl -X POST "http://localhost:8000/chat" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "prompt": "What documents are available to you?",
+    "previous_response_id": null
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+<details closed>
+<summary>Response</summary>
+
+```
+{
+  "response": "I have access to a wide range of documents depending on the context and the tools enabled in this environment. Specifically, I can search for and retrieve documents related to various topics such as technical papers, articles, manuals, guides, knowledge base entries, and other text-based resources. If you specify a particular subject or type of document you're interested in, I can try to locate relevant materials for you. Let me know what you need!",
+  "response_id": "resp_68d3fdbac93081958b8781b97919fe7007f98bd83932fa1a"
+}
+```
+
+</details>
+
+### Search your documents
+
+Search your document knowledge base at the `/search` endpoint.
+
+<Tabs>
+<TabItem value="python" label="Python">
+
+```python
+import requests
+
+url = "http://localhost:8000/search"
+payload = {"query": "healthcare data quality", "limit": 5}
+
+response = requests.post(url, json=payload)
+results = response.json()
+
+print("Search Results:")
+for result in results.get("results", []):
+    print(f"- {result.get('filename')}: {result.get('text', '')[:100]}...")
+```
+
+</TabItem>
+<TabItem value="typescript" label="TypeScript">
+
+```typescript
+const response = await fetch("http://localhost:8000/search", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({
+        query: "healthcare data quality",
+        limit: 5
+    })
+});
+
+const results = await response.json();
+console.log("Search Results:");
+results.results?.forEach((result, index) => {
+    const filename = result.filename || 'Unknown';
+    const text = result.text?.substring(0, 100) || '';
+    console.log(`${index + 1}. ${filename}: ${text}...`);
+});
+```
+
+</TabItem>
+<TabItem value="curl" label="curl">
+
+```bash
+curl -X POST "http://localhost:8000/search" \
+  -H "Content-Type: application/json" \
+  -d '{"query": "healthcare data quality", "limit": 5}'
+```
+
+</TabItem>
+</Tabs>
+
+
+<details closed>
+<summary>Example response</summary>
+
+```
+Found 5 results
+1. 2506.08231v1.pdf: variables with high performance metrics. These variables might also require fewer replication analys...
+2. 2506.08231v1.pdf: on EHR data and may lack the clinical domain knowledge needed to perform well on the tasks where EHR...
+3. 2506.08231v1.pdf: Abstract Large language models (LLMs) are increasingly used to extract clinical data from electronic...
+4. 2506.08231v1.pdf: these multidimensional assessments, the framework not only quantifies accuracy, but can also be appl...
+5. 2506.08231v1.pdf: observed in only the model metrics, but not the abstractor metrics, it indicates that model errors m...
+```
+
+</details>
+
+### Use chat and search together 
+
+Create a complete chat application that combines an interactive terminal chat with session continuity and search functionality.
+
+<Tabs>
+<TabItem value="python" label="Python">
+
+```python
+import requests
+
+# Configuration
+OPENRAG_BASE_URL = "http://localhost:8000"
+CHAT_URL = f"{OPENRAG_BASE_URL}/chat"
+SEARCH_URL = f"{OPENRAG_BASE_URL}/search"
+DEFAULT_SEARCH_LIMIT = 5
+
+def chat_with_openrag(message, previous_response_id=None):
+    try:
+        response = requests.post(CHAT_URL, json={
+            "prompt": message, 
+            "previous_response_id": previous_response_id
+        })
+        response.raise_for_status()
+        data = response.json()
+        return data.get("response"), data.get("response_id")
+    except Exception as e:
+        return f"Error: {str(e)}", None
+
+def search_documents(query, limit=DEFAULT_SEARCH_LIMIT):
+    try:
+        response = requests.post(SEARCH_URL, json={
+            "query": query,
+            "limit": limit
+        })
+        response.raise_for_status()
+        data = response.json()
+        return data.get("results", [])
+    except Exception as e:
+        return []
+
+# Interactive chat with session continuity and search
+previous_response_id = None
+while True:
+    question = input("Your question (or 'search <query>' to search): ").strip()
+    if question.lower() in ['quit', 'exit', 'q']:
+        break
+    if not question:
+        continue
+    
+    if question.lower().startswith('search '):
+        query = question[7:].strip()
+        print("Searching documents...")
+        results = search_documents(query)
+        print(f"\nFound {len(results)} results:")
+        for i, result in enumerate(results, 1):
+            filename = result.get('filename', 'Unknown')
+            text = result.get('text', '')[:100]
+            print(f"{i}. {filename}: {text}...")
+        print()
+    else:
+        print("OpenRAG is thinking...")
+        result, response_id = chat_with_openrag(question, previous_response_id)
+        print(f"OpenRAG: {result}\n")
+        previous_response_id = response_id
+```
+
+</TabItem>
+<TabItem value="typescript" label="TypeScript">
+
+```ts
+import fetch from 'node-fetch';
+
+// Configuration
+const OPENRAG_BASE_URL = "http://localhost:8000";
+const CHAT_URL = `${OPENRAG_BASE_URL}/chat`;
+const SEARCH_URL = `${OPENRAG_BASE_URL}/search`;
+const DEFAULT_SEARCH_LIMIT = 5;
+
+async function chatWithOpenRAG(message: string, previousResponseId?: string | null) {
+    try {
+        const response = await fetch(CHAT_URL, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({
+                prompt: message,
+                previous_response_id: previousResponseId
+            })
+        });
+        const data = await response.json();
+        return [data.response || "No response received", data.response_id || null];
+    } catch (error) {
+        return [`Error: ${error}`, null];
+    }
+}
+
+async function searchDocuments(query: string, limit: number = DEFAULT_SEARCH_LIMIT) {
+    try {
+        const response = await fetch(SEARCH_URL, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ query, limit })
+        });
+        const data = await response.json();
+        return data.results || [];
+    } catch (error) {
+        return [];
+    }
+}
+
+// Interactive chat with session continuity and search
+let previousResponseId = null;
+const readline = require('readline');
+const rl = readline.createInterface({ input: process.stdin, output: process.stdout });
+
+const askQuestion = () => {
+    rl.question("Your question (or 'search <query>' to search): ", async (question) => {
+        if (question.toLowerCase() === 'quit' || question.toLowerCase() === 'exit' || question.toLowerCase() === 'q') {
+            console.log("Goodbye!");
+            rl.close();
+            return;
+        }
+        if (!question.trim()) {
+            askQuestion();
+            return;
+        }
+        
+        if (question.toLowerCase().startsWith('search ')) {
+            const query = question.substring(7).trim();
+            console.log("Searching documents...");
+            const results = await searchDocuments(query);
+            console.log(`\nFound ${results.length} results:`);
+            results.forEach((result, i) => {
+                const filename = result.filename || 'Unknown';
+                const text = result.text?.substring(0, 100) || '';
+                console.log(`${i + 1}. ${filename}: ${text}...`);
+            });
+            console.log();
+        } else {
+            console.log("OpenRAG is thinking...");
+            const [result, responseId] = await chatWithOpenRAG(question, previousResponseId);
+            console.log(`\nOpenRAG: ${result}\n`);
+            previousResponseId = responseId;
+        }
+        askQuestion();
+    });
+};
+
+console.log("OpenRAG Chat Interface");
+console.log("Ask questions about your documents. Type 'quit' to exit.");
+console.log("Use 'search <query>' to search documents directly.\n");
+askQuestion();
+```
+
+</TabItem>
+</Tabs>
+
+<details closed>
+<summary>Example response</summary>
+
+```
+Your question (or 'search <query>' to search): search healthcare
+Searching documents...
+
+Found 5 results:
+1. 2506.08231v1.pdf: variables with high performance metrics. These variables might also require fewer replication analys...
+2. 2506.08231v1.pdf: on EHR data and may lack the clinical domain knowledge needed to perform well on the tasks where EHR...
+3. 2506.08231v1.pdf: Abstract Large language models (LLMs) are increasingly used to extract clinical data from electronic...
+4. 2506.08231v1.pdf: Acknowledgements Darren Johnson for support in publication planning and management. The authors used...
+5. 2506.08231v1.pdf: Ensuring Reliability of Curated EHR-Derived Data: The Validation of Accuracy for LLM/ML-Extracted In...
+
+Your question (or 'search <query>' to search): what's the weather today?
+OpenRAG is thinking...
+OpenRAG: I don't have access to real-time weather data. Could you please provide me with your location? Then I can help you find the weather information.
+
+Your question (or 'search <query>' to search): newark nj
+OpenRAG is thinking...               
+```
+
+</details>
+## Next steps
+
+TBD
\ No newline at end of file
diff --git a/docs/docs/get-started/what-is-openrag.mdx b/docs/docs/get-started/what-is-openrag.mdx
index 7b411617..d52fe12f 100644
--- a/docs/docs/get-started/what-is-openrag.mdx
+++ b/docs/docs/get-started/what-is-openrag.mdx
@@ -1,6 +1,6 @@
 ---
 title: What is OpenRAG?
-slug: /what-is-openrag
+slug: /
 ---
 
 OpenRAG is an open-source package for building agentic RAG systems.
diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js
index 4248c3e2..c4175c09 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -71,7 +71,7 @@ const config = {
         logo: {
           alt: 'OpenRAG Logo',
           src: 'img/logo.svg',
-          href: 'what-is-openrag',
+          href: '/',
         },
         items: [
           {
@@ -89,7 +89,7 @@ const config = {
             items: [
               {
                 label: 'Getting Started',
-                to: 'what-is-openrag',
+                to: '/',
               },
             ],
           },
diff --git a/docs/sidebars.js b/docs/sidebars.js
index 568989e5..588e7665 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -23,7 +23,17 @@ const sidebars = {
         {
           type: "doc",
           id: "get-started/what-is-openrag",
-          label: "Introduction"
+          label: "About OpenRAG"
+        },
+        {
+          type: "doc",
+          id: "get-started/install",
+          label: "Installation"
+        },
+        {
+          type: "doc",
+          id: "get-started/quickstart",
+          label: "Quickstart"
         },
         {
           type: "doc",
diff --git a/docs/static/img/opensearch-agent-flow.png b/docs/static/img/opensearch-agent-flow.png
new file mode 100644
index 00000000..d201aef9
Binary files /dev/null and b/docs/static/img/opensearch-agent-flow.png differ
diff --git a/flows/openrag_ingest_docling.json b/flows/openrag_ingest_docling.json
index 05088ae2..889f8425 100644
--- a/flows/openrag_ingest_docling.json
+++ b/flows/openrag_ingest_docling.json
@@ -30,34 +30,6 @@
         "target": "OpenSearchHybrid-XtKoA",
         "targetHandle": "{œfieldNameœ:œingest_dataœ,œidœ:œOpenSearchHybrid-XtKoAœ,œinputTypesœ:[œDataœ,œDataFrameœ],œtypeœ:œotherœ}"
       },
-      {
-        "animated": false,
-        "className": "",
-        "data": {
-          "sourceHandle": {
-            "dataType": "OpenAIEmbeddings",
-            "id": "OpenAIEmbeddings-mP45L",
-            "name": "embeddings",
-            "output_types": [
-              "Embeddings"
-            ]
-          },
-          "targetHandle": {
-            "fieldName": "embedding",
-            "id": "OpenSearchHybrid-XtKoA",
-            "inputTypes": [
-              "Embeddings"
-            ],
-            "type": "other"
-          }
-        },
-        "id": "reactflow__edge-OpenAIEmbeddings-mP45L{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-mP45Lœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-OpenSearchHybrid-XtKoA{œfieldNameœ:œembeddingœ,œidœ:œOpenSearchHybrid-XtKoAœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
-        "selected": false,
-        "source": "OpenAIEmbeddings-mP45L",
-        "sourceHandle": "{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-mP45Lœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}",
-        "target": "OpenSearchHybrid-XtKoA",
-        "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œOpenSearchHybrid-XtKoAœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}"
-      },
       {
         "animated": false,
         "className": "",
@@ -116,6 +88,34 @@
         "sourceHandle": "{œdataTypeœ:œExportDoclingDocumentœ,œidœ:œExportDoclingDocument-xFoCIœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}",
         "target": "SplitText-3ZI5B",
         "targetHandle": "{œfieldNameœ:œdata_inputsœ,œidœ:œSplitText-3ZI5Bœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}"
+      },
+      {
+        "animated": false,
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "EmbeddingModel",
+            "id": "EmbeddingModel-cxG9r",
+            "name": "embeddings",
+            "output_types": [
+              "Embeddings"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "embedding",
+            "id": "OpenSearchHybrid-XtKoA",
+            "inputTypes": [
+              "Embeddings"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "xy-edge__EmbeddingModel-cxG9r{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-cxG9rœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-OpenSearchHybrid-XtKoA{œfieldNameœ:œembeddingœ,œidœ:œOpenSearchHybrid-XtKoAœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
+        "selected": false,
+        "source": "EmbeddingModel-cxG9r",
+        "sourceHandle": "{œdataTypeœ:œEmbeddingModelœ,œidœ:œEmbeddingModel-cxG9rœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}",
+        "target": "OpenSearchHybrid-XtKoA",
+        "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œOpenSearchHybrid-XtKoAœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}"
       }
     ],
     "nodes": [
@@ -146,8 +146,9 @@
             "frozen": false,
             "icon": "scissors-line-dashed",
             "legacy": false,
+            "lf_version": "1.6.0",
             "metadata": {
-              "code_hash": "f2867efda61f",
+              "code_hash": "65a90e1f4fe6",
               "dependencies": {
                 "dependencies": [
                   {
@@ -155,8 +156,8 @@
                     "version": "0.3.9"
                   },
                   {
-                    "name": "lfx",
-                    "version": null
+                    "name": "langflow",
+                    "version": "1.5.0.post2"
                   }
                 ],
                 "total_dependencies": 2
@@ -239,7 +240,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "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"
+                "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"
               },
               "data_inputs": {
                 "_input_type": "HandleInput",
@@ -270,7 +271,6 @@
                 "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": [
@@ -361,585 +361,6 @@
         "type": "genericNode",
         "width": 320
       },
-      {
-        "data": {
-          "id": "OpenAIEmbeddings-mP45L",
-          "node": {
-            "base_classes": [
-              "Embeddings"
-            ],
-            "beta": false,
-            "conditional_paths": [],
-            "custom_fields": {},
-            "description": "Generate embeddings using OpenAI models.",
-            "display_name": "OpenAI Embeddings",
-            "documentation": "",
-            "edited": false,
-            "field_order": [
-              "default_headers",
-              "default_query",
-              "chunk_size",
-              "client",
-              "deployment",
-              "embedding_ctx_length",
-              "max_retries",
-              "model",
-              "model_kwargs",
-              "openai_api_key",
-              "openai_api_base",
-              "openai_api_type",
-              "openai_api_version",
-              "openai_organization",
-              "openai_proxy",
-              "request_timeout",
-              "show_progress_bar",
-              "skip_empty",
-              "tiktoken_model_name",
-              "tiktoken_enable",
-              "dimensions"
-            ],
-            "frozen": false,
-            "icon": "OpenAI",
-            "legacy": false,
-            "metadata": {
-              "code_hash": "8a658ed6d4c9",
-              "dependencies": {
-                "dependencies": [
-                  {
-                    "name": "langchain_openai",
-                    "version": "0.3.23"
-                  },
-                  {
-                    "name": "lfx",
-                    "version": null
-                  }
-                ],
-                "total_dependencies": 2
-              },
-              "module": "custom_components.openai_embeddings"
-            },
-            "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": {
-              "_type": "Component",
-              "chunk_size": {
-                "_input_type": "IntInput",
-                "advanced": true,
-                "display_name": "Chunk Size",
-                "dynamic": false,
-                "info": "",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "chunk_size",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "int",
-                "value": 1000
-              },
-              "client": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Client",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "client",
-                "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,
-                "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 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 BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageTextInput, SecretStrInput\n\n\nclass OpenAIEmbeddingsComponent(LCEmbeddingsModel):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIEmbeddings\"\n\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        MessageTextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        MessageTextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=OPENAI_EMBEDDING_MODEL_NAMES,\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\", value=\"OPENAI_API_KEY\", required=True),\n        MessageTextInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        MessageTextInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        MessageTextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        MessageTextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        MessageTextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        MessageTextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\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    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            client=self.client or None,\n            model=self.model,\n            dimensions=self.dimensions or None,\n            deployment=self.deployment or None,\n            api_version=self.openai_api_version or None,\n            base_url=self.openai_api_base or None,\n            openai_api_type=self.openai_api_type or None,\n            openai_proxy=self.openai_proxy or None,\n            embedding_ctx_length=self.embedding_ctx_length,\n            api_key=self.openai_api_key or None,\n            organization=self.openai_organization or None,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            max_retries=self.max_retries,\n            timeout=self.request_timeout or None,\n            tiktoken_enabled=self.tiktoken_enable,\n            tiktoken_model_name=self.tiktoken_model_name or None,\n            show_progress_bar=self.show_progress_bar,\n            model_kwargs=self.model_kwargs,\n            skip_empty=self.skip_empty,\n            default_headers=self.default_headers or None,\n            default_query=self.default_query or None,\n        )\n"
-              },
-              "default_headers": {
-                "_input_type": "DictInput",
-                "advanced": true,
-                "display_name": "Default Headers",
-                "dynamic": false,
-                "info": "Default headers to use for the API request.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "default_headers",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "type": "dict",
-                "value": {}
-              },
-              "default_query": {
-                "_input_type": "DictInput",
-                "advanced": true,
-                "display_name": "Default Query",
-                "dynamic": false,
-                "info": "Default query parameters to use for the API request.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "default_query",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "type": "dict",
-                "value": {}
-              },
-              "deployment": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "Deployment",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "deployment",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "dimensions": {
-                "_input_type": "IntInput",
-                "advanced": true,
-                "display_name": "Dimensions",
-                "dynamic": false,
-                "info": "The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "dimensions",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "int",
-                "value": ""
-              },
-              "embedding_ctx_length": {
-                "_input_type": "IntInput",
-                "advanced": true,
-                "display_name": "Embedding Context Length",
-                "dynamic": false,
-                "info": "",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "embedding_ctx_length",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "int",
-                "value": 1536
-              },
-              "max_retries": {
-                "_input_type": "IntInput",
-                "advanced": true,
-                "display_name": "Max Retries",
-                "dynamic": false,
-                "info": "",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "max_retries",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "int",
-                "value": 3
-              },
-              "model": {
-                "_input_type": "DropdownInput",
-                "advanced": false,
-                "combobox": false,
-                "dialog_inputs": {},
-                "display_name": "Model",
-                "dynamic": false,
-                "info": "",
-                "name": "model",
-                "options": [
-                  "text-embedding-3-small",
-                  "text-embedding-3-large",
-                  "text-embedding-ada-002"
-                ],
-                "options_metadata": [],
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "toggle": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": "text-embedding-3-small"
-              },
-              "model_kwargs": {
-                "_input_type": "DictInput",
-                "advanced": true,
-                "display_name": "Model Kwargs",
-                "dynamic": false,
-                "info": "",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "model_kwargs",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "type": "dict",
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-              },
-              "openai_api_base": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "OpenAI API Base",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "openai_api_base",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
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-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
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-              },
-              "openai_api_key": {
-                "_input_type": "SecretStrInput",
-                "advanced": false,
-                "display_name": "OpenAI API Key",
-                "dynamic": false,
-                "info": "",
-                "input_types": [],
-                "load_from_db": true,
-                "name": "openai_api_key",
-                "password": true,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "OPENAI_API_KEY"
-              },
-              "openai_api_type": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "OpenAI API Type",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "openai_api_type",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "openai_api_version": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "OpenAI API Version",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "openai_api_version",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_input": true,
-                "trace_as_metadata": true,
-                "type": "str",
-                "value": ""
-              },
-              "openai_organization": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "OpenAI Organization",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "openai_organization",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
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-              "openai_proxy": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "OpenAI Proxy",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "openai_proxy",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
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-              },
-              "request_timeout": {
-                "_input_type": "FloatInput",
-                "advanced": true,
-                "display_name": "Request Timeout",
-                "dynamic": false,
-                "info": "",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "request_timeout",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "float",
-                "value": ""
-              },
-              "show_progress_bar": {
-                "_input_type": "BoolInput",
-                "advanced": true,
-                "display_name": "Show Progress Bar",
-                "dynamic": false,
-                "info": "",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "show_progress_bar",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "bool",
-                "value": false
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-              "skip_empty": {
-                "_input_type": "BoolInput",
-                "advanced": true,
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-                "dynamic": false,
-                "info": "",
-                "list": false,
-                "list_add_label": "Add More",
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-              "tiktoken_enable": {
-                "_input_type": "BoolInput",
-                "advanced": true,
-                "display_name": "TikToken Enable",
-                "dynamic": false,
-                "info": "If False, you must have transformers installed.",
-                "list": false,
-                "list_add_label": "Add More",
-                "name": "tiktoken_enable",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "tool_mode": false,
-                "trace_as_metadata": true,
-                "type": "bool",
-                "value": true
-              },
-              "tiktoken_model_name": {
-                "_input_type": "MessageTextInput",
-                "advanced": true,
-                "display_name": "TikToken Model Name",
-                "dynamic": false,
-                "info": "",
-                "input_types": [
-                  "Message"
-                ],
-                "list": false,
-                "list_add_label": "Add More",
-                "load_from_db": false,
-                "name": "tiktoken_model_name",
-                "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
-          },
-          "selected_output": "embeddings",
-          "type": "OpenAIEmbeddings"
-        },
-        "dragging": false,
-        "height": 320,
-        "id": "OpenAIEmbeddings-mP45L",
-        "measured": {
-          "height": 320,
-          "width": 320
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-        "position": {
-          "x": 1704.8491676318172,
-          "y": 1879.144249471858
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-        "selected": false,
-        "type": "genericNode",
-        "width": 320
-      },
-      {
-        "data": {
-          "id": "note-59mzY",
-          "node": {
-            "description": "### 💡 Add your OpenAI API key here 👇",
-            "display_name": "",
-            "documentation": "",
-            "template": {
-              "backgroundColor": "transparent"
-            }
-          },
-          "type": "note"
-        },
-        "dragging": false,
-        "height": 324,
-        "id": "note-59mzY",
-        "measured": {
-          "height": 324,
-          "width": 324
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-          "x": 1692.2322233423606,
-          "y": 1821.9077961087607
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-        "selected": false,
-        "type": "noteNode",
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-      },
       {
         "data": {
           "id": "OpenSearchHybrid-XtKoA",
@@ -952,8 +373,8 @@
             "beta": false,
             "conditional_paths": [],
             "custom_fields": {},
-            "description": "Store and search documents using OpenSearch with hybrid semantic and keyword search capabilities.",
-            "display_name": "OpenSearch",
+            "description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
+            "display_name": "OpenSearch (Hybrid)",
             "documentation": "",
             "edited": true,
             "field_order": [
@@ -984,21 +405,21 @@
             "icon": "OpenSearch",
             "legacy": false,
             "metadata": {
-              "code_hash": "2720a7c68202",
+              "code_hash": "deee3f04cb47",
               "dependencies": {
                 "dependencies": [
                   {
-                    "name": "opensearchpy",
-                    "version": "2.8.0"
+                    "name": "langflow",
+                    "version": "1.5.0.post2"
                   },
                   {
-                    "name": "lfx",
-                    "version": null
+                    "name": "opensearchpy",
+                    "version": "2.8.0"
                   }
                 ],
                 "total_dependencies": 2
               },
-              "module": "custom_components.opensearch"
+              "module": "custom_components.opensearch_hybrid"
             },
             "minimized": false,
             "output_types": [],
@@ -1042,7 +463,7 @@
                 "cache": true,
                 "display_name": "Vector Store Connection",
                 "group_outputs": false,
-                "hidden": false,
+                "hidden": true,
                 "method": "as_vector_store",
                 "name": "vectorstoreconnection",
                 "options": null,
@@ -1063,10 +484,9 @@
                 "advanced": false,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Authentication Mode",
+                "display_name": "Auth Mode",
                 "dynamic": false,
-                "external_options": {},
-                "info": "Authentication method: 'basic' for username/password authentication, or 'jwt' for JSON Web Token (Bearer) authentication.",
+                "info": "Choose Basic (username/password) or JWT (Bearer token).",
                 "load_from_db": false,
                 "name": "auth_mode",
                 "options": [
@@ -1119,14 +539,14 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "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        ),\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=True,\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            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            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"
+                "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"
               },
               "docs_metadata": {
                 "_input_type": "TableInput",
                 "advanced": true,
-                "display_name": "Document Metadata",
+                "display_name": "Ingestion 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.",
+                "info": "Key value pairs to be inserted into each ingested document.",
                 "is_list": true,
                 "list_add_label": "Add More",
                 "name": "docs_metadata",
@@ -1134,20 +554,36 @@
                 "required": false,
                 "show": true,
                 "table_icon": "Table",
-                "table_schema": [
-                  {
-                    "description": "Key name",
-                    "display_name": "Key",
-                    "name": "key",
-                    "type": "str"
-                  },
-                  {
-                    "description": "Value of the metadata",
-                    "display_name": "Value",
-                    "name": "value",
-                    "type": "str"
-                  }
-                ],
+                "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"
+                    }
+                  ]
+                },
                 "title_case": false,
                 "tool_mode": false,
                 "trace_as_metadata": true,
@@ -1161,7 +597,7 @@
                 "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.",
+                "info": "Size of the dynamic list used during k-NN graph creation.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "ef_construction",
@@ -1199,10 +635,9 @@
                 "advanced": true,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Vector Engine",
+                "display_name": "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'.",
+                "info": "Vector search engine to use.",
                 "load_from_db": false,
                 "name": "engine",
                 "options": [
@@ -1226,9 +661,9 @@
                 "_input_type": "MultilineInput",
                 "advanced": false,
                 "copy_field": false,
-                "display_name": "Search Filters (JSON)",
+                "display_name": "Filter Expression (JSON)",
                 "dynamic": false,
-                "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.",
+                "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.",
                 "input_types": [
                   "Message"
                 ],
@@ -1252,7 +687,7 @@
                 "advanced": false,
                 "display_name": "Index Name",
                 "dynamic": false,
-                "info": "The OpenSearch index name where documents will be stored and searched. Will be created automatically if it doesn't exist.",
+                "info": "The index to search.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1311,7 +746,7 @@
                 "advanced": false,
                 "display_name": "JWT Token",
                 "dynamic": false,
-                "info": "Valid JSON Web Token for authentication. Will be sent in the Authorization header (with optional 'Bearer ' prefix).",
+                "info": "Paste a valid JWT (sent as a header).",
                 "input_types": [],
                 "load_from_db": true,
                 "name": "jwt_token",
@@ -1321,14 +756,14 @@
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "JWT"
+                "value": ""
               },
               "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.",
+                "info": "Number of bidirectional links created for each new element.",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "m",
@@ -1344,9 +779,9 @@
               "number_of_results": {
                 "_input_type": "IntInput",
                 "advanced": true,
-                "display_name": "Default Result Limit",
+                "display_name": "Default Size (limit)",
                 "dynamic": false,
-                "info": "Default maximum number of search results to return when no limit is specified in the filter expression.",
+                "info": "Default number of hits when no limit provided in filter_expression.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1365,7 +800,7 @@
                 "advanced": false,
                 "display_name": "OpenSearch URL",
                 "dynamic": false,
-                "info": "The connection URL for your OpenSearch cluster (e.g., http://localhost:9200 for local development or your cloud endpoint).",
+                "info": "URL for your OpenSearch cluster.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1382,7 +817,7 @@
               "password": {
                 "_input_type": "SecretStrInput",
                 "advanced": false,
-                "display_name": "OpenSearch Password",
+                "display_name": "Password",
                 "dynamic": false,
                 "info": "",
                 "input_types": [],
@@ -1442,10 +877,9 @@
                 "advanced": true,
                 "combobox": false,
                 "dialog_inputs": {},
-                "display_name": "Distance Metric",
+                "display_name": "Space Type",
                 "dynamic": false,
-                "external_options": {},
-                "info": "Distance metric for calculating vector similarity. 'l2' (Euclidean) is most common, 'cosinesimil' for cosine similarity, 'innerproduct' for dot product.",
+                "info": "Distance metric for vector similarity.",
                 "name": "space_type",
                 "options": [
                   "l2",
@@ -1468,9 +902,9 @@
               "use_ssl": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Use SSL/TLS",
+                "display_name": "Use SSL",
                 "dynamic": false,
-                "info": "Enable SSL/TLS encryption for secure connections to OpenSearch.",
+                "info": "",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "use_ssl",
@@ -1505,9 +939,9 @@
               "vector_field": {
                 "_input_type": "StrInput",
                 "advanced": true,
-                "display_name": "Vector Field Name",
+                "display_name": "Vector Field",
                 "dynamic": false,
-                "info": "Name of the field in OpenSearch documents that stores the vector embeddings for similarity search.",
+                "info": "Vector field used for KNN.",
                 "list": false,
                 "list_add_label": "Add More",
                 "load_from_db": false,
@@ -1524,9 +958,9 @@
               "verify_certs": {
                 "_input_type": "BoolInput",
                 "advanced": true,
-                "display_name": "Verify SSL Certificates",
+                "display_name": "Verify Certificates",
                 "dynamic": false,
-                "info": "Verify SSL certificates when connecting. Disable for self-signed certificates in development environments.",
+                "info": "",
                 "list": false,
                 "list_add_label": "Add More",
                 "name": "verify_certs",
@@ -1544,12 +978,12 @@
           },
           "selected_output": "search_results",
           "showNode": true,
-          "type": "OpenSearchVectorStoreComponent"
+          "type": "OpenSearchHybrid"
         },
         "dragging": false,
         "id": "OpenSearchHybrid-XtKoA",
         "measured": {
-          "height": 737,
+          "height": 760,
           "width": 320
         },
         "position": {
@@ -1561,6 +995,8 @@
       },
       {
         "data": {
+          "description": "Uses Docling to process input documents connecting to your instance of Docling Serve.",
+          "display_name": "Docling Serve",
           "id": "DoclingRemote-78KoX",
           "node": {
             "base_classes": [
@@ -1590,9 +1026,8 @@
             "frozen": false,
             "icon": "Docling",
             "legacy": false,
-            "lf_version": "1.6.0",
             "metadata": {
-              "code_hash": "930312ffe40c",
+              "code_hash": "880538860431",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1608,13 +1043,13 @@
                     "version": "2.10.6"
                   },
                   {
-                    "name": "lfx",
+                    "name": "langflow",
                     "version": null
                   }
                 ],
                 "total_dependencies": 4
               },
-              "module": "lfx.components.docling.docling_remote.DoclingRemoteComponent"
+              "module": "custom_components.docling_serve"
             },
             "minimized": false,
             "output_types": [],
@@ -1626,6 +1061,8 @@
                 "group_outputs": false,
                 "method": "load_files",
                 "name": "dataframe",
+                "options": null,
+                "required_inputs": null,
                 "selected": "DataFrame",
                 "tool_mode": true,
                 "types": [
@@ -1691,7 +1128,7 @@
                 "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 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"
+                "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"
               },
               "delete_server_file_after_processing": {
                 "_input_type": "BoolInput",
@@ -1719,6 +1156,7 @@
                 "info": "Optional dictionary of additional options. See https://github.com/docling-project/docling-serve/blob/main/docs/usage.md for more information.",
                 "list": false,
                 "list_add_label": "Add More",
+                "load_from_db": false,
                 "name": "docling_serve_opts",
                 "placeholder": "",
                 "required": false,
@@ -1926,7 +1364,7 @@
         "dragging": false,
         "id": "DoclingRemote-78KoX",
         "measured": {
-          "height": 475,
+          "height": 472,
           "width": 320
         },
         "position": {
@@ -1938,6 +1376,8 @@
       },
       {
         "data": {
+          "description": "Export DoclingDocument to markdown, html or other formats.",
+          "display_name": "Export DoclingDocument",
           "id": "ExportDoclingDocument-xFoCI",
           "node": {
             "base_classes": [
@@ -1962,9 +1402,8 @@
             "frozen": false,
             "icon": "Docling",
             "legacy": false,
-            "lf_version": "1.6.0",
             "metadata": {
-              "code_hash": "4de16ddd37ac",
+              "code_hash": "451c9673bd4c",
               "dependencies": {
                 "dependencies": [
                   {
@@ -1972,13 +1411,13 @@
                     "version": "2.45.0"
                   },
                   {
-                    "name": "lfx",
+                    "name": "langflow",
                     "version": null
                   }
                 ],
                 "total_dependencies": 2
               },
-              "module": "lfx.components.docling.export_docling_document.ExportDoclingDocumentComponent"
+              "module": "custom_components.export_doclingdocument"
             },
             "minimized": false,
             "output_types": [],
@@ -1990,6 +1429,8 @@
                 "group_outputs": false,
                 "method": "export_document",
                 "name": "data",
+                "options": null,
+                "required_inputs": null,
                 "selected": "Data",
                 "tool_mode": true,
                 "types": [
@@ -2004,6 +1445,9 @@
                 "group_outputs": false,
                 "method": "as_dataframe",
                 "name": "dataframe",
+                "options": null,
+                "required_inputs": null,
+                "selected": "DataFrame",
                 "tool_mode": true,
                 "types": [
                   "DataFrame"
@@ -2030,7 +1474,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "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"
+                "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"
               },
               "data_inputs": {
                 "_input_type": "HandleInput",
@@ -2175,7 +1619,7 @@
         "dragging": false,
         "id": "ExportDoclingDocument-xFoCI",
         "measured": {
-          "height": 347,
+          "height": 344,
           "width": 320
         },
         "position": {
@@ -2184,19 +1628,328 @@
         },
         "selected": false,
         "type": "genericNode"
+      },
+      {
+        "data": {
+          "id": "EmbeddingModel-cxG9r",
+          "node": {
+            "base_classes": [
+              "Embeddings"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Generate embeddings using a specified provider.",
+            "display_name": "Embedding Model",
+            "documentation": "https://docs.langflow.org/components-embedding-models",
+            "edited": false,
+            "field_order": [
+              "provider",
+              "model",
+              "api_key",
+              "api_base",
+              "dimensions",
+              "chunk_size",
+              "request_timeout",
+              "max_retries",
+              "show_progress_bar",
+              "model_kwargs"
+            ],
+            "frozen": false,
+            "icon": "binary",
+            "last_updated": "2025-09-24T16:02:07.998Z",
+            "legacy": false,
+            "metadata": {
+              "code_hash": "93faf11517da",
+              "dependencies": {
+                "dependencies": [
+                  {
+                    "name": "langchain_openai",
+                    "version": "0.3.23"
+                  },
+                  {
+                    "name": "langflow",
+                    "version": null
+                  }
+                ],
+                "total_dependencies": 2
+              },
+              "module": "langflow.components.models.embedding_model.EmbeddingModelComponent"
+            },
+            "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": {
+              "_type": "Component",
+              "api_base": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "API Base URL",
+                "dynamic": false,
+                "info": "Base URL for the API. Leave empty for default.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "list_add_label": "Add More",
+                "load_from_db": false,
+                "name": "api_base",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
+              },
+              "api_key": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "OpenAI API Key",
+                "dynamic": false,
+                "info": "Model Provider API key",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "api_key",
+                "password": true,
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "chunk_size": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "Chunk Size",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "chunk_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "int",
+                "value": 1000
+              },
+              "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\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"
+              },
+              "dimensions": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "Dimensions",
+                "dynamic": false,
+                "info": "The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "dimensions",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "int",
+                "value": ""
+              },
+              "max_retries": {
+                "_input_type": "IntInput",
+                "advanced": true,
+                "display_name": "Max Retries",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "max_retries",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "int",
+                "value": 3
+              },
+              "model": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Model Name",
+                "dynamic": false,
+                "info": "Select the embedding model to use",
+                "name": "model",
+                "options": [
+                  "text-embedding-3-small",
+                  "text-embedding-3-large",
+                  "text-embedding-ada-002"
+                ],
+                "options_metadata": [],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "toggle": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "text-embedding-3-small"
+              },
+              "model_kwargs": {
+                "_input_type": "DictInput",
+                "advanced": true,
+                "display_name": "Model Kwargs",
+                "dynamic": false,
+                "info": "Additional keyword arguments to pass to the model.",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "model_kwargs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_input": true,
+                "type": "dict",
+                "value": {}
+              },
+              "provider": {
+                "_input_type": "DropdownInput",
+                "advanced": false,
+                "combobox": false,
+                "dialog_inputs": {},
+                "display_name": "Model Provider",
+                "dynamic": false,
+                "info": "Select the embedding model provider",
+                "name": "provider",
+                "options": [
+                  "OpenAI"
+                ],
+                "options_metadata": [
+                  {
+                    "icon": "OpenAI"
+                  }
+                ],
+                "placeholder": "",
+                "real_time_refresh": true,
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "toggle": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": "OpenAI"
+              },
+              "request_timeout": {
+                "_input_type": "FloatInput",
+                "advanced": true,
+                "display_name": "Request Timeout",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "request_timeout",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "float",
+                "value": ""
+              },
+              "show_progress_bar": {
+                "_input_type": "BoolInput",
+                "advanced": true,
+                "display_name": "Show Progress Bar",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "list_add_label": "Add More",
+                "name": "show_progress_bar",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": false,
+                "trace_as_metadata": true,
+                "type": "bool",
+                "value": false
+              }
+            },
+            "tool_mode": false
+          },
+          "showNode": true,
+          "type": "EmbeddingModel"
+        },
+        "dragging": false,
+        "id": "EmbeddingModel-cxG9r",
+        "measured": {
+          "height": 366,
+          "width": 320
+        },
+        "position": {
+          "x": 1743.8608432729177,
+          "y": 1808.780792406514
+        },
+        "selected": false,
+        "type": "genericNode"
       }
     ],
     "viewport": {
-      "x": -747.2506879716952,
-      "y": -1001.2271322529787,
-      "zoom": 0.8347026947181932
+      "x": -767.6929603556041,
+      "y": -1196.6455082358875,
+      "zoom": 0.9277466102702023
     }
   },
   "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": "1.6.0",
+  "last_tested_version": "1.5.0.post2",
   "name": "OpenSearch Ingestion Flow Docling Serve",
   "tags": [
     "openai",
diff --git a/frontend/components.json b/frontend/components.json
index 8e7f1638..53d2101e 100644
--- a/frontend/components.json
+++ b/frontend/components.json
@@ -10,9 +10,13 @@
     "cssVariables": true,
     "prefix": ""
   },
+  "iconLibrary": "lucide",
   "aliases": {
     "components": "components",
     "utils": "lib/utils",
     "ui": "components/ui"
+  },
+  "registries": {
+    "@magicui": "https://magicui.design/r/{name}.json"
   }
-}
\ No newline at end of file
+}
diff --git a/frontend/components/delete-session-modal.tsx b/frontend/components/delete-session-modal.tsx
new file mode 100644
index 00000000..7b57a44f
--- /dev/null
+++ b/frontend/components/delete-session-modal.tsx
@@ -0,0 +1,58 @@
+"use client";
+
+import { AlertTriangle } from "lucide-react";
+import { Button } from "@/components/ui/button";
+import {
+  Dialog,
+  DialogContent,
+  DialogDescription,
+  DialogFooter,
+  DialogHeader,
+  DialogTitle,
+} from "@/components/ui/dialog";
+
+interface DeleteSessionModalProps {
+  isOpen: boolean;
+  onClose: () => void;
+  onConfirm: () => void;
+  sessionTitle: string;
+  isDeleting?: boolean;
+}
+
+export function DeleteSessionModal({
+  isOpen,
+  onClose,
+  onConfirm,
+  sessionTitle,
+  isDeleting = false,
+}: DeleteSessionModalProps) {
+  return (
+    <Dialog open={isOpen} onOpenChange={onClose}>
+      <DialogContent>
+        <DialogHeader>
+          <DialogTitle className="flex items-center gap-2">
+            <AlertTriangle className="h-5 w-5 text-destructive" />
+            Delete Conversation
+          </DialogTitle>
+          <DialogDescription>
+            Are you sure you want to delete &quot;{sessionTitle}&quot;? This
+            action cannot be undone and will permanently remove the conversation
+            and all its messages.
+          </DialogDescription>
+        </DialogHeader>
+        <DialogFooter>
+          <Button variant="outline" onClick={onClose} disabled={isDeleting}>
+            Cancel
+          </Button>
+          <Button
+            variant="destructive"
+            onClick={onConfirm}
+            disabled={isDeleting}
+          >
+            {isDeleting ? "Deleting..." : "Delete"}
+          </Button>
+        </DialogFooter>
+      </DialogContent>
+    </Dialog>
+  );
+}
diff --git a/frontend/components/logo/ibm-logo.tsx b/frontend/components/logo/ibm-logo.tsx
index 6f7fc2cd..44b6e08c 100644
--- a/frontend/components/logo/ibm-logo.tsx
+++ b/frontend/components/logo/ibm-logo.tsx
@@ -11,7 +11,7 @@ export default function IBMLogo(props: React.SVGProps<SVGSVGElement>) {
       <title>IBM Logo</title>
       <path
         d="M15.696 10.9901C15.7213 10.9901 15.7356 10.979 15.7356 10.9552V10.9313C15.7356 10.9076 15.7213 10.8964 15.696 10.8964H15.6359V10.9901H15.696ZM15.6359 11.1649H15.5552V10.8329H15.7055C15.7799 10.8329 15.8179 10.8773 15.8179 10.9378C15.8179 10.9901 15.7942 11.0235 15.7577 11.0378L15.8321 11.1649H15.7436L15.6818 11.0504H15.6359V11.1649ZM15.9255 11.0171V10.9759C15.9255 10.8424 15.821 10.7376 15.6833 10.7376C15.5456 10.7376 15.4412 10.8424 15.4412 10.9759V11.0171C15.4412 11.1505 15.5456 11.2554 15.6833 11.2554C15.821 11.2554 15.9255 11.1505 15.9255 11.0171ZM15.3668 10.9964C15.3668 10.8107 15.5077 10.6693 15.6833 10.6693C15.859 10.6693 16 10.8107 16 10.9964C16 11.1823 15.859 11.3237 15.6833 11.3237C15.5077 11.3237 15.3668 11.1823 15.3668 10.9964ZM10.8069 5.74885L10.6627 5.33301H8.28904V5.74885H10.8069ZM11.0821 6.54285L10.9379 6.12691H8.28904V6.54285H11.0821ZM12.8481 11.3067H14.9203V10.8908H12.8481V11.3067ZM12.8481 10.5126H14.9203V10.0968H12.8481V10.5126ZM12.8481 9.71873H14.0914V9.3028H12.8481V9.71873ZM12.8481 8.92474H14.0914V8.50889H12.8481V8.92474ZM12.8481 8.13084H14.0914V7.7149H11.7212L11.6047 8.05102L11.4882 7.7149H9.11794V8.13084H10.3613V7.74863L10.4951 8.13084H12.7143L12.8481 7.74863V8.13084ZM14.0914 6.921H11.9964L11.8522 7.33675H14.0914V6.921ZM9.11794 8.92474H10.3613V8.50889H9.11794V8.92474ZM9.11794 9.71873H10.3613V9.3028H9.11794V9.71873ZM8.28904 10.5126H10.3613V10.0968H8.28904V10.5126ZM8.28904 11.3067H10.3613V10.8908H8.28904V11.3067ZM12.5466 5.33301L12.4025 5.74885H14.9203V5.33301H12.5466ZM12.1273 6.54285H14.9203V6.12691H12.2714L12.1273 6.54285ZM9.11794 7.33675H11.3572L11.213 6.921H9.11794V7.33675ZM10.7727 8.92474H12.4366L12.5821 8.50889H10.6272L10.7727 8.92474ZM11.0505 9.71873H12.1588L12.3042 9.3028H10.9051L11.0505 9.71873ZM11.3283 10.5126H11.881L12.0265 10.0969H11.1828L11.3283 10.5126ZM11.604 11.3067L11.7487 10.8908H11.4606L11.604 11.3067ZM3.31561 11.3026L6.36754 11.3067C6.78195 11.3067 7.15365 11.1491 7.43506 10.8908H3.31561V11.3026ZM6.55592 9.3028V9.71873H7.94994C7.94994 9.57477 7.93029 9.43551 7.89456 9.3028H6.55592ZM4.14452 9.71873H5.38783V9.3028H4.14452V9.71873ZM6.55592 7.33675H7.89456C7.93029 7.20422 7.94994 7.06486 7.94994 6.921H6.55592V7.33675ZM4.14452 7.33675H5.38783V6.9209H4.14452V7.33675ZM6.36754 5.33301H3.31561V5.74885H7.43506C7.15365 5.49061 6.77892 5.33301 6.36754 5.33301ZM7.73778 6.12691H3.31561V6.54285H7.90448C7.86839 6.39502 7.81172 6.25539 7.73778 6.12691ZM4.14452 7.7149V8.13084H7.39152C7.5292 8.01333 7.64621 7.87268 7.73732 7.7149H4.14452ZM7.39152 8.50889H4.14452V8.92474H7.73732C7.64621 8.76695 7.5292 8.62631 7.39152 8.50889ZM3.31561 10.5126H7.73778C7.81172 10.3843 7.86839 10.2447 7.90448 10.0969H3.31561V10.5126ZM0 5.74885H2.90121V5.33301H0V5.74885ZM0 6.54285H2.90121V6.12691H0V6.54285ZM0.828996 7.33684H2.0723V6.921H0.828996V7.33684ZM0.828996 8.13084H2.0723V7.7149H0.828996V8.13084ZM0.828996 8.92474H2.0723V8.50889H0.828996V8.92474ZM0.828996 9.71873H2.0723V9.3028H0.828996V9.71873ZM0 10.5126H2.90121V10.0968H0V10.5126ZM0 11.3067H2.90121V10.8908H0V11.3067Z"
-        fill="#A1A1AA"
+        fill="currentColor"
       />
     </svg>
   );
diff --git a/frontend/components/logo/openai-logo.tsx b/frontend/components/logo/openai-logo.tsx
index 639c130e..330211b9 100644
--- a/frontend/components/logo/openai-logo.tsx
+++ b/frontend/components/logo/openai-logo.tsx
@@ -23,7 +23,7 @@ export default function OpenAILogo(props: React.SVGProps<SVGSVGElement>) {
       <g mask="url(#mask0_2162_638)">
         <path
           d="M6.52523 5.82397V4.30397C6.52523 4.17595 6.57329 4.07991 6.68523 4.01599L9.74132 2.25599C10.1573 2.016 10.6533 1.90406 11.1653 1.90406C13.0852 1.90406 14.3013 3.39209 14.3013 4.97602C14.3013 5.088 14.3013 5.21601 14.2853 5.34403L11.1172 3.48799C10.9253 3.37605 10.7332 3.37605 10.5412 3.48799L6.52523 5.82397ZM13.6612 11.744V8.11194C13.6612 7.88789 13.5652 7.7279 13.3732 7.61592L9.35724 5.27993L10.6692 4.52788C10.7812 4.46396 10.8772 4.46396 10.9892 4.52788L14.0453 6.28788C14.9254 6.79995 15.5173 7.88789 15.5173 8.94382C15.5173 10.1598 14.7973 11.2798 13.6612 11.7439V11.744ZM5.58124 8.54404L4.26924 7.77608C4.1573 7.71216 4.10925 7.61609 4.10925 7.48807V3.9681C4.10925 2.25616 5.42125 0.960064 7.19729 0.960064C7.86938 0.960064 8.49325 1.18412 9.02138 1.5841L5.86938 3.40816C5.67744 3.5201 5.58141 3.6801 5.58141 3.90418V8.54417L5.58124 8.54404ZM8.40528 10.176L6.52523 9.12002V6.88011L8.40528 5.82414L10.2852 6.88011V9.12002L8.40528 10.176ZM9.61327 15.0401C8.94122 15.0401 8.31735 14.816 7.78921 14.4161L10.9412 12.592C11.1331 12.48 11.2292 12.32 11.2292 12.096V7.45596L12.5573 8.22392C12.6692 8.28784 12.7172 8.38388 12.7172 8.51193V12.0319C12.7172 13.7438 11.3892 15.0399 9.61327 15.0399V15.0401ZM5.82123 11.4721L2.76514 9.71212C1.88507 9.20002 1.29315 8.11211 1.29315 7.05614C1.29315 5.82414 2.02916 4.72016 3.16509 4.25611V7.9041C3.16509 8.12815 3.26116 8.28814 3.4531 8.40012L7.45319 10.72L6.14119 11.4721C6.02925 11.536 5.93318 11.536 5.82123 11.4721ZM5.64533 14.0961C3.83731 14.0961 2.50928 12.7361 2.50928 11.0561C2.50928 10.928 2.52532 10.8 2.54122 10.672L5.69322 12.4961C5.88516 12.608 6.07726 12.608 6.2692 12.4961L10.2852 10.1762V11.6962C10.2852 11.8242 10.2372 11.9202 10.1252 11.9841L7.06914 13.7441C6.65312 13.9841 6.15709 14.0961 5.64517 14.0961H5.64533ZM9.61327 16C11.5493 16 13.1652 14.624 13.5334 12.8C15.3253 12.3359 16.4773 10.6559 16.4773 8.94399C16.4773 7.82393 15.9974 6.73602 15.1334 5.95199C15.2134 5.61596 15.2614 5.27994 15.2614 4.94407C15.2614 2.65611 13.4053 0.943991 11.2613 0.943991C10.8294 0.943991 10.4134 1.00792 9.99735 1.152C9.27724 0.44797 8.28523 0 7.19729 0C5.26129 0 3.64537 1.37592 3.27724 3.19998C1.48526 3.66402 0.333252 5.34403 0.333252 7.05598C0.333252 8.17603 0.8132 9.26395 1.67723 10.048C1.59723 10.384 1.54921 10.72 1.54921 11.0559C1.54921 13.3439 3.40525 15.056 5.54926 15.056C5.98119 15.056 6.39722 14.9921 6.81324 14.848C7.53318 15.552 8.52519 16 9.61327 16Z"
-          fill="white"
+          fill="currentColor"
         />
       </g>
     </svg>
diff --git a/frontend/components/navigation-layout.tsx b/frontend/components/navigation-layout.tsx
index fae8da62..a2fdbe8f 100644
--- a/frontend/components/navigation-layout.tsx
+++ b/frontend/components/navigation-layout.tsx
@@ -1,8 +1,12 @@
-"use client"
+"use client";
 
-import { Navigation } from "@/components/navigation";
-import { ModeToggle } from "@/components/mode-toggle";
+import { usePathname } from "next/navigation";
+import { useGetConversationsQuery, type ChatConversation } from "@/app/api/queries/useGetConversationsQuery";
 import { KnowledgeFilterDropdown } from "@/components/knowledge-filter-dropdown";
+import { ModeToggle } from "@/components/mode-toggle";
+import { Navigation } from "@/components/navigation";
+import { useAuth } from "@/contexts/auth-context";
+import { useChat } from "@/contexts/chat-context";
 import { useKnowledgeFilter } from "@/contexts/knowledge-filter-context";
 
 interface NavigationLayoutProps {
@@ -11,11 +15,35 @@ interface NavigationLayoutProps {
 
 export function NavigationLayout({ children }: NavigationLayoutProps) {
   const { selectedFilter, setSelectedFilter } = useKnowledgeFilter();
-  
+  const pathname = usePathname();
+  const { isAuthenticated, isNoAuthMode } = useAuth();
+  const {
+    endpoint,
+    refreshTrigger,
+    refreshConversations,
+    startNewConversation,
+  } = useChat();
+
+  // Only fetch conversations on chat page
+  const isOnChatPage = pathname === "/" || pathname === "/chat";
+  const { data: conversations = [], isLoading: isConversationsLoading } =
+    useGetConversationsQuery(endpoint, refreshTrigger, {
+      enabled: isOnChatPage && (isAuthenticated || isNoAuthMode),
+    }) as { data: ChatConversation[]; isLoading: boolean };
+
+  const handleNewConversation = () => {
+    refreshConversations();
+    startNewConversation();
+  };
+
   return (
     <div className="h-full relative">
       <div className="hidden h-full md:flex md:w-72 md:flex-col md:fixed md:inset-y-0 z-[80] border-r border-border/40">
-        <Navigation />
+        <Navigation
+          conversations={conversations}
+          isConversationsLoading={isConversationsLoading}
+          onNewConversation={handleNewConversation}
+        />
       </div>
       <main className="md:pl-72">
         <div className="flex flex-col min-h-screen">
@@ -31,7 +59,7 @@ export function NavigationLayout({ children }: NavigationLayoutProps) {
                   {/* Search component could go here */}
                 </div>
                 <nav className="flex items-center space-x-2">
-                  <KnowledgeFilterDropdown 
+                  <KnowledgeFilterDropdown
                     selectedFilter={selectedFilter}
                     onFilterSelect={setSelectedFilter}
                   />
@@ -41,12 +69,10 @@ export function NavigationLayout({ children }: NavigationLayoutProps) {
             </div>
           </header>
           <div className="flex-1">
-            <div className="container py-6 lg:py-8">
-              {children}
-            </div>
+            <div className="container py-6 lg:py-8">{children}</div>
           </div>
         </div>
       </main>
     </div>
   );
-} 
\ No newline at end of file
+}
diff --git a/frontend/components/navigation.tsx b/frontend/components/navigation.tsx
index b651ef6a..339b7d22 100644
--- a/frontend/components/navigation.tsx
+++ b/frontend/components/navigation.tsx
@@ -1,24 +1,35 @@
 "use client";
 
-import { useChat } from "@/contexts/chat-context";
-import { cn } from "@/lib/utils";
 import {
+  EllipsisVertical,
   FileText,
   Library,
   MessageSquare,
+  MoreHorizontal,
   Plus,
   Settings2,
+  Trash2,
 } from "lucide-react";
 import Link from "next/link";
 import { usePathname } from "next/navigation";
-import { useCallback, useEffect, useRef, useState } from "react";
-
-import { EndpointType } from "@/contexts/chat-context";
-import { useLoadingStore } from "@/stores/loadingStore";
-import { KnowledgeFilterList } from "./knowledge-filter-list";
+import { useEffect, useRef, useState } from "react";
+import { toast } from "sonner";
+import { useDeleteSessionMutation } from "@/app/api/queries/useDeleteSessionMutation";
+import {
+  DropdownMenu,
+  DropdownMenuContent,
+  DropdownMenuItem,
+  DropdownMenuTrigger,
+} from "@/components/ui/dropdown-menu";
+import { type EndpointType, useChat } from "@/contexts/chat-context";
 import { useKnowledgeFilter } from "@/contexts/knowledge-filter-context";
+import { cn } from "@/lib/utils";
+import { useLoadingStore } from "@/stores/loadingStore";
+import { DeleteSessionModal } from "./delete-session-modal";
+import { KnowledgeFilterList } from "./knowledge-filter-list";
 
-interface RawConversation {
+// Re-export the types for backward compatibility
+export interface RawConversation {
   response_id: string;
   title: string;
   endpoint: string;
@@ -35,7 +46,7 @@ interface RawConversation {
   [key: string]: unknown;
 }
 
-interface ChatConversation {
+export interface ChatConversation {
   response_id: string;
   title: string;
   endpoint: EndpointType;
@@ -52,11 +63,20 @@ interface ChatConversation {
   [key: string]: unknown;
 }
 
-export function Navigation() {
+interface NavigationProps {
+  conversations?: ChatConversation[];
+  isConversationsLoading?: boolean;
+  onNewConversation?: () => void;
+}
+
+export function Navigation({
+  conversations = [],
+  isConversationsLoading = false,
+  onNewConversation,
+}: NavigationProps = {}) {
   const pathname = usePathname();
   const {
     endpoint,
-    refreshTrigger,
     loadConversation,
     currentConversationId,
     setCurrentConversationId,
@@ -70,18 +90,64 @@ export function Navigation() {
 
   const { loading } = useLoadingStore();
 
-  const [conversations, setConversations] = useState<ChatConversation[]>([]);
-  const [loadingConversations, setLoadingConversations] = useState(false);
   const [loadingNewConversation, setLoadingNewConversation] = useState(false);
   const [previousConversationCount, setPreviousConversationCount] = useState(0);
+  const [deleteModalOpen, setDeleteModalOpen] = useState(false);
+  const [conversationToDelete, setConversationToDelete] =
+    useState<ChatConversation | null>(null);
   const fileInputRef = useRef<HTMLInputElement>(null);
 
   const { selectedFilter, setSelectedFilter } = useKnowledgeFilter();
 
+  // Delete session mutation
+  const deleteSessionMutation = useDeleteSessionMutation({
+    onSuccess: () => {
+      toast.success("Conversation deleted successfully");
+
+      // If we deleted the current conversation, select another one
+      if (
+        conversationToDelete &&
+        currentConversationId === conversationToDelete.response_id
+      ) {
+        // Filter out the deleted conversation and find the next one
+        const remainingConversations = conversations.filter(
+          (conv) => conv.response_id !== conversationToDelete.response_id,
+        );
+
+        if (remainingConversations.length > 0) {
+          // Load the first available conversation (most recent)
+          loadConversation(remainingConversations[0]);
+        } else {
+          // No conversations left, start a new one
+          setCurrentConversationId(null);
+          if (onNewConversation) {
+            onNewConversation();
+          } else {
+            refreshConversations();
+            startNewConversation();
+          }
+        }
+      }
+
+      setDeleteModalOpen(false);
+      setConversationToDelete(null);
+    },
+    onError: (error) => {
+      toast.error(`Failed to delete conversation: ${error.message}`);
+    },
+  });
+
   const handleNewConversation = () => {
     setLoadingNewConversation(true);
-    refreshConversations();
-    startNewConversation();
+
+    // Use the prop callback if provided, otherwise use the context method
+    if (onNewConversation) {
+      onNewConversation();
+    } else {
+      refreshConversations();
+      startNewConversation();
+    }
+
     if (typeof window !== "undefined") {
       window.dispatchEvent(new CustomEvent("newConversation"));
     }
@@ -98,7 +164,7 @@ export function Navigation() {
     window.dispatchEvent(
       new CustomEvent("fileUploadStart", {
         detail: { filename: file.name },
-      })
+      }),
     );
 
     try {
@@ -122,7 +188,7 @@ export function Navigation() {
               filename: file.name,
               error: "Failed to process document",
             },
-          })
+          }),
         );
 
         // Trigger loading end event
@@ -142,7 +208,7 @@ export function Navigation() {
       window.dispatchEvent(
         new CustomEvent("fileUploaded", {
           detail: { file, result },
-        })
+        }),
       );
 
       // Trigger loading end event
@@ -156,7 +222,7 @@ export function Navigation() {
       window.dispatchEvent(
         new CustomEvent("fileUploadError", {
           detail: { filename: file.name, error: "Failed to process document" },
-        })
+        }),
       );
     }
   };
@@ -176,6 +242,41 @@ export function Navigation() {
     }
   };
 
+  const handleDeleteConversation = (
+    conversation: ChatConversation,
+    event?: React.MouseEvent,
+  ) => {
+    if (event) {
+      event.preventDefault();
+      event.stopPropagation();
+    }
+    setConversationToDelete(conversation);
+    setDeleteModalOpen(true);
+  };
+
+  const handleContextMenuAction = (
+    action: string,
+    conversation: ChatConversation,
+  ) => {
+    switch (action) {
+      case "delete":
+        handleDeleteConversation(conversation);
+        break;
+      // Add more actions here in the future (rename, duplicate, etc.)
+      default:
+        break;
+    }
+  };
+
+  const confirmDeleteConversation = () => {
+    if (conversationToDelete) {
+      deleteSessionMutation.mutate({
+        sessionId: conversationToDelete.response_id,
+        endpoint: endpoint,
+      });
+    }
+  };
+
   const routes = [
     {
       label: "Chat",
@@ -200,91 +301,6 @@ export function Navigation() {
   const isOnChatPage = pathname === "/" || pathname === "/chat";
   const isOnKnowledgePage = pathname.startsWith("/knowledge");
 
-  const createDefaultPlaceholder = useCallback(() => {
-    return {
-      response_id: "new-conversation-" + Date.now(),
-      title: "New conversation",
-      endpoint: endpoint,
-      messages: [
-        {
-          role: "assistant",
-          content: "How can I assist?",
-          timestamp: new Date().toISOString(),
-        },
-      ],
-      created_at: new Date().toISOString(),
-      last_activity: new Date().toISOString(),
-      total_messages: 1,
-    } as ChatConversation;
-  }, [endpoint]);
-
-  const fetchConversations = useCallback(async () => {
-    setLoadingConversations(true);
-    try {
-      // Fetch from the selected endpoint only
-      const apiEndpoint =
-        endpoint === "chat" ? "/api/chat/history" : "/api/langflow/history";
-
-      const response = await fetch(apiEndpoint);
-      if (response.ok) {
-        const history = await response.json();
-        const rawConversations = history.conversations || [];
-
-        // Cast conversations to proper type and ensure endpoint is correct
-        const conversations: ChatConversation[] = rawConversations.map(
-          (conv: RawConversation) => ({
-            ...conv,
-            endpoint: conv.endpoint as EndpointType,
-          })
-        );
-
-        // Sort conversations by last activity (most recent first)
-        conversations.sort((a: ChatConversation, b: ChatConversation) => {
-          const aTime = new Date(
-            a.last_activity || a.created_at || 0
-          ).getTime();
-          const bTime = new Date(
-            b.last_activity || b.created_at || 0
-          ).getTime();
-          return bTime - aTime;
-        });
-
-        setConversations(conversations);
-
-        // If no conversations exist and no placeholder is shown, create a default placeholder
-        if (conversations.length === 0 && !placeholderConversation) {
-          setPlaceholderConversation(createDefaultPlaceholder());
-        }
-      } else {
-        setConversations([]);
-
-        // Also create placeholder when request fails and no conversations exist
-        if (!placeholderConversation) {
-          setPlaceholderConversation(createDefaultPlaceholder());
-        }
-      }
-
-      // Conversation documents are now managed in chat context
-    } catch (error) {
-      console.error(`Failed to fetch ${endpoint} conversations:`, error);
-      setConversations([]);
-    } finally {
-      setLoadingConversations(false);
-    }
-  }, [
-    endpoint,
-    placeholderConversation,
-    setPlaceholderConversation,
-    createDefaultPlaceholder,
-  ]);
-
-  // Fetch chat conversations when on chat page, endpoint changes, or refresh is triggered
-  useEffect(() => {
-    if (isOnChatPage) {
-      fetchConversations();
-    }
-  }, [isOnChatPage, endpoint, refreshTrigger, fetchConversations]);
-
   // Clear placeholder when conversation count increases (new conversation was created)
   useEffect(() => {
     const currentCount = conversations.length;
@@ -326,7 +342,7 @@ export function Navigation() {
                   "text-sm group flex p-3 w-full justify-start font-medium cursor-pointer hover:bg-accent hover:text-accent-foreground rounded-lg transition-all",
                   route.active
                     ? "bg-accent text-accent-foreground shadow-sm"
-                    : "text-foreground hover:text-accent-foreground"
+                    : "text-foreground hover:text-accent-foreground",
                 )}
               >
                 <div className="flex items-center flex-1">
@@ -335,7 +351,7 @@ export function Navigation() {
                       "h-4 w-4 mr-3 shrink-0",
                       route.active
                         ? "text-accent-foreground"
-                        : "text-muted-foreground group-hover:text-foreground"
+                        : "text-muted-foreground group-hover:text-foreground",
                     )}
                   />
                   {route.label}
@@ -366,6 +382,7 @@ export function Navigation() {
                 Conversations
               </h3>
               <button
+                type="button"
                 className="p-1 hover:bg-accent rounded"
                 onClick={handleNewConversation}
                 title="Start new conversation"
@@ -379,7 +396,7 @@ export function Navigation() {
           <div className="px-3 flex-1 min-h-0 flex flex-col">
             {/* Conversations List - grows naturally, doesn't fill all space */}
             <div className="flex-shrink-0 overflow-y-auto scrollbar-hide space-y-1 max-h-full">
-              {loadingNewConversation ? (
+              {loadingNewConversation || isConversationsLoading ? (
                 <div className="text-sm text-muted-foreground p-2">
                   Loading...
                 </div>
@@ -387,8 +404,9 @@ export function Navigation() {
                 <>
                   {/* Show placeholder conversation if it exists */}
                   {placeholderConversation && (
-                    <div
-                      className="p-2 rounded-lg bg-accent/50 border border-dashed border-accent cursor-pointer group"
+                    <button
+                      type="button"
+                      className="w-full p-2 rounded-lg bg-accent/50 border border-dashed border-accent cursor-pointer group text-left"
                       onClick={() => {
                         // Don't load placeholder as a real conversation, just focus the input
                         if (typeof window !== "undefined") {
@@ -402,7 +420,7 @@ export function Navigation() {
                       <div className="text-xs text-muted-foreground">
                         Start typing to begin...
                       </div>
-                    </div>
+                    </button>
                   )}
 
                   {/* Show regular conversations */}
@@ -412,9 +430,10 @@ export function Navigation() {
                     </div>
                   ) : (
                     conversations.map((conversation) => (
-                      <div
+                      <button
                         key={conversation.response_id}
-                        className={`p-2 rounded-lg group ${
+                        type="button"
+                        className={`w-full px-3 pr-2 h-11 rounded-lg group relative text-left ${
                           loading
                             ? "opacity-50 cursor-not-allowed"
                             : "hover:bg-accent cursor-pointer"
@@ -428,21 +447,53 @@ export function Navigation() {
                           loadConversation(conversation);
                           refreshConversations();
                         }}
+                        disabled={loading}
                       >
-                        <div className="text-sm font-medium text-foreground mb-1 truncate">
-                          {conversation.title}
-                        </div>
-                        <div className="text-xs text-muted-foreground">
-                          {conversation.total_messages} messages
-                        </div>
-                        {conversation.last_activity && (
-                          <div className="text-xs text-muted-foreground">
-                            {new Date(
-                              conversation.last_activity
-                            ).toLocaleDateString()}
+                        <div className="flex items-center justify-between">
+                          <div className="flex-1 min-w-0">
+                            <div className="text-sm font-medium text-foreground truncate">
+                              {conversation.title}
+                            </div>
                           </div>
-                        )}
-                      </div>
+                          <DropdownMenu>
+                            <DropdownMenuTrigger asChild>
+                              <button
+                                type="button"
+                                className="opacity-0 group-hover:opacity-100 data-[state=open]:opacity-100 data-[state=open]:text-foreground transition-opacity p-1 hover:bg-accent rounded text-muted-foreground hover:text-foreground ml-2 flex-shrink-0"
+                                title="More options"
+                                disabled={
+                                  loading || deleteSessionMutation.isPending
+                                }
+                                onClick={(e) => {
+                                  e.stopPropagation();
+                                }}
+                              >
+                                <EllipsisVertical className="h-4 w-4" />
+                              </button>
+                            </DropdownMenuTrigger>
+                            <DropdownMenuContent
+                              side="bottom"
+                              align="end"
+                              className="w-48"
+                              onClick={(e) => e.stopPropagation()}
+                            >
+                              <DropdownMenuItem
+                                onClick={(e) => {
+                                  e.stopPropagation();
+                                  handleContextMenuAction(
+                                    "delete",
+                                    conversation,
+                                  );
+                                }}
+                                className="cursor-pointer text-destructive focus:text-destructive"
+                              >
+                                <Trash2 className="mr-2 h-4 w-4" />
+                                Delete conversation
+                              </DropdownMenuItem>
+                            </DropdownMenuContent>
+                          </DropdownMenu>
+                        </div>
+                      </button>
                     ))
                   )}
                 </>
@@ -456,6 +507,7 @@ export function Navigation() {
                   Conversation knowledge
                 </h3>
                 <button
+                  type="button"
                   onClick={handleFilePickerClick}
                   className="p-1 hover:bg-accent rounded"
                   disabled={loading}
@@ -476,9 +528,9 @@ export function Navigation() {
                     No documents yet
                   </div>
                 ) : (
-                  conversationDocs.map((doc, index) => (
+                  conversationDocs.map((doc) => (
                     <div
-                      key={index}
+                      key={`${doc.filename}-${doc.uploadTime.getTime()}`}
                       className="p-2 rounded-lg hover:bg-accent cursor-pointer group flex items-center"
                     >
                       <FileText className="h-4 w-4 mr-2 text-muted-foreground flex-shrink-0" />
@@ -495,6 +547,18 @@ export function Navigation() {
           </div>
         </div>
       )}
+
+      {/* Delete Session Modal */}
+      <DeleteSessionModal
+        isOpen={deleteModalOpen}
+        onClose={() => {
+          setDeleteModalOpen(false);
+          setConversationToDelete(null);
+        }}
+        onConfirm={confirmDeleteConversation}
+        sessionTitle={conversationToDelete?.title || ""}
+        isDeleting={deleteSessionMutation.isPending}
+      />
     </div>
   );
 }
diff --git a/frontend/components/ui/button.tsx b/frontend/components/ui/button.tsx
index b9a83922..aff33335 100644
--- a/frontend/components/ui/button.tsx
+++ b/frontend/components/ui/button.tsx
@@ -14,8 +14,7 @@ const buttonVariants = cva(
           "border border-input 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-foreground text-warning-text hover:bg-warning-foreground/90 hover:shadow-sm",
+        warning: "bg-warning text-secondary hover:bg-warning/90",
         secondary:
           "border border-muted bg-muted text-secondary-foreground hover:bg-secondary-foreground/5",
         ghost:
@@ -39,14 +38,14 @@ const buttonVariants = cva(
       variant: "default",
       size: "default",
     },
-  },
+  }
 );
 
 function toTitleCase(text: string) {
   return text
     ?.split(" ")
     ?.map(
-      (word) => word?.charAt(0)?.toUpperCase() + word?.slice(1)?.toLowerCase(),
+      (word) => word?.charAt(0)?.toUpperCase() + word?.slice(1)?.toLowerCase()
     )
     ?.join(" ");
 }
@@ -72,7 +71,7 @@ const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
       ignoreTitleCase = false,
       ...props
     },
-    ref,
+    ref
   ) => {
     const Comp = asChild ? Slot : "button";
     let newChildren = children;
@@ -101,7 +100,7 @@ const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
         )}
       </Comp>
     );
-  },
+  }
 );
 
 Button.displayName = "Button";
diff --git a/frontend/components/ui/dot-pattern.tsx b/frontend/components/ui/dot-pattern.tsx
new file mode 100644
index 00000000..aa4b2028
--- /dev/null
+++ b/frontend/components/ui/dot-pattern.tsx
@@ -0,0 +1,158 @@
+"use client";
+
+import { motion } from "motion/react";
+import type React from "react";
+import { useEffect, useId, useRef, useState } from "react";
+import { cn } from "@/lib/utils";
+
+/**
+ *  DotPattern Component Props
+ *
+ * @param {number} [width=16] - The horizontal spacing between dots
+ * @param {number} [height=16] - The vertical spacing between dots
+ * @param {number} [x=0] - The x-offset of the entire pattern
+ * @param {number} [y=0] - The y-offset of the entire pattern
+ * @param {number} [cx=1] - The x-offset of individual dots
+ * @param {number} [cy=1] - The y-offset of individual dots
+ * @param {number} [cr=1] - The radius of each dot
+ * @param {string} [className] - Additional CSS classes to apply to the SVG container
+ * @param {boolean} [glow=false] - Whether dots should have a glowing animation effect
+ */
+interface DotPatternProps extends React.SVGProps<SVGSVGElement> {
+  width?: number;
+  height?: number;
+  x?: number;
+  y?: number;
+  cx?: number;
+  cy?: number;
+  cr?: number;
+  className?: string;
+  glow?: boolean;
+  [key: string]: unknown;
+}
+
+/**
+ * DotPattern Component
+ *
+ * A React component that creates an animated or static dot pattern background using SVG.
+ * The pattern automatically adjusts to fill its container and can optionally display glowing dots.
+ *
+ * @component
+ *
+ * @see DotPatternProps for the props interface.
+ *
+ * @example
+ * // Basic usage
+ * <DotPattern />
+ *
+ * // With glowing effect and custom spacing
+ * <DotPattern
+ *   width={20}
+ *   height={20}
+ *   glow={true}
+ *   className="opacity-50"
+ * />
+ *
+ * @notes
+ * - The component is client-side only ("use client")
+ * - Automatically responds to container size changes
+ * - When glow is enabled, dots will animate with random delays and durations
+ * - Uses Motion for animations
+ * - Dots color can be controlled via the text color utility classes
+ */
+
+export function DotPattern({
+  width = 16,
+  height = 16,
+  x = 0,
+  y = 0,
+  cx = 1,
+  cy = 1,
+  cr = 1,
+  className,
+  glow = false,
+  ...props
+}: DotPatternProps) {
+  const id = useId();
+  const containerRef = useRef<SVGSVGElement>(null);
+  const [dimensions, setDimensions] = useState({ width: 0, height: 0 });
+
+  useEffect(() => {
+    const updateDimensions = () => {
+      if (containerRef.current) {
+        const { width, height } = containerRef.current.getBoundingClientRect();
+        setDimensions({ width, height });
+      }
+    };
+
+    updateDimensions();
+    window.addEventListener("resize", updateDimensions);
+    return () => window.removeEventListener("resize", updateDimensions);
+  }, []);
+
+  const dots = Array.from(
+    {
+      length:
+        Math.ceil(dimensions.width / width) *
+        Math.ceil(dimensions.height / height),
+    },
+    (_, i) => {
+      const col = i % Math.ceil(dimensions.width / width);
+      const row = Math.floor(i / Math.ceil(dimensions.width / width));
+      return {
+        x: col * width + cx,
+        y: row * height + cy,
+        delay: Math.random() * 5,
+        duration: Math.random() * 3 + 2,
+      };
+    },
+  );
+
+  return (
+    <svg
+      ref={containerRef}
+      aria-hidden="true"
+      className={cn(
+        "pointer-events-none absolute inset-0 h-full w-full text-neutral-400/80",
+        className,
+      )}
+      {...props}
+    >
+      <defs>
+        <radialGradient id={`${id}-gradient`}>
+          <stop offset="0%" stopColor="currentColor" stopOpacity="1" />
+          <stop offset="100%" stopColor="currentColor" stopOpacity="0" />
+        </radialGradient>
+      </defs>
+      {dots.map((dot, index) => (
+        <motion.circle
+          key={`${dot.x}-${dot.y}`}
+          cx={dot.x}
+          cy={dot.y}
+          r={cr}
+          fill={glow ? `url(#${id}-gradient)` : "currentColor"}
+          initial={glow ? { opacity: 0.4, scale: 1 } : {}}
+          animate={
+            glow
+              ? {
+                  opacity: [0.4, 1, 0.4],
+                  scale: [1, 1.5, 1],
+                }
+              : {}
+          }
+          transition={
+            glow
+              ? {
+                  duration: dot.duration,
+                  repeat: Infinity,
+                  repeatType: "reverse",
+                  delay: dot.delay,
+                  ease: "easeInOut",
+                }
+              : {}
+          }
+        />
+      ))}
+    </svg>
+  );
+}
diff --git a/frontend/components/ui/input.tsx b/frontend/components/ui/input.tsx
index 5ba0eba0..04599fd0 100644
--- a/frontend/components/ui/input.tsx
+++ b/frontend/components/ui/input.tsx
@@ -1,3 +1,4 @@
+import { Eye, EyeOff } from "lucide-react";
 import * as React from "react";
 import { cn } from "@/lib/utils";
 
@@ -12,6 +13,11 @@ const Input = React.forwardRef<HTMLInputElement, InputProps>(
     const [hasValue, setHasValue] = React.useState(
       Boolean(props.value || props.defaultValue),
     );
+    const [showPassword, setShowPassword] = React.useState(false);
+
+    const handleTogglePassword = () => {
+      setShowPassword(!showPassword);
+    };
 
     const handleChange = (e: React.ChangeEvent<HTMLInputElement>) => {
       setHasValue(e.target.value.length > 0);
@@ -23,8 +29,8 @@ const Input = React.forwardRef<HTMLInputElement, InputProps>(
     return (
       <label
         className={cn(
-          "relative block h-fit w-full text-sm",
-          icon ? className : ""
+          "relative block h-fit w-full text-sm group",
+          icon ? className : "",
         )}
       >
         {icon && (
@@ -34,17 +40,32 @@ const Input = React.forwardRef<HTMLInputElement, InputProps>(
         )}
         <input
           autoComplete="off"
-          type={type}
+          type={type === "password" && showPassword ? "text" : type}
           placeholder={placeholder}
           className={cn(
             "primary-input !placeholder-transparent",
             icon && "pl-9",
-            icon ? inputClassName : className
+            type === "password" && "!pr-8",
+            icon ? inputClassName : className,
           )}
           ref={ref}
           {...props}
           onChange={handleChange}
         />
+        {type === "password" && (
+          <button
+            type="button"
+            className="absolute top-1/2 opacity-0 group-hover:opacity-100 hover:text-primary transition-all right-3 transform -translate-y-1/2 text-sm text-muted-foreground"
+            onMouseDown={(e) => e.preventDefault()}
+            onMouseUp={handleTogglePassword}
+          >
+            {showPassword ? (
+              <Eye className="w-4" />
+            ) : (
+              <EyeOff className="w-4" />
+            )}
+          </button>
+        )}
         <span
           className={cn(
             "pointer-events-none absolute top-1/2 -translate-y-1/2 pl-px text-placeholder-foreground font-mono",
@@ -56,7 +77,7 @@ const Input = React.forwardRef<HTMLInputElement, InputProps>(
         </span>
       </label>
     );
-  }
+  },
 );
 
 Input.displayName = "Input";
diff --git a/frontend/components/ui/radio-group.tsx b/frontend/components/ui/radio-group.tsx
index 0968c2a8..de9da9af 100644
--- a/frontend/components/ui/radio-group.tsx
+++ b/frontend/components/ui/radio-group.tsx
@@ -1,10 +1,10 @@
-"use client"
+"use client";
 
-import * as React from "react"
-import * as RadioGroupPrimitive from "@radix-ui/react-radio-group"
-import { Circle } from "lucide-react"
+import * as React from "react";
+import * as RadioGroupPrimitive from "@radix-ui/react-radio-group";
+import { Circle } from "lucide-react";
 
-import { cn } from "@/lib/utils"
+import { cn } from "@/lib/utils";
 
 const RadioGroup = React.forwardRef<
   React.ElementRef<typeof RadioGroupPrimitive.Root>,
@@ -16,9 +16,9 @@ const RadioGroup = React.forwardRef<
       {...props}
       ref={ref}
     />
-  )
-})
-RadioGroup.displayName = RadioGroupPrimitive.Root.displayName
+  );
+});
+RadioGroup.displayName = RadioGroupPrimitive.Root.displayName;
 
 const RadioGroupItem = React.forwardRef<
   React.ElementRef<typeof RadioGroupPrimitive.Item>,
@@ -28,7 +28,7 @@ const RadioGroupItem = React.forwardRef<
     <RadioGroupPrimitive.Item
       ref={ref}
       className={cn(
-        "aspect-square h-4 w-4 rounded-full border border-primary text-primary ring-offset-background focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50",
+        "aspect-square h-4 w-4 rounded-full border border-input text-primary ring-offset-background focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50",
         className
       )}
       {...props}
@@ -37,8 +37,8 @@ const RadioGroupItem = React.forwardRef<
         <Circle className="h-2.5 w-2.5 fill-current text-current" />
       </RadioGroupPrimitive.Indicator>
     </RadioGroupPrimitive.Item>
-  )
-})
-RadioGroupItem.displayName = RadioGroupPrimitive.Item.displayName
+  );
+});
+RadioGroupItem.displayName = RadioGroupPrimitive.Item.displayName;
 
-export { RadioGroup, RadioGroupItem }
\ No newline at end of file
+export { RadioGroup, RadioGroupItem };
diff --git a/frontend/components/ui/select.tsx b/frontend/components/ui/select.tsx
index f9571262..66665060 100644
--- a/frontend/components/ui/select.tsx
+++ b/frontend/components/ui/select.tsx
@@ -1,36 +1,48 @@
-"use client"
+"use client";
 
-import * as React from "react"
-import * as SelectPrimitive from "@radix-ui/react-select"
-import { Check, ChevronDown, ChevronUp } from "lucide-react"
+import * as React from "react";
+import * as SelectPrimitive from "@radix-ui/react-select";
+import {
+  Check,
+  ChevronDown,
+  ChevronsUpDown,
+  ChevronUp,
+  LockIcon,
+} from "lucide-react";
 
-import { cn } from "@/lib/utils"
+import { cn } from "@/lib/utils";
 
-const Select = SelectPrimitive.Root
+const Select = SelectPrimitive.Root;
 
-const SelectGroup = SelectPrimitive.Group
+const SelectGroup = SelectPrimitive.Group;
 
-const SelectValue = SelectPrimitive.Value
+const SelectValue = SelectPrimitive.Value;
 
 const SelectTrigger = React.forwardRef<
   React.ElementRef<typeof SelectPrimitive.Trigger>,
   React.ComponentPropsWithoutRef<typeof SelectPrimitive.Trigger>
->(({ className, children, ...props }, ref) => (
-  <SelectPrimitive.Trigger
-    ref={ref}
-    className={cn(
-      "flex h-10 w-full items-center justify-between rounded-md border border-input bg-background px-3 py-2 text-sm ring-offset-background placeholder:text-muted-foreground focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50 [&>span]:line-clamp-1",
-      className
-    )}
-    {...props}
-  >
-    {children}
-    <SelectPrimitive.Icon asChild>
-      <ChevronDown className="h-4 w-4 opacity-50" />
-    </SelectPrimitive.Icon>
-  </SelectPrimitive.Trigger>
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
+>(({ className, children, ...props }, ref) => {
+  return (
+    <SelectPrimitive.Trigger
+      ref={ref}
+      className={cn(
+        "flex h-10 w-full items-center justify-between rounded-md border border-input px-3 py-2 text-sm ring-offset-background placeholder:text-muted-foreground focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:cursor-not-allowed disabled:bg-muted [&>span]:line-clamp-1",
+        className
+      )}
+      {...props}
+    >
+      {children}
+      <SelectPrimitive.Icon asChild>
+        {props.disabled ? (
+          <LockIcon className="text-base h-5 w-5 opacity-50" />
+        ) : (
+          <ChevronsUpDown className="text-base h-5 w-5" />
+        )}
+      </SelectPrimitive.Icon>
+    </SelectPrimitive.Trigger>
+  );
+});
+SelectTrigger.displayName = SelectPrimitive.Trigger.displayName;
 
 const SelectScrollUpButton = React.forwardRef<
   React.ElementRef<typeof SelectPrimitive.ScrollUpButton>,
@@ -46,8 +58,8 @@ const SelectScrollUpButton = React.forwardRef<
   >
     <ChevronUp className="h-4 w-4" />
   </SelectPrimitive.ScrollUpButton>
-))
-SelectScrollUpButton.displayName = SelectPrimitive.ScrollUpButton.displayName
+));
+SelectScrollUpButton.displayName = SelectPrimitive.ScrollUpButton.displayName;
 
 const SelectScrollDownButton = React.forwardRef<
   React.ElementRef<typeof SelectPrimitive.ScrollDownButton>,
@@ -63,9 +75,9 @@ const SelectScrollDownButton = React.forwardRef<
   >
     <ChevronDown className="h-4 w-4" />
   </SelectPrimitive.ScrollDownButton>
-))
+));
 SelectScrollDownButton.displayName =
-  SelectPrimitive.ScrollDownButton.displayName
+  SelectPrimitive.ScrollDownButton.displayName;
 
 const SelectContent = React.forwardRef<
   React.ElementRef<typeof SelectPrimitive.Content>,
@@ -96,8 +108,8 @@ const SelectContent = React.forwardRef<
       <SelectScrollDownButton />
     </SelectPrimitive.Content>
   </SelectPrimitive.Portal>
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
+));
+SelectContent.displayName = SelectPrimitive.Content.displayName;
 
 const SelectLabel = React.forwardRef<
   React.ElementRef<typeof SelectPrimitive.Label>,
@@ -108,8 +120,8 @@ const SelectLabel = React.forwardRef<
     className={cn("py-1.5 pl-8 pr-2 text-sm font-semibold", className)}
     {...props}
   />
-))
-SelectLabel.displayName = SelectPrimitive.Label.displayName
+));
+SelectLabel.displayName = SelectPrimitive.Label.displayName;
 
 const SelectItem = React.forwardRef<
   React.ElementRef<typeof SelectPrimitive.Item>,
@@ -131,8 +143,8 @@ const SelectItem = React.forwardRef<
 
     <SelectPrimitive.ItemText>{children}</SelectPrimitive.ItemText>
   </SelectPrimitive.Item>
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
+));
+SelectItem.displayName = SelectPrimitive.Item.displayName;
 
 const SelectSeparator = React.forwardRef<
   React.ElementRef<typeof SelectPrimitive.Separator>,
@@ -143,8 +155,8 @@ const SelectSeparator = React.forwardRef<
     className={cn("-mx-1 my-1 h-px bg-muted", className)}
     {...props}
   />
-))
-SelectSeparator.displayName = SelectPrimitive.Separator.displayName
+));
+SelectSeparator.displayName = SelectPrimitive.Separator.displayName;
 
 export {
   Select,
@@ -157,4 +169,4 @@ export {
   SelectSeparator,
   SelectScrollUpButton,
   SelectScrollDownButton,
-} 
\ No newline at end of file
+};
diff --git a/frontend/public/images/background.png b/frontend/public/images/background.png
deleted file mode 100644
index 66d44a8a..00000000
Binary files a/frontend/public/images/background.png and /dev/null differ
diff --git a/frontend/src/app/api/queries/useDeleteSessionMutation.ts b/frontend/src/app/api/queries/useDeleteSessionMutation.ts
new file mode 100644
index 00000000..996e8a44
--- /dev/null
+++ b/frontend/src/app/api/queries/useDeleteSessionMutation.ts
@@ -0,0 +1,57 @@
+import {
+  type MutationOptions,
+  useMutation,
+  useQueryClient,
+} from "@tanstack/react-query";
+import type { EndpointType } from "@/contexts/chat-context";
+
+interface DeleteSessionParams {
+  sessionId: string;
+  endpoint: EndpointType;
+}
+
+interface DeleteSessionResponse {
+  success: boolean;
+  message: string;
+}
+
+export const useDeleteSessionMutation = (
+  options?: Omit<
+    MutationOptions<DeleteSessionResponse, Error, DeleteSessionParams>,
+    "mutationFn"
+  >,
+) => {
+  const queryClient = useQueryClient();
+
+  return useMutation<DeleteSessionResponse, Error, DeleteSessionParams>({
+    mutationFn: async ({ sessionId }: DeleteSessionParams) => {
+      const response = await fetch(`/api/sessions/${sessionId}`, {
+        method: "DELETE",
+      });
+
+      if (!response.ok) {
+        const errorData = await response.json().catch(() => ({}));
+        throw new Error(
+          errorData.error || `Failed to delete session: ${response.status}`,
+        );
+      }
+
+      return response.json();
+    },
+    onSettled: (_data, _error, variables) => {
+      // Invalidate conversations query to refresh the list
+      // Use a slight delay to ensure the success callback completes first
+      setTimeout(() => {
+        queryClient.invalidateQueries({
+          queryKey: ["conversations", variables.endpoint],
+        });
+
+        // Also invalidate any specific conversation queries
+        queryClient.invalidateQueries({
+          queryKey: ["conversations"],
+        });
+      }, 0);
+    },
+    ...options,
+  });
+};
diff --git a/frontend/src/app/api/queries/useGetConversationsQuery.ts b/frontend/src/app/api/queries/useGetConversationsQuery.ts
new file mode 100644
index 00000000..f7e579b3
--- /dev/null
+++ b/frontend/src/app/api/queries/useGetConversationsQuery.ts
@@ -0,0 +1,105 @@
+import {
+  type UseQueryOptions,
+  useQuery,
+  useQueryClient,
+} from "@tanstack/react-query";
+import type { EndpointType } from "@/contexts/chat-context";
+
+export interface RawConversation {
+  response_id: string;
+  title: string;
+  endpoint: string;
+  messages: Array<{
+    role: string;
+    content: string;
+    timestamp?: string;
+    response_id?: string;
+  }>;
+  created_at?: string;
+  last_activity?: string;
+  previous_response_id?: string;
+  total_messages: number;
+  [key: string]: unknown;
+}
+
+export interface ChatConversation {
+  response_id: string;
+  title: string;
+  endpoint: EndpointType;
+  messages: Array<{
+    role: string;
+    content: string;
+    timestamp?: string;
+    response_id?: string;
+  }>;
+  created_at?: string;
+  last_activity?: string;
+  previous_response_id?: string;
+  total_messages: number;
+  [key: string]: unknown;
+}
+
+export interface ConversationHistoryResponse {
+  conversations: RawConversation[];
+  [key: string]: unknown;
+}
+
+export const useGetConversationsQuery = (
+  endpoint: EndpointType,
+  refreshTrigger?: number,
+  options?: Omit<UseQueryOptions, "queryKey" | "queryFn">,
+) => {
+  const queryClient = useQueryClient();
+
+  async function getConversations(): Promise<ChatConversation[]> {
+    try {
+      // Fetch from the selected endpoint only
+      const apiEndpoint =
+        endpoint === "chat" ? "/api/chat/history" : "/api/langflow/history";
+
+      const response = await fetch(apiEndpoint);
+
+      if (!response.ok) {
+        console.error(`Failed to fetch conversations: ${response.status}`);
+        return [];
+      }
+
+      const history: ConversationHistoryResponse = await response.json();
+      const rawConversations = history.conversations || [];
+
+      // Cast conversations to proper type and ensure endpoint is correct
+      const conversations: ChatConversation[] = rawConversations.map(
+        (conv: RawConversation) => ({
+          ...conv,
+          endpoint: conv.endpoint as EndpointType,
+        }),
+      );
+
+      // Sort conversations by last activity (most recent first)
+      conversations.sort((a: ChatConversation, b: ChatConversation) => {
+        const aTime = new Date(a.last_activity || a.created_at || 0).getTime();
+        const bTime = new Date(b.last_activity || b.created_at || 0).getTime();
+        return bTime - aTime;
+      });
+
+      return conversations;
+    } catch (error) {
+      console.error(`Failed to fetch ${endpoint} conversations:`, error);
+      return [];
+    }
+  }
+
+  const queryResult = useQuery(
+    {
+      queryKey: ["conversations", endpoint, refreshTrigger],
+      placeholderData: (prev) => prev,
+      queryFn: getConversations,
+      staleTime: 0, // Always consider data stale to ensure fresh data on trigger changes
+      gcTime: 5 * 60 * 1000, // Keep in cache for 5 minutes
+      ...options,
+    },
+    queryClient,
+  );
+
+  return queryResult;
+};
diff --git a/frontend/src/app/api/queries/useGetModelsQuery.ts b/frontend/src/app/api/queries/useGetModelsQuery.ts
index 4ce55bd3..3a5eb77e 100644
--- a/frontend/src/app/api/queries/useGetModelsQuery.ts
+++ b/frontend/src/app/api/queries/useGetModelsQuery.ts
@@ -90,7 +90,6 @@ export const useGetOllamaModelsQuery = (
       queryKey: ["models", "ollama", params],
       queryFn: getOllamaModels,
       retry: 2,
-      enabled: !!params?.endpoint, // Only run if endpoint is provided
       staleTime: 0, // Always fetch fresh data
       gcTime: 0, // Don't cache results
       ...options,
diff --git a/frontend/src/app/login/page.tsx b/frontend/src/app/login/page.tsx
index c2347f1b..1639a4be 100644
--- a/frontend/src/app/login/page.tsx
+++ b/frontend/src/app/login/page.tsx
@@ -6,7 +6,9 @@ import { Suspense, useEffect } from "react";
 import GoogleLogo from "@/components/logo/google-logo";
 import Logo from "@/components/logo/logo";
 import { Button } from "@/components/ui/button";
+import { DotPattern } from "@/components/ui/dot-pattern";
 import { useAuth } from "@/contexts/auth-context";
+import { cn } from "@/lib/utils";
 import { useGetSettingsQuery } from "../api/queries/useGetSettingsQuery";
 
 function LoginPageContent() {
@@ -53,15 +55,19 @@ function LoginPageContent() {
   }
 
   return (
-    <div
-      className="min-h-dvh relative flex gap-4 flex-col items-center justify-center bg-background p-4"
-      style={{
-        backgroundImage: "url('/images/background.png')",
-        backgroundSize: "cover",
-        backgroundPosition: "center",
-      }}
-    >
-      <div className="flex flex-col items-center justify-center gap-4">
+    <div className="min-h-dvh relative flex gap-4 flex-col items-center justify-center bg-background p-4">
+      <DotPattern
+        width={24}
+        height={24}
+        cx={1}
+        cy={1}
+        cr={1}
+        className={cn(
+          "[mask-image:linear-gradient(to_bottom,white,transparent,transparent)]",
+          "text-input/70",
+        )}
+      />
+      <div className="flex flex-col items-center justify-center gap-4 z-10">
         <Logo className="fill-primary" width={32} height={28} />
         <h1 className="text-2xl font-medium font-chivo">Welcome to OpenRAG</h1>
         <p className="text-sm text-muted-foreground">
@@ -72,7 +78,7 @@ function LoginPageContent() {
           Continue with Google
         </Button>
       </div>
-      <div className="flex items-center justify-center gap-2 absolute bottom-6 text-xs text-muted-foreground">
+      <div className="flex items-center justify-center gap-2 absolute bottom-6 text-xs text-muted-foreground z-10">
         <p className="text-accent-emerald-foreground">Systems Operational</p>•
         <p>Privacy Policy</p>
       </div>
diff --git a/frontend/src/app/onboarding/components/advanced.tsx b/frontend/src/app/onboarding/components/advanced.tsx
index bb0089d5..20764aed 100644
--- a/frontend/src/app/onboarding/components/advanced.tsx
+++ b/frontend/src/app/onboarding/components/advanced.tsx
@@ -47,8 +47,7 @@ export function AdvancedOnboarding({
           {hasEmbeddingModels && (
             <LabelWrapper
               label="Embedding model"
-              description="It’s recommended that you use XYZ, ABC, or DEF models for best performance."
-              helperText="The embedding model for your Ollama server."
+              helperText="Model used for knowledge ingest and retrieval"
               id="embedding-model"
               required={true}
             >
@@ -63,8 +62,7 @@ export function AdvancedOnboarding({
           {hasLanguageModels && (
             <LabelWrapper
               label="Language model"
-              description="It’s recommended that you use XYZ, ABC, or DEF models for best performance."
-              helperText="The embedding model for your Ollama server."
+              helperText="Model used for chat"
               id="embedding-model"
               required={true}
             >
@@ -79,7 +77,7 @@ export function AdvancedOnboarding({
           {(hasLanguageModels || hasEmbeddingModels) && <Separator />}
           <LabelWrapper
             label="Sample dataset"
-            description="Ingest two small PDFs"
+            description="Load 2 sample PDFs to chat with data immediately."
             id="sample-dataset"
             flex
           >
diff --git a/frontend/src/app/onboarding/components/ibm-onboarding.tsx b/frontend/src/app/onboarding/components/ibm-onboarding.tsx
index 550f9d6b..63e3fe6a 100644
--- a/frontend/src/app/onboarding/components/ibm-onboarding.tsx
+++ b/frontend/src/app/onboarding/components/ibm-onboarding.tsx
@@ -1,5 +1,6 @@
 import { useState } from "react";
 import { LabelInput } from "@/components/label-input";
+import { LabelWrapper } from "@/components/label-wrapper";
 import IBMLogo from "@/components/logo/ibm-logo";
 import { useDebouncedValue } from "@/lib/debounce";
 import type { OnboardingVariables } from "../../api/mutations/useOnboardingMutation";
@@ -7,6 +8,7 @@ import { useGetIBMModelsQuery } from "../../api/queries/useGetModelsQuery";
 import { useModelSelection } from "../hooks/useModelSelection";
 import { useUpdateSettings } from "../hooks/useUpdateSettings";
 import { AdvancedOnboarding } from "./advanced";
+import { ModelSelector } from "./model-selector";
 
 export function IBMOnboarding({
   setSettings,
@@ -17,10 +19,42 @@ export function IBMOnboarding({
   sampleDataset: boolean;
   setSampleDataset: (dataset: boolean) => void;
 }) {
-  const [endpoint, setEndpoint] = useState("");
+  const [endpoint, setEndpoint] = useState("https://us-south.ml.cloud.ibm.com");
   const [apiKey, setApiKey] = useState("");
   const [projectId, setProjectId] = useState("");
 
+  const options = [
+    {
+      value: "https://us-south.ml.cloud.ibm.com",
+      label: "https://us-south.ml.cloud.ibm.com",
+      default: true,
+    },
+    {
+      value: "https://eu-de.ml.cloud.ibm.com",
+      label: "https://eu-de.ml.cloud.ibm.com",
+      default: false,
+    },
+    {
+      value: "https://eu-gb.ml.cloud.ibm.com",
+      label: "https://eu-gb.ml.cloud.ibm.com",
+      default: false,
+    },
+    {
+      value: "https://au-syd.ml.cloud.ibm.com",
+      label: "https://au-syd.ml.cloud.ibm.com",
+      default: false,
+    },
+    {
+      value: "https://jp-tok.ml.cloud.ibm.com",
+      label: "https://jp-tok.ml.cloud.ibm.com",
+      default: false,
+    },
+    {
+      value: "https://ca-tor.ml.cloud.ibm.com",
+      label: "https://ca-tor.ml.cloud.ibm.com",
+      default: false,
+    },
+  ];
   const debouncedEndpoint = useDebouncedValue(endpoint, 500);
   const debouncedApiKey = useDebouncedValue(apiKey, 500);
   const debouncedProjectId = useDebouncedValue(projectId, 500);
@@ -68,19 +102,26 @@ export function IBMOnboarding({
   return (
     <>
       <div className="space-y-4">
-        <LabelInput
+        <LabelWrapper
           label="watsonx.ai API Endpoint"
-          helperText="The API endpoint for your watsonx.ai account."
+          helperText="Base URL of the API"
           id="api-endpoint"
           required
-          placeholder="https://us-south.ml.cloud.ibm.com"
-          value={endpoint}
-          onChange={(e) => setEndpoint(e.target.value)}
-        />
+        >
+          <ModelSelector
+            options={options}
+            value={endpoint}
+            onValueChange={setEndpoint}
+            searchPlaceholder="Search endpoint..."
+            noOptionsPlaceholder="No endpoints available"
+            placeholder="Select endpoint..."
+          />
+        </LabelWrapper>
         <LabelInput
           label="IBM API key"
           helperText="The API key for your watsonx.ai account."
           id="api-key"
+          type="password"
           required
           placeholder="your-api-key"
           value={apiKey}
@@ -102,16 +143,9 @@ export function IBMOnboarding({
         )}
         {modelsError && (
           <p className="text-mmd text-accent-amber-foreground">
-            Invalid configuration or connection failed
+            Connection failed. Check your configuration.
           </p>
         )}
-        {modelsData &&
-          (modelsData.language_models?.length > 0 ||
-            modelsData.embedding_models?.length > 0) && (
-            <p className="text-mmd text-accent-emerald-foreground">
-              Configuration is valid
-            </p>
-          )}
       </div>
       <AdvancedOnboarding
         icon={<IBMLogo className="w-4 h-4" />}
diff --git a/frontend/src/app/onboarding/components/model-selector.tsx b/frontend/src/app/onboarding/components/model-selector.tsx
index 7a74bed2..dfed52ee 100644
--- a/frontend/src/app/onboarding/components/model-selector.tsx
+++ b/frontend/src/app/onboarding/components/model-selector.tsx
@@ -21,6 +21,9 @@ export function ModelSelector({
   value,
   onValueChange,
   icon,
+  placeholder = "Select model...",
+  searchPlaceholder = "Search model...",
+  noOptionsPlaceholder = "No models available",
 }: {
   options: {
     value: string;
@@ -29,6 +32,9 @@ export function ModelSelector({
   }[];
   value: string;
   icon?: React.ReactNode;
+  placeholder?: string;
+  searchPlaceholder?: string;
+  noOptionsPlaceholder?: string;
   onValueChange: (value: string) => void;
 }) {
   const [open, setOpen] = useState(false);
@@ -50,7 +56,7 @@ export function ModelSelector({
         >
           {value ? (
             <div className="flex items-center gap-2">
-              <div className="w-4 h-4">{icon}</div>
+              {icon && <div className="w-4 h-4">{icon}</div>}
               {options.find((framework) => framework.value === value)?.label}
               {options.find((framework) => framework.value === value)
                 ?.default && (
@@ -60,18 +66,18 @@ export function ModelSelector({
               )}
             </div>
           ) : options.length === 0 ? (
-            "No models available"
+            noOptionsPlaceholder
           ) : (
-            "Select model..."
+            placeholder
           )}
           <ChevronsUpDownIcon className="ml-2 h-4 w-4 shrink-0 opacity-50" />
         </Button>
       </PopoverTrigger>
       <PopoverContent align="start" className="w-[400px] p-0">
         <Command>
-          <CommandInput placeholder="Search model..." />
+          <CommandInput placeholder={searchPlaceholder} />
           <CommandList>
-            <CommandEmpty>No model found.</CommandEmpty>
+            <CommandEmpty>{noOptionsPlaceholder}</CommandEmpty>
             <CommandGroup>
               {options.map((option) => (
                 <CommandItem
diff --git a/frontend/src/app/onboarding/components/ollama-onboarding.tsx b/frontend/src/app/onboarding/components/ollama-onboarding.tsx
index 0803c438..0aaf991f 100644
--- a/frontend/src/app/onboarding/components/ollama-onboarding.tsx
+++ b/frontend/src/app/onboarding/components/ollama-onboarding.tsx
@@ -1,4 +1,4 @@
-import { useState } from "react";
+import { useEffect, useState } from "react";
 import { LabelInput } from "@/components/label-input";
 import { LabelWrapper } from "@/components/label-wrapper";
 import OllamaLogo from "@/components/logo/ollama-logo";
@@ -19,7 +19,8 @@ export function OllamaOnboarding({
   sampleDataset: boolean;
   setSampleDataset: (dataset: boolean) => void;
 }) {
-  const [endpoint, setEndpoint] = useState("");
+  const [endpoint, setEndpoint] = useState("http://localhost:11434");
+  const [showConnecting, setShowConnecting] = useState(false);
   const debouncedEndpoint = useDebouncedValue(endpoint, 500);
 
   // Fetch models from API when endpoint is provided (debounced)
@@ -41,6 +42,25 @@ export function OllamaOnboarding({
     embeddingModels,
   } = useModelSelection(modelsData);
 
+  // Handle delayed display of connecting state
+  useEffect(() => {
+    let timeoutId: NodeJS.Timeout;
+
+    if (debouncedEndpoint && isLoadingModels) {
+      timeoutId = setTimeout(() => {
+        setShowConnecting(true);
+      }, 500);
+    } else {
+      setShowConnecting(false);
+    }
+
+    return () => {
+      if (timeoutId) {
+        clearTimeout(timeoutId);
+      }
+    };
+  }, [debouncedEndpoint, isLoadingModels]);
+
   const handleSampleDatasetChange = (dataset: boolean) => {
     setSampleDataset(dataset);
   };
@@ -57,74 +77,75 @@ export function OllamaOnboarding({
   );
 
   // Check validation state based on models query
-  const isConnecting = debouncedEndpoint && isLoadingModels;
   const hasConnectionError = debouncedEndpoint && modelsError;
   const hasNoModels =
     modelsData &&
     !modelsData.language_models?.length &&
     !modelsData.embedding_models?.length;
-  const isValidConnection =
-    modelsData &&
-    (modelsData.language_models?.length > 0 ||
-      modelsData.embedding_models?.length > 0);
 
   return (
     <>
       <div className="space-y-4">
         <div className="space-y-1">
           <LabelInput
-            label="Ollama Endpoint"
-            helperText="The endpoint for your Ollama server."
+            label="Ollama Base URL"
+            helperText="Base URL of your Ollama server"
             id="api-endpoint"
             required
             placeholder="http://localhost:11434"
             value={endpoint}
             onChange={(e) => setEndpoint(e.target.value)}
           />
-          {isConnecting && (
+          {showConnecting && (
             <p className="text-mmd text-muted-foreground">
               Connecting to Ollama server...
             </p>
           )}
           {hasConnectionError && (
             <p className="text-mmd text-accent-amber-foreground">
-              Can’t reach Ollama at {debouncedEndpoint}. Update the endpoint or
+              Can’t reach Ollama at {debouncedEndpoint}. Update the base URL or
               start the server.
             </p>
           )}
           {hasNoModels && (
             <p className="text-mmd text-accent-amber-foreground">
-              No models found. Please install some models on your Ollama server.
-            </p>
-          )}
-          {isValidConnection && (
-            <p className="text-mmd text-accent-emerald-foreground">
-              Connected successfully
+              No models found. Install embedding and agent models on your Ollama
+              server.
             </p>
           )}
         </div>
         <LabelWrapper
           label="Embedding model"
-          helperText="The embedding model for your Ollama server."
+          helperText="Model used for knowledge ingest and retrieval"
           id="embedding-model"
           required={true}
         >
           <ModelSelector
             options={embeddingModels}
             icon={<OllamaLogo className="w-4 h-4" />}
+            noOptionsPlaceholder={
+              isLoadingModels
+                ? "Loading models..."
+                : "No embedding models detected. Install an embedding model to continue."
+            }
             value={embeddingModel}
             onValueChange={setEmbeddingModel}
           />
         </LabelWrapper>
         <LabelWrapper
           label="Language model"
-          helperText="The embedding model for your Ollama server."
+          helperText="Model used for chat"
           id="embedding-model"
           required={true}
         >
           <ModelSelector
             options={languageModels}
             icon={<OllamaLogo className="w-4 h-4" />}
+            noOptionsPlaceholder={
+              isLoadingModels
+                ? "Loading models..."
+                : "No language models detected. Install a language model to continue."
+            }
             value={languageModel}
             onValueChange={setLanguageModel}
           />
diff --git a/frontend/src/app/onboarding/components/openai-onboarding.tsx b/frontend/src/app/onboarding/components/openai-onboarding.tsx
index cf18fb53..236097a4 100644
--- a/frontend/src/app/onboarding/components/openai-onboarding.tsx
+++ b/frontend/src/app/onboarding/components/openai-onboarding.tsx
@@ -1,6 +1,8 @@
 import { useState } from "react";
 import { LabelInput } from "@/components/label-input";
+import { LabelWrapper } from "@/components/label-wrapper";
 import OpenAILogo from "@/components/logo/openai-logo";
+import { Switch } from "@/components/ui/switch";
 import { useDebouncedValue } from "@/lib/debounce";
 import type { OnboardingVariables } from "../../api/mutations/useOnboardingMutation";
 import { useGetOpenAIModelsQuery } from "../../api/queries/useGetModelsQuery";
@@ -18,6 +20,7 @@ export function OpenAIOnboarding({
   setSampleDataset: (dataset: boolean) => void;
 }) {
   const [apiKey, setApiKey] = useState("");
+  const [getFromEnv, setGetFromEnv] = useState(true);
   const debouncedApiKey = useDebouncedValue(apiKey, 500);
 
   // Fetch models from API when API key is provided
@@ -26,7 +29,12 @@ export function OpenAIOnboarding({
     isLoading: isLoadingModels,
     error: modelsError,
   } = useGetOpenAIModelsQuery(
-    debouncedApiKey ? { apiKey: debouncedApiKey } : undefined,
+    getFromEnv
+      ? { apiKey: "" }
+      : debouncedApiKey
+      ? { apiKey: debouncedApiKey }
+      : undefined,
+    { enabled: debouncedApiKey !== "" || getFromEnv },
   );
   // Use custom hook for model selection logic
   const {
@@ -41,6 +49,15 @@ export function OpenAIOnboarding({
     setSampleDataset(dataset);
   };
 
+  const handleGetFromEnvChange = (fromEnv: boolean) => {
+    setGetFromEnv(fromEnv);
+    if (fromEnv) {
+      setApiKey("");
+    }
+    setLanguageModel("");
+    setEmbeddingModel("");
+  };
+
   // Update settings when values change
   useUpdateSettings(
     "openai",
@@ -53,33 +70,41 @@ export function OpenAIOnboarding({
   );
   return (
     <>
-      <div className="space-y-1">
-        <LabelInput
-          label="OpenAI API key"
-          helperText="The API key for your OpenAI account."
-          id="api-key"
-          required
-          placeholder="sk-..."
-          value={apiKey}
-          onChange={(e) => setApiKey(e.target.value)}
-        />
-        {isLoadingModels && (
-          <p className="text-mmd text-muted-foreground">
-            Validating API key...
-          </p>
+      <div className="space-y-5">
+        <LabelWrapper
+          label="Get API key from environment variable"
+          id="get-api-key"
+          flex
+        >
+          <Switch
+            checked={getFromEnv}
+            onCheckedChange={handleGetFromEnvChange}
+          />
+        </LabelWrapper>
+        {!getFromEnv && (
+          <div className="space-y-1">
+            <LabelInput
+              label="OpenAI API key"
+              helperText="The API key for your OpenAI account."
+              id="api-key"
+              type="password"
+              required
+              placeholder="sk-..."
+              value={apiKey}
+              onChange={(e) => setApiKey(e.target.value)}
+            />
+            {isLoadingModels && (
+              <p className="text-mmd text-muted-foreground">
+                Validating API key...
+              </p>
+            )}
+            {modelsError && (
+              <p className="text-mmd text-accent-amber-foreground">
+                Invalid OpenAI API key. Verify or replace the key.
+              </p>
+            )}
+          </div>
         )}
-        {modelsError && (
-          <p className="text-mmd text-accent-amber-foreground">
-            Invalid API key
-          </p>
-        )}
-        {modelsData &&
-          (modelsData.language_models?.length > 0 ||
-            modelsData.embedding_models?.length > 0) && (
-            <p className="text-mmd text-accent-emerald-foreground">
-              API Key is valid
-            </p>
-          )}
       </div>
       <AdvancedOnboarding
         icon={<OpenAILogo className="w-4 h-4" />}
diff --git a/frontend/src/app/onboarding/page.tsx b/frontend/src/app/onboarding/page.tsx
index c58abfea..a82e5fab 100644
--- a/frontend/src/app/onboarding/page.tsx
+++ b/frontend/src/app/onboarding/page.tsx
@@ -4,8 +4,8 @@ import { useRouter } from "next/navigation";
 import { Suspense, useEffect, useState } from "react";
 import { toast } from "sonner";
 import {
-	type OnboardingVariables,
-	useOnboardingMutation,
+  type OnboardingVariables,
+  useOnboardingMutation,
 } from "@/app/api/mutations/useOnboardingMutation";
 import IBMLogo from "@/components/logo/ibm-logo";
 import OllamaLogo from "@/components/logo/ollama-logo";
@@ -13,198 +13,208 @@ import OpenAILogo from "@/components/logo/openai-logo";
 import { ProtectedRoute } from "@/components/protected-route";
 import { Button } from "@/components/ui/button";
 import {
-	Card,
-	CardContent,
-	CardFooter,
-	CardHeader,
+  Card,
+  CardContent,
+  CardFooter,
+  CardHeader,
 } from "@/components/ui/card";
+import { DotPattern } from "@/components/ui/dot-pattern";
 import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs";
 import {
-	Tooltip,
-	TooltipContent,
-	TooltipTrigger,
+  Tooltip,
+  TooltipContent,
+  TooltipTrigger,
 } from "@/components/ui/tooltip";
+import { cn } from "@/lib/utils";
 import { useGetSettingsQuery } from "../api/queries/useGetSettingsQuery";
 import { IBMOnboarding } from "./components/ibm-onboarding";
 import { OllamaOnboarding } from "./components/ollama-onboarding";
 import { OpenAIOnboarding } from "./components/openai-onboarding";
 
 function OnboardingPage() {
-	const { data: settingsDb, isLoading: isSettingsLoading } =
-		useGetSettingsQuery();
+  const { data: settingsDb, isLoading: isSettingsLoading } =
+    useGetSettingsQuery();
 
-	const redirect = "/";
+  const redirect = "/";
 
-	const router = useRouter();
+  const router = useRouter();
 
-	// Redirect if already authenticated or in no-auth mode
-	useEffect(() => {
-		if (!isSettingsLoading && settingsDb && settingsDb.edited) {
-			router.push(redirect);
-		}
-	}, [isSettingsLoading, settingsDb, router]);
+  // Redirect if already authenticated or in no-auth mode
+  useEffect(() => {
+    if (!isSettingsLoading && settingsDb && settingsDb.edited) {
+      router.push(redirect);
+    }
+  }, [isSettingsLoading, settingsDb, router]);
 
-	const [modelProvider, setModelProvider] = useState<string>("openai");
+  const [modelProvider, setModelProvider] = useState<string>("openai");
 
-	const [sampleDataset, setSampleDataset] = useState<boolean>(true);
+  const [sampleDataset, setSampleDataset] = useState<boolean>(true);
 
-	const handleSetModelProvider = (provider: string) => {
-		setModelProvider(provider);
-		setSettings({
-			model_provider: provider,
-			embedding_model: "",
-			llm_model: "",
-		});
-	};
+  const handleSetModelProvider = (provider: string) => {
+    setModelProvider(provider);
+    setSettings({
+      model_provider: provider,
+      embedding_model: "",
+      llm_model: "",
+    });
+  };
 
-	const [settings, setSettings] = useState<OnboardingVariables>({
-		model_provider: modelProvider,
-		embedding_model: "",
-		llm_model: "",
-	});
+  const [settings, setSettings] = useState<OnboardingVariables>({
+    model_provider: modelProvider,
+    embedding_model: "",
+    llm_model: "",
+  });
 
-	// Mutations
-	const onboardingMutation = useOnboardingMutation({
-		onSuccess: (data) => {
-			toast.success("Onboarding completed successfully!");
-			console.log("Onboarding completed successfully", data);
-			router.push(redirect);
-		},
-		onError: (error) => {
-			toast.error("Failed to complete onboarding", {
-				description: error.message,
-			});
-		},
-	});
+  // Mutations
+  const onboardingMutation = useOnboardingMutation({
+    onSuccess: (data) => {
+      toast.success("Onboarding completed successfully!");
+      console.log("Onboarding completed successfully", data);
+      router.push(redirect);
+    },
+    onError: (error) => {
+      toast.error("Failed to complete onboarding", {
+        description: error.message,
+      });
+    },
+  });
 
-	const handleComplete = () => {
-		if (
-			!settings.model_provider ||
-			!settings.llm_model ||
-			!settings.embedding_model
-		) {
-			toast.error("Please complete all required fields");
-			return;
-		}
+  const handleComplete = () => {
+    if (
+      !settings.model_provider ||
+      !settings.llm_model ||
+      !settings.embedding_model
+    ) {
+      toast.error("Please complete all required fields");
+      return;
+    }
 
-		// Prepare onboarding data
-		const onboardingData: OnboardingVariables = {
-			model_provider: settings.model_provider,
-			llm_model: settings.llm_model,
-			embedding_model: settings.embedding_model,
-			sample_data: sampleDataset,
-		};
+    // Prepare onboarding data
+    const onboardingData: OnboardingVariables = {
+      model_provider: settings.model_provider,
+      llm_model: settings.llm_model,
+      embedding_model: settings.embedding_model,
+      sample_data: sampleDataset,
+    };
 
-		// Add API key if available
-		if (settings.api_key) {
-			onboardingData.api_key = settings.api_key;
-		}
+    // Add API key if available
+    if (settings.api_key) {
+      onboardingData.api_key = settings.api_key;
+    }
 
-		// Add endpoint if available
-		if (settings.endpoint) {
-			onboardingData.endpoint = settings.endpoint;
-		}
+    // Add endpoint if available
+    if (settings.endpoint) {
+      onboardingData.endpoint = settings.endpoint;
+    }
 
-		// Add project_id if available
-		if (settings.project_id) {
-			onboardingData.project_id = settings.project_id;
-		}
+    // Add project_id if available
+    if (settings.project_id) {
+      onboardingData.project_id = settings.project_id;
+    }
 
-		onboardingMutation.mutate(onboardingData);
-	};
+    onboardingMutation.mutate(onboardingData);
+  };
 
-	const isComplete = !!settings.llm_model && !!settings.embedding_model;
+  const isComplete = !!settings.llm_model && !!settings.embedding_model;
 
-	return (
-		<div
-			className="min-h-dvh w-full flex gap-5 flex-col items-center justify-center bg-background p-4"
-			style={{
-				backgroundImage: "url('/images/background.png')",
-				backgroundSize: "cover",
-				backgroundPosition: "center",
-			}}
-		>
-			<div className="flex flex-col items-center gap-5 min-h-[550px] w-full">
-				<div className="flex flex-col items-center justify-center gap-4">
-					<h1 className="text-2xl font-medium font-chivo">
-						Configure your models
-					</h1>
-					<p className="text-sm text-muted-foreground">[description of task]</p>
-				</div>
-				<Card className="w-full max-w-[580px]">
-					<Tabs
-						defaultValue={modelProvider}
-						onValueChange={handleSetModelProvider}
-					>
-						<CardHeader>
-							<TabsList>
-								<TabsTrigger value="openai">
-									<OpenAILogo className="w-4 h-4" />
-									OpenAI
-								</TabsTrigger>
-								<TabsTrigger value="watsonx">
-									<IBMLogo className="w-4 h-4" />
-									IBM
-								</TabsTrigger>
-								<TabsTrigger value="ollama">
-									<OllamaLogo className="w-4 h-4" />
-									Ollama
-								</TabsTrigger>
-							</TabsList>
-						</CardHeader>
-						<CardContent>
-							<TabsContent value="openai">
-								<OpenAIOnboarding
-									setSettings={setSettings}
-									sampleDataset={sampleDataset}
-									setSampleDataset={setSampleDataset}
-								/>
-							</TabsContent>
-							<TabsContent value="watsonx">
-								<IBMOnboarding
-									setSettings={setSettings}
-									sampleDataset={sampleDataset}
-									setSampleDataset={setSampleDataset}
-								/>
-							</TabsContent>
-							<TabsContent value="ollama">
-								<OllamaOnboarding
-									setSettings={setSettings}
-									sampleDataset={sampleDataset}
-									setSampleDataset={setSampleDataset}
-								/>
-							</TabsContent>
-						</CardContent>
-					</Tabs>
-					<CardFooter className="flex justify-end">
-						<Tooltip>
-							<TooltipTrigger asChild>
-								<Button
-									size="sm"
-									onClick={handleComplete}
-									disabled={!isComplete}
-									loading={onboardingMutation.isPending}
-								>
-									Complete
-								</Button>
-							</TooltipTrigger>
-							<TooltipContent>
-								{!isComplete ? "Please fill in all required fields" : ""}
-							</TooltipContent>
-						</Tooltip>
-					</CardFooter>
-				</Card>
-			</div>
-		</div>
-	);
+  return (
+    <div className="min-h-dvh w-full flex gap-5 flex-col items-center justify-center bg-background relative p-4">
+      <DotPattern
+        width={24}
+        height={24}
+        cx={1}
+        cy={1}
+        cr={1}
+        className={cn(
+          "[mask-image:linear-gradient(to_bottom,white,transparent,transparent)]",
+          "text-input/70",
+        )}
+      />
+
+      <div className="flex flex-col items-center gap-5 min-h-[550px] w-full z-10">
+        <div className="flex flex-col items-center justify-center gap-4">
+          <h1 className="text-2xl font-medium font-chivo">
+            Connect a model provider
+          </h1>
+        </div>
+        <Card className="w-full max-w-[580px]">
+          <Tabs
+            defaultValue={modelProvider}
+            onValueChange={handleSetModelProvider}
+          >
+            <CardHeader>
+              <TabsList>
+                <TabsTrigger value="openai">
+                  <OpenAILogo className="w-4 h-4" />
+                  OpenAI
+                </TabsTrigger>
+                <TabsTrigger value="watsonx">
+                  <IBMLogo className="w-4 h-4" />
+                  IBM
+                </TabsTrigger>
+                <TabsTrigger value="ollama">
+                  <OllamaLogo className="w-4 h-4" />
+                  Ollama
+                </TabsTrigger>
+              </TabsList>
+            </CardHeader>
+            <CardContent>
+              <TabsContent value="openai">
+                <OpenAIOnboarding
+                  setSettings={setSettings}
+                  sampleDataset={sampleDataset}
+                  setSampleDataset={setSampleDataset}
+                />
+              </TabsContent>
+              <TabsContent value="watsonx">
+                <IBMOnboarding
+                  setSettings={setSettings}
+                  sampleDataset={sampleDataset}
+                  setSampleDataset={setSampleDataset}
+                />
+              </TabsContent>
+              <TabsContent value="ollama">
+                <OllamaOnboarding
+                  setSettings={setSettings}
+                  sampleDataset={sampleDataset}
+                  setSampleDataset={setSampleDataset}
+                />
+              </TabsContent>
+            </CardContent>
+          </Tabs>
+          <CardFooter className="flex justify-end">
+            <Tooltip>
+              <TooltipTrigger asChild>
+                <div>
+                  <Button
+                    size="sm"
+                    onClick={handleComplete}
+                    disabled={!isComplete}
+                    loading={onboardingMutation.isPending}
+                  >
+                    Complete
+                  </Button>
+                </div>
+              </TooltipTrigger>
+              {!isComplete && (
+                <TooltipContent>
+                  Please fill in all required fields
+                </TooltipContent>
+              )}
+            </Tooltip>
+          </CardFooter>
+        </Card>
+      </div>
+    </div>
+  );
 }
 
 export default function ProtectedOnboardingPage() {
-	return (
-		<ProtectedRoute>
-			<Suspense fallback={<div>Loading onboarding...</div>}>
-				<OnboardingPage />
-			</Suspense>
-		</ProtectedRoute>
-	);
+  return (
+    <ProtectedRoute>
+      <Suspense fallback={<div>Loading onboarding...</div>}>
+        <OnboardingPage />
+      </Suspense>
+    </ProtectedRoute>
+  );
 }
diff --git a/frontend/src/app/settings/page.tsx b/frontend/src/app/settings/page.tsx
index f49ff393..a63d91d3 100644
--- a/frontend/src/app/settings/page.tsx
+++ b/frontend/src/app/settings/page.tsx
@@ -1,6 +1,6 @@
 "use client";
 
-import { Loader2, PlugZap, RefreshCw } from "lucide-react";
+import { ArrowUpRight, Loader2, PlugZap, RefreshCw } from "lucide-react";
 import { useSearchParams } from "next/navigation";
 import { Suspense, useCallback, useEffect, useState } from "react";
 import { useUpdateFlowSettingMutation } from "@/app/api/mutations/useUpdateFlowSettingMutation";
@@ -35,10 +35,17 @@ 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 } from "@/lib/constants";
 import { getFallbackModels, type ModelProvider } from "./helpers/model-helpers";
 import { ModelSelectItems } from "./helpers/model-select-item";
+import { LabelWrapper } from "@/components/label-wrapper";
+import {
+  Tooltip,
+  TooltipContent,
+  TooltipTrigger,
+} from "@radix-ui/react-tooltip";
 
-const MAX_SYSTEM_PROMPT_CHARS = 2000;
+const { MAX_SYSTEM_PROMPT_CHARS } = UI_CONSTANTS;
 
 interface GoogleDriveFile {
   id: string;
@@ -122,7 +129,7 @@ function KnowledgeSourcesPage() {
     {
       enabled:
         (isAuthenticated || isNoAuthMode) && currentProvider === "openai",
-    },
+    }
   );
 
   const { data: ollamaModelsData } = useGetOllamaModelsQuery(
@@ -130,14 +137,14 @@ function KnowledgeSourcesPage() {
     {
       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
@@ -165,7 +172,7 @@ function KnowledgeSourcesPage() {
     (variables: Parameters<typeof updateFlowSettingMutation.mutate>[0]) => {
       updateFlowSettingMutation.mutate(variables);
     },
-    500,
+    500
   );
 
   // Sync system prompt state with settings data
@@ -293,7 +300,7 @@ function KnowledgeSourcesPage() {
           const data = await response.json();
           const connections = data.connections || [];
           const activeConnection = connections.find(
-            (conn: Connection) => conn.is_active,
+            (conn: Connection) => conn.is_active
           );
           const isConnected = activeConnection !== undefined;
 
@@ -305,8 +312,8 @@ function KnowledgeSourcesPage() {
                     status: isConnected ? "connected" : "not_connected",
                     connectionId: activeConnection?.connection_id,
                   }
-                : c,
-            ),
+                : c
+            )
           );
         }
       }
@@ -349,7 +356,7 @@ function KnowledgeSourcesPage() {
             `response_type=code&` +
             `scope=${result.oauth_config.scopes.join(" ")}&` +
             `redirect_uri=${encodeURIComponent(
-              result.oauth_config.redirect_uri,
+              result.oauth_config.redirect_uri
             )}&` +
             `access_type=offline&` +
             `prompt=consent&` +
@@ -498,7 +505,7 @@ function KnowledgeSourcesPage() {
 
   const handleEditInLangflow = (
     flowType: "chat" | "ingest",
-    closeDialog: () => void,
+    closeDialog: () => void
   ) => {
     // Select the appropriate flow ID and edit URL based on flow type
     const targetFlowId =
@@ -529,8 +536,17 @@ function KnowledgeSourcesPage() {
     fetch(`/api/reset-flow/retrieval`, {
       method: "POST",
     })
-      .then((response) => response.json())
+      .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) => {
@@ -543,8 +559,17 @@ function KnowledgeSourcesPage() {
     fetch(`/api/reset-flow/ingest`, {
       method: "POST",
     })
-      .then((response) => response.json())
+      .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);
+        setProcessingMode(DEFAULT_KNOWLEDGE_SETTINGS.processing_mode);
         closeDialog(); // Close after successful completion
       })
       .catch((error) => {
@@ -555,349 +580,6 @@ function KnowledgeSourcesPage() {
 
   return (
     <div className="space-y-8">
-      {/* Agent Behavior Section */}
-      <Card>
-        <CardHeader>
-          <div className="flex items-center justify-between">
-            <div>
-              <CardTitle className="text-lg">Agent</CardTitle>
-              <CardDescription>
-                Quick Agent settings. Edit in Langflow for full control.
-              </CardDescription>
-            </div>
-            <div className="flex gap-2">
-              <ConfirmationDialog
-                trigger={<Button variant="outline">Restore flow</Button>}
-                title="Restore default Agent 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 Agent flow in Langflow"
-                description="You're entering Langflow. You can edit the Agent flow and other underlying flows. Manual changes to components, wiring, or I/O can break this experience."
-                confirmText="Proceed"
-                onConfirm={(closeDialog) =>
-                  handleEditInLangflow("chat", closeDialog)
-                }
-              />
-            </div>
-          </div>
-        </CardHeader>
-        <CardContent>
-          <div className="space-y-6">
-            <div className="space-y-2">
-              <Label htmlFor="model-select" className="text-base font-medium">
-                Language Model
-              </Label>
-              <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>
-            </div>
-            <div className="space-y-2">
-              <Label htmlFor="system-prompt" className="text-base font-medium">
-                Agent Instructions
-              </Label>
-              <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>
-            </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">Knowledge</CardTitle>
-              <CardDescription>
-                Quick ingest options. Edit in Langflow for full control.
-              </CardDescription>
-            </div>
-            <div className="flex gap-2">
-              <ConfirmationDialog
-                trigger={<Button 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="You're entering Langflow. You can edit the Ingest flow and other underlying flows. Manual changes to components, wiring, or I/O can break this experience."
-                confirmText="Proceed"
-                onConfirm={(closeDialog) =>
-                  handleEditInLangflow("ingest", closeDialog)
-                }
-              />
-            </div>
-          </div>
-        </CardHeader>
-        <CardContent>
-          <div className="space-y-6">
-            <div className="space-y-2">
-              <Label
-                htmlFor="embedding-model-select"
-                className="text-base font-medium"
-              >
-                Embedding Model
-              </Label>
-              <Select
-                value={
-                  settings.knowledge?.embedding_model ||
-                  modelsData?.embedding_models?.find((m) => m.default)?.value ||
-                  "text-embedding-ada-002"
-                }
-                onValueChange={handleEmbeddingModelChange}
-              >
-                <SelectTrigger id="embedding-model-select">
-                  <SelectValue placeholder="Select an embedding model" />
-                </SelectTrigger>
-                <SelectContent>
-                  <ModelSelectItems
-                    models={modelsData?.embedding_models}
-                    fallbackModels={
-                      getFallbackModels(currentProvider).embedding
-                    }
-                    provider={currentProvider}
-                  />
-                </SelectContent>
-              </Select>
-            </div>
-            <div className="grid grid-cols-2 gap-4">
-              <div className="space-y-2">
-                <Label htmlFor="chunk-size" className="text-base font-medium">
-                  Chunk Size
-                </Label>
-                <div className="relative">
-                  <Input
-                    id="chunk-size"
-                    type="number"
-                    min="1"
-                    value={chunkSize}
-                    onChange={(e) => handleChunkSizeChange(e.target.value)}
-                    className="w-full pr-20"
-                  />
-                  <div className="absolute inset-y-0 right-0 flex items-center pr-8 pointer-events-none">
-                    <span className="text-sm text-muted-foreground">
-                      characters
-                    </span>
-                  </div>
-                </div>
-              </div>
-              <div className="space-y-2">
-                <Label
-                  htmlFor="chunk-overlap"
-                  className="text-base font-medium"
-                >
-                  Chunk Overlap
-                </Label>
-                <div className="relative">
-                  <Input
-                    id="chunk-overlap"
-                    type="number"
-                    min="0"
-                    value={chunkOverlap}
-                    onChange={(e) => handleChunkOverlapChange(e.target.value)}
-                    className="w-full pr-20"
-                  />
-                  <div className="absolute inset-y-0 right-0 flex items-center pr-8 pointer-events-none">
-                    <span className="text-sm text-muted-foreground">
-                      characters
-                    </span>
-                  </div>
-                </div>
-              </div>
-            </div>
-            <div className="space-y-3">
-              <Label className="text-base font-medium">Ingest Presets</Label>
-              <RadioGroup
-                value={processingMode}
-                onValueChange={handleProcessingModeChange}
-                className="space-y-3"
-              >
-                <div className="flex items-center space-x-3">
-                  <RadioGroupItem value="standard" id="standard" />
-                  <div className="flex-1">
-                    <Label
-                      htmlFor="standard"
-                      className="text-base font-medium cursor-pointer"
-                    >
-                      Standard
-                    </Label>
-                    <div className="text-sm text-muted-foreground">
-                      Fast ingest for text-based documents without images
-                    </div>
-                  </div>
-                </div>
-                <div className="flex items-center space-x-3">
-                  <RadioGroupItem value="ocr" id="ocr" />
-                  <div className="flex-1">
-                    <Label
-                      htmlFor="ocr"
-                      className="text-base font-medium cursor-pointer"
-                    >
-                      Extract text from images
-                    </Label>
-                    <div className="text-sm text-muted-foreground">
-                      Uses OCR to extract text from images/PDFs. Ingest is
-                      slower when enabled
-                    </div>
-                  </div>
-                </div>
-                <div className="flex items-center space-x-3">
-                  <RadioGroupItem
-                    value="picture_description"
-                    id="picture_description"
-                  />
-                  <div className="flex-1">
-                    <Label
-                      htmlFor="picture_description"
-                      className="text-base font-medium cursor-pointer"
-                    >
-                      Generate Description
-                    </Label>
-                    <div className="text-sm text-muted-foreground">
-                      Text extraction plust AI generated image descriptions
-                    </div>
-                  </div>
-                </div>
-                <div className="flex items-center space-x-3">
-                  <RadioGroupItem value="VLM" id="VLM" />
-                  <div className="flex-1">
-                    <Label
-                      htmlFor="VLM"
-                      className="text-base font-medium cursor-pointer"
-                    >
-                      AI Vision
-                    </Label>
-                    <div className="text-sm text-muted-foreground">
-                      Advanced processing with vision language models. Highest
-                      quality but most expensive
-                    </div>
-                  </div>
-                </div>
-              </RadioGroup>
-            </div>
-          </div>
-        </CardContent>
-      </Card>
-
       {/* Connectors Section */}
       <div className="space-y-6">
         <div>
@@ -1063,6 +745,398 @@ function KnowledgeSourcesPage() {
           ))}
         </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">
+              <Label htmlFor="system-prompt" className="text-base font-medium">
+                Agent Instructions
+              </Label>
+              <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>
+            </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</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>
+                        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">
+                <Label htmlFor="chunk-size" className="text-base font-medium">
+                  Chunk size
+                </Label>
+                <div className="relative">
+                  <Input
+                    id="chunk-size"
+                    type="number"
+                    min="1"
+                    value={chunkSize}
+                    onChange={(e) => handleChunkSizeChange(e.target.value)}
+                    className="w-full pr-20"
+                  />
+                  <div className="absolute inset-y-0 right-0 flex items-center pr-8 pointer-events-none">
+                    <span className="text-sm text-muted-foreground">
+                      characters
+                    </span>
+                  </div>
+                </div>
+              </div>
+              <div className="space-y-2">
+                <Label
+                  htmlFor="chunk-overlap"
+                  className="text-base font-medium"
+                >
+                  Chunk overlap
+                </Label>
+                <div className="relative">
+                  <Input
+                    id="chunk-overlap"
+                    type="number"
+                    min="0"
+                    value={chunkOverlap}
+                    onChange={(e) => handleChunkOverlapChange(e.target.value)}
+                    className="w-full pr-20"
+                  />
+                  <div className="absolute inset-y-0 right-0 flex items-center pr-8 pointer-events-none">
+                    <span className="text-sm text-muted-foreground">
+                      characters
+                    </span>
+                  </div>
+                </div>
+              </div>
+            </div>
+            <div className="space-y-3">
+              <Label className="text-base font-medium">Ingestion presets</Label>
+              <RadioGroup
+                value={processingMode}
+                onValueChange={handleProcessingModeChange}
+                className="space-y-3"
+              >
+                <div className="flex items-center space-x-3">
+                  <RadioGroupItem value="standard" id="standard" />
+                  <div className="flex-1">
+                    <Label
+                      htmlFor="standard"
+                      className="text-base font-medium cursor-pointer"
+                    >
+                      No OCR
+                    </Label>
+                    <div className="text-sm text-muted-foreground">
+                      Fast ingest for documents with selectable text. Images are
+                      ignored.
+                    </div>
+                  </div>
+                </div>
+                <div className="flex items-center space-x-3">
+                  <RadioGroupItem value="ocr" id="ocr" />
+                  <div className="flex-1">
+                    <Label
+                      htmlFor="ocr"
+                      className="text-base font-medium cursor-pointer"
+                    >
+                      OCR
+                    </Label>
+                    <div className="text-sm text-muted-foreground">
+                      Extracts text from images and scanned pages.
+                    </div>
+                  </div>
+                </div>
+                <div className="flex items-center space-x-3">
+                  <RadioGroupItem
+                    value="picture_description"
+                    id="picture_description"
+                  />
+                  <div className="flex-1">
+                    <Label
+                      htmlFor="picture_description"
+                      className="text-base font-medium cursor-pointer"
+                    >
+                      OCR + Captions
+                    </Label>
+                    <div className="text-sm text-muted-foreground">
+                      Extracts text from images and scanned pages. Generates
+                      short image captions.
+                    </div>
+                  </div>
+                </div>
+                <div className="flex items-center space-x-3">
+                  <RadioGroupItem value="VLM" id="VLM" />
+                  <div className="flex-1">
+                    <Label
+                      htmlFor="VLM"
+                      className="text-base font-medium cursor-pointer"
+                    >
+                      VLM
+                    </Label>
+                    <div className="text-sm text-muted-foreground">
+                      Extracts text from layout-aware parsing of text, tables,
+                      and sections.
+                    </div>
+                  </div>
+                </div>
+              </RadioGroup>
+            </div>
+          </div>
+        </CardContent>
+      </Card>
     </div>
   );
 }
diff --git a/frontend/src/components/confirmation-dialog.tsx b/frontend/src/components/confirmation-dialog.tsx
index c424e437..51d26335 100644
--- a/frontend/src/components/confirmation-dialog.tsx
+++ b/frontend/src/components/confirmation-dialog.tsx
@@ -1,6 +1,6 @@
-"use client"
+"use client";
 
-import { ReactNode, useState } from "react"
+import { ReactNode, useState } from "react";
 import {
   Dialog,
   DialogContent,
@@ -9,18 +9,19 @@ import {
   DialogHeader,
   DialogTitle,
   DialogTrigger,
-} from "@/components/ui/dialog"
-import { Button } from "@/components/ui/button"
+} from "@/components/ui/dialog";
+import { Button } from "@/components/ui/button";
 
 interface ConfirmationDialogProps {
-  trigger: ReactNode
-  title: string
-  description: string
-  confirmText?: string
-  cancelText?: string
-  onConfirm: (closeDialog: () => void) => void
-  onCancel?: () => void
-  variant?: "default" | "destructive"
+  trigger: ReactNode;
+  title: string;
+  description: ReactNode;
+  confirmText?: string;
+  cancelText?: string;
+  onConfirm: (closeDialog: () => void) => void;
+  onCancel?: () => void;
+  variant?: "default" | "destructive" | "warning";
+  confirmIcon?: ReactNode | null;
 }
 
 export function ConfirmationDialog({
@@ -31,25 +32,24 @@ export function ConfirmationDialog({
   cancelText = "Cancel",
   onConfirm,
   onCancel,
-  variant = "default"
+  variant = "default",
+  confirmIcon = null,
 }: ConfirmationDialogProps) {
-  const [open, setOpen] = useState(false)
+  const [open, setOpen] = useState(false);
 
   const handleConfirm = () => {
-    const closeDialog = () => setOpen(false)
-    onConfirm(closeDialog)
-  }
+    const closeDialog = () => setOpen(false);
+    onConfirm(closeDialog);
+  };
 
   const handleCancel = () => {
-    onCancel?.()
-    setOpen(false)
-  }
+    onCancel?.();
+    setOpen(false);
+  };
 
   return (
     <Dialog open={open} onOpenChange={setOpen}>
-      <DialogTrigger asChild>
-        {trigger}
-      </DialogTrigger>
+      <DialogTrigger asChild>{trigger}</DialogTrigger>
       <DialogContent>
         <DialogHeader>
           <DialogTitle className="mb-4">{title}</DialogTitle>
@@ -58,20 +58,15 @@ export function ConfirmationDialog({
           </DialogDescription>
         </DialogHeader>
         <DialogFooter>
-          <Button
-            variant="outline"
-            onClick={handleCancel}
-          >
+          <Button variant="ghost" onClick={handleCancel} size="sm">
             {cancelText}
           </Button>
-          <Button
-            variant={variant}
-            onClick={handleConfirm}
-          >
+          <Button variant={variant} onClick={handleConfirm} size="sm">
             {confirmText}
+            {confirmIcon}
           </Button>
         </DialogFooter>
       </DialogContent>
     </Dialog>
-  )
-}
\ No newline at end of file
+  );
+}
diff --git a/frontend/src/components/layout-wrapper.tsx b/frontend/src/components/layout-wrapper.tsx
index 9be42730..5c2f0e13 100644
--- a/frontend/src/components/layout-wrapper.tsx
+++ b/frontend/src/components/layout-wrapper.tsx
@@ -2,28 +2,48 @@
 
 import { Bell, Loader2 } from "lucide-react";
 import { usePathname } from "next/navigation";
+import { useGetConversationsQuery, type ChatConversation } from "@/app/api/queries/useGetConversationsQuery";
 import { useGetSettingsQuery } from "@/app/api/queries/useGetSettingsQuery";
 import { KnowledgeFilterPanel } from "@/components/knowledge-filter-panel";
+import Logo from "@/components/logo/logo";
 import { Navigation } from "@/components/navigation";
 import { TaskNotificationMenu } from "@/components/task-notification-menu";
 import { Button } from "@/components/ui/button";
 import { UserNav } from "@/components/user-nav";
 import { useAuth } from "@/contexts/auth-context";
+import { useChat } from "@/contexts/chat-context";
 import { useKnowledgeFilter } from "@/contexts/knowledge-filter-context";
 // import { GitHubStarButton } from "@/components/github-star-button"
 // import { DiscordLink } from "@/components/discord-link"
 import { useTask } from "@/contexts/task-context";
-import Logo from "@/components/logo/logo";
 
 export function LayoutWrapper({ children }: { children: React.ReactNode }) {
   const pathname = usePathname();
   const { tasks, isMenuOpen, toggleMenu } = useTask();
   const { isPanelOpen } = useKnowledgeFilter();
   const { isLoading, isAuthenticated, isNoAuthMode } = useAuth();
+  const {
+    endpoint,
+    refreshTrigger,
+    refreshConversations,
+    startNewConversation,
+  } = useChat();
   const { isLoading: isSettingsLoading, data: settings } = useGetSettingsQuery({
     enabled: isAuthenticated || isNoAuthMode,
   });
 
+  // Only fetch conversations on chat page
+  const isOnChatPage = pathname === "/" || pathname === "/chat";
+  const { data: conversations = [], isLoading: isConversationsLoading } =
+    useGetConversationsQuery(endpoint, refreshTrigger, {
+      enabled: isOnChatPage && (isAuthenticated || isNoAuthMode),
+    }) as { data: ChatConversation[]; isLoading: boolean };
+
+  const handleNewConversation = () => {
+    refreshConversations();
+    startNewConversation();
+  };
+
   // List of paths that should not show navigation
   const authPaths = ["/login", "/auth/callback", "/onboarding"];
   const isAuthPage = authPaths.includes(pathname);
@@ -33,7 +53,7 @@ export function LayoutWrapper({ children }: { children: React.ReactNode }) {
     (task) =>
       task.status === "pending" ||
       task.status === "running" ||
-      task.status === "processing"
+      task.status === "processing",
   );
 
   // Show loading state when backend isn't ready
@@ -99,7 +119,11 @@ export function LayoutWrapper({ children }: { children: React.ReactNode }) {
         </div>
       </header>
       <div className="side-bar-arrangement bg-background fixed left-0 top-[53px] bottom-0 md:flex hidden">
-        <Navigation />
+        <Navigation
+          conversations={conversations}
+          isConversationsLoading={isConversationsLoading}
+          onNewConversation={handleNewConversation}
+        />
       </div>
       <main
         className={`md:pl-72 transition-all duration-300 overflow-y-auto h-[calc(100vh-53px)] ${
diff --git a/frontend/src/lib/constants.ts b/frontend/src/lib/constants.ts
new file mode 100644
index 00000000..9c6ea7b0
--- /dev/null
+++ b/frontend/src/lib/constants.ts
@@ -0,0 +1,23 @@
+/**
+ * Default agent settings
+ */
+export const DEFAULT_AGENT_SETTINGS = {
+  llm_model: "gpt-4o-mini",
+  system_prompt: "You are a helpful assistant that can use tools to answer questions and perform tasks."
+} as const;
+
+/**
+ * Default knowledge/ingest settings
+ */
+export const DEFAULT_KNOWLEDGE_SETTINGS = {
+  chunk_size: 1000,
+  chunk_overlap: 200,
+  processing_mode: "standard"
+} as const;
+
+/**
+ * UI Constants
+ */
+export const UI_CONSTANTS = {
+  MAX_SYSTEM_PROMPT_CHARS: 2000,
+} as const;
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index cc4db78a..5673adcd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "openrag"
-version = "0.1.13"
+version = "0.1.14.dev1"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
diff --git a/src/agent.py b/src/agent.py
index 1eb362bd..eceb2ac4 100644
--- a/src/agent.py
+++ b/src/agent.py
@@ -636,6 +636,34 @@ async def async_langflow_chat_stream(
             logger.debug(
                 f"Stored langflow conversation thread for user {user_id} with response_id: {response_id}"
             )
-            logger.debug(
-                f"Stored langflow conversation thread for user {user_id} with response_id: {response_id}"
-            )
+
+
+def delete_user_conversation(user_id: str, response_id: str) -> bool:
+    """Delete a conversation for a user from both memory and persistent storage"""
+    deleted = False
+
+    try:
+        # Delete from in-memory storage
+        if user_id in active_conversations and response_id in active_conversations[user_id]:
+            del active_conversations[user_id][response_id]
+            logger.debug(f"Deleted conversation {response_id} from memory for user {user_id}")
+            deleted = True
+
+        # Delete from persistent storage
+        conversation_deleted = conversation_persistence.delete_conversation_thread(user_id, response_id)
+        if conversation_deleted:
+            logger.debug(f"Deleted conversation {response_id} from persistent storage for user {user_id}")
+            deleted = True
+
+        # Release session ownership
+        try:
+            from services.session_ownership_service import session_ownership_service
+            session_ownership_service.release_session(user_id, response_id)
+            logger.debug(f"Released session ownership for {response_id} for user {user_id}")
+        except Exception as e:
+            logger.warning(f"Failed to release session ownership: {e}")
+
+        return deleted
+    except Exception as e:
+        logger.error(f"Error deleting conversation {response_id} for user {user_id}: {e}")
+        return False
diff --git a/src/api/chat.py b/src/api/chat.py
index b9dea5ef..58492118 100644
--- a/src/api/chat.py
+++ b/src/api/chat.py
@@ -155,3 +155,27 @@ async def langflow_history_endpoint(request: Request, chat_service, session_mana
         return JSONResponse(
             {"error": f"Failed to get langflow history: {str(e)}"}, status_code=500
         )
+
+
+async def delete_session_endpoint(request: Request, chat_service, session_manager):
+    """Delete a chat session"""
+    user = request.state.user
+    user_id = user.user_id
+    session_id = request.path_params["session_id"]
+
+    try:
+        # Delete from both local storage and Langflow
+        result = await chat_service.delete_session(user_id, session_id)
+
+        if result.get("success"):
+            return JSONResponse({"message": "Session deleted successfully"})
+        else:
+            return JSONResponse(
+                {"error": result.get("error", "Failed to delete session")},
+                status_code=500
+            )
+    except Exception as e:
+        logger.error(f"Error deleting session: {e}")
+        return JSONResponse(
+            {"error": f"Failed to delete session: {str(e)}"}, status_code=500
+        )
diff --git a/src/api/settings.py b/src/api/settings.py
index 560eb400..3e242c4b 100644
--- a/src/api/settings.py
+++ b/src/api/settings.py
@@ -47,9 +47,6 @@ def get_docling_preset_configs():
     }
 
 
-
-
-
 async def get_settings(request, session_manager):
     """Get application settings"""
     try:
@@ -182,6 +179,7 @@ async def update_settings(request, session_manager):
             "chunk_size",
             "chunk_overlap",
             "doclingPresets",
+            "embedding_model",
         }
 
         # Check for invalid fields
@@ -202,11 +200,61 @@ async def update_settings(request, session_manager):
             current_config.agent.llm_model = body["llm_model"]
             config_updated = True
 
+            # 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"])
+                logger.info(
+                    f"Successfully updated chat flow model to '{body['llm_model']}'"
+                )
+            except Exception as e:
+                logger.error(f"Failed to update chat flow model: {str(e)}")
+                # Don't fail the entire settings update if flow update fails
+                # The config will still be saved
+
         if "system_prompt" in body:
             current_config.agent.system_prompt = body["system_prompt"]
             config_updated = True
 
+            # Also update the chat flow with the new system prompt
+            try:
+                flows_service = _get_flows_service()
+                await flows_service.update_chat_flow_system_prompt(
+                    body["system_prompt"]
+                )
+                logger.info(f"Successfully updated chat flow system prompt")
+            except Exception as e:
+                logger.error(f"Failed to update chat flow system prompt: {str(e)}")
+                # Don't fail the entire settings update if flow update fails
+                # The config will still be saved
+
         # Update knowledge settings
+        if "embedding_model" in body:
+            if (
+                not isinstance(body["embedding_model"], str)
+                or not body["embedding_model"].strip()
+            ):
+                return JSONResponse(
+                    {"error": "embedding_model must be a non-empty string"},
+                    status_code=400,
+                )
+            current_config.knowledge.embedding_model = body["embedding_model"].strip()
+            config_updated = True
+
+            # Also update the ingest flow with the new embedding model
+            try:
+                flows_service = _get_flows_service()
+                await flows_service.update_ingest_flow_embedding_model(
+                    body["embedding_model"].strip()
+                )
+                logger.info(
+                    f"Successfully updated ingest flow embedding model to '{body['embedding_model'].strip()}'"
+                )
+            except Exception as e:
+                logger.error(f"Failed to update ingest flow embedding model: {str(e)}")
+                # Don't fail the entire settings update if flow update fails
+                # The config will still be saved
+
         if "doclingPresets" in body:
             preset_configs = get_docling_preset_configs()
             valid_presets = list(preset_configs.keys())
@@ -222,8 +270,13 @@ async def update_settings(request, session_manager):
 
             # Also update the flow with the new docling preset
             try:
-                await _update_flow_docling_preset(body["doclingPresets"], preset_configs[body["doclingPresets"]])
-                logger.info(f"Successfully updated docling preset in flow to '{body['doclingPresets']}'")
+                flows_service = _get_flows_service()
+                await flows_service.update_flow_docling_preset(
+                    body["doclingPresets"], preset_configs[body["doclingPresets"]]
+                )
+                logger.info(
+                    f"Successfully updated docling preset in flow to '{body['doclingPresets']}'"
+                )
             except Exception as e:
                 logger.error(f"Failed to update docling preset in flow: {str(e)}")
                 # Don't fail the entire settings update if flow update fails
@@ -237,6 +290,18 @@ async def update_settings(request, session_manager):
             current_config.knowledge.chunk_size = body["chunk_size"]
             config_updated = True
 
+            # Also update the ingest flow with the new chunk size
+            try:
+                flows_service = _get_flows_service()
+                await flows_service.update_ingest_flow_chunk_size(body["chunk_size"])
+                logger.info(
+                    f"Successfully updated ingest flow chunk size to {body['chunk_size']}"
+                )
+            except Exception as e:
+                logger.error(f"Failed to update ingest flow chunk size: {str(e)}")
+                # Don't fail the entire settings update if flow update fails
+                # The config will still be saved
+
         if "chunk_overlap" in body:
             if not isinstance(body["chunk_overlap"], int) or body["chunk_overlap"] < 0:
                 return JSONResponse(
@@ -246,6 +311,20 @@ async def update_settings(request, session_manager):
             current_config.knowledge.chunk_overlap = body["chunk_overlap"]
             config_updated = True
 
+            # Also update the ingest flow with the new chunk overlap
+            try:
+                flows_service = _get_flows_service()
+                await flows_service.update_ingest_flow_chunk_overlap(
+                    body["chunk_overlap"]
+                )
+                logger.info(
+                    f"Successfully updated ingest flow chunk overlap to {body['chunk_overlap']}"
+                )
+            except Exception as e:
+                logger.error(f"Failed to update ingest flow chunk overlap: {str(e)}")
+                # Don't fail the entire settings update if flow update fails
+                # The config will still be saved
+
         if not config_updated:
             return JSONResponse(
                 {"error": "No valid fields provided for update"}, status_code=400
@@ -524,48 +603,11 @@ async def onboarding(request, flows_service):
         )
 
 
-async def _update_flow_docling_preset(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")
+def _get_flows_service():
+    """Helper function to get flows service instance"""
+    from services.flows_service import FlowsService
 
-    # Get the current flow data from Langflow
-    response = await clients.langflow_request(
-        "GET", f"/api/v1/flows/{LANGFLOW_INGEST_FLOW_ID}"
-    )
-
-    if response.status_code != 200:
-        raise Exception(f"Failed to get ingest flow: HTTP {response.status_code} - {response.text}")
-
-    flow_data = response.json()
-
-    # Find the target node in the flow using environment variable
-    nodes = flow_data.get("data", {}).get("nodes", [])
-    target_node = None
-    target_node_index = None
-
-    for i, node in enumerate(nodes):
-        if node.get("id") == DOCLING_COMPONENT_ID:
-            target_node = node
-            target_node_index = i
-            break
-
-    if target_node is None:
-        raise Exception(f"Docling component '{DOCLING_COMPONENT_ID}' not found in ingest flow")
-
-    # Update the docling_serve_opts value directly in the existing node
-    if (target_node.get("data", {}).get("node", {}).get("template", {}).get("docling_serve_opts")):
-        flow_data["data"]["nodes"][target_node_index]["data"]["node"]["template"]["docling_serve_opts"]["value"] = preset_config
-    else:
-        raise Exception(f"docling_serve_opts field not found in node '{DOCLING_COMPONENT_ID}'")
-
-    # Update the flow via PATCH request
-    patch_response = await clients.langflow_request(
-        "PATCH", f"/api/v1/flows/{LANGFLOW_INGEST_FLOW_ID}", json=flow_data
-    )
-
-    if patch_response.status_code != 200:
-        raise Exception(f"Failed to update ingest flow: HTTP {patch_response.status_code} - {patch_response.text}")
+    return FlowsService()
 
 
 async def update_docling_preset(request, session_manager):
@@ -577,8 +619,7 @@ async def update_docling_preset(request, session_manager):
         # Validate preset parameter
         if "preset" not in body:
             return JSONResponse(
-                {"error": "preset parameter is required"},
-                status_code=400
+                {"error": "preset parameter is required"}, status_code=400
             )
 
         preset = body["preset"]
@@ -587,28 +628,31 @@ async def update_docling_preset(request, session_manager):
         if preset not in preset_configs:
             valid_presets = list(preset_configs.keys())
             return JSONResponse(
-                {"error": f"Invalid preset '{preset}'. Valid presets: {', '.join(valid_presets)}"},
-                status_code=400
+                {
+                    "error": f"Invalid preset '{preset}'. Valid presets: {', '.join(valid_presets)}"
+                },
+                status_code=400,
             )
 
         # Get the preset configuration
         preset_config = preset_configs[preset]
 
         # Use the helper function to update the flow
-        await _update_flow_docling_preset(preset, preset_config)
+        flows_service = _get_flows_service()
+        await flows_service.update_flow_docling_preset(preset, preset_config)
 
         logger.info(f"Successfully updated docling preset to '{preset}' in ingest flow")
 
-        return JSONResponse({
-            "message": f"Successfully updated docling preset to '{preset}'",
-            "preset": preset,
-            "preset_config": preset_config
-        })
+        return JSONResponse(
+            {
+                "message": f"Successfully updated docling preset to '{preset}'",
+                "preset": preset,
+                "preset_config": preset_config,
+            }
+        )
 
     except Exception as e:
         logger.error("Failed to update docling preset", error=str(e))
         return JSONResponse(
-            {"error": f"Failed to update docling preset: {str(e)}"},
-            status_code=500
+            {"error": f"Failed to update docling preset: {str(e)}"}, status_code=500
         )
-
diff --git a/src/services/chat_service.py b/src/services/chat_service.py
index 5ffe30f9..32536f4b 100644
--- a/src/services/chat_service.py
+++ b/src/services/chat_service.py
@@ -484,3 +484,55 @@ class ChatService:
             "total_conversations": len(all_conversations),
         }
 
+    async def delete_session(self, user_id: str, session_id: str):
+        """Delete a session from both local storage and Langflow"""
+        try:
+            # Delete from local conversation storage
+            from agent import delete_user_conversation
+            local_deleted = delete_user_conversation(user_id, session_id)
+
+            # Delete from Langflow using the monitor API
+            langflow_deleted = await self._delete_langflow_session(session_id)
+
+            success = local_deleted or langflow_deleted
+            error_msg = None
+
+            if not success:
+                error_msg = "Session not found in local storage or Langflow"
+
+            return {
+                "success": success,
+                "local_deleted": local_deleted,
+                "langflow_deleted": langflow_deleted,
+                "error": error_msg
+            }
+
+        except Exception as e:
+            logger.error(f"Error deleting session {session_id} for user {user_id}: {e}")
+            return {
+                "success": False,
+                "error": str(e)
+            }
+
+    async def _delete_langflow_session(self, session_id: str):
+        """Delete a session from Langflow using the monitor API"""
+        try:
+            response = await clients.langflow_request(
+                "DELETE",
+                f"/api/v1/monitor/messages/session/{session_id}"
+            )
+
+            if response.status_code == 200 or response.status_code == 204:
+                logger.info(f"Successfully deleted session {session_id} from Langflow")
+                return True
+            else:
+                logger.warning(
+                    f"Failed to delete session {session_id} from Langflow: "
+                    f"{response.status_code} - {response.text}"
+                )
+                return False
+
+        except Exception as e:
+            logger.error(f"Error deleting session {session_id} from Langflow: {e}")
+            return False
+
diff --git a/src/services/conversation_persistence_service.py b/src/services/conversation_persistence_service.py
index fa5717c1..c6b62c24 100644
--- a/src/services/conversation_persistence_service.py
+++ b/src/services/conversation_persistence_service.py
@@ -86,12 +86,14 @@ class ConversationPersistenceService:
         user_conversations = self.get_user_conversations(user_id)
         return user_conversations.get(response_id, {})
     
-    def delete_conversation_thread(self, user_id: str, response_id: str):
+    def delete_conversation_thread(self, user_id: str, response_id: str) -> bool:
         """Delete a specific conversation thread"""
         if user_id in self._conversations and response_id in self._conversations[user_id]:
             del self._conversations[user_id][response_id]
             self._save_conversations()
             logger.debug(f"Deleted conversation {response_id} for user {user_id}")
+            return True
+        return False
     
     def clear_user_conversations(self, user_id: str):
         """Clear all conversations for a user"""
diff --git a/src/services/flows_service.py b/src/services/flows_service.py
index 4c3872ca..0d7a7bc8 100644
--- a/src/services/flows_service.py
+++ b/src/services/flows_service.py
@@ -400,6 +400,123 @@ class FlowsService:
                 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.
+        Returns tuple of (node, node_index) or (None, None) if not found.
+        """
+        nodes = flow_data.get("data", {}).get("nodes", [])
+
+        for i, node in enumerate(nodes):
+            node_data = node.get("data", {})
+            node_template = node_data.get("node", {})
+
+            # Check by ID if provided
+            if node_id and node_data.get("id") == node_id:
+                return node, i
+
+            # Check by display_name if provided
+            if display_name and node_template.get("display_name") == display_name:
+                return node, i
+
+        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):
+        """
+        Generic helper function to update any field in any Langflow component.
+
+        Args:
+            flow_id: The ID of the flow to update
+            field_name: The name of the field to update (e.g., 'model_name', 'system_message', 'docling_serve_opts')
+            field_value: The new value to set
+            node_display_name: The display name to search for (optional)
+            node_id: The node ID to search for (optional, used as fallback or primary)
+        """
+        if not flow_id:
+            raise ValueError("flow_id is required")
+
+        # Get the current flow data from Langflow
+        response = await clients.langflow_request(
+            "GET", f"/api/v1/flows/{flow_id}"
+        )
+
+        if response.status_code != 200:
+            raise Exception(f"Failed to get flow: HTTP {response.status_code} - {response.text}")
+
+        flow_data = response.json()
+
+        # Find the target component by display name first, then by ID as fallback
+        target_node, target_node_index = None, None
+        if node_display_name:
+            target_node, target_node_index = self._find_node_in_flow(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
+            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
+        else:
+            identifier = node_display_name or node_id
+            raise Exception(f"{field_name} field not found in {identifier} component")
+
+        # Update the flow via PATCH request
+        patch_response = await clients.langflow_request(
+            "PATCH", f"/api/v1/flows/{flow_id}", json=flow_data
+        )
+
+        if patch_response.status_code != 200:
+            raise Exception(f"Failed to update flow: HTTP {patch_response.status_code} - {patch_response.text}")
+
+    async def update_chat_flow_model(self, model_name: 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):
+        """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")
+
+    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)
+
+    async def update_ingest_flow_chunk_size(self, chunk_size: int):
+        """Helper function to update chunk size 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, "chunk_size", chunk_size,
+                                node_display_name="Split Text")
+
+    async def update_ingest_flow_chunk_overlap(self, chunk_overlap: int):
+        """Helper function to update chunk overlap 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, "chunk_overlap", chunk_overlap,
+                                node_display_name="Split Text")
+
+    async def update_ingest_flow_embedding_model(self, embedding_model: 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):
         """Replace a node in the flow data"""
         nodes = flow_data.get("data", {}).get("nodes", [])
diff --git a/src/services/session_ownership_service.py b/src/services/session_ownership_service.py
index 220a6d96..d700c5c3 100644
--- a/src/services/session_ownership_service.py
+++ b/src/services/session_ownership_service.py
@@ -74,6 +74,20 @@ class SessionOwnershipService:
         """Filter a list of sessions to only include those owned by the user"""
         user_sessions = self.get_user_sessions(user_id)
         return [session for session in session_ids if session in user_sessions]
+
+    def release_session(self, user_id: str, session_id: str) -> bool:
+        """Release a session from a user (delete ownership record)"""
+        if session_id in self.ownership_data:
+            # Verify the user owns this session before deleting
+            if self.ownership_data[session_id].get("user_id") == user_id:
+                del self.ownership_data[session_id]
+                self._save_ownership_data()
+                logger.debug(f"Released session {session_id} from user {user_id}")
+                return True
+            else:
+                logger.warning(f"User {user_id} tried to release session {session_id} they don't own")
+                return False
+        return False
     
     def get_ownership_stats(self) -> Dict[str, any]:
         """Get statistics about session ownership"""