Add workflow to update uv.lock on version bump

Introduces a GitHub Actions workflow that automatically updates and commits the uv.lock file when pyproject.toml changes on the main branch or via manual dispatch. This helps ensure dependency lock file stays in sync with project version updates.

.env.example

@@ -2,14 +2,6 @@
 # Set to true to disable Langflow ingestion and use traditional OpenRAG processor
 # If unset or false, Langflow pipeline will be used (default: upload -> ingest -> delete)
 DISABLE_INGEST_WITH_LANGFLOW=false
-
-# Langflow HTTP timeout configuration (in seconds)
-# For large documents (300+ pages), ingestion can take 30+ minutes
-# Increase these values if you experience timeouts with very large PDFs
-# Default: 2400 seconds (40 minutes) total timeout, 30 seconds connection timeout
-# LANGFLOW_TIMEOUT=2400
-# LANGFLOW_CONNECT_TIMEOUT=30
-
 # make one like so https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key
 LANGFLOW_SECRET_KEY=
 
@@ -40,36 +32,17 @@ GOOGLE_OAUTH_CLIENT_SECRET=
 MICROSOFT_GRAPH_OAUTH_CLIENT_ID=
 MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET=
 
-# AWS Access Key ID and Secret Access Key with access to your S3 instance
-AWS_ACCESS_KEY_ID=
-AWS_SECRET_ACCESS_KEY=
-
 # OPTIONAL: dns routable from google (etc.) to handle continous ingest (something like ngrok works). This enables continous ingestion
 WEBHOOK_BASE_URL=
 
-# Model Provider API Keys
 OPENAI_API_KEY=
-ANTHROPIC_API_KEY=
-OLLAMA_ENDPOINT=
-WATSONX_API_KEY=
-WATSONX_ENDPOINT=
-WATSONX_PROJECT_ID=
 
-# LLM Provider configuration. Providers can be "anthropic", "watsonx", "ibm" or "ollama".
-LLM_PROVIDER=
-LLM_MODEL=
-
-# Embedding provider configuration. Providers can be "watsonx", "ibm" or "ollama".
-EMBEDDING_PROVIDER=
-EMBEDDING_MODEL=
+AWS_ACCESS_KEY_ID=
+AWS_SECRET_ACCESS_KEY=
 
 # OPTIONAL url for openrag link to langflow in the UI
 LANGFLOW_PUBLIC_URL=
 
-# OPTIONAL: Override the full docling-serve URL (e.g., for remote instances)
-# If not set, auto-detects host and uses port 5001
-# DOCLING_SERVE_URL=http://my-docling-server:5001
-
 # OPTIONAL: Override host for docling service (for special networking setups)
 # HOST_DOCKER_INTERNAL=host.containers.internal
 
@@ -79,10 +52,3 @@ LANGFLOW_SUPERUSER=
 LANGFLOW_SUPERUSER_PASSWORD=
 LANGFLOW_NEW_USER_IS_ACTIVE=False
 LANGFLOW_ENABLE_SUPERUSER_CLI=False
-
-# Langfuse tracing (optional)
-# Get keys from https://cloud.langfuse.com or your self-hosted instance
-LANGFUSE_SECRET_KEY=
-LANGFUSE_PUBLIC_KEY=
-# Leave empty for Langfuse Cloud, or set for self-hosted (e.g., http://localhost:3002)
-LANGFUSE_HOST=

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,155 +0,0 @@
-name: Bug Report
-description: Report a bug or unexpected behavior in OpenRAG
-title: "[Bug]: "
-labels: ["bug"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to report a bug! Please fill out the form below to help us understand and fix the issue.
-
-  - type: input
-    id: openrag-version
-    attributes:
-      label: OpenRAG Version
-      description: What version of OpenRAG are you using? Run `openrag --version` or check your package version.
-      placeholder: "e.g., 0.1.0"
-    validations:
-      required: true
-
-  - type: dropdown
-    id: deployment-method
-    attributes:
-      label: Deployment Method
-      description: How are you running OpenRAG?
-      options:
-        - uvx (uvx openrag)
-        - uv add (installed in project)
-        - Docker
-        - Podman
-        - Local development (make dev)
-        - Other
-    validations:
-      required: true
-
-  - type: input
-    id: os
-    attributes:
-      label: Operating System
-      description: What operating system are you using?
-      placeholder: "e.g., macOS 14.0, Ubuntu 22.04, Windows 11"
-    validations:
-      required: true
-
-  - type: input
-    id: python-version
-    attributes:
-      label: Python Version
-      description: What Python version are you using? Run `python --version` to check.
-      placeholder: "e.g., 3.13.0"
-    validations:
-      required: false
-
-  - type: dropdown
-    id: affected-area
-    attributes:
-      label: Affected Area
-      description: Which area(s) of OpenRAG does this bug affect? Select all that apply.
-      multiple: true
-      options:
-        - Ingestion (document processing, upload, Docling)
-        - Retrieval (search, OpenSearch, hybrid search)
-        - Chat (chat interface, conversations, AI responses)
-        - Knowledge Filters (partitions, document filtering)
-        - Settings (configuration, model providers)
-        - TUI (Terminal User Interface)
-        - Connectors (Google Drive, OneDrive, SharePoint)
-        - Frontend (Next.js UI, components)
-        - Backend/API (Python/Starlette)
-        - Infrastructure (Docker, OpenSearch, Langflow)
-        - SDK (Python or TypeScript SDK)
-        - Onboarding (setup wizard, initial configuration)
-        - Authentication (OIDC, API keys)
-        - Other
-    validations:
-      required: true
-
-  - type: textarea
-    id: bug-description
-    attributes:
-      label: Bug Description
-      description: A clear and concise description of what the bug is.
-      placeholder: Describe the bug...
-    validations:
-      required: true
-
-  - type: textarea
-    id: steps-to-reproduce
-    attributes:
-      label: Steps to Reproduce
-      description: Steps to reproduce the behavior.
-      placeholder: |
-        1. Go to '...'
-        2. Click on '...'
-        3. Scroll down to '...'
-        4. See error
-    validations:
-      required: true
-
-  - type: textarea
-    id: expected-behavior
-    attributes:
-      label: Expected Behavior
-      description: A clear and concise description of what you expected to happen.
-      placeholder: What should have happened?
-    validations:
-      required: true
-
-  - type: textarea
-    id: actual-behavior
-    attributes:
-      label: Actual Behavior
-      description: A clear and concise description of what actually happened.
-      placeholder: What actually happened?
-    validations:
-      required: true
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant Logs
-      description: |
-        Please copy and paste any relevant log output. 
-        You can get logs using `make logs` for Docker deployments or check the terminal output.
-        This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: false
-
-  - type: textarea
-    id: screenshots
-    attributes:
-      label: Screenshots
-      description: If applicable, add screenshots to help explain your problem.
-    validations:
-      required: false
-
-  - type: textarea
-    id: additional-context
-    attributes:
-      label: Additional Context
-      description: Add any other context about the problem here (e.g., browser version, specific document types, model provider being used).
-    validations:
-      required: false
-
-  - type: checkboxes
-    id: checklist
-    attributes:
-      label: Checklist
-      description: Please confirm the following before submitting.
-      options:
-        - label: I have searched existing issues to ensure this bug hasn't been reported before.
-          required: true
-        - label: I have provided all the requested information.
-          required: true
-

.github/ISSUE_TEMPLATE/config.yml

@@ -1,15 +0,0 @@
-blank_issues_enabled: false
-contact_links:
-  - name: OpenRAG Documentation
-    url: https://docs.openr.ag/
-    about: Learn more about OpenRAG's features, installation, and configuration.
-  - name: Troubleshooting Guide
-    url: https://docs.openr.ag/support/troubleshoot
-    about: Check the troubleshooting guide for common issues and solutions.
-  - name: GitHub Discussions
-    url: https://github.com/langflow-ai/openrag/discussions
-    about: Ask questions and discuss ideas with the community.
-  - name: Contributing Guide
-    url: https://github.com/langflow-ai/openrag/blob/main/CONTRIBUTING.md
-    about: Learn how to contribute to OpenRAG development.
-

.github/ISSUE_TEMPLATE/documentation.yml

@@ -1,106 +0,0 @@
-name: Documentation Issue
-description: Report an issue with documentation or request new documentation
-title: "[Docs]: "
-labels: ["documentation"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for helping improve OpenRAG's documentation! Please provide details about the issue or your request.
-
-  - type: dropdown
-    id: issue-type
-    attributes:
-      label: Issue Type
-      description: What type of documentation issue is this?
-      options:
-        - Incorrect information
-        - Missing documentation
-        - Outdated content
-        - Unclear or confusing
-        - Typo or grammatical error
-        - Broken links
-        - Request for new documentation
-        - Other
-    validations:
-      required: true
-
-  - type: dropdown
-    id: doc-area
-    attributes:
-      label: Documentation Area
-      description: Which area of documentation does this relate to?
-      multiple: true
-      options:
-        - Getting Started / Quickstart
-        - Installation (uvx, Docker, Podman)
-        - Configuration / Settings
-        - Ingestion & Document Processing
-        - Search & Retrieval
-        - Chat Interface
-        - Knowledge Filters
-        - Connectors (Google Drive, OneDrive, SharePoint)
-        - TUI (Terminal User Interface)
-        - API Reference
-        - SDK Documentation (Python/TypeScript)
-        - Troubleshooting
-        - Contributing Guide
-        - Other
-    validations:
-      required: true
-
-  - type: input
-    id: doc-url
-    attributes:
-      label: Documentation URL
-      description: If applicable, provide a link to the specific documentation page.
-      placeholder: "https://docs.openr.ag/..."
-    validations:
-      required: false
-
-  - type: textarea
-    id: current-content
-    attributes:
-      label: Current Content
-      description: If reporting an issue, what does the documentation currently say?
-      placeholder: Quote or describe the current documentation content.
-    validations:
-      required: false
-
-  - type: textarea
-    id: issue-description
-    attributes:
-      label: Issue Description
-      description: Describe the problem or what documentation you'd like to see added.
-      placeholder: |
-        For issues: Explain what's wrong or confusing about the current documentation.
-        For requests: Describe what topic you'd like documented and why it would be helpful.
-    validations:
-      required: true
-
-  - type: textarea
-    id: suggested-content
-    attributes:
-      label: Suggested Content
-      description: If you have suggestions for how to fix or improve the documentation, please share them.
-      placeholder: Provide suggested text, corrections, or an outline for new documentation.
-    validations:
-      required: false
-
-  - type: textarea
-    id: additional-context
-    attributes:
-      label: Additional Context
-      description: Add any other context, screenshots, or examples here.
-    validations:
-      required: false
-
-  - type: checkboxes
-    id: contribution
-    attributes:
-      label: Contribution
-      description: Would you be interested in contributing to fix this documentation issue?
-      options:
-        - label: I would be willing to submit a pull request to fix this issue.
-          required: false
-

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,113 +0,0 @@
-name: Feature Request
-description: Suggest a new feature or enhancement for OpenRAG
-title: "[Feature]: "
-labels: ["enhancement"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for suggesting a feature! Please provide as much detail as possible to help us understand your request.
-
-  - type: dropdown
-    id: feature-area
-    attributes:
-      label: Feature Area
-      description: Which area(s) of OpenRAG does this feature relate to?
-      multiple: true
-      options:
-        - Ingestion (document processing, upload, Docling)
-        - Retrieval (search, OpenSearch, hybrid search)
-        - Chat (chat interface, conversations, AI responses)
-        - Knowledge Filters (partitions, document filtering)
-        - Settings (configuration, model providers)
-        - TUI (Terminal User Interface)
-        - Connectors (Google Drive, OneDrive, SharePoint)
-        - Frontend (Next.js UI, components)
-        - Backend/API (Python/Starlette)
-        - Infrastructure (Docker, OpenSearch, Langflow)
-        - SDK (Python or TypeScript SDK)
-        - Onboarding (setup wizard, initial configuration)
-        - Authentication (OIDC, API keys)
-        - New Area
-    validations:
-      required: true
-
-  - type: textarea
-    id: problem-description
-    attributes:
-      label: Problem Description
-      description: Is your feature request related to a problem? Please describe.
-      placeholder: A clear and concise description of what the problem is. E.g., "I'm always frustrated when..."
-    validations:
-      required: true
-
-  - type: textarea
-    id: proposed-solution
-    attributes:
-      label: Proposed Solution
-      description: Describe the solution you'd like to see implemented.
-      placeholder: A clear and concise description of what you want to happen.
-    validations:
-      required: true
-
-  - type: textarea
-    id: use-case
-    attributes:
-      label: Use Case
-      description: Describe your use case and how this feature would benefit you or others.
-      placeholder: |
-        As a [type of user], I want [goal] so that [benefit].
-        
-        Example: As a developer, I want to filter documents by custom metadata so that I can organize my knowledge base more effectively.
-    validations:
-      required: true
-
-  - type: textarea
-    id: alternatives
-    attributes:
-      label: Alternatives Considered
-      description: Describe any alternative solutions or features you've considered.
-      placeholder: What other approaches have you thought about? Why wouldn't they work as well?
-    validations:
-      required: false
-
-  - type: dropdown
-    id: priority
-    attributes:
-      label: Priority
-      description: How important is this feature to your workflow?
-      options:
-        - Nice to have
-        - Would improve my workflow
-        - Critical for my use case
-    validations:
-      required: true
-
-  - type: textarea
-    id: additional-context
-    attributes:
-      label: Additional Context
-      description: Add any other context, mockups, screenshots, or examples about the feature request here.
-    validations:
-      required: false
-
-  - type: checkboxes
-    id: contribution
-    attributes:
-      label: Contribution
-      description: Would you be interested in contributing to this feature?
-      options:
-        - label: I would be willing to help implement this feature.
-          required: false
-        - label: I can help test this feature once implemented.
-          required: false
-
-  - type: checkboxes
-    id: checklist
-    attributes:
-      label: Checklist
-      description: Please confirm the following before submitting.
-      options:
-        - label: I have searched existing issues and discussions to ensure this feature hasn't been requested before.
-          required: true
-

.github/dependabot.yml

@@ -1,11 +0,0 @@
-version: 2
-
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "monthly"
-    commit-message:
-      prefix: "build(deps):"
-      include: scope
-

.github/workflows/build-multiarch.yml

@@ -14,7 +14,6 @@ jobs:
     outputs:
       skip_release: ${{ steps.version.outputs.skip_release }}
       version: ${{ steps.version.outputs.version }}
-      docker_version: ${{ steps.version.outputs.docker_version }}
       is_prerelease: ${{ steps.version.outputs.is_prerelease }}
     steps:
       - name: Checkout
@@ -27,12 +26,6 @@ jobs:
           echo "version=$VERSION" >> $GITHUB_OUTPUT
           echo "Version: $VERSION"
 
-          # Normalize version per PEP 440 for Docker tags
-          # e.g., "0.1.53-rc2" -> "0.1.53rc2" to match Python's importlib.metadata
-          DOCKER_VERSION=$(echo "$VERSION" | sed -E 's/-?(rc|alpha|beta|dev|post)/\1/g')
-          echo "docker_version=$DOCKER_VERSION" >> $GITHUB_OUTPUT
-          echo "Docker Version: $DOCKER_VERSION"
-
           # Check if tag already exists
           if git rev-parse "v$VERSION" >/dev/null 2>&1; then
             echo "Tag v$VERSION already exists, skipping release"
@@ -69,7 +62,8 @@ jobs:
             tag: langflowai/openrag-backend
             platform: linux/arm64
             arch: arm64
-            runs-on: [self-hosted, Linux, ARM64, langflow-ai-arm64-40gb-ephemeral]
+            #runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2]
+            runs-on: RagRunner
 
           # frontend
           - image: frontend
@@ -83,7 +77,8 @@ jobs:
             tag: langflowai/openrag-frontend
             platform: linux/arm64
             arch: arm64
-            runs-on: [self-hosted, Linux, ARM64, langflow-ai-arm64-40gb-ephemeral]
+            #runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2]
+            runs-on: RagRunner
 
           # langflow
           - image: langflow
@@ -97,7 +92,8 @@ jobs:
             tag: langflowai/openrag-langflow
             platform: linux/arm64
             arch: arm64
-            runs-on: [self-hosted, Linux, ARM64, langflow-ai-arm64-40gb-ephemeral]
+            #runs-on: self-hosted
+            runs-on: RagRunner
 
           # opensearch
           - image: opensearch
@@ -111,7 +107,9 @@ jobs:
             tag: langflowai/openrag-opensearch
             platform: linux/arm64
             arch: arm64
-            runs-on: [self-hosted, Linux, ARM64, langflow-ai-arm64-40gb-ephemeral]
+            #runs-on: [self-hosted, linux, ARM64, langflow-ai-arm64-2]
+            #runs-on: self-hosted
+            runs-on: RagRunner
 
     runs-on: ${{ matrix.runs-on }}
 
@@ -119,6 +117,13 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v4
 
+      - name: Extract version from pyproject.toml
+        id: version
+        run: |
+          VERSION=$(grep '^version = ' pyproject.toml | cut -d '"' -f 2)
+          echo "version=$VERSION" >> $GITHUB_OUTPUT
+          echo "Version: $VERSION"
+
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
@@ -136,7 +141,7 @@ jobs:
           file: ${{ matrix.file }}
           platforms: ${{ matrix.platform }}
           push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ matrix.tag }}:${{ needs.check-version.outputs.docker_version }}-${{ matrix.arch }}
+          tags: ${{ matrix.tag }}:${{ steps.version.outputs.version }}-${{ matrix.arch }}
           cache-from: type=gha,scope=${{ matrix.image }}-${{ matrix.arch }}
           cache-to: type=gha,mode=max,scope=${{ matrix.image }}-${{ matrix.arch }}
 
@@ -148,6 +153,12 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v4
 
+      - name: Extract version from pyproject.toml
+        id: version
+        run: |
+          VERSION=$(grep '^version = ' pyproject.toml | cut -d '"' -f 2)
+          echo "version=$VERSION" >> $GITHUB_OUTPUT
+
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
@@ -156,7 +167,7 @@ jobs:
 
       - name: Create and push multi-arch manifests
         run: |
-          VERSION=${{ needs.check-version.outputs.docker_version }}
+          VERSION=${{ steps.version.outputs.version }}
 
           # Create versioned tags
           docker buildx imagetools create -t langflowai/openrag-backend:$VERSION \
@@ -206,13 +217,20 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
           python-version: '3.13'
 
       - name: Install uv
         uses: astral-sh/setup-uv@v3
 
+      - name: Extract version from pyproject.toml
+        id: version
+        run: |
+          VERSION=$(grep '^version = ' pyproject.toml | cut -d '"' -f 2)
+          echo "version=$VERSION" >> $GITHUB_OUTPUT
+          echo "Version: $VERSION"
+
       - name: Build wheel and source distribution
         run: |
           uv build
@@ -235,8 +253,8 @@ jobs:
       - name: Create Release
         uses: softprops/action-gh-release@v2
         with:
-          tag_name: v${{ needs.check-version.outputs.version }}
-          name: Release ${{ needs.check-version.outputs.version }}
+          tag_name: v${{ steps.version.outputs.version }}
+          name: Release ${{ steps.version.outputs.version }}
           draft: false
           prerelease: ${{ needs.check-version.outputs.is_prerelease }}
           generate_release_notes: true
@@ -246,8 +264,8 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Publish to PyPI
-        run: |
-          uv publish
-        env:
-          UV_PUBLISH_TOKEN: ${{ secrets.UV_PUBLISH_TOKEN }}
+     # - name: Publish to PyPI
+      #  run: |
+       #   uv publish
+        # env:
+          # UV_PUBLISH_TOKEN: ${{ secrets.UV_PUBLISH_TOKEN }}

.github/workflows/codeql.yml

@@ -1,66 +0,0 @@
-name: "CodeQL"
-
-on:
-  push:
-    branches: [ 'main' ]
-  pull_request:
-    # The branches below must be a subset of the branches above
-    branches: [ 'main' ]
-  schedule:
-    - cron: '17 2 * * 1'
-
-jobs:
-  analyze:
-    name: Analyze
-    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
-    timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }}
-    permissions:
-      actions: read
-      contents: read
-      security-events: write
-
-    strategy:
-      fail-fast: false
-      matrix:
-        language: [ 'python', 'javascript' ]
-        # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ]
-        # Use only 'java' to analyze code written in Java, Kotlin or both
-        # Use only 'javascript' to analyze code written in JavaScript, TypeScript or both
-        # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support
-
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@v6
-
-    # Initializes the CodeQL tools for scanning.
-    - name: Initialize CodeQL
-      uses: github/codeql-action/init@v3
-      with:
-        languages: ${{ matrix.language }}
-        # If you wish to specify custom queries, you can do so here or in a config file.
-        # By default, queries listed here will override any specified in a config file.
-        # Prefix the list here with "+" to use these queries and those in the config file.
-
-        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
-        # queries: security-extended,security-and-quality
-
-    # Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift).
-    # If this step fails, then you should remove it and run the build manually (see below)
-    - name: Autobuild
-      uses: github/codeql-action/autobuild@v3
-
-    # ℹ️ Command-line programs to run using the OS shell.
-    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
-
-    #   If the Autobuild fails above, remove it and uncomment the following three lines.
-    #   modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance.
-
-    # - run: |
-    #     echo "Run, Build Application using script"
-    #     ./location_of_script_within_repo/buildscript.sh
-
-    - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v3
-      with:
-        category: "/language:${{matrix.language}}"
-

.github/workflows/dependency-audit.yml

@@ -1,60 +0,0 @@
-name: Dependency Audit
-
-on:
-  schedule:
-    # Run Monday, Thursday at 9am UTC
-    - cron: '0 9 * * 1,4'
-  workflow_dispatch: # Allow manual trigger
-
-jobs:
-  npm-audit:
-    name: NPM Audit
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        directory: ['frontend', 'docs', 'sdks/typescript']
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '20'
-
-      - name: Run npm audit
-        working-directory: ${{ matrix.directory }}
-        run: |
-          echo "::group::NPM Audit for ${{ matrix.directory }}"
-          npm audit --audit-level=moderate || echo "::warning::NPM audit found vulnerabilities in ${{ matrix.directory }}"
-          echo "::endgroup::"
-
-      - name: Check for outdated packages
-        working-directory: ${{ matrix.directory }}
-        run: |
-          echo "::group::Outdated packages in ${{ matrix.directory }}"
-          npm outdated || true
-          echo "::endgroup::"
-
-  python-audit:
-    name: Python Audit
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        directory: ['.', 'sdks/python']
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.11'
-
-      - name: Install pip-audit
-        run: pip install pip-audit
-
-      - name: Run pip-audit
-        working-directory: ${{ matrix.directory }}
-        run: |
-          echo "::group::Python Audit for ${{ matrix.directory }}"
-          pip-audit --desc || echo "::warning::pip-audit found vulnerabilities in ${{ matrix.directory }}"
-          echo "::endgroup::"

.github/workflows/deploy-docs-draft.yml

@@ -23,9 +23,9 @@ jobs:
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: 20.20.0
-          cache: npm
-          cache-dependency-path: ./docs/package-lock.json
+          node-version: 20
+          cache: yarn
+          cache-dependency-path: ./docs/yarn.lock
 
       - name: Validate Branch Names
         run: |
@@ -74,14 +74,14 @@ jobs:
           echo "url=${{ vars.DOCS_DRAFT_BASE_URL }}/langflow-drafts/${{ steps.extract_branch.outputs.draft_directory }}/index.html" >> $GITHUB_OUTPUT
 
       - name: Install dependencies
-        run: cd docs && npm install
+        run: cd docs && yarn install
 
       - name: Build website
         if: success()
         run: |
           set -o pipefail
           cd docs
-          npm run build |& tee $GITHUB_WORKSPACE/build.log
+          yarn build |& tee $GITHUB_WORKSPACE/build.log
         env:
           BASE_URL: /langflow-drafts/${{ steps.extract_branch.outputs.draft_directory }}
           FORCE_COLOR: 0 # Disable color output

.github/workflows/deploy-gh-pages.yml

@@ -16,14 +16,14 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-node@v4
         with:
-          node-version: 20.20.0
-          cache: npm
-          cache-dependency-path: ./docs/package-lock.json
+          node-version: 20
+          cache: yarn
+          cache-dependency-path: ./docs/yarn.lock
 
       - name: Install dependencies
-        run: cd docs && npm install
+        run: cd docs && yarn install
       - name: Build website
-        run: cd docs && npm run build
+        run: cd docs && yarn build
         # env:
         #   SEGMENT_PUBLIC_WRITE_KEY: ${{ vars.DOCS_PROD_SEGMENT_PUBLIC_WRITE_KEY }}
 

.github/workflows/publish-sdk-python.yml

@@ -1,59 +0,0 @@
-name: Publish Python SDK
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - 'sdks/python/pyproject.toml'
-  workflow_dispatch:
-
-jobs:
-  publish:
-    name: Publish to PyPI
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: sdks/python
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.12'
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Extract version from pyproject.toml
-        id: version
-        run: |
-          VERSION=$(grep -Po '(?<=^version = ")[^"]*' pyproject.toml)
-          echo "version=$VERSION" >> $GITHUB_OUTPUT
-
-      - name: Check if version already published
-        id: check
-        run: |
-          HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" https://pypi.org/pypi/openrag-sdk/${{ steps.version.outputs.version }}/json)
-          if [ "$HTTP_STATUS" = "200" ]; then
-            echo "exists=true" >> $GITHUB_OUTPUT
-          else
-            echo "exists=false" >> $GITHUB_OUTPUT
-          fi
-
-      - name: Build package
-        if: steps.check.outputs.exists == 'false'
-        run: uv build
-
-      - name: Publish to PyPI
-        if: steps.check.outputs.exists == 'false'
-        run: uv publish
-        env:
-          UV_PUBLISH_TOKEN: ${{ secrets.UV_PUBLISH_TOKEN }}
-
-      - name: Skip publish (version exists)
-        if: steps.check.outputs.exists == 'true'
-        run: echo "Version ${{ steps.version.outputs.version }} already exists on PyPI, skipping publish"

.github/workflows/publish-sdk-typescript.yml

@@ -1,64 +0,0 @@
-name: Publish TypeScript SDK
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - 'sdks/typescript/package.json'
-  workflow_dispatch:
-
-jobs:
-  publish:
-    name: Publish to npm
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      id-token: write
-    defaults:
-      run:
-        working-directory: sdks/typescript
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '20'
-          registry-url: 'https://registry.npmjs.org'
-
-      - name: Update npm to latest
-        run: npm install -g npm@latest
-
-      - name: Extract version from package.json
-        id: version
-        run: |
-          VERSION=$(node -p "require('./package.json').version")
-          echo "version=$VERSION" >> $GITHUB_OUTPUT
-
-      - name: Check if version already published
-        id: check
-        run: |
-          if npm view openrag-sdk@${{ steps.version.outputs.version }} version 2>/dev/null; then
-            echo "exists=true" >> $GITHUB_OUTPUT
-          else
-            echo "exists=false" >> $GITHUB_OUTPUT
-          fi
-
-      - name: Install dependencies
-        if: steps.check.outputs.exists == 'false'
-        run: npm ci
-
-      - name: Build
-        if: steps.check.outputs.exists == 'false'
-        run: npm run build
-
-      - name: Publish to npm
-        if: steps.check.outputs.exists == 'false'
-        run: npm publish --access public --provenance
-
-      - name: Skip publish (version exists)
-        if: steps.check.outputs.exists == 'true'
-        run: echo "Version ${{ steps.version.outputs.version }} already exists on npm, skipping publish"

.github/workflows/test-integration.yml

@@ -7,7 +7,6 @@ on:
       - 'tests/**.py'
       - 'pyproject.toml'
       - 'uv.lock'
-      - 'sdks/**'
       - '.github/workflows/test-integration.yml'
   workflow_dispatch:
     inputs:
@@ -15,7 +14,7 @@ on:
         description: 'Build images locally instead of pulling from DockerHub'
         required: false
         type: boolean
-        default: true
+        default: false
 
 jobs:
   tests:
@@ -28,7 +27,6 @@ jobs:
       LANGFLOW_NEW_USER_IS_ACTIVE: "True"
       LANGFLOW_ENABLE_SUPERUSER_CLI: "True"
       LANGFLOW_CHAT_FLOW_ID: ${{ vars.LANGFLOW_CHAT_FLOW_ID || '1098eea1-6649-4e1d-aed1-b77249fb8dd0' }}
-      LANGFLOW_INGEST_FLOW_ID: ${{ vars.LANGFLOW_INGEST_FLOW_ID || '5488df7c-b93f-4f87-a446-b67028bc0813' }}
       NUDGES_FLOW_ID: ${{ vars.NUDGES_FLOW_ID || 'ebc01d31-1976-46ce-a385-b0240327226c' }}
 
     steps:
@@ -40,16 +38,8 @@ jobs:
           docker builder prune -af || true
           docker-compose -f docker-compose.yml down -v --remove-orphans || true
 
-      - name: Cleanup root-owned files (OpenSearch data, config)
-        run: |
-          for i in 1 2 3; do
-            docker run --rm -v $(pwd):/work alpine sh -c "rm -rf /work/opensearch-data /work/config" && break
-            echo "Attempt $i failed, retrying in 5s..."
-            sleep 5
-          done || true
-
       - run: df -h
-
+      
       - name: Checkout
         uses: actions/checkout@v4
         
@@ -64,11 +54,6 @@ jobs:
         with:
           version: latest
 
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '20'
-
       - name: Python version
         run: uv python install 3.13
 
@@ -88,10 +73,7 @@ jobs:
           # Disable startup ingest noise unless a test enables it
           DISABLE_STARTUP_INGEST: "true"
         run: |
-          # For PRs, always build locally since we're testing new code
-          # For workflow_dispatch, use the input (defaults to true)
-          USE_LOCAL="${{ inputs.use_local_images }}"
-          if [ "${{ github.event_name }}" == "pull_request" ] || [ "$USE_LOCAL" != "false" ]; then
+          if [ "${{ inputs.use_local_images }}" == "true" ]; then
             echo "Running tests with locally built images..."
             make test-ci-local
           else

.github/workflows/update-uv-lock.yml

@@ -20,7 +20,7 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
           python-version: '3.13'
 

.gitignore

@@ -17,9 +17,6 @@ wheels/
 
 1001*.pdf
 *.json
-!**/package.json
-!**/package-lock.json
-!**/tsconfig.json
 !flows/*.json
 !src/tui/_assets/flows/*.json
 !src/tui/_assets/flows/components/*.json
@@ -32,5 +29,3 @@ config/
 
 # OpenSearch data directory
 opensearch-data/
-
-node_modules

.pre-commit-config.yaml

@@ -1,7 +0,0 @@
-repos:
-  - repo: https://github.com/Yelp/detect-secrets
-    rev: v1.5.0
-    hooks:
-      - id: detect-secrets
-        args: ["--baseline", ".secrets.baseline", "--exclude-lines", "code_hash"]
-

.secrets.baseline

@@ -1,180 +0,0 @@
-{
-  "version": "1.5.0",
-  "plugins_used": [
-    {
-      "name": "ArtifactoryDetector"
-    },
-    {
-      "name": "AWSKeyDetector"
-    },
-    {
-      "name": "AzureStorageKeyDetector"
-    },
-    {
-      "name": "Base64HighEntropyString",
-      "limit": 4.5
-    },
-    {
-      "name": "BasicAuthDetector"
-    },
-    {
-      "name": "CloudantDetector"
-    },
-    {
-      "name": "DiscordBotTokenDetector"
-    },
-    {
-      "name": "GitHubTokenDetector"
-    },
-    {
-      "name": "GitLabTokenDetector"
-    },
-    {
-      "name": "HexHighEntropyString",
-      "limit": 3.0
-    },
-    {
-      "name": "IbmCloudIamDetector"
-    },
-    {
-      "name": "IbmCosHmacDetector"
-    },
-    {
-      "name": "IPPublicDetector"
-    },
-    {
-      "name": "JwtTokenDetector"
-    },
-    {
-      "name": "KeywordDetector",
-      "keyword_exclude": ""
-    },
-    {
-      "name": "MailchimpDetector"
-    },
-    {
-      "name": "NpmDetector"
-    },
-    {
-      "name": "OpenAIDetector"
-    },
-    {
-      "name": "PrivateKeyDetector"
-    },
-    {
-      "name": "PypiTokenDetector"
-    },
-    {
-      "name": "SendGridDetector"
-    },
-    {
-      "name": "SlackDetector"
-    },
-    {
-      "name": "SoftlayerDetector"
-    },
-    {
-      "name": "SquareOAuthDetector"
-    },
-    {
-      "name": "StripeDetector"
-    },
-    {
-      "name": "TelegramBotTokenDetector"
-    },
-    {
-      "name": "TwilioKeyDetector"
-    }
-  ],
-  "filters_used": [
-    {
-      "path": "detect_secrets.filters.allowlist.is_line_allowlisted"
-    },
-    {
-      "path": "detect_secrets.filters.common.is_baseline_file",
-      "filename": ".secrets.baseline"
-    },
-    {
-      "path": "detect_secrets.filters.common.is_ignored_due_to_verification_policies",
-      "min_level": 2
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_indirect_reference"
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_likely_id_string"
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_lock_file"
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_not_alphanumeric_string"
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_potential_uuid"
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_prefixed_with_dollar_sign"
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_sequential_string"
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_swagger_file"
-    },
-    {
-      "path": "detect_secrets.filters.heuristic.is_templated_secret"
-    },
-    {
-      "path": "detect_secrets.filters.regex.should_exclude_file",
-      "pattern": [
-        "flows/.*\\.json$"
-      ]
-    },
-    {
-      "path": "detect_secrets.filters.regex.should_exclude_line",
-      "pattern": [
-        "code_hash"
-      ]
-    }
-  ],
-  "results": {
-    "docs/docs/_partial-integrate-chat.mdx": [
-      {
-        "type": "Secret Keyword",
-        "filename": "docs/docs/_partial-integrate-chat.mdx",
-        "hashed_secret": "e42fd8b9ad15d8fa5f4718cad7cf19b522807996",
-        "is_verified": false,
-        "line_number": 30
-      }
-    ],
-    "src/main.py": [
-      {
-        "type": "Base64 High Entropy String",
-        "filename": "src/main.py",
-        "hashed_secret": "131a83e9ef8660d7dd0771da7ce5954d9ea801ee",
-        "is_verified": false,
-        "line_number": 404
-      }
-    ],
-    "src/models/processors.py": [
-      {
-        "type": "Base64 High Entropy String",
-        "filename": "src/models/processors.py",
-        "hashed_secret": "131a83e9ef8660d7dd0771da7ce5954d9ea801ee",
-        "is_verified": false,
-        "line_number": 763
-      }
-    ],
-    "src/services/langflow_file_service.py": [
-      {
-        "type": "Base64 High Entropy String",
-        "filename": "src/services/langflow_file_service.py",
-        "hashed_secret": "131a83e9ef8660d7dd0771da7ce5954d9ea801ee",
-        "is_verified": false,
-        "line_number": 97
-      }
-    ]
-  },
-  "generated_at": "2025-12-09T20:33:13Z"
-}

Dockerfile.frontend

@@ -1,4 +1,4 @@
-FROM node:20.20.0-slim
+FROM node:18-slim
 
 # Set working directory
 WORKDIR /app

Makefile

@@ -11,7 +11,7 @@ ifneq (,$(wildcard .env))
 endif
 
 .PHONY: help dev dev-cpu dev-local infra stop clean build logs shell-backend shell-frontend install \
-       test test-integration test-ci test-ci-local test-sdk \
+       test test-integration test-ci test-ci-local \
        backend frontend install-be install-fe build-be build-fe logs-be logs-fe logs-lf logs-os \
        shell-be shell-lf shell-os restart status health db-reset flow-upload quick setup
 
@@ -46,19 +46,15 @@ help:
 	@echo "Testing:"
 	@echo "  test             - Run all backend tests"
 	@echo "  test-integration - Run integration tests (requires infra)"
-	@echo "  test-ci          - Start infra, run integration + SDK tests, tear down (uses DockerHub images)"
+	@echo "  test-ci          - Start infra, run integration tests, tear down (uses DockerHub images)"
 	@echo "  test-ci-local    - Same as test-ci but builds all images locally"
-	@echo "  test-sdk         - Run SDK integration tests (requires running OpenRAG at localhost:3000)"
 	@echo "  lint         - Run linting checks"
 	@echo ""
 
 # Development environments
-# Use centralized env file from TUI if it exists, otherwise fall back to local .env
-OPENRAG_ENV_FILE := $(shell if [ -f ~/.openrag/tui/.env ]; then echo "--env-file ~/.openrag/tui/.env"; fi)
-
 dev:
 	@echo "🚀 Starting OpenRAG with GPU support..."
-	docker compose $(OPENRAG_ENV_FILE) -f docker-compose.yml -f docker-compose.gpu.yml up -d
+	docker compose up -d
 	@echo "✅ Services started!"
 	@echo "   Backend: http://localhost:8000"
 	@echo "   Frontend: http://localhost:3000"
@@ -68,7 +64,7 @@ dev:
 
 dev-cpu:
 	@echo "🚀 Starting OpenRAG with CPU only..."
-	docker compose $(OPENRAG_ENV_FILE) up -d
+	docker compose -f docker-compose-cpu.yml up -d
 	@echo "✅ Services started!"
 	@echo "   Backend: http://localhost:8000"
 	@echo "   Frontend: http://localhost:3000"
@@ -78,7 +74,7 @@ dev-cpu:
 
 dev-local:
 	@echo "🔧 Starting infrastructure only (for local development)..."
-	docker compose $(OPENRAG_ENV_FILE) up -d opensearch dashboards langflow
+	docker compose up -d opensearch dashboards langflow
 	@echo "✅ Infrastructure started!"
 	@echo "   Langflow: http://localhost:7860"
 	@echo "   OpenSearch: http://localhost:9200"
@@ -88,7 +84,7 @@ dev-local:
 
 infra:
 	@echo "🔧 Starting infrastructure services only..."
-	docker compose $(OPENRAG_ENV_FILE) up -d opensearch dashboards langflow
+	docker compose up -d opensearch dashboards langflow
 	@echo "✅ Infrastructure services started!"
 	@echo "   Langflow: http://localhost:7860"
 	@echo "   OpenSearch: http://localhost:9200"
@@ -96,7 +92,7 @@ infra:
 
 infra-cpu:
 	@echo "🔧 Starting infrastructure services only..."
-	docker compose $(OPENRAG_ENV_FILE) up -d opensearch dashboards langflow
+	docker-compose -f docker-compose-cpu.yml up -d opensearch dashboards langflow
 	@echo "✅ Infrastructure services started!"
 	@echo "   Langflow: http://localhost:7860"
 	@echo "   OpenSearch: http://localhost:9200"
@@ -105,13 +101,15 @@ infra-cpu:
 # Container management
 stop:
 	@echo "🛑 Stopping all containers..."
-	docker compose $(OPENRAG_ENV_FILE) down
+	docker compose down
+	docker compose -f docker-compose-cpu.yml down 2>/dev/null || true
 
 restart: stop dev
 
 clean: stop
 	@echo "🧹 Cleaning up containers and volumes..."
-	docker compose $(OPENRAG_ENV_FILE) down -v --remove-orphans
+	docker compose down -v --remove-orphans
+	docker compose -f docker-compose-cpu.yml down -v --remove-orphans 2>/dev/null || true
 	docker system prune -f
 
 # Local development
@@ -139,53 +137,50 @@ install-fe:
 
 # Building
 build:
-	@echo "Building all Docker images locally..."
-	docker build -t langflowai/openrag-opensearch:latest -f Dockerfile .
-	docker build -t langflowai/openrag-backend:latest -f Dockerfile.backend .
-	docker build -t langflowai/openrag-frontend:latest -f Dockerfile.frontend .
-	docker build -t langflowai/openrag-langflow:latest -f Dockerfile.langflow .
+	@echo "🔨 Building Docker images..."
+	docker compose build
 
 build-be:
-	@echo "Building backend image..."
-	docker build -t langflowai/openrag-backend:latest -f Dockerfile.backend .
+	@echo "🔨 Building backend image..."
+	docker build -t openrag-backend -f Dockerfile.backend .
 
 build-fe:
-	@echo "Building frontend image..."
-	docker build -t langflowai/openrag-frontend:latest -f Dockerfile.frontend .
+	@echo "🔨 Building frontend image..."
+	docker build -t openrag-frontend -f Dockerfile.frontend .
 
 # Logging and debugging
 logs:
 	@echo "📋 Showing all container logs..."
-	docker compose $(OPENRAG_ENV_FILE) logs -f
+	docker compose logs -f
 
 logs-be:
 	@echo "📋 Showing backend logs..."
-	docker compose $(OPENRAG_ENV_FILE) logs -f openrag-backend
+	docker compose logs -f openrag-backend
 
 logs-fe:
 	@echo "📋 Showing frontend logs..."
-	docker compose $(OPENRAG_ENV_FILE) logs -f openrag-frontend
+	docker compose logs -f openrag-frontend
 
 logs-lf:
 	@echo "📋 Showing langflow logs..."
-	docker compose $(OPENRAG_ENV_FILE) logs -f langflow
+	docker compose logs -f langflow
 
 logs-os:
 	@echo "📋 Showing opensearch logs..."
-	docker compose $(OPENRAG_ENV_FILE) logs -f opensearch
+	docker compose logs -f opensearch
 
 # Shell access
 shell-be:
 	@echo "🐚 Opening shell in backend container..."
-	docker compose $(OPENRAG_ENV_FILE) exec openrag-backend /bin/bash
+	docker compose exec openrag-backend /bin/bash
 
 shell-lf:
 	@echo "🐚 Opening shell in langflow container..."
-	docker compose $(OPENRAG_ENV_FILE) exec langflow /bin/bash
+	docker compose exec langflow /bin/bash
 
 shell-os:
 	@echo "🐚 Opening shell in opensearch container..."
-	docker compose $(OPENRAG_ENV_FILE) exec opensearch /bin/bash
+	docker compose exec opensearch /bin/bash
 
 # Testing and quality
 test:
@@ -211,13 +206,13 @@ test-ci:
 		chmod 644 keys/public_key.pem 2>/dev/null || true; \
 	fi; \
 	echo "Cleaning up old containers and volumes..."; \
-	docker compose down -v 2>/dev/null || true; \
+	docker compose -f docker-compose-cpu.yml down -v 2>/dev/null || true; \
 	echo "Pulling latest images..."; \
-	docker compose pull; \
+	docker compose -f docker-compose-cpu.yml pull; \
 	echo "Building OpenSearch image override..."; \
 	docker build --no-cache -t langflowai/openrag-opensearch:latest -f Dockerfile .; \
-	echo "Starting infra (OpenSearch + Dashboards + Langflow + Backend + Frontend) with CPU containers"; \
-	docker compose up -d opensearch dashboards langflow openrag-backend openrag-frontend; \
+	echo "Starting infra (OpenSearch + Dashboards + Langflow) with CPU containers"; \
+	docker compose -f docker-compose-cpu.yml up -d opensearch dashboards langflow; \
 	echo "Starting docling-serve..."; \
 	DOCLING_ENDPOINT=$$(uv run python scripts/docling_ctl.py start --port 5001 | grep "Endpoint:" | awk '{print $$2}'); \
 	echo "Docling-serve started at $$DOCLING_ENDPOINT"; \
@@ -262,21 +257,6 @@ test-ci:
 	uv run pytest tests/integration -vv -s -o log_cli=true --log-cli-level=DEBUG; \
 	TEST_RESULT=$$?; \
 	echo ""; \
-	echo "Waiting for frontend at http://localhost:3000..."; \
-	for i in $$(seq 1 60); do \
-		curl -s http://localhost:3000/ >/dev/null 2>&1 && break || sleep 2; \
-	done; \
-	echo "Running Python SDK integration tests"; \
-	cd sdks/python && \
-	uv sync --extra dev && \
-	OPENRAG_URL=http://localhost:3000 uv run pytest tests/test_integration.py -vv -s || TEST_RESULT=1; \
-	cd ../..; \
-	echo "Running TypeScript SDK integration tests"; \
-	cd sdks/typescript && \
-	npm install && npm run build && \
-	OPENRAG_URL=http://localhost:3000 npm test || TEST_RESULT=1; \
-	cd ../..; \
-	echo ""; \
 	echo "=== Post-test JWT diagnostics ==="; \
 	echo "Generating test JWT token..."; \
 	TEST_TOKEN=$$(uv run python -c "from src.session_manager import SessionManager, AnonymousUser; sm = SessionManager('test'); print(sm.create_jwt_token(AnonymousUser()))" 2>/dev/null || echo ""); \
@@ -289,7 +269,7 @@ test-ci:
 	echo ""; \
 	echo "Tearing down infra"; \
 	uv run python scripts/docling_ctl.py stop || true; \
-	docker compose down -v 2>/dev/null || true; \
+	docker compose -f docker-compose-cpu.yml down -v 2>/dev/null || true; \
 	exit $$TEST_RESULT
 
 # CI-friendly integration test target with local builds: builds all images, brings up infra, waits, runs tests, tears down
@@ -306,14 +286,14 @@ test-ci-local:
 		chmod 644 keys/public_key.pem 2>/dev/null || true; \
 	fi; \
 	echo "Cleaning up old containers and volumes..."; \
-	docker compose down -v 2>/dev/null || true; \
+	docker compose -f docker-compose-cpu.yml down -v 2>/dev/null || true; \
 	echo "Building all images locally..."; \
 	docker build -t langflowai/openrag-opensearch:latest -f Dockerfile .; \
 	docker build -t langflowai/openrag-backend:latest -f Dockerfile.backend .; \
 	docker build -t langflowai/openrag-frontend:latest -f Dockerfile.frontend .; \
 	docker build -t langflowai/openrag-langflow:latest -f Dockerfile.langflow .; \
-	echo "Starting infra (OpenSearch + Dashboards + Langflow + Backend + Frontend) with CPU containers"; \
-	docker compose up -d opensearch dashboards langflow openrag-backend openrag-frontend; \
+	echo "Starting infra (OpenSearch + Dashboards + Langflow) with CPU containers"; \
+	docker compose -f docker-compose-cpu.yml up -d opensearch dashboards langflow; \
 	echo "Starting docling-serve..."; \
 	DOCLING_ENDPOINT=$$(uv run python scripts/docling_ctl.py start --port 5001 | grep "Endpoint:" | awk '{print $$2}'); \
 	echo "Docling-serve started at $$DOCLING_ENDPOINT"; \
@@ -358,21 +338,6 @@ test-ci-local:
 	uv run pytest tests/integration -vv -s -o log_cli=true --log-cli-level=DEBUG; \
 	TEST_RESULT=$$?; \
 	echo ""; \
-	echo "Waiting for frontend at http://localhost:3000..."; \
-	for i in $$(seq 1 60); do \
-		curl -s http://localhost:3000/ >/dev/null 2>&1 && break || sleep 2; \
-	done; \
-	echo "Running Python SDK integration tests"; \
-	cd sdks/python && \
-	uv sync --extra dev && \
-	OPENRAG_URL=http://localhost:3000 uv run pytest tests/test_integration.py -vv -s || TEST_RESULT=1; \
-	cd ../..; \
-	echo "Running TypeScript SDK integration tests"; \
-	cd sdks/typescript && \
-	npm install && npm run build && \
-	OPENRAG_URL=http://localhost:3000 npm test || TEST_RESULT=1; \
-	cd ../..; \
-	echo ""; \
 	echo "=== Post-test JWT diagnostics ==="; \
 	echo "Generating test JWT token..."; \
 	TEST_TOKEN=$$(uv run python -c "from src.session_manager import SessionManager, AnonymousUser; sm = SessionManager('test'); print(sm.create_jwt_token(AnonymousUser()))" 2>/dev/null || echo ""); \
@@ -383,32 +348,11 @@ test-ci-local:
 	fi; \
 	echo "================================="; \
 	echo ""; \
-	if [ $$TEST_RESULT -ne 0 ]; then \
-		echo "=== Tests failed, dumping container logs ==="; \
-		echo ""; \
-		echo "=== Langflow logs (last 500 lines) ==="; \
-		docker logs langflow 2>&1 | tail -500 || echo "Could not get Langflow logs"; \
-		echo ""; \
-		echo "=== Backend logs (last 200 lines) ==="; \
-		docker logs openrag-backend 2>&1 | tail -200 || echo "Could not get backend logs"; \
-		echo ""; \
-	fi; \
 	echo "Tearing down infra"; \
 	uv run python scripts/docling_ctl.py stop || true; \
-	docker compose down -v 2>/dev/null || true; \
+	docker compose -f docker-compose-cpu.yml down -v 2>/dev/null || true; \
 	exit $$TEST_RESULT
 
-# SDK integration tests (requires running OpenRAG instance)
-test-sdk:
-	@echo "Running SDK integration tests..."
-	@echo "Make sure OpenRAG is running at localhost:3000 (make up)"
-	@echo ""
-	@echo "Running Python SDK tests..."
-	cd sdks/python && uv sync --extra dev && OPENRAG_URL=http://localhost:3000 uv run pytest tests/test_integration.py -vv -s
-	@echo ""
-	@echo "Running TypeScript SDK tests..."
-	cd sdks/typescript && npm install && npm run build && OPENRAG_URL=http://localhost:3000 npm test
-
 lint:
 	@echo "🔍 Running linting checks..."
 	cd frontend && npm run lint
@@ -417,7 +361,7 @@ lint:
 # Service status
 status:
 	@echo "📊 Container status:"
-	@docker compose $(OPENRAG_ENV_FILE) ps 2>/dev/null || echo "No containers running"
+	@docker compose ps 2>/dev/null || echo "No containers running"
 
 health:
 	@echo "🏥 Health check:"

README.md

@@ -15,20 +15,68 @@ OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables
 
 <a href="https://deepwiki.com/langflow-ai/openrag"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
 
-## Install OpenRAG
+</div>
+<div align="center">
+  <a href="#quickstart" style="color: #0366d6;">Quickstart</a> &nbsp;&nbsp;|&nbsp;&nbsp;
+  <a href="#install-python-package" style="color: #0366d6;">Python package</a> &nbsp;&nbsp;|&nbsp;&nbsp;
+  <a href="#docker-or-podman-installation" style="color: #0366d6;">Docker or Podman</a> &nbsp;&nbsp;|&nbsp;&nbsp;
+  <a href="#development" style="color: #0366d6;">Development</a> &nbsp;&nbsp;|&nbsp;&nbsp;
+  <a href="#troubleshooting" style="color: #0366d6;">Troubleshooting</a>
+</div>
 
-To get started with OpenRAG, see the installation guides in the OpenRAG documentation:
+## Quickstart
 
-* [Quickstart](https://docs.openr.ag/quickstart)
-* [Install the OpenRAG Python package](https://docs.openr.ag/install-options)
-* [Deploy self-managed services with Docker or Podman](https://docs.openr.ag/docker)
+To run OpenRAG without creating or modifying any project files, use `uvx`:
+
+```bash
+uvx openrag
+```
+
+This command runs OpenRAG without installing it to your project or globally.
+
+To run a specific version of OpenRAG, run `uvx --from openrag==VERSION openrag`.
+
+## Install Python package
+
+To add the OpenRAG Python package to a Python project, use `uv`:
+
+1. Create a new project with a virtual environment using `uv init`:
+
+   ```bash
+   uv init YOUR_PROJECT_NAME
+   cd YOUR_PROJECT_NAME
+   ```
+
+   The `(venv)` prompt doesn't change, but `uv` commands will automatically use the project's virtual environment.
+   For more information on virtual environments, see the [uv documentation](https://docs.astral.sh/uv/pip/environments).
+
+2. Add OpenRAG to your project:
+
+   ```bash
+   uv add openrag
+   ```
+
+   To add a specific version of OpenRAG, run `uv add openrag==VERSION`.
+
+3. Start the OpenRAG terminal user interface (TUI):
+
+   ```bash
+   uv run openrag
+   ```
+
+4. Continue with the [Quickstart](https://docs.openr.ag/quickstart).
+
+For all installation options, see the [OpenRAG installation guide](https://docs.openr.ag/install).
+
+## Docker or Podman installation
+
+By default, OpenRAG automatically starts the required containers and helps you manage them.
+To install OpenRAG with self-managed containers, see the [OpenRAG installation guide](https://docs.openr.ag/docker).
 
 ## Development
 
-For developers who want to [contribute to OpenRAG](https://docs.openr.ag/support/contribute) or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md).
+For developers wanting to contribute to OpenRAG or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md).
 
 ## Troubleshooting
 
-For assistance with OpenRAG, see [Troubleshoot OpenRAG](https://docs.openr.ag/support/troubleshoot) and visit the [Discussions page](https://github.com/langflow-ai/openrag/discussions).
-
-To report a bug or submit a feature request, visit the [Issues page](https://github.com/langflow-ai/openrag/issues).
+For common issues and fixes, see [Troubleshoot OpenRAG](https://docs.openr.ag/support/troubleshoot).

SECURITY.md

@@ -1,64 +0,0 @@
-# OpenRAG security policy and responsible disclosure
-
-## Security policy
-
-This security policy applies to all public projects under the langflow-ai organization on GitHub. We prioritize security and continuously work to safeguard our systems. However, vulnerabilities can still exist. If you identify a security issue, please report it to us so we can address it promptly.
-
-### Security and bug fix versions
-
-- Fixes are released either as part of the next minor version (e.g., 1.3.0 → 1.4.0) or as an on-demand patch version (e.g., 1.3.0 → 1.3.1)
-- Security fixes are given priority and might be enough to cause a new version to be released
-
-## Report a vulnerability
-
-We encourage responsible disclosure of security vulnerabilities. If you find or suspect a security issue, please discreetly report it to us so we can address it promptly:
-
-### Submit a report
-
-Go to the [OpenRAG Security page](https://github.com/langflow-ai/openrag/security), and then click **Report a vulnerability** to start a private conversation between you and the repository's maintainers.
-
-Provide as many specific details as possible to help us reproduce and fix the issue quickly, including the following:
-
-- Steps to reproduce the issue
-- Potential impact or concerns
-- Any suggested fixes
-
-Your report is kept confidential, and these details aren't shared without your consent.
-
-### Response timeline
-
-We will acknowledge your report within 5 business days.
-
-We will provide an estimated resolution timeline.
-
-We will keep you updated on our progress.
-
-### Disclosure guidelines
-
-- Don't publicly disclose vulnerabilities until we have assessed, resolved, and notified affected users.
-- If you plan to present your research (e.g., at a conference or in a blog), share a draft with us at least 30 days in advance for review.
-- Disclosures must not include the following:
-  - Data from any OpenRAG customer projects
-  - OpenRAG user/customer information
-  - Details about OpenRAG employees, contractors, or partners
-
-We appreciate your efforts in helping us maintain a secure platform, and we look forward to working together to resolve any issues responsibly.
-
-## Known vulnerabilities
-
-The following known vulnerabilities are for the OpenRAG codebase.
-
-This list doesn't include vulnerabilities within OpenRAG dependencies like OpenSearch and Langflow.
-For Langflow vulnerabilities, see the [Langflow SECURITY.md](https://github.com/langflow-ai/langflow/blob/main/SECURITY.md).
-
-There are no known vulnerabilities exclusive to the OpenRAG application at this time.
-
-## Security configuration guidelines
-
-### Start the Langflow server with authentication enabled
-
-It is recommended that you set a Langflow password (`LANGFLOW_SUPERUSER_PASSWORD`) so the Langflow server starts with authentication enabled and the `langflow superuser` command disabled.
-
-You can set this password when you install OpenRAG, or you can [edit the OpenRAG `.env` file and redeploy the OpenRAG containers](https://docs.openr.ag/reference/configuration#set-environment-variables).
-
-For more information, see [OpenRAG's Langflow settings reference](https://docs.openr.ag/reference/configuration#langflow-settings).

docker-compose-cpu.yml

@@ -0,0 +1,143 @@
+services:
+  opensearch:
+    image: langflowai/openrag-opensearch:${OPENRAG_VERSION:-latest}
+    #build:
+    #  context: .
+    #  dockerfile: Dockerfile
+    container_name: os
+    depends_on:
+      - openrag-backend
+    environment:
+      - discovery.type=single-node
+      - OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_PASSWORD}
+    # Run security setup in background after OpenSearch starts
+    command: >
+      bash -c "
+        # Ensure data directory has correct permissions
+        sudo chown -R opensearch:opensearch /usr/share/opensearch/data || true
+        
+        # Start OpenSearch in background
+        /usr/share/opensearch/opensearch-docker-entrypoint.sh opensearch &
+
+        # Wait a bit for OpenSearch to start, then apply security config
+        sleep 10 && /usr/share/opensearch/setup-security.sh &
+
+        # Wait for background processes
+        wait
+      "
+    ports:
+      - "9200:9200"
+      - "9600:9600"
+    volumes:
+      - ${OPENSEARCH_DATA_PATH:-./opensearch-data}:/usr/share/opensearch/data:Z
+
+  dashboards:
+    image: opensearchproject/opensearch-dashboards:3.0.0
+    container_name: osdash
+    depends_on:
+      - opensearch
+    environment:
+      OPENSEARCH_HOSTS: '["https://opensearch:9200"]'
+      OPENSEARCH_USERNAME: "admin"
+      OPENSEARCH_PASSWORD: ${OPENSEARCH_PASSWORD}
+    ports:
+      - "5601:5601"
+
+  openrag-backend:
+    image: langflowai/openrag-backend:${OPENRAG_VERSION:-latest}
+    # build:
+    #   context: .
+    #   dockerfile: Dockerfile.backend
+    container_name: openrag-backend
+    depends_on:
+      - langflow
+    environment:
+      - OPENSEARCH_HOST=opensearch
+      - LANGFLOW_URL=http://langflow:7860
+      - LANGFLOW_PUBLIC_URL=${LANGFLOW_PUBLIC_URL}
+      - LANGFLOW_AUTO_LOGIN=${LANGFLOW_AUTO_LOGIN}
+      - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY}
+      - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER}
+      - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD}
+      - LANGFLOW_CHAT_FLOW_ID=${LANGFLOW_CHAT_FLOW_ID}
+      - LANGFLOW_INGEST_FLOW_ID=${LANGFLOW_INGEST_FLOW_ID}
+      - LANGFLOW_URL_INGEST_FLOW_ID=${LANGFLOW_URL_INGEST_FLOW_ID}
+      - DISABLE_INGEST_WITH_LANGFLOW=${DISABLE_INGEST_WITH_LANGFLOW:-false}
+      - NUDGES_FLOW_ID=${NUDGES_FLOW_ID}
+      - OPENSEARCH_PORT=9200
+      - OPENSEARCH_USERNAME=admin
+      - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD}
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+      - WATSONX_API_KEY=${WATSONX_API_KEY}
+      - WATSONX_ENDPOINT=${WATSONX_ENDPOINT}
+      - WATSONX_PROJECT_ID=${WATSONX_PROJECT_ID}
+      - OLLAMA_ENDPOINT=${OLLAMA_ENDPOINT}
+      - GOOGLE_OAUTH_CLIENT_ID=${GOOGLE_OAUTH_CLIENT_ID}
+      - GOOGLE_OAUTH_CLIENT_SECRET=${GOOGLE_OAUTH_CLIENT_SECRET}
+      - MICROSOFT_GRAPH_OAUTH_CLIENT_ID=${MICROSOFT_GRAPH_OAUTH_CLIENT_ID}
+      - MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET=${MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET}
+      - WEBHOOK_BASE_URL=${WEBHOOK_BASE_URL}
+      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
+      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
+    volumes:
+      - ./openrag-documents:/app/openrag-documents:Z
+      - ./keys:/app/keys:Z
+      - ./flows:/app/flows:U,z
+      - ./config:/app/config:Z
+
+  openrag-frontend:
+    image: langflowai/openrag-frontend:${OPENRAG_VERSION:-latest}
+    # build:
+    #   context: .
+    #   dockerfile: Dockerfile.frontend
+    container_name: openrag-frontend
+    depends_on:
+      - openrag-backend
+    environment:
+      - OPENRAG_BACKEND_HOST=openrag-backend
+    ports:
+      - "3000:3000"
+
+  langflow:
+    volumes:
+      - ./flows:/app/flows:U,z
+    image: langflowai/openrag-langflow:${LANGFLOW_VERSION:-latest}
+    # build:
+    #   context: .
+    #   dockerfile: Dockerfile.langflow
+    container_name: langflow
+    ports:
+      - "7860:7860"
+    environment:
+      - LANGFLOW_DEACTIVATE_TRACING=true
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+      - WATSONX_API_KEY=${WATSONX_API_KEY}
+      - WATSONX_ENDPOINT=${WATSONX_ENDPOINT}
+      - WATSONX_PROJECT_ID=${WATSONX_PROJECT_ID}
+      - OLLAMA_BASE_URL=${OLLAMA_ENDPOINT}
+      - LANGFLOW_LOAD_FLOWS_PATH=/app/flows
+      - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY}
+      - JWT=None  
+      - OWNER=None
+      - OWNER_NAME=None
+      - OWNER_EMAIL=None
+      - CONNECTOR_TYPE=system
+      - CONNECTOR_TYPE_URL=url
+      - OPENRAG-QUERY-FILTER="{}"
+      - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD}
+      - FILENAME=None
+      - MIMETYPE=None
+      - FILESIZE=0
+      - SELECTED_EMBEDDING_MODEL=${SELECTED_EMBEDDING_MODEL:-}
+      - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT,OPENRAG-QUERY-FILTER,OPENSEARCH_PASSWORD,OWNER,OWNER_NAME,OWNER_EMAIL,CONNECTOR_TYPE,FILENAME,MIMETYPE,FILESIZE,SELECTED_EMBEDDING_MODEL
+      - LANGFLOW_LOG_LEVEL=DEBUG
+      - LANGFLOW_AUTO_LOGIN=${LANGFLOW_AUTO_LOGIN}
+      - LANGFLOW_SUPERUSER=${LANGFLOW_SUPERUSER}
+      - LANGFLOW_SUPERUSER_PASSWORD=${LANGFLOW_SUPERUSER_PASSWORD}
+      - LANGFLOW_NEW_USER_IS_ACTIVE=${LANGFLOW_NEW_USER_IS_ACTIVE}
+      - LANGFLOW_ENABLE_SUPERUSER_CLI=${LANGFLOW_ENABLE_SUPERUSER_CLI}
+      # - DEFAULT_FOLDER_NAME=OpenRAG
+      - HIDE_GETTING_STARTED_PROGRESS=true
+

docker-compose.gpu.yml

@@ -3,10 +3,5 @@ services:
     environment:
       - NVIDIA_DRIVER_CAPABILITIES=compute,utility
       - NVIDIA_VISIBLE_DEVICES=all
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
+    gpus: all
+

docker-compose.yml

@@ -29,7 +29,7 @@ services:
       - "9200:9200"
       - "9600:9600"
     volumes:
-      - ${OPENSEARCH_DATA_PATH:-./opensearch-data}:/usr/share/opensearch/data:U,z
+      - ${OPENSEARCH_DATA_PATH:-./opensearch-data}:/usr/share/opensearch/data:Z
 
   dashboards:
     image: opensearchproject/opensearch-dashboards:3.0.0
@@ -80,11 +80,10 @@ services:
       - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
       - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
     volumes:
-      - ${OPENRAG_DOCUMENTS_PATH:-./openrag-documents}:/app/openrag-documents:Z
-      - ${OPENRAG_KEYS_PATH:-./keys}:/app/keys:U,z
-      - ${OPENRAG_FLOWS_PATH:-./flows}:/app/flows:U,z
-      - ${OPENRAG_CONFIG_PATH:-./config}:/app/config:Z
-      - ${OPENRAG_DATA_PATH:-./data}:/app/data:Z
+      - ./openrag-documents:/app/openrag-documents:Z
+      - ./keys:/app/keys:Z
+      - ./flows:/app/flows:U,z
+      - ./config:/app/config:Z
 
   openrag-frontend:
     image: langflowai/openrag-frontend:${OPENRAG_VERSION:-latest}
@@ -97,11 +96,11 @@ services:
     environment:
       - OPENRAG_BACKEND_HOST=openrag-backend
     ports:
-      - "3003:3003"
+      - "3000:3000"
 
   langflow:
     volumes:
-      - ${OPENRAG_FLOWS_PATH:-./flows}:/app/flows:U,z
+      - ./flows:/app/flows:U,z
     image: langflowai/openrag-langflow:${OPENRAG_VERSION:-latest}
     build:
       context: .
@@ -110,9 +109,7 @@ services:
     ports:
       - "7860:7860"
     environment:
-      - LANGFUSE_SECRET_KEY=${LANGFUSE_SECRET_KEY:-}
-      - LANGFUSE_PUBLIC_KEY=${LANGFUSE_PUBLIC_KEY:-}
-      - LANGFUSE_HOST=${LANGFUSE_HOST:-}
+      - LANGFLOW_DEACTIVATE_TRACING=true
       - OPENAI_API_KEY=${OPENAI_API_KEY:-None}
       - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-None}
       - WATSONX_API_KEY=${WATSONX_API_KEY:-None}
@@ -133,6 +130,7 @@ services:
       - MIMETYPE=None
       - FILESIZE=0
       - SELECTED_EMBEDDING_MODEL=${SELECTED_EMBEDDING_MODEL:-}
+      - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD}
       - LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=JWT,OPENRAG-QUERY-FILTER,OPENSEARCH_PASSWORD,OWNER,OWNER_NAME,OWNER_EMAIL,CONNECTOR_TYPE,FILENAME,MIMETYPE,FILESIZE,SELECTED_EMBEDDING_MODEL,OPENAI_API_KEY,ANTHROPIC_API_KEY,WATSONX_API_KEY,WATSONX_ENDPOINT,WATSONX_PROJECT_ID,OLLAMA_BASE_URL
       - LANGFLOW_LOG_LEVEL=DEBUG
       - LANGFLOW_AUTO_LOGIN=${LANGFLOW_AUTO_LOGIN}

docs/README.md

@@ -5,13 +5,13 @@ This website is built using [Docusaurus](https://docusaurus.io/), a modern stati
 ## Installation
 
 ```bash
-npm install
+yarn
 ```
 
 ## Local Development
 
 ```bash
-npm start
+yarn start
 ```
 
 This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
@@ -19,7 +19,7 @@ This command starts a local development server and opens up a browser window. Mo
 ## Build
 
 ```bash
-npm run build
+yarn build
 ```
 
 This command generates static content into the `build` directory and can be served using any static contents hosting service.
@@ -29,13 +29,13 @@ This command generates static content into the `build` directory and can be serv
 Using SSH:
 
 ```bash
-USE_SSH=true npm run deploy
+USE_SSH=true yarn deploy
 ```
 
 Not using SSH:
 
 ```bash
-GIT_USER=<Your GitHub username> npm run deploy
+GIT_USER=<Your GitHub username> yarn deploy
 ```
 
 If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.

docs/VERSIONING_SETUP.md

@@ -28,6 +28,7 @@ docs: {
 See the [Docusaurus docs](https://docusaurus.io/docs/versioning) for more info.
 
 1. Use the Docusaurus CLI command to create a version.
+You can use `yarn` instead of `npm`.
 ```bash
 # Create version 1.0.0 from current docs
 npm run docusaurus docs:version 1.0.0

docs/docs/_partial-anonymous-user-owner.mdx

@@ -1 +0,0 @@
-In no-auth mode, all documents are attributed to **Anonymous User** because there is no distinct document ownership or unique JWTs. For more control over document ownership and visibility, use OAuth mode. For more information, see [OpenSearch authentication and document access](/knowledge#auth).

docs/docs/_partial-docker-compose-down-and-prune.mdx

@@ -1,9 +0,0 @@
-```bash title="Docker"
-docker compose down --volumes --remove-orphans --rmi local
-docker system prune -f
-```
-
-```bash title="Podman"
-podman compose down --volumes --remove-orphans --rmi local
-podman system prune -f
-```

docs/docs/_partial-docker-compose-up.mdx

@@ -1,7 +0,0 @@
-```bash title="Docker"
-docker compose up -d
-```
-
-```bash title="Podman"
-podman compose up -d
-```

docs/docs/_partial-docker-remove-and-cleanup-steps.mdx

@@ -1,49 +0,0 @@
-1. Remove all containers, including stopped containers:
-
-   ```bash title="Docker"
-   docker rm --force $(docker ps -aq)
-   ```
-
-   ```bash title="Podman"
-   podman rm --all --force
-   ```
-
-2. Remove all images:
-
-   ```bash title="Docker"
-   docker rmi --force $(docker images -q)
-   ```
-
-   ```bash title="Podman"
-   podman rmi --all --force
-   ```
-
-3. Remove all volumes:
-
-   ```bash title="Docker"
-   docker volume prune --force
-   ```
-
-   ```bash title="Podman"
-   podman volume prune --force
-   ```
-
-4. Remove all networks except the default network:
-
-   ```bash title="Docker"
-   docker network prune --force
-   ```
-
-   ```bash title="Podman"
-   podman network prune --force
-   ```
-
-5. Clean up any leftover data:
-
-   ```bash title="Docker"
-   docker system prune --all --force --volumes
-   ```
-
-   ```bash title="Podman"
-   podman system prune --all --force --volumes
-   ```

docs/docs/_partial-docker-stop-all.mdx

@@ -1,7 +0,0 @@
-```bash title="Docker"
-docker stop $(docker ps -q)
-```
-
-```bash title="Podman"
-podman stop --all
-```

docs/docs/_partial-export-flows.mdx

@@ -1 +0,0 @@
-1. If you modified the built-in flows or created custom flows in your [OpenRAG Langflow instance](/agents), [export your flows](https://docs.langflow.org/concepts-flows-import) before starting this process. Although OpenRAG can preserve changes to the built-in flows, it doesn't preserve user-created flows. As a general best practice, exporting your flows is recommended to create backups of your customizations. Afterwards, you can reimport your flows or reference the exported flow JSON as needed.

docs/docs/_partial-factory-reset-warning.mdx

@@ -1,22 +0,0 @@
-:::warning
-This is a destructive operation that does the following:
-
-* Destroys all OpenRAG containers, volumes, and local images.
-* Prunes any additional container objects.
-* Deletes the contents of the `~/.openrag` directory _except_ for OpenRAG's `.env` file and the `/documents` subdirectory.
-
-<p/>Destroyed containers and deleted data are lost and cannot be recovered after running this operation.
-:::
-
-<!--
-Author's note: Don't remove the <p/> tag!
-For some reason, this specific usage consistently enforces the line break and indentation whether the partial is nested or not.
-
-Without the <p/> tag, when this partial is used inside a list (ex. nested under a step in a numbered list), there is no implicit line break after the last bullet.
-When this partial is used outside of a list (as a top-level paragraph), there is an implicit line break after the last bullet.
-
-Neither <br/> nor wrapping the entire line in <p> </p> worked consistently for both use cases.
-Either the line break was missing or the indentation was incorrect.
-
-This behavior was observed in Docusaurus 3.9.2 on 05 Dec 2025. In a future release, if this is not longer an issue, you can remove the tag and this note. :)
--->

docs/docs/_partial-gpu-mode-tip.mdx

@@ -1,5 +0,0 @@
-GPU acceleration isn't required for most use cases.
-OpenRAG's CPU-only deployment doesn't prevent you from using GPU acceleration in external services, such as Ollama servers.
-
-GPU acceleration is required only for specific use cases, typically involving customization of the ingestion flows or ingestion logic.
-For example, writing alternate ingest logic in OpenRAG that uses GPUs directly in the container, or customizing the ingestion flows to use Langflow's Docling component with GPU acceleration instead of OpenRAG's Docling Serve service.

docs/docs/_partial-ingestion-flow.mdx

@@ -10,8 +10,8 @@ The **OpenSearch Ingestion** flow is comprised of several components that work t
 
 * [**Docling Serve** component](https://docs.langflow.org/bundles-docling#docling-serve): Ingests files and processes them by connecting to OpenRAG's local Docling Serve service. The output is `DoclingDocument` data that contains the extracted text and metadata from the documents.
 * [**Export DoclingDocument** component](https://docs.langflow.org/bundles-docling#export-doclingdocument): Exports processed `DoclingDocument` data to Markdown format with image placeholders. This conversion standardizes the document data in preparation for further processing.
-* [**DataFrame Operations** component](https://docs.langflow.org/dataframe-operations): Three of these components run sequentially to add metadata to the document data: `filename`, `file_size`, and `mimetype`.
-* [**Split Text** component](https://docs.langflow.org/split-text): Splits the processed text into chunks, based on the configured [chunk size and overlap settings](/knowledge#knowledge-ingestion-settings).
+* [**DataFrame Operations** component](https://docs.langflow.org/components-processing#dataframe-operations): Three of these components run sequentially to add metadata to the document data: `filename`, `file_size`, and `mimetype`.
+* [**Split Text** component](https://docs.langflow.org/components-processing#split-text): Splits the processed text into chunks, based on the configured [chunk size and overlap settings](/knowledge#knowledge-ingestion-settings).
 * **Secret Input** component: If needed, four of these components securely fetch the [OAuth authentication](/knowledge#auth) configuration variables: `CONNECTOR_TYPE`, `OWNER`, `OWNER_EMAIL`, and `OWNER_NAME`.
 * **Create Data** component: Combines the authentication credentials from the **Secret Input** components into a structured data object that is associated with the document embeddings.
 * [**Embedding Model** component](https://docs.langflow.org/components-embedding-models): Generates vector embeddings using your selected [embedding model](/knowledge#set-the-embedding-model-and-dimensions).

docs/docs/_partial-install-next-steps.mdx

@@ -1,5 +0,0 @@
-## Next steps
-
-* Try some of OpenRAG's core features in the [quickstart](/quickstart#chat-with-documents).
-* Learn how to [manage OpenRAG services](/manage-services).
-* [Upload documents](/ingestion), and then use the [**Chat**](/chat) to explore your data.

docs/docs/_partial-integrate-chat.mdx

@@ -2,10 +2,10 @@ import Icon from "@site/src/components/icon/icon";
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-1. Open the **OpenRAG OpenSearch Agent** flow in the Langflow visual editor: Click <Icon name="Settings2" aria-hidden="true"/> **Settings**, click **Edit in Langflow**, and then click **Proceed**.
+1. Open the **OpenRAG OpenSearch Agent** flow in the Langflow visual editor: From the **Chat** window, click <Icon name="Settings2" aria-hidden="true"/> **Settings**, click **Edit in Langflow**, and then click **Proceed**.
 
-2. Optional: If you don't want to use the Langflow API key that is generated automatically when you install OpenRAG, you can create a [Langflow API key](https://docs.langflow.org/api-keys-and-authentication).
-This key doesn't grant access to OpenRAG; it is only for authenticating with the Langflow API.
+2. Create a [Langflow API key](https://docs.langflow.org/api-keys-and-authentication), which is a user-specific token required to send requests to the Langflow server.
+This key doesn't grant access to OpenRAG.
 
    1. In the Langflow visual editor, click your user icon in the header, and then select **Settings**.
    2. Click **Langflow API Keys**, and then click <Icon name="Plus" aria-hidden="true"/> **Add New**.
@@ -109,6 +109,6 @@ This key doesn't grant access to OpenRAG; it is only for authenticating with the
 If the request is successful, the response includes many details about the flow run, including the session ID, inputs, outputs, components, durations, and more.
 
 In production, you won't pass the raw response to the user in its entirety.
-Instead, you extract and reformat relevant fields for different use cases, as demonstrated in the [Langflow quickstart](https://docs.langflow.org/get-started-quickstart#extract-data-from-the-response).
+Instead, you extract and reformat relevant fields for different use cases, as demonstrated in the [Langflow quickstart](https://docs.langflow.org/quickstart#extract-data-from-the-response).
 For example, you could pass the chat output text to a front-end user-facing application, and store specific fields in logs and backend data stores for monitoring, chat history, or analytics.
 You could also pass the output from one flow as input to another flow.

docs/docs/_partial-ollama-models.mdx

@@ -1,13 +0,0 @@
-OpenRAG isn't guaranteed to be compatible with all models that are available through Ollama.
-For example, some models might produce unexpected results, such as JSON-formatted output instead of natural language responses, and some models aren't appropriate for the types of tasks that OpenRAG performs, such as those that generate media.
-
-The OpenRAG team recommends the following models when using Ollama as your model provider:
-
-* **Language models**: `gpt-oss:20b` or `mistral-nemo:12b`.
-
-   If you choose `gpt-oss:20b`, consider using Ollama Cloud or running Ollama on a remote machine because this model requires at least 16GB of RAM.
-
-* **Embedding models**: [`nomic-embed-text:latest`](https://ollama.com/library/nomic-embed-text), `mxbai-embed-large:latest`, or `embeddinggemma:latest`.
-
-You can experiment with other models, but if you encounter issues that you are unable to resolve through other RAG best practices (like context filters and prompt engineering), try switching to one of the recommended models.
-You can submit an [OpenRAG GitHub issue](https://github.com/langflow-ai/openrag/issues) to request support for specific models.

docs/docs/_partial-ollama.mdx

@@ -0,0 +1,24 @@
+import Icon from "@site/src/components/icon/icon";
+
+Using Ollama for your OpenRAG language model provider offers greater flexibility and configuration, but can also be overwhelming to start.
+These recommendations are a reasonable starting point for users with at least one GPU and experience running LLMs locally.
+
+For best performance, OpenRAG recommends OpenAI's `gpt-oss:20b` language model. However, this model uses 16GB of RAM, so consider using Ollama Cloud or running Ollama on a remote machine.
+
+For generating embeddings, OpenRAG recommends the [`nomic-embed-text`](https://ollama.com/library/nomic-embed-text) embedding model, which provides high-quality embeddings optimized for retrieval tasks.
+
+To run models in [**Ollama Cloud**](https://docs.ollama.com/cloud), follow these steps:
+
+    1. Sign in to Ollama Cloud. 
+    In a terminal, enter `ollama signin` to connect your local environment with Ollama Cloud.
+    2. To run the model, in Ollama, select the `gpt-oss:20b-cloud` model, or run `ollama run gpt-oss:20b-cloud` in a terminal.
+    Ollama Cloud models are run at the same URL as your local Ollama server at `http://localhost:11434`, and automatically offloaded to Ollama's cloud service.
+    3. Connect OpenRAG to the same local Ollama server as you would for local models in onboarding, using the default address of `http://localhost:11434`.
+    4. In the **Language model** field, select the `gpt-oss:20b-cloud` model.
+<br></br>
+To run models on a **remote Ollama server**, follow these steps:
+
+    1. Ensure your remote Ollama server is accessible from your OpenRAG instance.
+    2. In the **Ollama Base URL** field, enter your remote Ollama server's base URL, such as `http://your-remote-server:11434`.
+    OpenRAG connects to the remote Ollama server and populates the lists with the server's available models.
+    3. Select your **Embedding model** and **Language model** from the available options.

docs/docs/_partial-onboarding.mdx

@@ -1,136 +1,79 @@
 import Icon from "@site/src/components/icon/icon";
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
-import PartialOllamaModels from '@site/docs/_partial-ollama-models.mdx';
+import PartialOllama from '@site/docs/_partial-ollama.mdx';
 
-## Complete the application onboarding process {#application-onboarding}
+## Application onboarding
 
-The first time you start the OpenRAG application, you must complete the application onboarding process to select language and embedding models that are essential for OpenRAG features like the [**Chat**](/chat).
+The first time you start OpenRAG, regardless of how you installed it, you must complete application onboarding.
 
 Some of these variables, such as the embedding models, can be changed seamlessly after onboarding.
 Others are immutable and require you to destroy and recreate the OpenRAG containers.
-For more information, see the [OpenRAG environment variables reference](/reference/configuration).
+For more information, see [Environment variables](/reference/configuration).
 
-You can use different providers for your language model and embedding model, such as Anthropic for the language model and OpenAI for the embedding model.
+You can use different providers for your language model and embedding model, such as Anthropic for the language model and OpenAI for the embeddings model.
 Additionally, you can set multiple embedding models.
 
 You only need to complete onboarding for your preferred providers.
 
-<Tabs groupId="Provider">
-<TabItem value="Anthropic" label="Anthropic" default>
+    <Tabs groupId="Provider">
+    <TabItem value="Anthropic" label="Anthropic" default>
 
-:::info
-Anthropic doesn't provide embedding models. If you select Anthropic for your language model, you must select a different provider for the embedding model.
-:::
+    :::info
+    Anthropic doesn't provide embedding models. If you select Anthropic for your language model, you must select a different provider for embeddings.
+    :::
 
-1. Enter your Anthropic API key, or enable **Use environment API key** to pull the key from your [OpenRAG `.env` file](/reference/configuration).
+    1. Enable **Use environment Anthropic API key** to automatically use your key from the `.env` file.
+    Alternatively, paste an Anthropic API key into the field.
+    2. Under **Advanced settings**, select your **Language Model**.
+    3. Click **Complete**.
+    4. In the second onboarding panel, select a provider for embeddings and select your **Embedding Model**.
+    5. To complete the onboarding tasks, click **What is OpenRAG**, and then click **Add a Document**.
+    Alternatively, click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
+    6. Continue with the [Quickstart](/quickstart).
 
-2. Under **Advanced settings**, select the language model that you want to use.
+    </TabItem>
+    <TabItem value="OpenAI" label="OpenAI">
 
-3. Click **Complete**.
+    1. Enable **Get API key from environment variable** to automatically enter your key from the TUI-generated `.env` file.
+    Alternatively, paste an OpenAI API key into the field.
+    2. Under **Advanced settings**, select your **Language Model**.
+    3. Click **Complete**.
+    4. In the second onboarding panel, select a provider for embeddings and select your **Embedding Model**.
+    5. To complete the onboarding tasks, click **What is OpenRAG**, and then click **Add a Document**.
+    Alternatively, click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
+    6. Continue with the [Quickstart](/quickstart).
 
-4. Select a provider for embeddings, provide the required information, and then select the embedding model you want to use.
-For information about another provider's credentials and settings, see the instructions for that provider.
+    </TabItem>
+    <TabItem value="IBM watsonx.ai" label="IBM watsonx.ai">
 
-5. Click **Complete**.
+    1. Complete the fields for **watsonx.ai API Endpoint**, **IBM Project ID**, and **IBM API key**.
+    These values are found in your IBM watsonx deployment.
+    2. Under **Advanced settings**, select your **Language Model**.
+    3. Click **Complete**.
+    4. In the second onboarding panel, select a provider for embeddings and select your **Embedding Model**.
+    5. To complete the onboarding tasks, click **What is OpenRAG**, and then click **Add a Document**.
+    Alternatively, click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
+    6. Continue with the [Quickstart](/quickstart).
 
-   After you configure the embedding model, OpenRAG uses your credentials and models to ingest some [initial documents](/knowledge#default-documents). This tests the connection, and it allows you to ask OpenRAG about itself in the [**Chat**](/chat).
-   If there is a problem with the model configuration, an error occurs and you are redirected back to the application onboarding screen.
-   Verify that the credential is valid and has access to the selected model, and then click **Complete** to retry ingestion.
+    </TabItem>
+    <TabItem value="Ollama" label="Ollama">
 
-6. Continue through the overview slides for a brief introduction to OpenRAG, or click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
-The overview demonstrates some basic functionality that is covered in the [quickstart](/quickstart#chat-with-documents) and in other parts of the OpenRAG documentation.
+    :::info
+    Ollama isn't installed with OpenRAG. To install Ollama, see the [Ollama documentation](https://docs.ollama.com/).
+    :::
 
-</TabItem>
-<TabItem value="IBM watsonx.ai" label="IBM watsonx.ai">
+    1. To connect to an Ollama server running on your local machine, enter your Ollama server's base URL address.
+    The default Ollama server address is `http://localhost:11434`.
+    OpenRAG connects to the Ollama server and populates the model lists with the server's available models.
+    2. Select the **Embedding Model** and **Language Model** your Ollama server is running.
+        <details closed>
+        <summary>Ollama model selection and external server configuration</summary>
+        <PartialOllama />
+        </details>
+    3. Click **Complete**.
+    4. To complete the onboarding tasks, click **What is OpenRAG**, and then click **Add a Document**.
+    5. Continue with the [Quickstart](/quickstart).
 
-1. For **watsonx.ai API Endpoint**, select the base URL for your watsonx.ai model deployment.
-
-2. Enter your watsonx.ai deployment's project ID and API key.
-
-   You can enable **Use environment API key** to pull the key from your [OpenRAG `.env` file](/reference/configuration).
-
-3. Under **Advanced settings**, select the language model that you want to use.
-
-4. Click **Complete**.
-
-5. Select a provider for embeddings, provide the required information, and then select the embedding model you want to use.
-For information about another provider's credentials and settings, see the instructions for that provider.
-
-6. Click **Complete**.
-
-   After you configure the embedding model, OpenRAG uses your credentials and models to ingest some [initial documents](/knowledge#default-documents). This tests the connection, and it allows you to ask OpenRAG about itself in the [**Chat**](/chat).
-   If there is a problem with the model configuration, an error occurs and you are redirected back to the application onboarding screen.
-   Verify that the credentials are valid and have access to the selected model, and then click **Complete** to retry ingestion.
-
-7. Continue through the overview slides for a brief introduction to OpenRAG, or click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
-The overview demonstrates some basic functionality that is covered in the [quickstart](/quickstart#chat-with-documents) and in other parts of the OpenRAG documentation.
-
-</TabItem>
-<TabItem value="Ollama" label="Ollama">
-
-Using Ollama as your language and embedding model provider offers greater flexibility and configuration options for hosting models.
-However, it requires additional setup because Ollama isn't included with OpenRAG.
-You must deploy Ollama separately if you want to use Ollama as a model provider.
-
-:::info
-<PartialOllamaModels />
-:::
-
-1. [Install Ollama locally or on a remote server](https://docs.ollama.com/), or [run models in Ollama Cloud](https://docs.ollama.com/cloud).
-
-   If you are running a remote server, it must be accessible from your OpenRAG deployment.
-
-2. In the OpenRAG onboarding dialog, enter your Ollama server's base URL:
-
-   * **Local Ollama server**: Enter your Ollama server's base URL and port. The default Ollama server address is `http://localhost:11434`.
-   * **Ollama Cloud**: Because Ollama Cloud models run at the same address as a local Ollama server and automatically offload to Ollama's cloud service, you can use the same base URL and port as you would for a local Ollama server. The default address is `http://localhost:11434`.
-   * **Remote server**: Enter your remote Ollama server's base URL and port, such as `http://your-remote-server:11434`.
-
-3. Select the language model that your Ollama server is running.
-
-   If your server isn't running any language models, you must either deploy a language model on your Ollama server, or use another provider for the language model.
-
-   Language model and embedding model selections are independent.
-   You can use the same or different servers for each model.
-
-   To use different providers for each model, you must configure both providers, and select the relevant model for each provider.
-
-4. Click **Complete**.
-
-5. Select a provider for embeddings, provide the required information, and then select the embedding model you want to use.
-For information about another provider's credentials and settings, see the instructions for that provider.
-
-6. Click **Complete**.
-
-   After you configure the embedding model, OpenRAG uses your credentials and models to ingest some [initial documents](/knowledge#default-documents). This tests the connection, and it allows you to ask OpenRAG about itself in the [**Chat**](/chat).
-   If there is a problem with the model configuration, an error occurs and you are redirected back to the application onboarding screen.
-   Verify that the server address is valid, and that the selected model is running on the server.
-   Then, click **Complete** to retry ingestion.
-
-7. Continue through the overview slides for a brief introduction to OpenRAG, or click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
-The overview demonstrates some basic functionality that is covered in the [quickstart](/quickstart#chat-with-documents) and in other parts of the OpenRAG documentation.
-
-</TabItem>
-<TabItem value="OpenAI" label="OpenAI (default)">
-
-1. Enter your OpenAI API key, or enable **Use environment API key** to pull the key from your [OpenRAG `.env` file](/reference/configuration).
-
-2. Under **Advanced settings**, select the language model that you want to use.
-
-3. Click **Complete**.
-
-4. Select a provider for embeddings, provide the required information, and then select the embedding model you want to use.
-For information about another provider's credentials and settings, see the instructions for that provider.
-
-5. Click **Complete**.
-
-   After you configure the embedding model, OpenRAG uses your credentials and models to ingest some [initial documents](/knowledge#default-documents). This tests the connection, and it allows you to ask OpenRAG about itself in the [**Chat**](/chat).
-   If there is a problem with the model configuration, an error occurs and you are redirected back to the application onboarding screen.
-   Verify that the credential is valid and has access to the selected model, and then click **Complete** to retry ingestion.
-
-6. Continue through the overview slides for a brief introduction to OpenRAG, or click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
-The overview demonstrates some basic functionality that is covered in the [quickstart](/quickstart#chat-with-documents) and in other parts of the OpenRAG documentation.
-
-</TabItem>
-</Tabs>
+    </TabItem>
+    </Tabs>

docs/docs/_partial-opensearch-auth-mode.mdx

@@ -1,11 +0,0 @@
-* **No-auth mode**: If you select **Basic Setup** in the [TUI](/tui), or your [OpenRAG `.env` file](/reference/configuration) doesn't include OAuth credentials, then the OpenRAG OpenSearch instance runs in no-auth mode.
-
-   This mode uses one anonymous JWT token for OpenSearch authentication.
-   There is no differentiation between users; all users that access your OpenRAG instance can access all documents uploaded to your knowledge base.
-
-* **OAuth mode**: If you select **Advanced Setup** in the [TUI](/tui), or your [OpenRAG `.env` file](/reference/configuration) includes OAuth credentials, then the OpenRAG OpenSearch instance runs in OAuth mode.
-
-   This mode uses a unique JWT token for each OpenRAG user, and each document is tagged with user ownership.
-   Documents are filtered by user owner; users see only the documents that they uploaded or have access to through their cloud storage accounts.
-
-   To enable OAuth mode after initial setup, see [Ingest files with OAuth connectors](/ingestion#oauth-ingestion).

docs/docs/_partial-prereq-common.mdx

@@ -1,20 +0,0 @@
-import PartialOllamaModels from '@site/docs/_partial-ollama-models.mdx';
-
-* Gather the credentials and connection details for your preferred model providers.
-You must have access to at least one language model and one embedding model.
-If a provider offers both types, you can use the same provider for both models.
-If a provider offers only one type, you must select two providers.
-
-   * **OpenAI**: Create an [OpenAI API key](https://platform.openai.com/api-keys).
-   * **Anthropic**: Create an [Anthropic API key](https://www.anthropic.com/docs/api/reference).
-   Anthropic provides language models only; you must select an additional provider for embeddings.
-   * **IBM watsonx.ai**: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
-   * **Ollama**: Deploy an [Ollama instance and models](https://docs.ollama.com/) locally, in the cloud, or on a remote server. Then, get your Ollama server's base URL and the names of the models that you want to use.
-
-      :::info
-      <PartialOllamaModels />
-      :::
-
-* Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine.
-If you don't have GPU capabilities, OpenRAG provides an alternate CPU-only deployment that is suitable for most use cases.
-The default CPU-only deployment doesn't prevent you from using GPU acceleration in external services, such as Ollama servers.

docs/docs/_partial-prereq-no-script.mdx

@@ -1,10 +0,0 @@
-* Install [uv](https://docs.astral.sh/uv/getting-started/installation/).
-
-* Install [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/).
-
-   The OpenRAG team recommends, at minimum, 8 GB of RAM for container VMs.
-   However, if you plan to upload large files regularly, more RAM is recommended.
-   For more information, see [Troubleshoot OpenRAG](/support/troubleshoot).
-
-* Install [`podman-compose`](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or [Docker Compose](https://docs.docker.com/compose/install/).
-To use Docker Compose with Podman, you must alias Docker Compose commands to Podman commands.

docs/docs/_partial-prereq-python.mdx

@@ -1 +0,0 @@
-* Install [Python](https://www.python.org/downloads/release/python-3100/) version 3.13 or later.

docs/docs/_partial-prereq-windows.mdx

@@ -1,2 +0,0 @@
-* For Microsoft Windows, you must use the Windows Subsystem for Linux (WSL).
-See [Install OpenRAG on Windows](/install-windows) before proceeding.

docs/docs/_partial-setup.mdx

@@ -1,126 +0,0 @@
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-import PartialOpenSearchAuthMode from '@site/docs/_partial-opensearch-auth-mode.mdx';
-
-You can use either **Basic Setup** or **Advanced Setup** to configure OpenRAG.
-This choice determines how OpenRAG authenticates with your deployment's [OpenSearch instance](/knowledge), and it controls user access to documents stored in your OpenSearch knowledge base:
-
-<PartialOpenSearchAuthMode />
-
-:::info
-You must use **Advanced Setup** if you want to [use OAuth connectors to upload documents from cloud storage](/ingestion#oauth-ingestion).
-:::
-
-If OpenRAG detects OAuth credentials during setup, it recommends **Advanced Setup** in the TUI.
-
-<Tabs groupId="Setup method">
-<TabItem value="Basic setup" label="Basic setup" default>
-
-1. In the TUI, select **Basic Setup**.
-
-2. Enter administrator passwords for the OpenRAG OpenSearch and Langflow services.
-
-   The OpenSearch password is required, and a secure password is automatically generated if you don't provide one manually.
-
-   The Langflow password is recommended but optional.
-   If the Langflow password is empty, the Langflow server starts without authentication enabled. For more information, see [Langflow settings](/reference/configuration#langflow-settings).
-
-   You can click **Generate Password** to create a Langflow password and username automatically.
-
-3. Optional: Under **API Keys**, enter your model provider credentials, or leave these fields empty if you want to configure model provider credentials during the application onboarding process.
-
-   There is no material difference between providing these values now or during the [application onboarding process](#application-onboarding).
-   If you provide a credential now, it can be populated automatically during the application onboarding process if you enable the **Use environment API key** option.
-
-   OpenRAG's core functionality requires access to language and embedding models.
-   By default, OpenRAG uses OpenAI models.
-   If you aren't sure which models or providers to use, you must provide an OpenAI API key to use OpenRAG's default model configuration.
-
-4. Optional: Under **Others**, edit the [knowledge base](/knowledge) paths if you don't want to use the default paths:
-
-   * **Documents Paths**: One or more paths to directories are where OpenRAG looks for documents to ingest.
-   * **OpenSearch Data Path**: Specify the path where you want OpenRAG to create your OpenSearch index.
-
-5. Click **Save Configuration**.
-
-   Your passwords and API keys, if provided, are stored in the [OpenRAG `.env` file](/reference/configuration) at `~/.openrag/tui`.
-   If you modified any credentials that were pulled from an existing `.env` file, those values are updated in the `.env` file.
-
-6. Click **Start OpenRAG** to start the OpenRAG services.
-
-   This process can take some time while OpenRAG pulls and runs the container images.
-   If all services start successfully, the TUI prints a confirmation message:
-
-   ```text
-   Services started successfully
-   Command completed successfully
-   ```
-
-7. Click **Close**, and then click **Launch OpenRAG** or navigate to `localhost:3000` in your browser.
-
-8. Continue with the [application onboarding process](#application-onboarding).
-
-</TabItem>
-<TabItem value="Advanced setup" label="Advanced setup">
-
-1. In the TUI, select **Advanced Setup**.
-
-2. Enter administrator passwords for the OpenRAG OpenSearch and Langflow services.
-
-   The OpenSearch password is required, and a secure password is automatically generated if you don't provide one manually.
-
-   The Langflow password is recommended but optional.
-   If the Langflow password is empty, the Langflow server starts without authentication enabled. For more information, see [Langflow settings](/reference/configuration#langflow-settings).
-
-   You can click **Generate Password** to create a Langflow password and username automatically.
-
-3. Optional: Under **API Keys**, enter your model provider credentials, or leave the **OpenAI**, **Anthropic**, **Ollama**, and **IBM watsonx.ai** fields empty if you want to configure model provider credentials during the application onboarding process.
-
-   There is no material difference between providing these values now or during the [application onboarding process](#application-onboarding).
-   If you provide a credential now, it can be populated automatically during the application onboarding process if you enable the **Use environment API key** option.
-
-   OpenRAG's core functionality requires access to language and embedding models.
-   By default, OpenRAG uses OpenAI models.
-   If you aren't sure which models or providers to use, you must provide an OpenAI API key to use OpenRAG's default model configuration.
-
-4. Recommended: To upload documents from external storage, such as Google Drive, add the required OAuth credentials for the connectors that you want to use under **API Keys**. These settings can be populated automatically if OpenRAG detects these credentials in an [OpenRAG `.env` file](/reference/configuration) at `~/.openrag/tui`.
-
-   * **Google**: Provide your Google OAuth Client ID and Google OAuth Client Secret. You can generate these in the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). For more information, see the [Google OAuth client documentation](https://developers.google.com/identity/protocols/oauth2).
-   * **Microsoft**: For the Microsoft OAuth Client ID and Microsoft OAuth Client Secret, provide [Azure application registration credentials for SharePoint and OneDrive](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/app-registration?view=odsp-graph-online). For more information, see the [Microsoft Graph OAuth client documentation](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/graph-oauth).
-   * **Amazon**: Provide your AWS Access Key ID and AWS Secret Access Key with access to your S3 instance. For more information, see the AWS documentation on [Configuring access to AWS applications](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html).
-
-   You can [manage OAuth credentials](/ingestion#oauth-ingestion) later, but it is recommended to configure them during initial set up.
-
-5. Register the redirect URIs shown in the TUI in your OAuth provider.
-These are the URLs your OAuth provider will use to redirect users back to OpenRAG after they sign in.
-
-6. Optional: Under **Others**, you can edit the following settings if needed:
-
-   * **Documents Paths**: Use the default path or provide one or more paths to directories are where OpenRAG looks for documents to ingest in to your [knowledge base](/knowledge).
-   * **OpenSearch Data Path**: Specify the path where you want OpenRAG to create your OpenSearch index.
-   * **Langflow Public URL (`LANGFLOW_PUBLIC_URL`)** : Sets the base address to access the Langflow web interface. This is where users interact with flows in a browser.
-   * **Webhook Base URL (`WEBHOOK_BASE_URL`)**: If applicable, set the base address for your OAuth connector endpoints. If set, the OAuth connector webhook URLs are constructed as `WEBHOOK_BASE_URL/connectors/${provider}/webhook`.
-
-7. Click **Save Configuration**.
-
-   Your passwords, API key, and OAuth credentials, if provided, are stored in the [OpenRAG `.env` file](/reference/configuration) at `~/.openrag/tui`.
-   If you modified any credentials that were pulled from an existing `.env` file, those values are updated in the `.env` file.
-
-8. Click **Start OpenRAG** to start the OpenRAG services.
-
-   This process can take some time while OpenRAG pulls and runs the container images.
-   If all services start successfully, the TUI prints a confirmation message:
-
-   ```text
-   Services started successfully
-   Command completed successfully
-   ```
-
-9. Click **Close**, and then click **Launch OpenRAG** or navigate to `localhost:3000` in your browser.
-
-10. If you enabled OAuth connectors, you must sign in to your OAuth provider before being redirected to your OpenRAG instance.
-
-11. Continue with the [application onboarding process](#application-onboarding).
-
-</TabItem>
-</Tabs>

docs/docs/_partial-temp-knowledge.mdx

@@ -1,5 +1,5 @@
 import Icon from "@site/src/components/icon/icon";
 
-When using the OpenRAG **Chat**, click <Icon name="Plus" aria-hidden="true"/> **Add** in the chat input field to upload a file to the current chat session.
+When using the OpenRAG **Chat**, click <Icon name="Plus" aria-hidden="true"/> in the chat input field to upload a file to the current chat session.
 Files added this way are processed and made available to the agent for the current conversation only.
 These files aren't stored in the knowledge base permanently.

docs/docs/_partial-wsl-install.mdx

@@ -1,21 +1,4 @@
----
-title: Install OpenRAG on Microsoft Windows
-slug: /install-windows
----
-
-If you're using Windows, you must install OpenRAG within the Windows Subsystem for Linux (WSL).
-
-:::warning
-Nested virtualization isn't supported.
-
-OpenRAG isn't compatible with nested virtualization, which can cause networking issues.
-Don't install OpenRAG on a WSL distribution that is installed inside a Windows VM.
-Instead, install OpenRAG on your base OS or a non-nested Linux VM.
-:::
-
-## Install OpenRAG in the WSL
-
-1. [Install WSL](https://learn.microsoft.com/en-us/windows/wsl/install) with an Ubuntu distribution using WSL 2:
+1. [Install WSL](https://learn.microsoft.com/en-us/windows/wsl/install) with the Ubuntu distribution using WSL 2:
 
    ```powershell
    wsl --install -d Ubuntu
@@ -25,18 +8,18 @@ Instead, install OpenRAG on your base OS or a non-nested Linux VM.
 
    For existing WSL installations, you can [change the distribution](https://learn.microsoft.com/en-us/windows/wsl/install#change-the-default-linux-distribution-installed) and [check the WSL version](https://learn.microsoft.com/en-us/windows/wsl/install#upgrade-version-from-wsl-1-to-wsl-2).
 
+   :::warning Known limitation
+   OpenRAG isn't compatible with nested virtualization, which can cause networking issues.
+   Don't install OpenRAG on a WSL distribution that is installed inside a Windows VM.
+   Instead, install OpenRAG on your base OS or a non-nested Linux VM.
+   :::
+
 2. [Start your WSL Ubuntu distribution](https://learn.microsoft.com/en-us/windows/wsl/install#ways-to-run-multiple-linux-distributions-with-wsl) if it doesn't start automatically.
 
 3. [Set up a username and password for your WSL distribution](https://learn.microsoft.com/en-us/windows/wsl/setup/environment#set-up-your-linux-username-and-password).
 
 4. [Install Docker Desktop for Windows with WSL 2](https://learn.microsoft.com/en-us/windows/wsl/tutorials/wsl-containers). When you reach the Docker Desktop **WSL integration** settings, make sure your Ubuntu distribution is enabled, and then click **Apply & Restart** to enable Docker support in WSL.
 
-   The Docker Desktop WSL integration makes Docker available within your WSL distribution.
-   You don't need to install Docker or Podman separately in your WSL distribution before you install OpenRAG.
-
 5. Install and run OpenRAG from within your WSL Ubuntu distribution.
-You can install OpenRAG in your WSL distribution using any of the [OpenRAG installation methods](/install-options).
-
-## Troubleshoot OpenRAG in WSL
-
+<br/>
 If you encounter issues with port forwarding or the Windows Firewall, you might need to adjust the [Hyper-V firewall settings](https://learn.microsoft.com/en-us/windows/security/operating-system-security/network-security/windows-firewall/hyper-v-firewall) to allow communication between your WSL distribution and the Windows host. For more troubleshooting advice for networking issues, see [Troubleshooting WSL common issues](https://learn.microsoft.com/en-us/windows/wsl/troubleshooting#common-issues).

docs/docs/core-components/agents.mdx

@@ -4,6 +4,8 @@ slug: /agents
 ---
 
 import Icon from "@site/src/components/icon/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 
 OpenRAG includes a built-in [Langflow](https://docs.langflow.org/) instance for creating and managing functional application workflows called _flows_.
 In a flow, the individual workflow steps are represented by [_components_](https://docs.langflow.org/concepts-components) that are connected together to form a complete process.
@@ -20,48 +22,35 @@ You can customize these flows and create your own flows using OpenRAG's embedded
 
 All OpenRAG flows are designed to be modular, performant, and provider-agnostic.
 
-To view and modify a flow in OpenRAG, click <Icon name="Settings2" aria-hidden="true"/> **Settings**.
-From here, you can manage OAuth connectors, model providers, and common parameters for the **Agent** and **Knowledge Ingestion** flows.
+To modify a flow in OpenRAG, click <Icon name="Settings2" aria-hidden="true"/> **Settings**.
+From here, you can quickly edit commonly used parameters, such as the **Language model** and **Agent Instructions**.
+To further explore and edit the flow, click **Edit in Langflow** to launch the embedded [Langflow visual editor](https://docs.langflow.org/concepts-overview) where you can fully [customize the flow](https://docs.langflow.org/concepts-flows) to suit your use case.
 
-To further explore and edit flows, click **Edit in Langflow** to launch the embedded [Langflow visual editor](https://docs.langflow.org/concepts-overview) where you can fully [customize the flow](https://docs.langflow.org/concepts-flows) to suit your use case.
+For example, to view and edit the built-in **Chat** flow (the **OpenRAG OpenSearch Agent** flow), do the following:
 
-:::tip
-After you click **Edit in Langflow**, you can access and edit all of OpenRAG's built-in flows from the Langflow editor's [**Projects** page](https://docs.langflow.org/concepts-flows#projects).
+1. In OpenRAG, click <Icon name="MessageSquare" aria-hidden="true"/> **Chat**.
 
-If you edit any flows other than the **Agent** or **Knowledge Ingestion** flows, it is recommended that you [export the flows](https://docs.langflow.org/concepts-flows-import) before editing so you can revert them to their original state if needed.
-:::
-
-For example, the following steps explain how to edit the built-in **Agent** flow, which is the **OpenRAG OpenSearch Agent** flow used for the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**:
-
-1. In OpenRAG, click <Icon name="Settings2" aria-hidden="true"/> **Settings**, and then find the **Agent** section.
-
-2. If you only need to edit the language model or agent instructions, edit those fields directly on the **Settings** page.
-Language model changes are saved automatically.
-To apply new instructions, click **Save Agent Instructions**.
-
-3. To edit all flow settings and components with full customization capabilities, click **Edit in Langflow** to launch the Langflow visual editor in a new browser tab.
+2. Click <Icon name="Settings2" aria-hidden="true"/> **Settings**, and then click **Edit in Langflow** to launch the Langflow visual editor in a new browser window.
 
    If prompted to acknowledge that you are entering Langflow, click **Proceed**.
 
-   If Langflow requests login information, enter the `LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD` from your [OpenRAG `.env` file](/reference/configuration).
+   If Langflow requests login information, enter the `LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD` from the `.env` file in your OpenRAG installation directory.
 
    ![OpenRAG OpenSearch Agent flow](/img/opensearch-agent-flow.png)
 
-4. Modify the flow as desired, and then press <kbd>Command</kbd>+<kbd>S</kbd> (<kbd>Ctrl</kbd>+<kbd>S</kbd>) to save your changes.
+3. Modify the flow as desired, and then press <kbd>Command</kbd>+<kbd>S</kbd> (<kbd>Ctrl</kbd>+<kbd>S</kbd>) to save your changes.
 
-   You can close the Langflow browser tab, or leave it open if you want to continue experimenting with the flow editor.
+   You can close the Langflow browser window, or leave it open if you want to continue experimenting with the flow editor.
 
-5. After you modify any **Agent** flow settings, go to the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**, and then click <Icon name="Plus" aria-hidden="true"/> **Start new conversation** in the **Conversations** list.
-This ensures that the chat doesn't persist any context from the previous conversation with the original flow settings.
+   :::tip
+   If you modify the built-in **Chat** flow, make sure you click <Icon name="Plus" aria-hidden="true"/> in the **Conversations** tab to start a new conversation. This ensures that the chat doesn't persist any context from the previous conversation with the original flow settings.
+   :::
 
 ### Revert a built-in flow to its original configuration {#revert-a-built-in-flow-to-its-original-configuration}
 
-After you edit the **Agent** or **Knowledge Ingestion** built-in flows, you can click **Restore flow** on the **Settings** page to revert either flow to its original state when you first installed OpenRAG.
+After you edit a built-in flow, you can click **Restore flow** on the **Settings** page to revert the flow to its original state when you first installed OpenRAG.
 This is a destructive action that discards all customizations to the flow.
 
-This option isn't available for other built-in flows such as the **Nudges** flow.
-To restore these flows to their original state, you must reimport the flow from a backup (if you exported one before editing), or [reset](/manage-services#reset-containers) or [reinstall](/reinstall) OpenRAG.
-
 ## Build custom flows and use other Langflow functionality
 
 In addition to OpenRAG's built-in flows, all Langflow features are available through OpenRAG, including the ability to [create your own flows](https://docs.langflow.org/concepts-flows) and popular extensibility features such as the following:
@@ -72,17 +61,11 @@ In addition to OpenRAG's built-in flows, all Langflow features are available thr
 
 Explore the [Langflow documentation](https://docs.langflow.org/) to learn more about the Langflow platform, features, and visual editor.
 
-## Modify a flow at runtime {#modify-a-flow-at-runtime}
-
-You can use _tweaks_ to modify flow settings at runtime without permanently changing the flow's configuration.
-Tweaks are one-time parameter modifications that are passed to specific Langflow components during flow execution.
-For more information on tweaks, see the Langflow documentation on [Input schema (tweaks)](https://docs.langflow.org/concepts-publish#input-schema).
-
 ## Set the Langflow version
 
 By default, OpenRAG is pinned to the latest Langflow Docker image for stability.
 
-If necessary, you can set a specific Langflow version with the `LANGFLOW_VERSION` [environment variable](/reference/configuration). However, there are risks to changing this setting:
+If necessary, you can set a specific Langflow version with the [`LANGFLOW_VERSION`](/reference/configuration). However, there are risks to changing this setting:
 
 * The [Langflow documentation](https://docs.langflow.org/) describes the functionality present in the latest release of the Langflow OSS Python package. If your `LANGFLOW_VERSION` is different, the Langflow documentation might not align with the features and default settings in your OpenRAG installation.
 

docs/docs/core-components/chat.mdx

@@ -11,7 +11,7 @@ import PartialTempKnowledge from '@site/docs/_partial-temp-knowledge.mdx';
 
 After you [upload documents to your knowledge base](/ingestion), you can use the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat** feature to interact with your knowledge through natural language queries.
 
-The OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat** uses an LLM-powered agent to understand your queries, retrieve relevant information from your knowledge base, and generate context-aware responses.
+The OpenRAG **Chat** uses an LLM-powered agent to understand your queries, retrieve relevant information from your knowledge base, and generate context-aware responses.
 The agent can also fetch information from URLs and new documents that you provide during the chat session.
 To limit the knowledge available to the agent, use [filters](/knowledge-filters).
 
@@ -24,17 +24,17 @@ Try chatting, uploading documents, and modifying chat settings in the [quickstar
 
 ## OpenRAG OpenSearch Agent flow {#flow}
 
-When you use the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**, the **OpenRAG OpenSearch Agent** flow runs in the background to retrieve relevant information from your knowledge base and generate a response.
+When you use the OpenRAG **Chat**, the **OpenRAG OpenSearch Agent** flow runs in the background to retrieve relevant information from your knowledge base and generate a response.
 
 If you [inspect the flow in Langflow](/agents#inspect-and-modify-flows), you'll see that it is comprised of eight components that work together to ingest chat messages, retrieve relevant information from your knowledge base, and then generate responses.
 When you inspect this flow, you can edit the components to customize the agent's behavior.
 
 ![OpenRAG Open Search Agent Flow](/img/opensearch-agent-flow.png)
 
-* [**Chat Input** component](https://docs.langflow.org/chat-input-and-output#chat-input): This component starts the flow when it receives a chat message. It is connected to the **Agent** component's **Input** port.
-When you use the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**, your chat messages are passed to the **Chat Input** component, which then sends them to the **Agent** component for processing.
+* [**Chat Input** component](https://docs.langflow.org/components-io): This component starts the flow when it receives a chat message. It is connected to the **Agent** component's **Input** port.
+When you use the OpenRAG **Chat**, your chat messages are passed to the **Chat Input** component, which then sends them to the **Agent** component for processing.
 
-* [**Agent** component](https://docs.langflow.org/components-agents): This component orchestrates the entire flow by processing chat messages, searching the knowledge base, and organizing the retrieved information into a cohesive response.
+* [**Agent** component](https://docs.langflow.org/agents): This component orchestrates the entire flow by processing chat messages, searching the knowledge base, and organizing the retrieved information into a cohesive response.
 The agent's general behavior is defined by the prompt in the **Agent Instructions** field and the model connected to the **Language Model** port.
 One or more specialized tools can be attached to the **Tools** port to extend the agent's capabilities. In this case, there are two tools: **MCP Tools** and **OpenSearch**.
 
@@ -66,25 +66,19 @@ This flow fetches content from URLs, and then stores the content in your OpenRAG
 
    It is critical that the embedding model used here matches the embedding model used when you [upload documents to your knowledge base](/ingestion). Mismatched models and dimensions can degrade the quality of similarity search results causing the agent to retrieve irrelevant documents from your knowledge base.
 
-* [**Text Input** component](https://docs.langflow.org/text-input-and-output#text-input): Connected to the **OpenSearch** component's **Search Filters** port, this component is populated with a Langflow global variable named `OPENRAG-QUERY-FILTER`. If a global or chat-level [knowledge filter](/knowledge-filters) is set, then the variable contains the filter expression, which limits the documents that the agent can access in the knowledge base.
+* [**Text Input** component](https://docs.langflow.org/components-io): Connected to the **OpenSearch** component's **Search Filters** port, this component is populated with a Langflow global variable named `OPENRAG-QUERY-FILTER`. If a global or chat-level [knowledge filter](/knowledge-filters) is set, then the variable contains the filter expression, which limits the documents that the agent can access in the knowledge base.
 If no knowledge filter is set, then the `OPENRAG-QUERY-FILTER` variable is empty, and the agent can access all documents in the knowledge base.
 
-* [**Chat Output** component](https://docs.langflow.org/chat-input-and-output#chat-output): Connected to the **Agent** component's **Output** port, this component returns the agent's generated response as a chat message.
+* [**Chat Output** component](https://docs.langflow.org/components-io): Connected to the **Agent** component's **Output** port, this component returns the agent's generated response as a chat message.
 
 ## Nudges {#nudges}
 
-When you use the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**, the **OpenRAG OpenSearch Nudges** flow runs in the background to pull additional context from your knowledge base and chat history.
+When you use the OpenRAG **Chat**, the **OpenRAG OpenSearch Nudges** flow runs in the background to pull additional context from your knowledge base and chat history.
 
-Nudges appear as prompts in the chat, and they are based on the contents of your OpenRAG OpenSearch knowledge base.
-Click a nudge to accept it and start a chat based on the nudge.
+Nudges appear as prompts in the chat.
+Click a nudge to accept it and provide the nudge's context to the OpenRAG **Chat** agent (the **OpenRAG OpenSearch Agent** flow).
 
 Like OpenRAG's other built-in flows, you can [inspect the flow in Langflow](/agents#inspect-and-modify-flows), and you can customize it if you want to change the nudge behavior.
-However, this flow is specifically designed to work with the OpenRAG chat and knowledge base.
-Major changes to this flow might break the nudge functionality or produce irrelevant nudges.
-
-The **Nudges** flow consists of **Embedding model**, **Language model**, **OpenSearch**, **Input/Output*, and other components that browse your knowledge base, identify key themes and possible insights, and then produce prompts based on the findings.
-
-For example, if your knowledge base contains documents about cybersecurity, possible nudges might include `Explain zero trust architecture principles` or `How to identify a social engineering attack`.
 
 ## Upload documents to the chat
 
@@ -105,12 +99,4 @@ To simplify this integration, you can get pre-configured code snippets directly
 
 The following example demonstrates how to generate and use code snippets for the **OpenRAG OpenSearch Agent** flow:
 
-<PartialIntegrateChat />
-
-## Troubleshoot chat {#troubleshoot-chat}
-
-The following issues can occur when using the OpenRAG **Chat** feature:
-
-* Documents seem to be missing or interpreted incorrectly: See [Troubleshoot ingestion](/ingestion#troubleshoot-ingestion).
-* Service is suddenly unavailable when it was working previously: If there is no other obvious cause, such as the service or container VM being stopped or disconnected, there might be a problem with the flow configuration. Use the [**Restore flow** option](/agents#revert-a-built-in-flow-to-its-original-configuration) to revert the **OpenRAG OpenSearch Agent** flow to its original configuration.
-If you made customizations to the flow, make sure to [export your flow](https://docs.langflow.org/concepts-flows-import) before restoring the flow.
+<PartialIntegrateChat />

docs/docs/core-components/ingestion.mdx

@@ -8,8 +8,6 @@ import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import PartialTempKnowledge from '@site/docs/_partial-temp-knowledge.mdx';
 import PartialIngestionFlow from '@site/docs/_partial-ingestion-flow.mdx';
-import PartialDockerComposeUp from '@site/docs/_partial-docker-compose-up.mdx';
-import PartialDockerStopAll from '@site/docs/_partial-docker-stop-all.mdx';
 
 Upload documents to your [OpenRAG OpenSearch instance](/knowledge) to populate your knowledge base with unique content, such as your own company documents, research papers, or websites.
 Documents are processed through OpenRAG's knowledge ingestion flows with Docling.
@@ -32,7 +30,7 @@ You can upload files and folders from your local machine to your knowledge base:
 
 3. To upload one file, click <Icon name="File" aria-hidden="true"/> **File**. To upload all documents in a folder, click <Icon name="Folder" aria-hidden="true"/> **Folder**.
 
-   The default path is `~/.openrag/documents`.
+   The default path is the `./documents` subdirectory in your OpenRAG installation directory.
    To change this path, see [Set the local documents path](/knowledge#set-the-local-documents-path).
 
 The selected files are processed in the background through the **OpenSearch Ingestion** flow.
@@ -64,41 +62,63 @@ Before users can connect their own cloud storage accounts, you must configure th
 To enable multiple connectors, you must register an app and generate credentials for each provider.
 
 <Tabs>
-<TabItem value="TUI" label="TUI-managed services" default>
+<TabItem value="TUI" label="TUI Advanced Setup" default>
+
+If you use the TUI to manage your OpenRAG containers, provide OAuth credentials in the **Advanced Setup**.
 
-If you use the [Terminal User Interface (TUI)](/tui) to manage your OpenRAG services, enter OAuth credentials on the **Advanced Setup** page.
 You can do this during [installation](/install#setup), or you can add the credentials afterwards:
 
-1. If OpenRAG is running, click **Stop All Services** in the TUI.
+1. If OpenRAG is running, stop it: Go to [**Status**](/install#tui-container-management), and then click **Stop Services**.
 
-2. Open the **Advanced Setup** page, and then add the OAuth credentials for the cloud storage providers that you want to use under **API Keys**:
+2. Click **Advanced Setup**, and then add the OAuth credentials for the cloud storage providers that you want to use:
 
+   * **Amazon**: Provide your AWS Access Key ID and AWS Secret Access Key with access to your S3 instance. For more information, see the AWS documentation on [Configuring access to AWS applications](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html).
    * **Google**: Provide your Google OAuth Client ID and Google OAuth Client Secret. You can generate these in the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). For more information, see the [Google OAuth client documentation](https://developers.google.com/identity/protocols/oauth2).
    * **Microsoft**: For the Microsoft OAuth Client ID and Microsoft OAuth Client Secret, provide [Azure application registration credentials for SharePoint and OneDrive](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/app-registration?view=odsp-graph-online). For more information, see the [Microsoft Graph OAuth client documentation](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/graph-oauth).
-   * **Amazon**: Provide your AWS Access Key ID and AWS Secret Access Key with access to your S3 instance. For more information, see the AWS documentation on [Configuring access to AWS applications](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html).
 
-3. Register the redirect URIs shown in the TUI in your OAuth provider.
-These are the URLs your OAuth provider will use to redirect users back to OpenRAG after they sign in.
+3. The OpenRAG TUI presents redirect URIs for your OAuth app that you must register with your OAuth provider.
+These are the URLs your OAuth provider will redirect back to after users authenticate and grant access to their cloud storage.
 
-4. Click **Save Configuration** to add the OAuth credentials to your [OpenRAG `.env` file](/reference/configuration).
+4. Click **Save Configuration**.
 
-5. Click **Start Services** to restart the OpenRAG containers with OAuth enabled.
+   OpenRAG regenerates the [`.env`](/reference/configuration) file with the given credentials.
 
-6. Launch the OpenRAG app.
-You should be prompted to sign in to your OAuth provider before being redirected to your OpenRAG instance.
+5. Click **Start Container Services**.
 
 </TabItem>
-<TabItem value="env" label="Self-managed services">
+<TabItem value="env" label="Docker Compose .env file">
 
-If you [installed OpenRAG with self-managed services](/docker), set OAuth credentials in your [OpenRAG `.env` file](/reference/configuration).
+If you [install OpenRAG with self-managed containers](/docker), set OAuth credentials in the `.env` file for Docker Compose.
 
-You can do this during [initial set up](/docker#setup), or you can add the credentials afterwards:
+You can do this during [initial set up](/docker#install-openrag-with-docker-compose), or you can add the credentials afterwards:
 
-1. Stop all OpenRAG containers:
+1. Stop your OpenRAG deployment.
 
-   <PartialDockerStopAll />
+   <Tabs>
+   <TabItem value="podman" label="Podman">
 
-2. Edit your OpenRAG `.env` file to add the OAuth credentials for the cloud storage providers that you want to use:
+   ```bash
+   podman stop --all
+   ```
+
+   </TabItem>
+   <TabItem value="docker" label="Docker">
+
+   ```bash
+   docker stop $(docker ps -q)
+   ```
+
+   </TabItem>
+   </Tabs>
+
+2. Edit the `.env` file for Docker Compose to add the OAuth credentials for the cloud storage providers that you want to use:
+
+   * **Amazon**: Provide your AWS Access Key ID and AWS Secret Access Key with access to your S3 instance. For more information, see the AWS documentation on [Configuring access to AWS applications](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html).
+
+      ```env
+      AWS_ACCESS_KEY_ID=
+      AWS_SECRET_ACCESS_KEY=
+      ```
 
    * **Google**: Provide your Google OAuth Client ID and Google OAuth Client Secret. You can generate these in the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). For more information, see the [Google OAuth client documentation](https://developers.google.com/identity/protocols/oauth2).
 
@@ -113,21 +133,27 @@ You can do this during [initial set up](/docker#setup), or you can add the crede
       MICROSOFT_GRAPH_OAUTH_CLIENT_ID=
       MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET=
       ```
-   * **Amazon**: Provide your AWS Access Key ID and AWS Secret Access Key with access to your S3 instance. For more information, see the AWS documentation on [Configuring access to AWS applications](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html).
-
-      ```env
-      AWS_ACCESS_KEY_ID=
-      AWS_SECRET_ACCESS_KEY=
-      ```
 
 3. Save the `.env` file.
 
-4. Restart your OpenRAG containers:
+4. Restart your OpenRAG deployment:
 
-   <PartialDockerComposeUp />
+   <Tabs>
+   <TabItem value="podman" label="Podman">
 
-5. Access the OpenRAG frontend at `http://localhost:3000`.
-You should be prompted to sign in to your OAuth provider before being redirected to your OpenRAG instance.
+   ```bash
+   podman-compose up -d
+   ```
+
+   </TabItem>
+   <TabItem value="docker" label="Docker">
+
+   ```bash
+   docker-compose up -d
+   ```
+
+   </TabItem>
+   </Tabs>
 
 </TabItem>
 </Tabs>
@@ -157,51 +183,35 @@ You can [monitor ingestion](#monitor-ingestion) to see the progress of the uploa
 
 ## Ingest knowledge from URLs {#url-flow}
 
-When using the OpenRAG chat, you can enter URLs into the chat to be ingested in real-time during your conversation.
-
-:::info
-The chat cannot ingest URLs that end in static document file extensions like `.pdf`.
-To upload these types of files, see [Ingest local files and folders](#ingest-local-files-and-folders) and [Ingest files with OAuth connectors](#oauth-ingestion).
-:::
-
-OpenRAG runs the **OpenSearch URL Ingestion** flow to ingest web content from URLs.
+The **OpenSearch URL Ingestion** flow is used to ingest web content from URLs.
 This flow isn't directly accessible from the OpenRAG user interface.
 Instead, this flow is called by the [**OpenRAG OpenSearch Agent** flow](/chat#flow) as a Model Context Protocol (MCP) tool.
 The agent can call this component to fetch web content from a given URL, and then ingest that content into your OpenSearch knowledge base.
+
 Like all OpenRAG flows, you can [inspect the flow in Langflow](/agents#inspect-and-modify-flows), and you can customize it.
+
 For more information about MCP in Langflow, see the Langflow documentation on [MCP clients](https://docs.langflow.org/mcp-client) and [MCP servers](https://docs.langflow.org/mcp-tutorial).
 
-## Monitor ingestion {#monitor-ingestion}
+## Monitor ingestion
 
-Depending on the amount of data to ingest, document ingestion can take a few seconds, minutes, or longer.
-For this reason, document ingestion tasks run in the background.
+Document ingestion tasks run in the background.
 
 In the OpenRAG user interface, a badge is shown on <Icon name="Bell" aria-hidden="true"/> **Tasks** when OpenRAG tasks are active.
-Click <Icon name="Bell" aria-hidden="true"/> **Tasks** to inspect and cancel tasks.
-Tasks are separated into multiple sections:
+Click <Icon name="Bell" aria-hidden="true"/> **Tasks** to inspect and cancel tasks:
 
-* The **Active Tasks** section includes all tasks that are **Pending**, **Running**, or **Processing**:
+* **Active Tasks**: All tasks that are **Pending**, **Running**, or **Processing**.
+For each active task, depending on its state, you can find the task ID, start time, duration, number of files processed, and the total files enqueued for processing.
 
-   * **Pending**: The task is queued and waiting to start.
-   * **Running**: The task is actively processing files.
-   * **Processing**: The task is performing ingestion operations.
+* **Pending**: The task is queued and waiting to start.
 
-   To stop an active task, click <Icon name="X" aria-hidden="true"/> **Cancel**. Canceling a task stops processing immediately and marks the ingestion as failed.
+* **Running**: The task is actively processing files.
 
-* The **Recent Tasks** section lists recently finished tasks.
+* **Processing**: The task is performing ingestion operations.
 
-   :::warning
-   **Completed** doesn't mean success.
+* **Failed**: Something went wrong during ingestion, or the task was manually canceled.
+For troubleshooting advice, see [Troubleshoot ingestion](#troubleshoot-ingestion).
 
-   A completed task can report successful ingestions, failed ingestions, or both, depending on the number of files processed.
-   :::
-
-   Check the **Success** and **Failed** counts for each completed task to determine the overall success rate.
-
-   **Failed** means something went wrong during ingestion, or the task was manually canceled.
-   For more information, see [Troubleshoot ingestion](#troubleshoot-ingestion).
-
-For each task, depending on its state, you can find the task ID, start time, duration, number of files processed successfully, number of files that failed, and the number of files enqueued for processing.
+To stop an active task, click <Icon name="X" aria-hidden="true"/> **Cancel**. Canceling a task stops processing immediately and marks the task as **Failed**.
 
 ### Ingestion performance expectations
 
@@ -228,7 +238,6 @@ All errors were file-specific, and they didn't stop the pipeline.
 
    * Machine: Apple M4 Pro
    * Podman VM:
-
      * Name: podman-machine-default
      * Type: applehv
      * vCPUs: 7
@@ -252,37 +261,26 @@ All errors were file-specific, and they didn't stop the pipeline.
 
 ## Troubleshoot ingestion {#troubleshoot-ingestion}
 
-The following issues can occur during document ingestion.
-
-### Failed or slow ingestion
-
 If an ingestion task fails, do the following:
 
-* Make sure you uploaded only supported file types.
-* Split very large files into smaller files.
-* Remove unusual or complex embedded content, such as videos or animations. Although Docling can replace some non-text content with placeholders during ingestion, some embedded content might cause errors.
-* Make sure your Podman/Docker VM has sufficient memory for the ingestion tasks.
-The minimum recommendation is 8 GB of RAM.
-If you regularly upload large files, more RAM is recommended.
-For more information, see [Memory issue with Podman on macOS](/support/troubleshoot#memory-issue-with-podman-on-macos) and [Container out of memory errors](/support/troubleshoot#container-out-of-memory-errors).
-* If OCR ingestion fails due to OCR missing, see [OCR ingestion fails (easyocr not installed)](/support/troubleshoot#ocr-ingestion-fails-easyocr-not-installed).
-
-### Problems when referencing documents in chat
+* Make sure you are uploading supported file types.
+* Split excessively large files into smaller files before uploading.
+* Remove unusual embedded content, such as videos or animations, before uploading. Although Docling can replace some non-text content with placeholders during ingestion, some embedded content might cause errors.
 
 If the OpenRAG **Chat** doesn't seem to use your documents correctly, [browse your knowledge base](/knowledge#browse-knowledge) to confirm that the documents are uploaded in full, and the chunks are correct.
 
 If the documents are present and well-formed, check your [knowledge filters](/knowledge-filters).
-If you applied a filter to the chat, make sure the expected documents aren't excluded by the filter settings.
-You can test this by applying the filter when you [browse the knowledge base](/knowledge#browse-knowledge).
-If the filter excludes any documents, the agent cannot access those documents.
-Be aware that some settings create dynamic filters that don't always produce the same results, such as a **Search query** combined with a low **Response limit**.
+If a global filter is applied, make sure the expected documents are included in the global filter.
+If the global filter excludes any documents, the agent cannot access those documents unless you apply a chat-level filter or change the global filter.
 
-If the document chunks have missing, incorrect, or unexpected text, you must [delete the documents](/knowledge#delete-knowledge) from your knowledge base, modify the [ingestion parameters](/knowledge#knowledge-ingestion-settings) or the documents themselves, and then reingest the documents.
+If text is missing or incorrectly processed, you need to reupload the documents after modifying the ingestion parameters or the documents themselves.
 For example:
 
 * Break combined documents into separate files for better metadata context.
 * Make sure scanned documents are legible enough for extraction, and enable the **OCR** option. Poorly scanned documents might require additional preparation or rescanning before ingestion.
-* Adjust the **Chunk size** and **Chunk overlap** settings to better suit your documents. Larger chunks provide more context but can include irrelevant information, while smaller chunks yield more precise semantic search but can lack context.
+* Adjust the **Chunk Size** and **Chunk Overlap** settings to better suit your documents. Larger chunks provide more context but can include irrelevant information, while smaller chunks yield more precise semantic search but can lack context.
+
+For more information about modifying ingestion parameters and flows, see [Knowledge ingestion settings](/knowledge#knowledge-ingestion-settings).
 
 ## See also
 

docs/docs/core-components/knowledge-filters.mdx

@@ -4,7 +4,8 @@ slug: /knowledge-filters
 ---
 
 import Icon from "@site/src/components/icon/icon";
-import PartialAnonymousUserOwner from '@site/docs/_partial-anonymous-user-owner.mdx';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 
 OpenRAG's knowledge filters help you organize and manage your [knowledge base](/knowledge) by creating pre-defined views of your documents.
 
@@ -27,61 +28,36 @@ After uploading your own documents, it is recommended that you create your own f
 
 To create a knowledge filter, do the following:
 
-1. Click <Icon name="Library" aria-hidden="true"/> **Knowledge**, and then click <Icon name="Plus" aria-hidden="true"/> **Knowledge Filters**.
+1. Click **Knowledge**, and then click <Icon name="Plus" aria-hidden="true"/> **Knowledge Filters**.
 
-2. Enter a **Name**.
+2. Enter a **Name** and **Description**, and then click **Create Filter**.
 
-3. Optional: Click the filter icon next to the filter name to select a different icon and color for the filter.
-This is purely cosmetic, but it can help you visually distinguish different sets of filters, such as different projects or sources.
+   By default, new filters match all documents in your knowledge base.
+   Modify the filter to customize it.
 
-4. Optional: Enter a **Description**.
-
-5. Customize the filter settings.
-
-   By default, filters match all documents in your knowledge base.
-   Use the filter settings to narrow the scope of documents that the filter captures:
-
-   * **Search Query**: Enter a natural language text string for semantic search.
-
-      When you apply a filter that has a **Search Query**, only documents matching the search query are included.
-      It is recommended that you also use the **Score Threshold** setting to avoid returning irrelevant documents.
-
-   * **Data Sources**: Select specific files and folders to include in the filter.
-
-      This is useful if you want to create a filter for a specific project or topic and you know the specific documents you want to include.
-      Similarly, if you upload a folder of documents or enable an OAuth connector, you might want to create a filter that only includes the documents from that source.
+3. To modify the filter, click <Icon name="Library" aria-hidden="true"/> **Knowledge**, and then click your new filter. You can edit the following settings:
 
+   * **Search Query**: Enter text for semantic search, such as `financial reports from Q4`.
+   * **Data Sources**: Select specific data sources or folders to include.
    * **Document Types**: Filter by file type.
-
    * **Owners**: Filter by the user that uploaded the documents.
+   * **Connectors**: Filter by [upload source](/ingestion), such as the local file system or a Google Drive OAuth connector.
+   * **Response Limit**: Set the maximum number of results to return from the knowledge base. The default is `10`.
+   * **Score Threshold**: Set the minimum relevance score for similarity search. The default score is `0`.
 
-      <PartialAnonymousUserOwner />
-
-   * **Connectors**: Filter by [upload source](/ingestion), such as the local file system or an OAuth connector.
-
-   * **Response Limit**: Set the maximum number of results to return from the knowledge base. The default is `10`, which means the filter returns only the top 10 most relevant documents.
-
-   * **Score Threshold**: Set the minimum relevance score for similarity search. The default score is `0`. A threshold is recommended to avoid returned irrelevant documents.
-
-6. Click **Create Filter**.
-
-## Edit a filter
-
-To modify a filter, click <Icon name="Library" aria-hidden="true"/> **Knowledge**, and then click the filter you want to edit in the **Knowledge Filters** list.
-On the filter settings pane, edit the filter as desired, and then click **Update Filter**.
+4. To save your changes, click **Update Filter**.
 
 ## Apply a filter {#apply-a-filter}
 
-In the OpenRAG <Icon name="MessageSquare" aria-hidden="true"/> **Chat**, click <Icon name="Funnel" aria-hidden="true"/> **Filter**, and then select the filter to apply.
-Chat filters apply to one chat session only.
+* **Apply a global filter**: Click <Icon name="Library" aria-hidden="true"/> **Knowledge**, and then enable the toggle next to your preferred filter. Only one filter can be the global filter. The global filter applies to all chat sessions.
 
-You can also use filters when [browsing your knowledge base](/knowledge#browse-knowledge).
-This is a helpful way to test filters and manage knowledge bases that have many documents.
+* **Apply a chat filter**: In the <Icon name="MessageSquare" aria-hidden="true"/> **Chat** window, click <Icon name="Funnel" aria-hidden="true"/> **Filter**, and then select the filter to apply.
+Chat filters apply to one chat session only.
 
 ## Delete a filter
 
 1. Click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
 
-2. In the **Knowledge Filters** list, click the filter that you want to delete.
+2. Click the filter that you want to delete.
 
-3. In the filter settings pane, click **Delete Filter**.
+3. Click **Delete Filter**.

docs/docs/core-components/knowledge.mdx

@@ -4,13 +4,13 @@ slug: /knowledge
 ---
 
 import Icon from "@site/src/components/icon/icon";
-import PartialOpenSearchAuthMode from '@site/docs/_partial-opensearch-auth-mode.mdx';
-import PartialAnonymousUserOwner from '@site/docs/_partial-anonymous-user-owner.mdx';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 
 OpenRAG includes a built-in [OpenSearch](https://docs.opensearch.org/latest/) instance that serves as the underlying datastore for your _knowledge_ (documents).
 This specialized database is used to store and retrieve your documents and the associated vector data (embeddings).
 
-The documents in your OpenSearch knowledge base provide specialized context in addition to the general knowledge available to the language model that you select when you [install OpenRAG](/install-options) or [edit a flow](/agents).
+The documents in your OpenSearch knowledge base provide specialized context in addition to the general knowledge available to the language model that you select when you [install OpenRAG](/install) or [edit a flow](/agents).
 
 You can [upload documents](/ingestion) from a variety of sources to populate your knowledge base with unique content, such as your own company documents, research papers, or websites.
 Documents are processed through OpenRAG's knowledge ingestion flows with Docling.
@@ -24,68 +24,28 @@ You can configure how documents are ingested and how the **Chat** interacts with
 The **Knowledge** page lists the documents OpenRAG has ingested into your OpenSearch database, specifically in an [OpenSearch index](https://docs.opensearch.org/latest/getting-started/intro/#index) named `documents`.
 
 To explore the raw contents of your knowledge base, click <Icon name="Library" aria-hidden="true"/> **Knowledge** to get a list of all ingested documents.
+Click a document to view the chunks produced from splitting the document during ingestion.
 
-### Inspect knowledge
-
-For each document, the **Knowledge** page provides the following information:
-
-* **Source**: Name of the ingested content, such as the file name.
-
-* **Size**
-
-* **Type**
-
-* **Owner**: User that uploaded the document.
-
-   <PartialAnonymousUserOwner />
-
-* **Chunks**: Number of chunks created by splitting the document during ingestion.
-
-   Click a document to view the individual chunks and technical details related to chunking.
-   If the chunks seem incorrect or incomplete, see [Troubleshoot ingestion](/ingestion#troubleshoot-ingestion).
-
-* **Avg score**: Average similarity score across all chunks of the document.
-
-   If you [search the knowledge base](#search-knowledge), the **Avg score** column shows the similarity score for your search query or filter.
-
-* **Embedding model** and **Dimensions**: The embedding model and dimensions used to embed the chunks.
-
-* **Status**: Status of document ingestion.
-If ingestion is complete and successful, then the status is **Active**.
-For more information, see [Monitor ingestion](/ingestion#monitor-ingestion).
-
-### Search knowledge {#search-knowledge}
-
-You can use the search field on the **Knowledge** page to find documents using semantic search and knowledge filters:
-
-To search all documents, enter a search string in the search field, and then press <kbd>Enter</kbd>.
-
-To apply a [knowledge filter](/knowledge-filters), select the filter from the **Knowledge Filters** list.
-The filter settings pane opens, and the filter appears in the search field.
-To remove the filter, close the filter settings pane or clear the filter from the search field.
-
-You can use the filter alone or in combination with a search string.
-If a knowledge filter has a **Search Query**, that query is applied in addition to any text string you enter in the search field.
-
-Only one filter can be applied at a time.
-
-### Default documents {#default-documents}
-
-By default, OpenRAG includes some initial documents about OpenRAG.
-These documents are ingested automatically during the [application onboarding process](/install#application-onboarding).
-
-You can use these documents to ask OpenRAG about itself, or to test the [**Chat**](/chat) feature before uploading your own documents.
-
-If you [delete these documents](#delete-knowledge), then you won't be able to ask OpenRAG about itself and it's own functionality.
+By default, OpenRAG includes some initial documents about OpenRAG. You can use these documents to ask OpenRAG about itself, and to test the [**Chat**](/chat) feature before uploading your own documents.
+If you [delete these documents](#delete-knowledge), you won't be able to ask OpenRAG about itself and it's own functionality.
 It is recommended that you keep these documents, and use [filters](/knowledge-filters) to separate them from your other knowledge.
-An **OpenRAG Docs** filter is created automatically for these documents.
 
 ## OpenSearch authentication and document access {#auth}
 
-When you [install OpenRAG](/install-options), you provide the initial configuration values for your OpenRAG services, including authentication credentials for OpenSearch and OAuth connectors.
-This configuration determines how OpenRAG authenticates with your deployment's OpenSearch instance, and it controls user access to documents in your knowledge base:
+When you [install OpenRAG](/install), you can choose between two setup modes: **Basic Setup** and **Advanced Setup**.
+The mode you choose determines how OpenRAG authenticates with OpenSearch and controls access to documents:
 
-<PartialOpenSearchAuthMode />
+* **Basic Setup (no-auth mode)**: If you choose **Basic Setup**, then OpenRAG is installed in no-auth mode.
+This mode uses one, anonymous JWT token for OpenSearch authentication.
+There is no differentiation between users.
+All users that access your OpenRAG instance can access all documents uploaded to your OpenSearch knowledge base.
+
+* **Advanced Setup (OAuth mode)**: If you choose **Advanced Setup**, then OpenRAG is installed in OAuth mode.
+This mode uses a unique JWT token for each OpenRAG user, and each document is tagged with user ownership. Documents are filtered by user owner.
+This means users see only the documents that they uploaded or have access to.
+
+You can enable OAuth mode after installation.
+For more information, see [Ingest files with OAuth connectors](/ingestion#oauth-ingestion).
 
 ## OpenSearch indexes
 
@@ -94,7 +54,7 @@ An [OpenSearch index](https://docs.opensearch.org/latest/getting-started/intro/#
 By default, all documents you upload to your OpenRAG knowledge base are stored in an index named `documents`.
 
 It is possible to change the index name by [editing the ingestion flow](/agents#inspect-and-modify-flows).
-However, this can impact dependent processes, such as the [filters](/knowledge-filters) and [**Chat**](/chat), that reference the `documents` index by default.
+However, this can impact dependent processes, such as the [filters](/knowledge-filters) and [**Chat**](/chat) flow, that reference the `documents` index by default.
 Make sure you edit other flows as needed to ensure all processes use the same index name.
 
 If you encounter errors or unexpected behavior after changing the index name, you can [revert the flows to their original configuration](/agents#revert-a-built-in-flow-to-its-original-configuration), or [delete knowledge](/knowledge#delete-knowledge) to clear the existing documents from your knowledge base.
@@ -115,21 +75,19 @@ If needed, you can use [filters](/knowledge-filters) to separate documents that
 
 ### Set the embedding model and dimensions {#set-the-embedding-model-and-dimensions}
 
-When you [install OpenRAG](/install-options), you select at least one embedding model during the [application onboarding process](/install#application-onboarding).
+When you [install OpenRAG](/install), you select at least one embedding model during [application onboarding](/install#application-onboarding).
 OpenRAG automatically detects and configures the appropriate vector dimensions for your selected embedding model, ensuring optimal search performance and compatibility.
 
 In the OpenRAG repository, you can find the complete list of supported models in [`models_service.py`](https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py) and the corresponding vector dimensions in [`settings.py`](https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py).
 
-During the application onboarding process, you can select from the supported models.
+During application onboarding, you can select from the supported models.
 The default embedding dimension is `1536`, and the default model is the OpenAI `text-embedding-3-small`.
 
-If you want to use an unsupported model, you must manually set the model in your [OpenRAG `.env` file](/reference/configuration).
+If you want to use an unsupported model, you must manually set the model in your [OpenRAG configuration](/reference/configuration).
 If you use an unsupported embedding model that doesn't have defined dimensions in `settings.py`, then OpenRAG falls back to the default dimensions (1536) and logs a warning. OpenRAG's OpenSearch instance and flows continue to work, but [similarity search](https://www.ibm.com/think/topics/vector-search) quality can be affected if the actual model dimensions aren't 1536.
 
-To change the embedding model after onboarding, modify the embedding model configuration on the OpenRAG **Settings** page or in your [OpenRAG `.env` file](/reference/configuration).
-This ensures that all relevant [OpenRAG flows](/agents) are updated to use the new embedding model configuration.
-
-If you edit these settings in the `.env` file, you must [stop and restart the OpenRAG containers](/manage-services#stop-and-start-containers) to apply the changes.
+To change the embedding model after onboarding, it is recommended that you modify the embedding model setting in the OpenRAG **Settings** page or in your [OpenRAG configuration](/reference/configuration).
+This will automatically update all relevant [OpenRAG flows](/agents) to use the new embedding model configuration.
 
 ### Set Docling parameters
 
@@ -137,39 +95,41 @@ OpenRAG uses [Docling](https://docling-project.github.io/docling/) for document
 
 When you [upload documents](/ingestion), Docling processes the files, splits them into chunks, and stores them as separate, structured documents in your OpenSearch knowledge base.
 
-#### Select a Docling implementation {#select-a-docling-implementation}
-
 You can use either Docling Serve or OpenRAG's built-in Docling ingestion pipeline to process documents.
 
-* **Docling Serve ingestion**: By default, OpenRAG uses [Docling Serve](https://github.com/docling-project/docling-serve).
-It starts a local `docling serve` process, and then runs Docling ingestion through the Docling Serve API.
+<Tabs>
+<TabItem value="serve" label="Docling Serve ingestion" default>
 
-   To use a remote `docling serve` instance or your own local instance, set `DOCLING_SERVE_URL=http://**HOST_IP**:5001` in your [OpenRAG `.env` file](/reference/configuration#document-processing-settings).
-   The service must run on port 5001.
+By default, OpenRAG uses [Docling Serve](https://github.com/docling-project/docling-serve).
+This means that OpenRAG starts a `docling serve` process on your local machine and runs Docling ingestion through an API service.
+
+</TabItem>
+<TabItem value="docling" label="Built-in Docling ingestion">
+
+If you want to use OpenRAG's built-in Docling ingestion pipeline instead of the separate Docling Serve service, set `DISABLE_INGEST_WITH_LANGFLOW=true` in your [OpenRAG environment variables](/reference/configuration#document-processing).
 
-* **Built-in Docling ingestion**: If you want to use OpenRAG's built-in Docling ingestion pipeline instead of the separate Docling Serve service, set `DISABLE_INGEST_WITH_LANGFLOW=true` in your [OpenRAG `.env` file](/reference/configuration#document-processing-settings).
 The built-in pipeline uses the Docling processor directly instead of through the Docling Serve API.
+
 For the underlying functionality, see [`processors.py`](https://github.com/langflow-ai/openrag/blob/main/src/models/processors.py#L58) in the OpenRAG repository.
 
-#### Configure Docling ingestion settings
+</TabItem>
+</Tabs>
 
-To modify the Docling document processing and embedding parameters, click <Icon name="Settings2" aria-hidden="true"/> **Settings** in OpenRAG, and then find the **Knowledge Ingest** section.
+To modify the Docling ingestion and embedding parameters, click <Icon name="Settings2" aria-hidden="true"/> **Settings** in the OpenRAG user interface.
 
 :::tip
-The TUI warns you if `docling serve` isn't running.
-For information about starting and stopping OpenRAG native services, like Docling, see [Manage OpenRAG services](/manage-services).
+OpenRAG warns you if `docling serve` isn't running.
+You can [start and stop OpenRAG services](/install#tui-container-management) from the TUI main menu with **Start Native Services** or **Stop Native Services**.
 :::
 
-You can edit the following parameters:
-
 * **Embedding model**: Select the model to use to generate vector embeddings for your documents.
 
    This is initially set during installation.
-   The recommended way to change this setting is in the OpenRAG <Icon name="Settings2" aria-hidden="true"/> **Settings** or your [OpenRAG `.env` file](/reference/configuration).
-   This ensures that all relevant [OpenRAG flows](/agents) are updated to use the new embedding model configuration.
+   The recommended way to change this setting is in the OpenRAG **Settings** or your [OpenRAG configuration](/reference/configuration).
+   This will automatically update all relevant [OpenRAG flows](/agents) to use the new embedding model configuration.
 
    If you uploaded documents prior to changing the embedding model, you can [create filters](/knowledge-filters) to separate documents embedded with different models, or you can reupload all documents to regenerate embeddings with the new model.
-   If you want to use multiple embeddings models, similarity search (in the **Chat**) can take longer as it searches each model's embeddings separately.
+   If you want to use multiple embeddings models, similarity search (in the **Chat**) can take longer as it searching each model's embeddings separately.
 
 * **Chunk size**: Set the number of characters for each text chunk when breaking down a file.
 Larger chunks yield more context per chunk, but can include irrelevant information. Smaller chunks yield more precise semantic search, but can lack context.
@@ -179,7 +139,7 @@ The default value is 1000 characters, which is usually a good balance between co
 Use larger overlap values for documents where context is most important. Use smaller overlap values for simpler documents or when optimization is most important.
 The default value is 200 characters, which represents an overlap of 20 percent if the **Chunk size** is 1000. This is suitable for general use. For faster processing, decrease the overlap to approximately 10 percent. For more complex documents where you need to preserve context across chunks, increase it to approximately 40 percent.
 
-* **Table structure**: Enables Docling's [`DocumentConverter`](https://docling-project.github.io/docling/reference/document_converter/) tool for parsing tables. Instead of treating tables as plain text, tables are output as structured table data with preserved relationships and metadata. This option is enabled by default.
+* **Table Structure**: Enables Docling's [`DocumentConverter`](https://docling-project.github.io/docling/reference/document_converter/) tool for parsing tables. Instead of treating tables as plain text, tables are output as structured table data with preserved relationships and metadata. This option is enabled by default.
 
 * **OCR**: Enables Optical Character Recognition (OCR) processing when extracting text from images and ingesting scanned documents. This setting is best suited for processing text-based documents faster with Docling's [`DocumentConverter`](https://docling-project.github.io/docling/reference/document_converter/). Images are ignored and not processed.
 
@@ -191,22 +151,14 @@ The default value is 200 characters, which represents an overlap of 20 percent i
 
 ### Set the local documents path {#set-the-local-documents-path}
 
-The default path for local uploads is `~/.openrag/documents`. This is mounted to the `/app/openrag-documents/` directory inside the OpenRAG container. Files added to the host or container directory are visible in both locations.
+The default path for local uploads is the `./openrag-documents` subdirectory in your OpenRAG installation directory. This is mounted to the `/app/openrag-documents/` directory inside the OpenRAG container. Files added to the host or container directory are visible in both locations.
 
-To change this location, modify the **Documents Paths** variable in either the [**Basic/Advanced Setup** menu](/install#setup) or in your [OpenRAG `.env` file](/reference/configuration).
+To change this location, modify the **Documents Paths** variable in either the [**Advanced Setup** menu](/install#setup) or in the `.env` used by Docker Compose.
 
 ## Delete knowledge {#delete-knowledge}
 
-:::warning
+To clear your entire knowledge base, delete the contents of the `./opensearch-data` folder in your OpenRAG installation directory.
 This is a destructive operation that cannot be undone.
-:::
-
-To delete documents from your knowledge base, click <Icon name="Library" aria-hidden="true"/> **Knowledge**, use the checkboxes to select one or more documents, and then click **Delete**.
-If you select the checkbox at the top of the list, all documents are selected and your entire knowledge base will be deleted.
-
-To delete an individual document, you can also click <Icon name="Ellipsis" aria-hidden="true"/> **More** next to that document, and then select **Delete**.
-
-To completely clear your entire knowledge base and OpenSearch index, [reset your OpenRAG containers](/manage-services#reset-containers) or [reinstall OpenRAG](/reinstall).
 
 ## See also
 

docs/docs/get-started/docker.mdx

@@ -1,119 +1,118 @@
 ---
-title: Deploy OpenRAG with self-managed services
+title: Install OpenRAG containers
 slug: /docker
 ---
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
-import PartialPrereqCommon from '@site/docs/_partial-prereq-common.mdx';
-import PartialPrereqNoScript from '@site/docs/_partial-prereq-no-script.mdx';
-import PartialPrereqWindows from '@site/docs/_partial-prereq-windows.mdx';
-import PartialPrereqPython from '@site/docs/_partial-prereq-python.mdx';
-import PartialInstallNextSteps from '@site/docs/_partial-install-next-steps.mdx';
-import PartialOllamaModels from '@site/docs/_partial-ollama-models.mdx';
-import PartialGpuModeTip from '@site/docs/_partial-gpu-mode-tip.mdx';
+import PartialWsl from '@site/docs/_partial-wsl-install.mdx';
 
-To manage your own OpenRAG services, deploy OpenRAG with Docker or Podman.
+OpenRAG has two Docker Compose files. Both files deploy the same applications and containers locally, but they are for different environments:
 
-Use this installation method if you don't want to [use the Terminal User Interface (TUI)](/tui), or you need to run OpenRAG in an environment where using the TUI is unfeasible.
+- [`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. This Docker Compose file requires an NVIDIA GPU with [CUDA](https://docs.nvidia.com/cuda/) support.
+
+- [`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 NVIDIA GPU support. Use this Docker Compose file for environments where GPU drivers aren't available.
 
 ## Prerequisites
 
-<PartialPrereqWindows />
+- Install the following:
 
-<PartialPrereqPython />
+   - [Python](https://www.python.org/downloads/release/python-3100/) version 3.13 or later.
+   - [uv](https://docs.astral.sh/uv/getting-started/installation/).
+   - [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/).
+   - [`podman-compose`](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or [Docker Compose](https://docs.docker.com/compose/install/). To use Docker Compose with Podman, you must alias Docker Compose commands to Podman commands.
 
-<PartialPrereqNoScript />
+- Microsoft Windows only: To run OpenRAG on Windows, you must use the Windows Subsystem for Linux (WSL).
 
-<PartialPrereqCommon />
+   <details>
+   <summary>Install WSL for OpenRAG</summary>
 
-## Prepare your deployment {#setup}
+   <PartialWsl />
 
-1. Clone the OpenRAG repository:
+   </details>
 
+- Prepare model providers and credentials.
+
+   During [application onboarding](#application-onboarding), you must select language model and embedding model providers.
+   If your chosen provider offers both types, you can use the same provider for both selections.
+   If your provider offers only one type, such as Anthropic, you must select two providers.
+
+   Gather the credentials and connection details for your chosen model providers before starting onboarding:
+
+   - OpenAI: Create an [OpenAI API key](https://platform.openai.com/api-keys).
+   - Anthropic language models: Create an [Anthropic API key](https://www.anthropic.com/docs/api/reference).
+   - IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
+   - Ollama: Use the [Ollama documentation](https://docs.ollama.com/) to set up your Ollama instance locally, in the cloud, or on a remote server, and then get your Ollama server's base URL.
+
+- Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine. This is required to use the GPU-accelerated Docker Compose file. If you choose not to use GPU support, you must use the CPU-only Docker Compose file instead.
+
+## Install OpenRAG with Docker Compose
+
+To install OpenRAG with Docker Compose, do the following:
+
+1. Clone the OpenRAG repository.
    ```bash
    git clone https://github.com/langflow-ai/openrag.git
-   ```
-
-2. Change to the root of the cloned repository:
-
-   ```bash
    cd openrag
    ```
 
-3. Install dependencies:
-
+2. Install dependencies.
    ```bash
    uv sync
    ```
 
-4. Create a `.env` file at the root of the cloned repository.
-
-   You can create an empty file or copy the repository's [`.env.example`](https://github.com/langflow-ai/openrag/blob/main/.env.example) file.
-   The example file contains some of the [OpenRAG environment variables](/reference/configuration) to get you started with configuring your deployment.
-
+3. Copy the example `.env` file 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
    ```
 
-5. Edit the `.env` file to configure your deployment using [OpenRAG environment variables](/reference/configuration).
-The OpenRAG Docker Compose files pull values from your `.env` file to configure the OpenRAG containers.
-The following variables are required or recommended:
+   Alternatively, create a new `.env` file in the repository root.
+   ```
+   touch .env
+   ```
 
-   * **`OPENSEARCH_PASSWORD` (Required)**: Sets the OpenSearch administrator password. It must adhere to the [OpenSearch password complexity requirements](https://docs.opensearch.org/latest/security/configuration/demo-configuration/#setting-up-a-custom-admin-password).
+4. The Docker Compose files are populated with the values from your `.env` file. 
+   The `OPENSEARCH_PASSWORD` value must be set.
+   `OPENSEARCH_PASSWORD` can be automatically generated when using the TUI, but for a Docker Compose installation, you can set it manually instead. To generate an OpenSearch admin password, see the [OpenSearch documentation](https://docs.opensearch.org/latest/security/configuration/demo-configuration/#setting-up-a-custom-admin-password).
 
-   * **`LANGFLOW_SUPERUSER`**: The username for the Langflow administrator user. If `LANGFLOW_SUPERUSER` isn't set, then the default value is `admin`.
+   The following values are optional:
 
-   * **`LANGFLOW_SUPERUSER_PASSWORD` (Strongly recommended)**: Sets the Langflow administrator password, and determines the Langflow server's default authentication mode. If `LANGFLOW_SUPERUSER_PASSWORD` isn't set, then the Langflow server starts without authentication enabled. For more information, see [Langflow settings](/reference/configuration#langflow-settings).
+   ```bash
+   OPENAI_API_KEY=your_openai_api_key
+   LANGFLOW_SECRET_KEY=your_secret_key
+   ```
 
-   * **`LANGFLOW_SECRET_KEY` (Strongly recommended)**: A secret encryption key for internal Langflow operations. It is recommended to [generate your own Langflow secret key](https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key). If `LANGFLOW_SECRET_KEY` isn't set, then Langflow generates a secret key automatically.
+   `OPENAI_API_KEY` is optional. You can provide it during [application onboarding](#application-onboarding) or choose a different model provider. If you want to set it in your `.env` file, you can find your OpenAI API key in your [OpenAI account](https://platform.openai.com/api-keys).
 
-   * **Model provider credentials**: Provide credentials for your preferred model providers. If none of these are set in the `.env` file, you must configure at least one provider during the [application onboarding process](#application-onboarding).
+   `LANGFLOW_SECRET_KEY` is optional. Langflow will auto-generate it if not set. For more information, see the [Langflow documentation](https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key).
 
-      * `OPENAI_API_KEY`
-      * `ANTHROPIC_API_KEY`
-      * `OLLAMA_ENDPOINT`
-      * `WATSONX_API_KEY`
-      * `WATSONX_ENDPOINT`
-      * `WATSONX_PROJECT_ID`
+   The following Langflow configuration values are optional but important to consider:
 
-   * **OAuth provider credentials**: To upload documents from external storage, such as Google Drive, set the required OAuth credentials for the connectors that you want to use. You can [manage OAuth credentials](/ingestion#oauth-ingestion) later, but it is recommended to configure them during initial set up so you don't have to rebuild the containers.
+   ```bash
+   LANGFLOW_SUPERUSER=admin
+   LANGFLOW_SUPERUSER_PASSWORD=your_langflow_password
+   ```
 
-      * **Google**: Provide your Google OAuth Client ID and Google OAuth Client Secret. You can generate these in the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). For more information, see the [Google OAuth client documentation](https://developers.google.com/identity/protocols/oauth2).
-      * **Microsoft**: For the Microsoft OAuth Client ID and Microsoft OAuth Client Secret, provide [Azure application registration credentials for SharePoint and OneDrive](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/app-registration?view=odsp-graph-online). For more information, see the [Microsoft Graph OAuth client documentation](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/graph-oauth).
-      * **Amazon**: Provide your AWS Access Key ID and AWS Secret Access Key with access to your S3 instance. For more information, see the AWS documentation on [Configuring access to AWS applications](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html).
-
-   For more information and variables, see [OpenRAG environment variables](/reference/configuration).
-
-## Start services
-
-1. To use the default Docling Serve implementation, start `docling serve` on port 5001 on the host machine using the included script:
+   `LANGFLOW_SUPERUSER` defaults to `admin`. You can omit it or set it to a different username. `LANGFLOW_SUPERUSER_PASSWORD` is optional. If omitted, Langflow runs in [autologin mode](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login) with no password required. If set, Langflow requires password authentication.
+   
+   For more information on configuring OpenRAG with environment variables, see [Environment variables](/reference/configuration).
 
+5. Start `docling serve` on the host machine.
+   OpenRAG Docker installations require that `docling serve` is running on port 5001 on the host machine.
+   This enables [Mac MLX](https://opensource.apple.com/projects/mlx/) support for document processing.
+   
    ```bash
    uv run python scripts/docling_ctl.py start --port 5001
    ```
-
-   Docling cannot run inside a Docker container due to system-level dependencies, so you must manage it as a separate service on the host machine.
-   For more information, see [Stop, start, and inspect native services](/manage-services#start-native-services).
-
-   Port 5001 is required to deploy OpenRAG successfully; don't use a different port.
-   Additionally, this enables the [MLX framework](https://opensource.apple.com/projects/mlx/) for accelerated performance on Apple Silicon Mac machines.
-
-   :::tip
-   If you don't want to use the default Docling Serve implementation, see [Select a Docling implementation](/knowledge#select-a-docling-implementation).
-   :::
-
-2. Confirm `docling serve` is running.
-
-   The following command checks the status of the default Docling Serve implementation:
-
-   ```bash
+   
+6. Confirm `docling serve` is running.
+   ```
    uv run python scripts/docling_ctl.py status
    ```
 
-   If `docling serve` is running, the output includes the status, address, and process ID (PID):
-
+   Make sure the response shows that `docling serve` is running, for example:
    ```bash
    Status: running
    Endpoint: http://127.0.0.1:5001
@@ -121,56 +120,239 @@ The following variables are required or recommended:
    PID: 27746
    ```
 
-3. Deploy the OpenRAG containers locally using the appropriate Docker Compose configuration for your environment:
+7. Deploy OpenRAG locally with Docker Compose based on your deployment type.
 
-   * **CPU-only deployment** (default, recommended): If your host machine doesn't have NVIDIA GPU support, use the base `docker-compose.yml` file:
+   <Tabs groupId="Compose file">
+     <TabItem value="docker-compose.yml" label="docker-compose.yml" default>
+   ```bash
+   docker compose build
+   docker compose up -d
+   ```   
+     </TabItem>
+     <TabItem value="docker-compose-cpu.yml" label="docker-compose-cpu.yml">
+   
+   ```bash
+   docker compose -f docker-compose-cpu.yml up -d
+   ```
+   
+     </TabItem>
+   </Tabs>
 
-      ```bash title="Docker"
-      docker compose 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. |
 
-      ```bash title="Podman"
-      podman compose up -d
-      ```
+8. Verify installation by confirming all services are running.
 
-   * **GPU-accelerated deployment**: If your host machine has an NVIDIA GPU with CUDA support and compatible NVIDIA drivers, use the base `docker-compose.yml` file with the `docker-compose.gpu.yml` override:
-
-      ```bash title="Docker"
-      docker compose -f docker-compose.yml -f docker-compose.gpu.yml up -d
-      ```
-
-      ```bash title="Podman"
-      podman compose -f docker-compose.yml -f docker-compose.gpu.yml up -d
-      ```
-
-   :::tip
-   <PartialGpuModeTip />
-   :::
-
-4. Wait for the OpenRAG containers to start, and then confirm that all containers are running:
-
-   ```bash title="Docker"
+   ```bash
    docker compose ps
    ```
 
-   ```bash title="Podman"
-   podman compose ps
-   ```
+   You can now access OpenRAG at the following endpoints:
 
-   The OpenRAG Docker Compose files deploy the following containers:
+   - **Frontend**: http://localhost:3000
+   - **Backend API**: http://localhost:8000
+   - **Langflow**: http://localhost:7860
 
-   | Container Name | Default address | Purpose |
-   |---|---|---|
-   | OpenRAG Backend | http://localhost:8000 | FastAPI server and core functionality. |
-   | OpenRAG Frontend | http://localhost:3000 | React web interface for user interaction. |
-   | Langflow | http://localhost:7860 | [AI workflow engine](/agents). |
-   | OpenSearch | http://localhost:9200 | Datastore for [knowledge](/knowledge). |
-   | OpenSearch Dashboards | http://localhost:5601 | OpenSearch database administration interface. |
+9. Continue with [application onboarding](#application-onboarding).
 
-   When the containers are running, you can access your OpenRAG services at their addresses.
+To stop `docling serve` when you're done with your OpenRAG deployment, run:
 
-5. Access the OpenRAG frontend at `http://localhost:3000`, and then continue with the [application onboarding process](#application-onboarding).
+```bash
+uv run python scripts/docling_ctl.py stop
+```
 
 <PartialOnboarding />
 
-<PartialInstallNextSteps />
+## Container management commands
+
+Manage your OpenRAG containers with the following commands.
+These commands are also available in the TUI's [Status menu](/install#status).
+
+### Upgrade containers {#upgrade-containers}
+
+Upgrade your containers to the latest version while preserving your data.
+
+```bash
+docker compose pull
+docker compose up -d --force-recreate
+```
+
+### Reset containers (destructive) {#reset-containers}
+
+:::warning
+These are destructive operations that reset your OpenRAG deployment to an initial state.
+Be aware that data is lost and cannot be recovered after running these commands.
+:::
+
+<Tabs>
+<TabItem value="docker-compose" label="Docker Compose" default>
+
+* Rebuild containers: This command destroys and recreates the containers. Data stored exclusively on the containers is lost, such as Langflow flows.
+The `.env` file, `config` directory, `./openrag-documents` directory, `./opensearch-data` directory, and the `conversations.json` file are preserved.
+
+   ```bash
+   docker compose up --build --force-recreate --remove-orphans
+   ```
+
+* Destroy and recreate containers with the option for additional data removal: These commands destroy the containers, and then recreate them.
+This allows you to delete other OpenRAG data before recreating the containers.
+
+   1. Destroy the containers, volumes, and local images, and then remove (prune) any additional Docker objects:
+
+      ```bash
+      docker compose down --volumes --remove-orphans --rmi local
+      docker system prune -f
+      ```
+
+   2. Optional: Remove data that wasn't deleted by the previous commands:
+
+      * OpenRAG's `.env` file
+      * The contents of OpenRAG's `config` directory
+      * The contents of the `./openrag-documents` directory
+      * The contents of the `./opensearch-data` directory
+      * The `conversations.json` file
+
+   3. Recreate the containers:
+
+      ```bash
+      docker compose up -d
+      ```
+
+</TabItem>
+<TabItem value="Podman-compose" label="Podman Compose">
+
+* Rebuild containers: This command destroys and recreates the containers. Data stored exclusively on the containers is lost, such as Langflow flows.
+The `.env` file, `config` directory, `./openrag-documents` directory, `./opensearch-data` directory, and the `conversations.json` file are preserved.
+
+   ```bash
+   podman-compose up --build --force-recreate --remove-orphans
+   ```
+
+* Destroy and recreate containers with the option for additional data removal: These commands destroy the containers, and then recreate them.
+This allows you to delete other OpenRAG data before recreating the containers.
+
+   1. Destroy the containers, volumes, and local images, and then remove (prune) any additional Podman objects:
+
+      ```bash
+      podman-compose down --volumes --remove-orphans --rmi local
+      podman system prune -f
+      ```
+
+   2. Optional: Remove data that wasn't deleted by the previous commands:
+
+      * OpenRAG's `.env` file
+      * The contents of OpenRAG's `config` directory
+      * The contents of the `./openrag-documents` directory
+      * The contents of the `./opensearch-data` directory
+      * The `conversations.json` file
+
+   3. Recreate the containers:
+
+      ```bash
+      podman-compose up -d
+      ```
+
+</TabItem>
+<TabItem value="docker" label="Docker">
+
+1. Stop all running containers:
+
+   ```bash
+   docker stop $(docker ps -q)
+   ```
+
+2. Remove all containers, including stopped containers:
+
+   ```bash
+   docker rm --force $(docker ps -aq)
+   ```
+
+3. Remove all images:
+
+   ```bash
+   docker rmi --force $(docker images -q)
+   ```
+
+4. Remove all volumes:
+
+   ```bash
+   docker volume prune --force
+   ```
+
+5. Remove all networks except the default network:
+
+   ```bash
+   docker network prune --force
+   ```
+
+6. Clean up any leftover data:
+
+   ```bash
+   docker system prune --all --force --volumes
+   ```
+
+7. Optional: Remove data that wasn't deleted by the previous commands:
+
+   * OpenRAG's `.env` file
+   * The contents of OpenRAG's `config` directory
+   * The contents of the `./openrag-documents` directory
+   * The contents of the `./opensearch-data` directory
+   * The `conversations.json` file
+
+</TabItem>
+<TabItem value="podman" label="Podman">
+
+1. Stop all running containers:
+
+   ```bash
+   podman stop --all
+   ```
+
+2. Remove all containers, including stopped containers:
+
+   ```bash
+   podman rm --all --force
+   ```
+
+3. Remove all images:
+
+   ```bash
+   podman rmi --all --force
+   ```
+
+4. Remove all volumes:
+
+   ```bash
+   podman volume prune --force
+   ```
+
+5. Remove all networks except the default network:
+
+   ```bash
+   podman network prune --force
+   ```
+
+6. Clean up any leftover data:
+
+   ```bash
+   podman system prune --all --force --volumes
+   ```
+
+7. Optional: Remove data that wasn't deleted by the previous commands:
+
+   * OpenRAG's `.env` file
+   * The contents of OpenRAG's `config` directory
+   * The contents of the `./openrag-documents` directory
+   * The contents of the `./opensearch-data` directory
+   * The `conversations.json` file
+
+</TabItem>
+</Tabs>
+
+After resetting your containers, you must repeat [application onboarding](#application-onboarding).

docs/docs/get-started/install-options.mdx

@@ -1,34 +0,0 @@
----
-title: Select an installation method
-slug: /install-options
----
-
-The [OpenRAG architecture](/#openrag-architecture) is lightweight and container-based with a central OpenRAG backend that orchestrates the various services and external connectors.
-Depending on your use case, OpenRAG can assist with service management, or you can manage the services yourself.
-
-Select the installation method that best fits your needs:
-
-* **Use the [Terminal User Interface (TUI)](/tui) to manage services**: For guided configuration and simplified service management, install OpenRAG with TUI-managed services. Use one of the following options:
-
-   * [**Automatic installer script**](/install): Run one script to install the required dependencies and OpenRAG.
-   * [**`uv`**](/install-uv): Install OpenRAG as a dependency of a new or existing Python project.
-   * [**`uvx`**](/install-uvx): Install OpenRAG without creating a project or modifying your project's dependencies.
-
-* [**Install OpenRAG on Microsoft Windows**](/install-windows): On Windows machines, you must install OpenRAG within the Windows Subsystem for Linux (WSL).
-
-   :::warning
-   OpenRAG doesn't support nested virtualization; don't run OpenRAG on a WSL distribution that is inside a Windows VM.
-   :::
-
-* [**Manage your own services**](/docker): You can use Docker or Podman to deploy self-managed OpenRAG services.
-
-The first time you start OpenRAG, you must complete the application onboarding process.
-This is required for all installation methods because it prepares the minimum required configuration for OpenRAG to run.
-For TUI-managed services, you must also complete initial setup before you start the OpenRAG services.
-For more information, see the instructions for your preferred installation method.
-
-Your OpenRAG configuration is stored in a `.env` file.
-When using TUI-managed services, this file is created automatically at `~/.openrag/tui`, or you can provide a pre-populated `.env` file in this directory before starting the TUI.
-The TUI prompts you for the required values during setup and onboarding, and any values detected in a preexisting `.env` file are populated automatically.
-When using self-managed services, you must provide a pre-populated `.env` file, as you would for any Docker or Podman deployment.
-For more information, see the instructions for your preferred installation method and the [OpenRAG environment variables reference](/reference/configuration).

docs/docs/get-started/install-uv.mdx

@@ -1,124 +0,0 @@
----
-title: Install OpenRAG in a Python project with uv
-slug: /install-uv
----
-
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
-import PartialSetup from '@site/docs/_partial-setup.mdx';
-import PartialPrereqCommon from '@site/docs/_partial-prereq-common.mdx';
-import PartialPrereqNoScript from '@site/docs/_partial-prereq-no-script.mdx';
-import PartialPrereqWindows from '@site/docs/_partial-prereq-windows.mdx';
-import PartialPrereqPython from '@site/docs/_partial-prereq-python.mdx';
-import PartialInstallNextSteps from '@site/docs/_partial-install-next-steps.mdx';
-import PartialOpenSearchAuthMode from '@site/docs/_partial-opensearch-auth-mode.mdx';
-import PartialOllamaModels from '@site/docs/_partial-ollama-models.mdx';
-
-Use [`uv`](https://docs.astral.sh/uv/getting-started/installation/) to install OpenRAG as a managed or unmanaged dependency in a new or existing Python project.
-
-When you install OpenRAG with `uv`, you will use the [Terminal User Interface (TUI)](/tui) to configure and manage your OpenRAG deployment.
-
-For other installation methods, see [Select an installation method](/install-options).
-
-## Prerequisites
-
-<PartialPrereqWindows />
-
-<PartialPrereqPython />
-
-<PartialPrereqNoScript />
-
-<PartialPrereqCommon />
-
-## Install and start OpenRAG with uv
-
-There are two ways to install OpenRAG with `uv`:
-
-* [**`uv add`** (Recommended)](#uv-add): Install OpenRAG as a managed dependency in a new or existing `uv` Python project.
-This is recommended because it adds OpenRAG to your `pyproject.toml` and lockfile for better management of dependencies and the virtual environment.
-
-* [**`uv pip install`**](#uv-pip-install): Use the [`uv pip` interface](https://docs.astral.sh/uv/pip/) to install OpenRAG into an existing Python project that uses `pip`, `pip-tools`, and `virtualenv` commands.
-
-If you encounter errors during installation, see [Troubleshoot OpenRAG](/support/troubleshoot).
-
-### Use uv add {#uv-add}
-
-1. Create a new `uv`-managed Python project:
-
-   ```bash
-   uv init PROJECT_NAME
-   ```
-
-2. Change into your new project directory:
-
-   ```bash
-   cd PROJECT_NAME
-   ```
-
-   Because `uv` manages the virtual environment for you, you won't see a `(venv)` prompt.
-   `uv` commands automatically use the project's virtual environment.
-
-3. Add OpenRAG to your project:
-
-   * Add the latest version:
-
-      ```bash
-      uv add openrag
-      ```
-
-   * Add a specific version:
-
-      ```bash
-      uv add openrag==0.1.30
-      ```
-
-   * Add a local wheel:
-
-      ```bash
-      uv add path/to/openrag-VERSION-py3-none-any.whl
-      ```
-
-   For more options, see [Managing dependencies with `uv`](https://docs.astral.sh/uv/concepts/projects/dependencies/).
-
-4. Optional: If you want to use a pre-populated [OpenRAG `.env` file](/reference/configuration), create one at `~/.openrag/tui` before starting OpenRAG.
-
-5. Start the OpenRAG TUI:
-
-   ```bash
-   uv run openrag
-   ```
-
-### Use uv pip install {#uv-pip-install}
-
-1. Activate your virtual environment.
-
-2. Install the OpenRAG Python package:
-
-   ```bash
-   uv pip install openrag
-   ```
-
-3. Optional: If you want to use a pre-populated [OpenRAG `.env` file](/reference/configuration), create one at `~/.openrag/tui` before starting OpenRAG.
-
-4. Start the OpenRAG TUI:
-
-   ```bash
-   uv run openrag
-   ```
-
-## Set up OpenRAG with the TUI {#setup}
-
-When you install OpenRAG with `uv`, you manage the OpenRAG services with the TUI.
-The TUI guides you through the initial configuration process before you start the OpenRAG services.
-
-Your configuration values are stored in an [OpenRAG `.env` file](/reference/configuration) that is created automatically at `~/.openrag/tui`.
-If OpenRAG detects an existing `.env` file in this directory, then the TUI can populate those values automatically during setup and onboarding.
-
-Container definitions are stored in the `docker-compose` files in the same directory as the OpenRAG `.env` file.
-
-<PartialSetup />
-
-<PartialOnboarding />
-
-<PartialInstallNextSteps />

docs/docs/get-started/install-uvx.mdx

@@ -1,83 +0,0 @@
----
-title: Invoke OpenRAG with uvx
-slug: /install-uvx
----
-
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
-import PartialSetup from '@site/docs/_partial-setup.mdx';
-import PartialPrereqCommon from '@site/docs/_partial-prereq-common.mdx';
-import PartialPrereqNoScript from '@site/docs/_partial-prereq-no-script.mdx';
-import PartialPrereqWindows from '@site/docs/_partial-prereq-windows.mdx';
-import PartialPrereqPython from '@site/docs/_partial-prereq-python.mdx';
-import PartialInstallNextSteps from '@site/docs/_partial-install-next-steps.mdx';
-import PartialOpenSearchAuthMode from '@site/docs/_partial-opensearch-auth-mode.mdx';
-import PartialOllamaModels from '@site/docs/_partial-ollama-models.mdx';
-
-Use [`uvx`](https://docs.astral.sh/uv/guides/tools/#running-tools) to invoke OpenRAG outside of a Python project or without modifying your project's dependencies.
-
-:::tip
-The [automatic installer script](/install) also uses `uvx` to install OpenRAG.
-:::
-
-When you install OpenRAG with `uvx`, you will use the [Terminal User Interface (TUI)](/tui) to configure and manage your OpenRAG deployment.
-
-This installation method is best for testing OpenRAG by running it outside of a Python project.
-For other installation methods, see [Select an installation method](/install-options).
-
-## Prerequisites
-
-<PartialPrereqWindows />
-
-<PartialPrereqPython />
-
-<PartialPrereqNoScript />
-
-<PartialPrereqCommon />
-
-## Install and run OpenRAG with uvx
-
-1. Create a directory to store your OpenRAG configuration files and data, and then change to that directory:
-
-   ```bash
-   mkdir openrag-workspace
-   cd openrag-workspace
-   ```
-
-2. Optional: If you want to use a pre-populated [OpenRAG `.env` file](/reference/configuration), create one at `~/.openrag/tui` before invoking OpenRAG.
-
-3. Invoke OpenRAG:
-
-   ```bash
-   uvx openrag
-   ```
-
-   You can invoke a specific version using any of the [`uvx` version specifiers](https://docs.astral.sh/uv/guides/tools/#requesting-specific-versions), such as `--from`:
-
-   ```bash
-   uvx --from openrag==0.1.30 openrag
-   ```
-
-   Invoking OpenRAG with `uvx openrag` creates a cached, ephemeral environment for the TUI in your local `uv` cache.
-   By invoking OpenRAG in a specific directory, your OpenRAG configuration files and data are stored separately from the `uv` cache.
-   Clearing the `uv` cache doesn't remove your entire OpenRAG installation.
-   After clearing the cache, you can re-invoke OpenRAG (`uvx openrag`) to restart the TUI with your preserved configuration and data.
-
-If you encounter errors during installation, see [Troubleshoot OpenRAG](/support/troubleshoot).
-
-## Set up OpenRAG with the TUI {#setup}
-
-When you install OpenRAG with `uvx`, you manage the OpenRAG services with the TUI.
-The TUI guides you through the initial configuration process before you start the OpenRAG services.
-
-Your configuration values are stored in an [OpenRAG `.env` file](/reference/configuration) that is created automatically at `~/.openrag/tui`.
-If OpenRAG detects an existing `.env` file in this directory, then the TUI can populate those values automatically during setup and onboarding.
-
-Container definitions are stored in the `docker-compose` files in the same directory as the OpenRAG `.env` file.
-
-<PartialSetup />
-
-<PartialOnboarding />
-
-<PartialInstallNextSteps />

docs/docs/get-started/install.mdx

@@ -1,82 +1,469 @@
 ---
-title: Install OpenRAG with the automatic installer script
+title: Install OpenRAG with TUI
 slug: /install
 ---
 
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
-import PartialSetup from '@site/docs/_partial-setup.mdx';
-import PartialPrereqCommon from '@site/docs/_partial-prereq-common.mdx';
-import PartialPrereqWindows from '@site/docs/_partial-prereq-windows.mdx';
-import PartialPrereqPython from '@site/docs/_partial-prereq-python.mdx';
-import PartialInstallNextSteps from '@site/docs/_partial-install-next-steps.mdx';
-import PartialOpenSearchAuthMode from '@site/docs/_partial-opensearch-auth-mode.mdx';
-import PartialOllamaModels from '@site/docs/_partial-ollama-models.mdx';
+import PartialWsl from '@site/docs/_partial-wsl-install.mdx';
 
-:::tip
-To quickly install and test OpenRAG's core features, try the [quickstart](/quickstart).
-:::
+[Install OpenRAG](#install) and then run the [OpenRAG Terminal User Interface(TUI)](#setup) to start your OpenRAG deployment with a guided setup process.
 
-The installer script installs `uv`, Docker or Podman, Docker Compose, and OpenRAG.
-Then, it installs and runs OpenRAG with `uvx`.
+The OpenRAG Terminal User Interface (TUI) allows you to set up, configure, and monitor your OpenRAG deployment directly from the terminal.
 
-When you install OpenRAG with the installer script, you will use the [Terminal User Interface (TUI)](/tui) to configure and manage your OpenRAG deployment.
+![OpenRAG TUI Interface](@site/static/img/OpenRAG_TUI_2025-09-10T13_04_11_757637.svg)
 
-This installation method is best for testing OpenRAG by running it outside of a Python project.
-For other installation methods, see [Select an installation method](/install-options).
+Instead of starting OpenRAG using Docker commands and manually editing values in the `.env` file, the TUI walks you through the setup. It prompts for variables where required, creates a `.env` file for you, and then starts OpenRAG.
+
+Once OpenRAG is running, use the TUI to monitor your application, control your containers, and retrieve logs.
+
+If you prefer running Podman or Docker containers and manually editing `.env` files, see [Install OpenRAG Containers](/docker).
 
 ## Prerequisites
 
-<PartialPrereqWindows />
+- All OpenRAG installations require [Python](https://www.python.org/downloads/release/python-3100/) version 3.13 or later.
 
-<PartialPrereqPython />
+- If you aren't using the automatic installer script, install the following:
 
-<PartialPrereqCommon />
+   - [uv](https://docs.astral.sh/uv/getting-started/installation/).
+   - [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/).
+   - [`podman-compose`](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or [Docker Compose](https://docs.docker.com/compose/install/). To use Docker Compose with Podman, you must alias Docker Compose commands to Podman commands.
 
-## Run the installer script {#install}
+- Microsoft Windows only: To run OpenRAG on Windows, you must use the Windows Subsystem for Linux (WSL).
 
-1. Create a directory to store your OpenRAG configuration files and data, and then change to that directory:
+   <details>
+   <summary>Install WSL for OpenRAG</summary>
 
-   ```bash
-   mkdir openrag-workspace
-   cd openrag-workspace
-   ```
+   <PartialWsl />
 
-2. Get and run the installer script:
+   </details>
 
-   ```bash
-   curl -fsSL https://docs.openr.ag/files/run_openrag_with_prereqs.sh | bash
-   ```
+- Prepare model providers and credentials.
 
-   The installer script installs OpenRAG with [`uvx`](https://docs.astral.sh/uv/guides/tools/#running-tools) in the directory where you run the script.
+   During [application onboarding](#application-onboarding), you must select language model and embedding model providers.
+   If your chosen provider offers both types, you can use the same provider for both selections.
+   If your provider offers only one type, such as Anthropic, you must select two providers.
 
-3. Wait while the installer script prepares your environment and installs OpenRAG.
-You might be prompted to install certain dependencies if they aren't already present in your environment.
+   Gather the credentials and connection details for your chosen model providers before starting onboarding:
 
-   The entire process can take a few minutes.
-   Once the environment is ready, the OpenRAG TUI starts.
+   - OpenAI: Create an [OpenAI API key](https://platform.openai.com/api-keys).
+   - Anthropic language models: Create an [Anthropic API key](https://www.anthropic.com/docs/api/reference).
+   - IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
+   - Ollama: Use the [Ollama documentation](https://docs.ollama.com/) to set up your Ollama instance locally, in the cloud, or on a remote server, and then get your Ollama server's base URL.
 
-   ![OpenRAG TUI Interface](@site/static/img/openrag_tui_dec_2025.png)
+- Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine. If you don't have GPU capabilities, OpenRAG provides an alternate CPU-only deployment.
 
-   Because the installer script uses `uvx`, it creates a cached, ephemeral environment in your local `uv` cache, and your OpenRAG configuration files and data are stored separately from the `uv` cache.
-   Clearing the cache doesn't delete your entire OpenRAG installation, only the temporary TUI environment.
-   After clearing the cache, run `uvx openrag` to [access the TUI](/tui) and continue with your preserved configuration and data.
+## Install OpenRAG {#install}
+
+Choose an installation method based on your needs:
+
+* For new users, the automatic installer script detects and installs prerequisites and then runs OpenRAG. 
+* For a quick test, use `uvx` to run OpenRAG without creating a project or modifying files.
+* Use `uv add` to install OpenRAG as a managed dependency in a new or existing Python project.
+* Use `uv pip install` to install OpenRAG into an existing virtual environment.
+
+<Tabs groupId="Installation method">
+  <TabItem value="installer" label="Automatic installer" default>
+
+    The script detects and installs uv, Docker/Podman, and Docker Compose prerequisites, then runs OpenRAG with `uvx`.
+
+    1. Create a directory to store the OpenRAG configuration files:
+       ```bash
+       mkdir openrag-workspace
+       cd openrag-workspace
+       ```
+
+    2. Run the installer:
+       ```bash
+       curl -fsSL https://docs.openr.ag/files/run_openrag_with_prereqs.sh | bash
+       ```
+
+    The TUI creates a `.env` file and docker-compose files in the current working directory.
+
+  </TabItem>
+  <TabItem value="uvx" label="Quick test with uvx">
+
+    Use `uvx` to quickly run OpenRAG without creating a project or modifying any files.
+
+    1. Create a directory to store the OpenRAG configuration files:
+       ```bash
+       mkdir openrag-workspace
+       cd openrag-workspace
+       ```
+
+    2. Run OpenRAG:
+       ```bash
+       uvx openrag
+       ```
+
+       To run a specific version:
+       ```bash
+       uvx --from openrag==0.1.30 openrag
+       ```
+
+    The TUI creates a `.env` file and docker-compose files in the current working directory.
+
+  </TabItem>
+  <TabItem value="uv-add" label="Python project with uv add">
+
+    Use `uv add` to install OpenRAG as a dependency in your Python project. This adds OpenRAG to your `pyproject.toml` and lockfile, making your installation reproducible and version-controlled.
+
+    1. Create a new project with a virtual environment:
+       ```bash
+       uv init YOUR_PROJECT_NAME
+       cd YOUR_PROJECT_NAME
+       ```
+
+       The `(venv)` prompt doesn't change, but `uv` commands will automatically use the project's virtual environment.
+
+    2. Add OpenRAG to your project:
+       ```bash
+       uv add openrag
+       ```
+
+       To add a specific version:
+       ```bash
+       uv add openrag==0.1.30
+       ```
+
+    3. Start the OpenRAG TUI:
+       ```bash
+       uv run openrag
+       ```
+
+    <details closed>
+      <summary>Install a local wheel</summary>
+      
+      If you downloaded the OpenRAG wheel to your local machine, install it by specifying its path:
+
+      1. Add the wheel to your project:
+         ```bash
+         uv add PATH/TO/openrag-VERSION-py3-none-any.whl
+         ```
+
+         Replace `PATH/TO/` and `VERSION` with the path and version of your downloaded OpenRAG `.whl` file.
+
+      2. Run OpenRAG:
+         ```bash
+         uv run openrag
+         ```
+    </details>
+
+  </TabItem>
+  <TabItem value="uv-pip" label="Existing virtual environment with uv pip install">
+
+    Use `uv pip install` to install OpenRAG into an existing virtual environment that isn't managed by `uv`.
+
+    :::tip
+    For new projects, `uv add` is recommended as it manages dependencies in your project's lockfile.
+    :::
+
+    1. Activate your virtual environment.
+
+    2. Install OpenRAG:
+       ```bash
+       uv pip install openrag
+       ```
+
+    3. Run OpenRAG:
+       ```bash
+       uv run openrag
+       ```
+
+  </TabItem>
+</Tabs>
+
+Continue with [Set up OpenRAG with the TUI](#setup).
 
 If you encounter errors during installation, see [Troubleshoot OpenRAG](/support/troubleshoot).
 
 ## Set up OpenRAG with the TUI {#setup}
 
-When you install OpenRAG with the installer script, you manage the OpenRAG services with the TUI.
-The TUI guides you through the initial configuration process before you start the OpenRAG services.
+The OpenRAG setup process creates a `.env` file at the root of your OpenRAG directory, and then starts OpenRAG.
+If it detects a `.env` file in the OpenRAG root directory, it sources any variables from the `.env` file.
 
-Your configuration values are stored in an [OpenRAG `.env` file](/reference/configuration) that is created automatically at `~/.openrag/tui`.
-If OpenRAG detects an existing `.env` file in this directory, then the TUI can populate those values automatically during setup and onboarding.
+The TUI offers two setup methods to populate the required values. **Basic Setup** can generate all minimum required values for OpenRAG. However, **Basic Setup** doesn't enable [OAuth connectors for cloud storage](/knowledge#auth). If you want to use OAuth connectors to upload documents from cloud storage, select **Advanced Setup**.
+If OpenRAG detects OAuth credentials, it recommends **Advanced Setup**.
 
-Container definitions are stored in the `docker-compose` files in the same directory as the OpenRAG `.env` file.
+<Tabs groupId="Setup method">
+  <TabItem value="Basic setup" label="Basic setup" default>
 
-<PartialSetup />
+   1. To install OpenRAG with **Basic Setup**, click **Basic Setup** or press <kbd>1</kbd>.
+   2. Click **Generate Passwords** to generate passwords for OpenSearch and Langflow.
+
+       The OpenSearch password is required. The Langflow admin password is optional.
+       If no Langflow admin password is generated, Langflow runs in [autologin mode](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login) with no password required.
+
+   3. Optional: Paste your OpenAI API key in the OpenAI API key field. You can also provide this during onboarding or choose a different model provider.
+   4. Click **Save Configuration**.
+   Your passwords are saved in the `.env` file used to start OpenRAG.
+   5. To start OpenRAG, click **Start All Services**.
+      Startup pulls container images and runs them, so it can take some time.
+      When startup is complete, the TUI displays the following:
+      ```bash
+      Services started successfully
+      Command completed successfully
+      ```
+   6. To start the Docling service, under **Native Services**, click **Start**.
+   7. To open the OpenRAG application, navigate to the TUI main menu, and then click **Open App**.
+   Alternatively, in your browser, navigate to `localhost:3000`.
+   8. Continue with [application onboarding](#application-onboarding).
+  </TabItem>
+  <TabItem value="Advanced setup" label="Advanced setup">
+
+   1. To install OpenRAG with **Advanced Setup**, click **Advanced Setup** or press <kbd>2</kbd>.
+   2. Click **Generate Passwords** to generate passwords for OpenSearch and Langflow.
+
+       The OpenSearch password is required. The Langflow admin password is optional.
+       If no Langflow admin password is generated, Langflow runs in [autologin mode](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login) with no password required.
+
+   3. Paste your OpenAI API key in the OpenAI API key field.
+   4. If you want to upload documents from external storage, such as Google Drive, add the required OAuth credentials for the connectors that you want to use. These settings can be populated automatically if OpenRAG detects these credentials in a `.env` file in the OpenRAG installation directory.
+
+       * **Amazon**: Provide your AWS Access Key ID and AWS Secret Access Key with access to your S3 instance. For more information, see the AWS documentation on [Configuring access to AWS applications](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html).
+       * **Google**: Provide your Google OAuth Client ID and Google OAuth Client Secret. You can generate these in the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). For more information, see the [Google OAuth client documentation](https://developers.google.com/identity/protocols/oauth2).
+       * **Microsoft**: For the Microsoft OAuth Client ID and Microsoft OAuth Client Secret, provide [Azure application registration credentials for SharePoint and OneDrive](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/app-registration?view=odsp-graph-online). For more information, see the [Microsoft Graph OAuth client documentation](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/graph-oauth).
+
+       You can [manage OAuth credentials](/ingestion#oauth-ingestion) later, but it is recommended to configure them during initial set up.
+
+   5. 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.
+   6. Click **Save Configuration**.
+   7. To start OpenRAG, click **Start All Services**.
+      Startup pulls container images and runs them, so it can take some time.
+      When startup is complete, the TUI displays the following:
+      ```bash
+      Services started successfully
+      Command completed successfully
+      ```
+   8. To start the Docling service, under **Native Services**, click **Start**.
+   9. To open the OpenRAG application, navigate to the TUI main menu, and then click **Open App**.
+   Alternatively, in your browser, navigate to `localhost:3000`.
+
+   10. If you enabled OAuth connectors, you must sign in to your OAuth provider before being redirected to your OpenRAG instance.
+
+   11. Two additional variables are available for **Advanced Setup** at this point.
+   Only change these variables if you have a non-default network configuration for your deployment, such as using a reverse proxy or custom domain.
+
+       * `LANGFLOW_PUBLIC_URL`: Sets the base address to access the Langflow web interface. This is where users interact with flows in a browser.
+
+       * `WEBHOOK_BASE_URL`: Sets the base address of the OpenRAG OAuth connector endpoint.
+       Supported webhook endpoints:
+
+           - Amazon S3: Not applicable.
+           - Google Drive: `/connectors/google_drive/webhook`
+           - OneDrive: `/connectors/onedrive/webhook`
+           - SharePoint: `/connectors/sharepoint/webhook`
+
+   12. Continue with [application onboarding](#application-onboarding).
+  </TabItem>
+</Tabs>
 
 <PartialOnboarding />
 
-<PartialInstallNextSteps />
+## Exit the OpenRAG TUI
+
+To exit the OpenRAG TUI, navigate to the main menu, and then press <kbd>q</kbd>.
+The OpenRAG containers continue to run until they are stopped.
+For more information, see [Manage OpenRAG containers with the TUI ](#tui-container-management).
+
+To relaunch the TUI, run `uv run openrag`.
+If you installed OpenRAG with `uvx`, run `uvx openrag`.
+
+## Manage OpenRAG containers with the TUI {#tui-container-management}
+
+After installation, the TUI can deploy, manage, and upgrade your OpenRAG containers.
+
+### Diagnostics
+
+The **Diagnostics** menu provides health monitoring for your container runtimes and monitoring of your OpenSearch security.
+
+### Status {#status}
+
+The **Status** menu displays information on your container deployment.
+Here you can check container health, find your service ports, view logs, and upgrade your containers.
+
+* **Logs**: To view streaming logs, select the container you want to view, and press <kbd>l</kbd>.
+To copy the logs, click **Copy to Clipboard**.
+
+* **Upgrade**: Check for updates. For more information, see [upgrade OpenRAG](#upgrade).
+
+* **Factory Reset**: This is a destructive action that [resets your containers](#reset-containers).
+
+* **Native services**: [View and manage OpenRAG services](#start-all-services) that run directly on your local machine instead of a container.
+
+### Reset containers {#reset-containers}
+
+Reset your OpenRAG deployment by recreating the containers and removing some related data.
+
+:::warning
+This is a destructive action that destroys the following:
+
+* All OpenRAG containers, volumes, and local images
+* Any additional Docker objects
+* The contents of OpenRAG's `config` and `./opensearch-data` directories
+* The `conversations.json` file
+
+This operation _doesn't_ remove the `.env` file or the contents of the `./openrag-documents` directory.
+:::
+
+1. To destroy and recreate your OpenRAG containers, go to the TUI [**Status** menu](#status), and then click **Factory Reset**.
+
+   This function runs the following commands _and_ deletes the contents of OpenRAG's `config` and `./opensearch-data` directories.
+
+   ```bash
+   docker compose down --volumes --remove-orphans --rmi local
+   docker system prune -f
+   ```
+
+2. If you reset your containers as part of reinstalling OpenRAG, continue the [reinstallation process](#reinstall) after resetting the containers.
+Otherwise, in the TUI **Setup** menu, repeat the [setup process](#setup) to start the services and launch the OpenRAG app. Your OpenRAG passwords, OAuth credentials (if previously set), and onboarding configuration are restored from the `.env` file.
+
+### Start all services {#start-all-services}
+
+Through the TUI, you can view and manage OpenRAG services that run in containers and directly on your local machine.
+
+#### Start containers
+
+On the TUI main page or the **Setup** menu, click **Start All Services** to start the OpenRAG containers and launch OpenRAG itself.
+
+When you start all services, the following processes happen:
+
+1. OpenRAG automatically detects your container runtime, and then checks if your machine has compatible GPU support by checking for `CUDA`, `NVIDIA_SMI`, and Docker/Podman runtime support. This check determines which Docker Compose file OpenRAG uses.
+
+2. OpenRAG pulls the OpenRAG container images with `docker compose pull` if any images are missing.
+
+3. OpenRAG deploys the containers with `docker compose up -d`.
+
+#### Start native services (Docling)
+
+A _native service_ in OpenRAG is a service that runs locally on your machine, not within a container. For example, the `docling serve` process is an OpenRAG native service because this document processing service runs on your local machine, separate from the OpenRAG containers.
+
+From the **Status** menu, you can view the status, port, and process ID (PID) of the OpenRAG native services.
+You can also click **Stop** or **Restart** to stop and start OpenRAG native services.
+
+## Upgrade OpenRAG {#upgrade}
+
+To upgrade OpenRAG, upgrade the OpenRAG Python package, and then upgrade the OpenRAG containers.
+
+This is a two part process because upgrading the OpenRAG Python package updates the TUI and Python code, but the container versions are controlled by environment variables in your `.env` file.
+
+1. Stop your OpenRAG containers: In the OpenRAG TUI, go to the **Status** menu, and then click **Stop Services**.
+
+2. Upgrade the OpenRAG Python package to the latest version from [PyPI](https://pypi.org/project/openrag/).
+
+    <Tabs groupId="Installation method">
+    <TabItem value="installer" label="Automatic installer or uvx" default>
+
+    Use these steps to upgrade the Python package if you installed OpenRAG using the automatic installer or `uvx`:
+
+    1. Navigate to your OpenRAG workspace directory:
+
+        ```bash
+        cd openrag-workspace
+        ```
+
+    2. Upgrade the OpenRAG package:
+
+        ```bash
+        uvx --from openrag openrag
+        ```
+
+        To upgrade to a specific version:
+
+        ```bash
+        uvx --from openrag==0.1.33 openrag
+        ```
+
+    </TabItem>
+    <TabItem value="uv-add" label="Python project (uv add)">
+
+    Use these steps to upgrade the Python package if you installed OpenRAG in a Python project with `uv add`:
+
+    1. Navigate to your project directory:
+
+        ```bash
+        cd YOUR_PROJECT_NAME
+        ```
+
+    2. Update OpenRAG to the latest version:
+
+        ```bash
+        uv add --upgrade openrag
+        ```
+
+        To upgrade to a specific version:
+
+        ```bash
+        uv add --upgrade openrag==0.1.33
+        ```
+
+    3. Start the OpenRAG TUI:
+
+        ```bash
+        uv run openrag
+        ```
+
+    </TabItem>
+    <TabItem value="uv-pip" label="Virtual environment (uv pip install)">
+
+    Use these steps to upgrade the Python package if you installed OpenRAG in a venv with `uv pip install`:
+
+    1. Activate your virtual environment.
+
+    2. Upgrade OpenRAG:
+
+        ```bash
+        uv pip install --upgrade openrag
+        ```
+
+        To upgrade to a specific version:
+
+        ```bash
+        uv pip install --upgrade openrag==0.1.33
+        ```
+
+    3. Start the OpenRAG TUI:
+
+        ```bash
+        uv run openrag
+        ```
+
+    </TabItem>
+    </Tabs>
+
+3. Start the upgraded OpenRAG containers: In the OpenRAG TUI, click **Start All Services**, and then wait while the containers start.
+
+    After upgrading the Python package, OpenRAG runs `docker compose pull` to get the appropriate container images matching the version specified in your OpenRAG `.env` file. Then, it recreates the containers with the new images using `docker compose up -d --force-recreate`.
+
+    In the `.env` file, the `OPENRAG_VERSION` [environment variable](/reference/configuration#system-settings) is set to `latest` by default, which it pulls the `latest` available container images.
+    To pin a specific container image version, you can set `OPENRAG_VERSION` to the desired container image version, such as `OPENRAG_VERSION=0.1.33`.
+
+    However, when you upgrade the Python package, OpenRAG automatically attempts to keep the `OPENRAG_VERSION` synchronized with the Python package version.
+    You might need to edit the `.env` file after upgrading the Python package to enforce a different container version.
+    The TUI warns you if it detects a version mismatch.
+
+    If you get an error that `langflow container already exists` error during upgrade, see [Langflow container already exists during upgrade](/support/troubleshoot#langflow-container-already-exists-during-upgrade).
+
+4. When the upgrade process is complete, you can close the **Status** window and continue using OpenRAG.
+
+## Reinstall OpenRAG {#reinstall}
+
+Reset your OpenRAG deployment by recreating the containers and, optionally, removing related data:
+
+1. In the TUI, [reset your containers](#reset-containers) to destroy the following:
+
+   * All existing OpenRAG containers, volumes, and local images
+   * Any additional Docker objects
+   * The contents of OpenRAG's `config` and `./opensearch-data` directories
+   * The `conversations.json` file
+
+2. Optional: Remove data that wasn't deleted by the **Factory Reset** operation. For a completely fresh installation, delete all of this data.
+
+    * **OpenRAG's `.env` file**: Contains your OpenRAG configuration, including OpenRAG passwords, API keys, OAuth settings, and other [environment variables](/reference/configuration). If you delete this file, you must either repeat the [setup process](#setup) to create a new `.env` file, or add a populated `.env` file to your OpenRAG installation directory before restarting OpenRAG.
+    * **The contents of the `./openrag-documents` directory**: Contains documents that you uploaded to OpenRAG. Delete these files to prevent documents from being reingested to your knowledge base after restarting OpenRAG. However, you might want to preserve OpenRAG's [default documents](https://github.com/langflow-ai/openrag/tree/main/openrag-documents).
+
+3. In the TUI **Setup** menu, repeat the [setup process](#setup) to configure OpenRAG, restart the services, and launch the OpenRAG app, and repeat [application onboarding](#application-onboarding).
+If OpenRAG detects a `.env` file, it automatically populates any OpenRAG passwords, OAuth credentials, and onboarding configuration set in that file.

docs/docs/get-started/manage-services.mdx

@@ -1,178 +0,0 @@
----
-title: Manage OpenRAG containers and services
-slug: /manage-services
----
-
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-import PartialDockerComposeUp from '@site/docs/_partial-docker-compose-up.mdx';
-import PartialDockerComposeDownAndPrune from '@site/docs/_partial-docker-compose-down-and-prune.mdx';
-import PartialFactorResetWarning from '@site/docs/_partial-factory-reset-warning.mdx';
-import PartialExportFlows from '@site/docs/_partial-export-flows.mdx';
-
-Service management is an essential part of maintaining your OpenRAG deployment.
-
-Most OpenRAG services run in containers.
-However, some services, like Docling, run directly on the local machine.
-
-If you [installed OpenRAG](/install-options) with the automated installer script, `uv`, or `uvx`, you can use the [Terminal User Interface (TUI)](/tui) to manage your OpenRAG configuration and services.
-
-For [self-managed deployments](/docker), run Docker or Podman commands to manage your OpenRAG services.
-
-## Monitor services and view logs
-
-<Tabs>
-<TabItem value="TUI" label="TUI-managed services" default>
-
-In the TUI, click **Status** to access diagnostics and controls for all OpenRAG services, including container health, ports, and image versions.
-
-To view streaming logs, click the name of a service, and then press <kbd>l</kbd>.
-
-For the Docling native service, see [Stop, start, and inspect native services](#start-native-services).
-
-</TabItem>
-<TabItem value="env" label="Self-managed services">
-
-For self-managed container services, you can get container logs with [`docker compose logs`](https://docs.docker.com/reference/cli/docker/compose/logs/) or [`podman logs`](https://docs.podman.io/en/latest/markdown/podman-logs.1.html).
-
-For the Docling native service, see [Stop, start, and inspect native services](#start-native-services).
-
-</TabItem>
-</Tabs>
-
-## Stop and start containers
-
-<Tabs>
-<TabItem value="TUI" label="TUI-managed services" default>
-
-On the TUI's **Status** page, you can stop, start, and restart OpenRAG's container-based services.
-
-When you click **Restart** or **Start Services**, the following processes are triggered:
-
-1. OpenRAG automatically detects your container runtime, and then checks if your machine has compatible GPU support by checking for `CUDA`, `NVIDIA_SMI`, and Docker/Podman runtime support. This check determines which Docker Compose file OpenRAG uses because there are separate Docker Compose files for GPU and CPU deployments.
-
-2. OpenRAG pulls the OpenRAG container images with `docker compose pull` if any images are missing.
-
-3. OpenRAG deploys the containers with `docker compose up -d`.
-
-</TabItem>
-<TabItem value="env" label="Self-managed services">
-
-Use [`docker compose down`](https://docs.docker.com/reference/cli/docker/compose/down/) and [`docker compose up -d`](https://docs.docker.com/reference/cli/docker/compose/up/).
-
-To stop or start individual containers, use targeted commands like `docker stop CONTAINER_ID` and `docker start CONTAINER_ID`.
-
-</TabItem>
-</Tabs>
-
-## Stop, start, and inspect native services (Docling) {#start-native-services}
-
-A _native service_ in OpenRAG is a service that runs locally on your machine, not within a container. For example, the `docling serve` process is an OpenRAG native service because this document processing service runs on your local machine, separate from the OpenRAG containers.
-
-<Tabs>
-<TabItem value="TUI" label="TUI-managed services" default>
-
-On the TUI's **Status** page, you can stop, start, restart, and inspect OpenRAG's native services.
-
-The **Native Services** section lists the status, port, and process ID (PID) for each native service.
-
-To manage a native service, click the service's name, and then click **Stop**, **Start** or **Restart**.
-
-To view the logs for a native service, click the service's name, and then press <kbd>l</kbd>.
-
-</TabItem>
-<TabItem value="env" label="Self-managed services">
-
-Because the Docling service doesn't run in a container, you must start and stop it manually on the host machine:
-
-* Stop `docling serve`:
-
-   ```bash
-   uv run python scripts/docling_ctl.py stop
-   ```
-
-* Start `docling serve`:
-
-   ```bash
-   uv run python scripts/docling_ctl.py start --port 5001
-   ```
-
-* Check that `docling serve` is running:
-
-   ```bash
-   uv run python scripts/docling_ctl.py status
-   ```
-
-   If `docling serve` is running, the output includes the status, address, and process ID (PID):
-
-   ```text
-   Status: running
-   Endpoint: http://127.0.0.1:5001
-   Docs: http://127.0.0.1:5001/docs
-   PID: 27746
-   ```
-
-</TabItem>
-</Tabs>
-
-## Upgrade services
-
-See [Upgrade OpenRAG](/upgrade).
-
-## Reset containers (destructive) {#reset-containers}
-
-<PartialFactorResetWarning />
-
-Use these steps to reset your OpenRAG deployment by recreating the containers and deleting all data in the `~/.openrag` directory _except_ for the `.env` file and the `/documents` subdirectory.
-
-This restores your OpenRAG deployment to a near-initial state while preserving your configuration (in `.env`) and uploaded documents (in `/documents`).
-Your documents are reingested into a fresh OpenSearch index after the reset.
-
-To reset your OpenRAG deployment _and_ delete all OpenRAG data, see [Reinstall OpenRAG](/reinstall).
-
-<Tabs>
-<TabItem value="TUI" label="TUI-managed services" default>
-
-<PartialExportFlows />
-
-2. To destroy and recreate your OpenRAG containers, click **Status** in the TUI, and then click **Factory Reset**.
-
-3. Repeat the [setup process](/install#setup) to restart the services and launch the OpenRAG app. Your OpenRAG passwords, OAuth credentials (if previously set), and onboarding configuration are restored from the `.env` file.
-
-4. If you exported customized flows, [import your flows](https://docs.langflow.org/concepts-flows-import) into Langflow after completing the onboarding process.
-
-</TabItem>
-<TabItem value="env" label="Self-managed services">
-
-<PartialExportFlows />
-
-2. Recreate the containers:
-
-   ```bash title="Docker"
-   docker compose up --build --force-recreate --remove-orphans
-   ```
-
-   ```bash title="Podman"
-   podman compose up --build --force-recreate --remove-orphans
-   ```
-
-3. Launch the OpenRAG app, and then repeat the [application onboarding process](/docker#application-onboarding).
-
-4. If you exported customized flows, [import your flows](https://docs.langflow.org/concepts-flows-import) into Langflow after completing the onboarding process.
-
-</TabItem>
-</Tabs>
-
-## Prune images
-
-Use image pruning to free up disk space by removing unused OpenRAG container images.
-
-For TUI-managed services, use the TUI's **Prune Images** option to clean up your OpenRAG container images.
-You can choose to prune unused images only or all images.
-If you prune all images, the OpenRAG services are stopped, all images are pruned, and then the required images are pulled the next time you start the OpenRAG services.
-
-For self-managed services, use [`docker image prune`](https://docs.docker.com/engine/reference/commandline/image_prune/) or [`podman image prune`](https://docs.podman.io/en/latest/markdown/podman-image-prune.1.html) to remove unused images.
-
-## See also
-
-* [Uninstall OpenRAG](/uninstall)

docs/docs/get-started/quickstart.mdx

@@ -6,28 +6,36 @@ slug: /quickstart
 import Icon from "@site/src/components/icon/icon";
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
+import PartialWsl from '@site/docs/_partial-wsl-install.mdx';
 import PartialIntegrateChat from '@site/docs/_partial-integrate-chat.mdx';
-import PartialPrereqWindows from '@site/docs/_partial-prereq-windows.mdx';
-import PartialPrereqPython from '@site/docs/_partial-prereq-python.mdx';
 
 Use this quickstart to install OpenRAG, and then try some of OpenRAG's core features.
 
 ## Prerequisites
 
-<PartialPrereqPython />
+This quickstart requires the following:
 
-* Get an [OpenAI API key](https://platform.openai.com/api-keys).
+- An [OpenAI API key](https://platform.openai.com/api-keys).
 This quickstart uses OpenAI for simplicity.
-For other providers, see the other [installation methods](/install-options).
+For other providers, see the complete [installation guide](/install).
+
+- [Python](https://www.python.org/downloads/release/python-3100/) version 3.13 or later.
+
+- Microsoft Windows only: To run OpenRAG on Windows, you must use the Windows Subsystem for Linux (WSL).
+
+   <details>
+   <summary>Install WSL for OpenRAG</summary>
+
+   <PartialWsl />
+
+   </details>
 
-<PartialPrereqWindows />
 
 ## Install OpenRAG
 
-For this quickstart, install OpenRAG with the automatic installer script and basic setup.
-The script installs OpenRAG dependencies, including Docker or Podman, and then it installs and runs OpenRAG with [`uvx`](https://docs.astral.sh/uv/guides/tools/#running-tools).
+For this quickstart, install OpenRAG with the automatic installer script and basic setup:
 
-1. Create a directory for your OpenRAG installation, and then change to that directory:
+1. Create a directory to store the OpenRAG configuration files, and then change to that directory:
 
    ```bash
    mkdir openrag-workspace
@@ -40,42 +48,40 @@ The script installs OpenRAG dependencies, including Docker or Podman, and then i
    bash run_openrag_with_prereqs.sh
    ```
 
-   Wait while the installer script prepares your environment and installs OpenRAG.
+   This script installs OpenRAG and its dependencies, including Docker or Podman, and it creates a `.env` file and `docker-compose` files in the current working directory.
    You might be prompted to install certain dependencies if they aren't already present in your environment.
+   This process can take a few minutes.
+   Once the environment is ready, OpenRAG starts.
 
-   The entire process can take a few minutes.
-   Once the environment is ready, the OpenRAG [Terminal User Interface (TUI)](/tui) starts.
+3. Click **Basic Setup**.
 
-   ![OpenRAG TUI Interface](@site/static/img/openrag_tui_dec_2025.png)
+4. Create passwords for your OpenRAG installation's OpenSearch and Langflow services. You can click **Generate Passwords** to automatically generate passwords.
 
-3. In the TUI, click **Basic Setup**.
+   The OpenSearch password is required. The Langflow admin password is optional.
+   If you don't generate a Langflow admin password, Langflow runs in [autologin mode](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login) with no password required.
 
-4. For **Langflow Admin Password**, click **Generate Password** to create a Langflow administrator password and username.
+   Your passwords are saved in the `.env` file that is used to start OpenRAG.
+   You can find this file in your OpenRAG installation directory.
 
-5. Use the default values for all other fields.
+5. Click **Save Configuration**, and then click **Start All Services**.
 
-6. Click **Save Configuration**.
+   Wait a few minutes while the startup process pulls and runs the necessary container images.
+   Proceed when you see the following messages in the terminal user interface (TUI):
 
-   Your OpenRAG configuration and passwords are stored in an [OpenRAG `.env` file](/reference/configuration) file that is created automatically at `~/.openrag/tui`.
-   OpenRAG container definitions are stored in the `docker-compose` files in the same directory.
-
-7. Click **Start OpenRAG** to start the OpenRAG services.
-
-   This process can take some time while OpenRAG pulls and runs the container images.
-   If all services start successfully, the TUI prints a confirmation message:
-
-   ```text
+   ```bash
    Services started successfully
    Command completed successfully
    ```
 
-8. Click **Close**, and then click **Launch OpenRAG** to access the OpenRAG application and start the application onboarding process.
+6. To open the OpenRAG application, go to the TUI main menu, and then click **Open App**.
+Alternatively, in your browser, navigate to `localhost:3000`.
 
-9. For this quickstart, select the **OpenAI** model provider, enter your OpenAI API key, and then click **Complete**. Use the default settings for all other model options.
+7. Select the **OpenAI** model provider, enter your OpenAI API key, and then click **Complete**.
 
-10. Click through the overview slides for a brief introduction to OpenRAG, or click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
+   For this quickstart, you can use the default options for the model settings.
+
+8. Click through the overview slides for a brief introduction to OpenRAG and basic setup, or click <Icon name="ArrowRight" aria-hidden="true"/> **Skip overview**.
 You can complete this quickstart without going through the overview.
-The overview demonstrates some basic functionality that is covered in the next section and in other parts of the OpenRAG documentation.
 
 ## Load and chat with documents {#chat-with-documents}
 
@@ -89,14 +95,14 @@ For example: `What documents are available to you?`
 
    The agent responds with a summary of OpenRAG's default documents.
 
-3. To verify the agent's response, click <Icon name="Library" aria-hidden="true"/> **Knowledge** to view the documents stored in the OpenRAG OpenSearch database.
+3. To verify the agent's response, click <Icon name="Library" aria-hidden="true"/> **Knowledge** to view the documents stored in the OpenRAG OpenSearch vector database.
 You can click a document to view the chunks of the document as they are stored in the database.
 
 4. Click **Add Knowledge** to add your own documents to your OpenRAG knowledge base.
 
    For this quickstart, use either the <Icon name="File" aria-hidden="true"/> **File** or <Icon name="Folder" aria-hidden="true"/> **Folder** upload options to load documents from your local machine.
    **Folder** uploads an entire directory.
-   The default directory is `~/.openrag/documents`.
+   The default directory is the `/openrag-documents` subdirectory in your OpenRAG installation directory.
 
    For information about the cloud storage provider options, see [Ingest files with OAuth connectors](/ingestion#oauth-ingestion).
 
@@ -106,7 +112,7 @@ You can click a document to view the chunks of the document as they are stored i
 
    * Click <Icon name="Gear" aria-hidden="true"/> **Function Call: search_documents (tool_call)** to view the log of tool calls made by the agent. This is helpful for troubleshooting because it shows you how the agent used particular tools.
 
-   * Click <Icon name="Library" aria-hidden="true"/> **Knowledge** to confirm that the documents are present in the OpenRAG OpenSearch database, and then click each document to see how the document was chunked.
+   * Click <Icon name="Library" aria-hidden="true"/> **Knowledge** to confirm that the documents are present in the OpenRAG OpenSearch vector database, and then click each document to see how the document was chunked.
    If a document was chunked improperly, you might need to tweak the ingestion or modify and reupload the document.
 
    * Click <Icon name="Settings2" aria-hidden="true"/> **Settings** to modify the knowledge ingestion settings.
@@ -121,7 +127,7 @@ You can click a document to view the chunks of the document as they are stored i
 
 2. For greater insight into the underlying [Langflow flow](/agents) that drives the OpenRAG chat, click **Edit in Langflow** and then click **Proceed** to launch the Langflow visual editor in a new browser window.
 
-   If Langflow requests login information, enter the `LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD` from the `.env` file at `~/.openrag/tui`.
+   If Langflow requests login information, enter the `LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD` from the `.env` file in your OpenRAG installation directory.
 
    The **OpenRAG OpenSearch Agent** flow opens in a new browser window.
 
@@ -152,8 +158,9 @@ You can send and receive requests with the Langflow API using Python, TypeScript
 
 ## Next steps
 
-* **Reinstall OpenRAG with your preferred settings**: This quickstart used `uvx` and a minimal setup to demonstrate OpenRAG's core functionality.
-It is recommended that you [reinstall OpenRAG](/reinstall) with your preferred configuration and [installation method](/install-options).
+* **Reinstall OpenRAG with your preferred settings**: This quickstart used a minimal setup to demonstrate OpenRAG's core functionality.
+It is recommended that you [reinstall OpenRAG](/install#reinstall) with your preferred configuration because some settings are immutable after initial setup.
+For all installation options, see [Install OpenRAG with TUI](/install) and [Install OpenRAG with containers](/docker).
 
 * **Learn more about OpenRAG**: Explore OpenRAG and the OpenRAG documentation to learn more about its features and functionality.
 

docs/docs/get-started/reinstall.mdx

@@ -1,87 +0,0 @@
----
-title: Reinstall OpenRAG
-slug: /reinstall
----
-
-import PartialDockerComposeUp from '@site/docs/_partial-docker-compose-up.mdx';
-import PartialDockerComposeDownAndPrune from '@site/docs/_partial-docker-compose-down-and-prune.mdx';
-import PartialDockerStopAll from '@site/docs/_partial-docker-stop-all.mdx';
-import PartialDockerRemoveAndCleanupSteps from '@site/docs/_partial-docker-remove-and-cleanup-steps.mdx';
-import PartialFactorResetWarning from '@site/docs/_partial-factory-reset-warning.mdx';
-import PartialExportFlows from '@site/docs/_partial-export-flows.mdx';
-
-You can reset your OpenRAG deployment to its initial state by recreating the containers and deleting accessory data, such as the `.env` file and ingested documents.
-
-:::warning
-These are destructive operations that reset your OpenRAG deployment to an initial state.
-Destroyed containers and deleted data are lost and cannot be recovered after running these operations.
-:::
-
-## Reinstall TUI-managed containers
-
-<PartialExportFlows />
-
-2. In the TUI, click **Status**, and then click **Factory Reset** to [reset your OpenRAG containers](/manage-services#reset-containers).
-
-   <PartialFactorResetWarning />
-
-3. Press <kbd>Esc</kbd> to close the **Status** page, and then press <kbd>q</kbd> to exit the TUI.
-
-4. Optional: Delete or edit [OpenRAG's `.env` file](/reference/configuration), which is stored at `~/.openrag/tui`.
-
-   This file contains your OpenRAG configuration, including OpenRAG passwords, API keys, OAuth settings, and other environment variables. If you delete this file, the TUI automatically generates a new one after you repeat the setup and onboarding process. If you preserve this file, the TUI can read values from the existing `.env` file during setup and onboarding.
-
-5. Optional: Remove any files from the `~/.openrag/documents` subdirectory that you don't want to reingest after redeploying the containers.
-It is recommended that you preserve OpenRAG's [default documents](https://github.com/langflow-ai/openrag/tree/main/openrag-documents).
-
-6. Restart the TUI with `uv run openrag` or `uvx openrag`.
-
-7. Repeat the [setup process](/install#setup) to configure OpenRAG and restart all services.
-Then, launch the OpenRAG app and repeat the [application onboarding process](/install#application-onboarding).
-
-## Reinstall self-managed containers with `docker compose` or `podman compose`
-
-Use these steps to reinstall OpenRAG containers with streamlined `docker compose` or `podman compose` commands:
-
-<PartialExportFlows />
-
-2. Destroy the containers, volumes, and local images, and then remove (prune) any additional container objects.
-
-   <PartialFactorResetWarning />
-
-   <PartialDockerComposeDownAndPrune />
-
-3. Optional: Edit OpenRAG's `.env` file if needed.
-
-4. Optional: Remove any files from the `~/.openrag/documents` subdirectory that you don't want to reingest after redeploying the containers.
-It is recommended that you preserve OpenRAG's [default documents](https://github.com/langflow-ai/openrag/tree/main/openrag-documents).
-
-5. Redeploy OpenRAG:
-
-   <PartialDockerComposeUp />
-
-6. Launch the OpenRAG app, and then repeat the [application onboarding process](/docker#application-onboarding).
-
-## Reinstall self-managed containers with discrete `docker` or `podman` commands
-
-Use these commands to remove and clean up OpenRAG containers with discrete `docker` or `podman` commands.
-
-If you want to reinstall one container, specify the container name in the commands instead of running the commands on all containers.
-
-<PartialExportFlows />
-
-2. Stop all running containers:
-
-   <PartialDockerStopAll />
-
-3. Remove and clean up containers:
-
-   <PartialDockerRemoveAndCleanupSteps />
-
-4. Optional: Edit OpenRAG's `.env` file if needed.
-
-5. Optional: If you removed all containers or specifically the OpenSearch container, then you can remove any files from the `~/.openrag/documents` subdirectory that you don't want to reingest after redeploying the containers.
-It is recommended that you preserve OpenRAG's [default documents](https://github.com/langflow-ai/openrag/tree/main/openrag-documents).
-
-6. If you removed all OpenRAG containers, [redeploy OpenRAG](/docker).
-If you removed only one container, redeploy that container with the appropriate `docker run` or `podman run` command.

docs/docs/get-started/tui.mdx

@@ -1,52 +0,0 @@
----
-title: Use the TUI
-slug: /tui
----
-
-import PartialGpuModeTip from '@site/docs/_partial-gpu-mode-tip.mdx';
-
-The OpenRAG Terminal User Interface (TUI) provides a simplified and guided experience for configuring, managing, and monitoring your OpenRAG deployment directly from the terminal.
-
-![OpenRAG TUI Interface](@site/static/img/openrag_tui_dec_2025.png)
-
-If you install OpenRAG with the [automatic installer script](/install), [`uv`](/install-uv), or [`uvx`](/install-uvx), you use the TUI to manage your OpenRAG deployment.
-The TUI guides you through the initial setup, automatically manages your OpenRAG `.env` and `docker-compose` files, and provides convenient access to [service management](/manage-services) controls.
-
-In contrast, when you [deploy OpenRAG with self-managed services](/docker), you must manually configure OpenRAG by preparing a `.env` file, and then use Docker or Podman commands to deploy and manage your OpenRAG services.
-
-## Access the TUI {#access-the-tui}
-
-If you installed OpenRAG with `uv`, access the TUI with `uv run openrag`.
-
-If you installed OpenRAG with the automatic installer script or `uvx`, access the TUI with `uvx openrag`.
-
-## Navigate the TUI
-
-You can navigate the TUI with your mouse or keyboard.
-Keyboard shortcuts for additional menus are printed at the bottom of the TUI screen.
-
-## Manage services with the TUI
-
-Use the TUI's **Status** page to access controls and information for your OpenRAG services.
-For more information, see [Manage OpenRAG services](/manage-services).
-
-## Toggle GPU/CPU mode
-
-You can toggle between GPU and CPU mode from within the TUI if your system has compatible GPU hardware and drivers installed.
-
-In the TUI, click **Status**, and then click **Switch to GPU Mode** or **Switch to CPU Mode**.
-
-This change requires restarting all OpenRAG services because each mode has its own `docker-compose` file.
-
-:::tip
-<PartialGpuModeTip />
-:::
-
-## Exit the OpenRAG TUI
-
-To exit the OpenRAG TUI, press <kbd>q</kbd> on the TUI main page.
-
-Exiting the TUI doesn't stop your OpenRAG services.
-Your OpenRAG services continue to run until they are stopped from within the TUI or by another process that inadvertently stops them.
-
-To restart the TUI, see [Access the TUI](#access-the-tui).

docs/docs/get-started/uninstall.mdx

@@ -1,58 +0,0 @@
----
-title: Remove OpenRAG
-slug: /uninstall
----
-
-import PartialDockerComposeDownAndPrune from '@site/docs/_partial-docker-compose-down-and-prune.mdx';
-import PartialDockerStopAll from '@site/docs/_partial-docker-stop-all.mdx';
-import PartialDockerRemoveAndCleanupSteps from '@site/docs/_partial-docker-remove-and-cleanup-steps.mdx';
-
-:::tip
-If you want to reset your OpenRAG containers without removing OpenRAG entirely, see [Reset OpenRAG containers](/manage-services) and [Reinstall OpenRAG](/reinstall).
-:::
-
-## Uninstall TUI-managed deployments
-
-If you used the [automated installer script](/install) or [`uvx`](/install-uvx) to install OpenRAG, clear your `uv` cache (`uv cache clean`) to remove the TUI environment, and then delete the `~/.openrag` directory.
-
-If you used [`uv`](/install-uv) to install OpenRAG, run `uv remove openrag` in your Python project, and then delete the `~/.openrag` directory.
-
-## Uninstall self-managed deployments
-
-For self-managed services, destroy the containers, prune any additional container objects, delete any remaining OpenRAG files, and then shut down the Docling service.
-
-### Uninstall with `docker compose` or `podman compose`
-
-Use these steps to uninstall a self-managed OpenRAG deployment with streamlined `docker compose` or `podman compose` commands:
-
-1. Destroy the containers, volumes, and local images, and then remove (prune) any additional container objects:
-
-   <PartialDockerComposeDownAndPrune />
-
-2. Remove OpenRAG's `.env` file and the `~/.openrag/documents` directory, which aren't deleted by the previous commands.
-
-3. Stop `docling-serve`:
-
-   ```bash
-   uv run python scripts/docling_ctl.py stop
-   ```
-
-### Uninstall with discrete `docker` or `podman` commands
-
-Use these commands to uninstall a self-managed OpenRAG deployment with discrete `docker` or `podman` commands:
-
-1. Stop all running containers:
-
-   <PartialDockerStopAll />
-
-2. Remove and clean up containers:
-
-   <PartialDockerRemoveAndCleanupSteps />
-
-3. Remove OpenRAG's `.env` file and the `~/.openrag/documents` directory, which aren't deleted by the previous commands.
-
-4. Stop `docling-serve`:
-
-   ```bash
-   uv run python scripts/docling_ctl.py stop
-   ```

docs/docs/get-started/upgrade.mdx

@@ -1,156 +0,0 @@
----
-title: Upgrade OpenRAG
-slug: /upgrade
----
-
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-import PartialExportFlows from '@site/docs/_partial-export-flows.mdx';
-
-Use these steps to upgrade your OpenRAG deployment to the latest version or a specific version.
-
-## Export customized flows before upgrading
-
-If you modified the built-in flows or created custom flows in your OpenRAG Langflow instance, [export your flows](https://docs.langflow.org/concepts-flows-import) before upgrading.
-This ensure that you won't lose your flows after upgrading, and you can reference the exported flows if there are any breaking changes in the new version.
-
-## Upgrade TUI-managed deployments
-
-To upgrade OpenRAG, you need to upgrade the OpenRAG Python package, and then upgrade the OpenRAG containers.
-
-Upgrading the Python package also upgrades Docling by bumping the dependency in `pyproject.toml`.
-
-This is a two-part process because upgrading the OpenRAG Python package updates the Terminal User Interface (TUI) and Python code, but the container versions are controlled by environment variables in your [OpenRAG `.env` file](/reference/configuration).
-
-<PartialExportFlows />
-
-2. To check for updates, click **Status** in the TUI, and then click **Upgrade**.
-
-3. If there is an update available, press <kbd>Esc</kbd> to close the **Status** page, then then click **Stop All Services**.
-
-4. Press <kbd>q</kbd> to exit the TUI.
-
-5. Upgrade the OpenRAG Python package to the latest version from [PyPI](https://pypi.org/project/openrag/).
-The commands to upgrade the package depend on how you installed OpenRAG.
-
-   <Tabs>
-   <TabItem value="installer" label="Script or uvx" default>
-
-   Use these steps to upgrade the Python package if you installed OpenRAG using the [installer script](/install) or [`uvx`](/install-uvx):
-
-   1. Navigate to your OpenRAG workspace directory:
-
-      ```bash
-      cd openrag-workspace
-      ```
-
-   2. Upgrade the OpenRAG package:
-
-      ```bash
-      uvx --from openrag openrag
-      ```
-
-      You can invoke a specific version using any of the [`uvx` version specifiers](https://docs.astral.sh/uv/guides/tools/#requesting-specific-versions), such as `--from`:
-
-      ```bash
-      uvx --from openrag==0.1.30 openrag
-      ```
-
-   </TabItem>
-   <TabItem value="uv-add" label="uv add">
-
-   Use these steps to upgrade the Python package if you installed OpenRAG with [`uv add`](/install-uv):
-
-   1. Navigate to your project directory:
-
-      ```bash
-      cd YOUR_PROJECT_NAME
-      ```
-
-   2. Update OpenRAG to the latest version:
-
-      ```bash
-      uv add --upgrade openrag
-      ```
-
-      To upgrade to a specific version:
-
-      ```bash
-      uv add --upgrade openrag==0.1.33
-      ```
-
-   3. Start the OpenRAG TUI:
-
-      ```bash
-      uv run openrag
-      ```
-
-   </TabItem>
-   <TabItem value="uv-pip" label="uv pip install">
-
-   Use these steps to upgrade the Python package if you installed OpenRAG with [`uv pip install`](/install-uv):
-
-   1. Activate your virtual environment.
-
-   2. Upgrade OpenRAG:
-
-      ```bash
-      uv pip install --upgrade openrag
-      ```
-
-      To upgrade to a specific version:
-
-      ```bash
-      uv pip install --upgrade openrag==0.1.33
-      ```
-
-   3. Start the OpenRAG TUI:
-
-      ```bash
-      uv run openrag
-      ```
-
-   </TabItem>
-   </Tabs>
-
-6. In the OpenRAG TUI, click **Start Services**, and then wait while the services start.
-
-   When you start services after upgrading the Python package, OpenRAG runs `docker compose pull` to get the appropriate container images matching the version specified in your OpenRAG `.env` file. Then, it recreates the containers with the new images using `docker compose up -d --force-recreate`.
-
-   :::tip Pin container versions
-   In the OpenRAG `.env` file, the `OPENRAG_VERSION` [environment variable](/reference/configuration#system-settings) is set to `latest` by default, which pulls the `latest` available container images.
-   To pin a specific container image version, you can set `OPENRAG_VERSION` to the desired container image version, such as `OPENRAG_VERSION=0.1.33`.
-
-   However, when you upgrade the Python package, OpenRAG automatically attempts to keep the `OPENRAG_VERSION` synchronized with the Python package version.
-   You might need to edit the `.env` file after upgrading the Python package to enforce a different container version.
-   The TUI warns you if it detects a version mismatch.
-   :::
-
-   If you get an error that `langflow container already exists` error during upgrade, see [Langflow container already exists during upgrade](/support/troubleshoot#langflow-container-already-exists-during-upgrade).
-
-7. After the containers start, click **Close**, and then click **Launch OpenRAG**.
-
-## Upgrade self-managed deployments
-
-<PartialExportFlows />
-
-2. Fetch and apply the latest container images while preserving your OpenRAG data:
-
-   ```bash title="Docker"
-   docker compose pull
-   docker compose up -d --force-recreate
-   ```
-
-   ```bash title="Podman"
-   podman compose pull
-   podman compose up -d --force-recreate
-   ```
-
-   By default, OpenRAG's `docker-compose` files pull the latest container images.
-
-3. After the containers start, access the OpenRAG application at `http://localhost:3000`.
-
-## See also
-
-* [Manage OpenRAG services](/manage-services)
-* [Troubleshoot OpenRAG](/support/troubleshoot)

docs/docs/get-started/what-is-openrag.mdx

@@ -4,11 +4,11 @@ slug: /
 hide_table_of_contents: true
 ---
 
-OpenRAG is an open-source package for building agentic RAG systems that integrates with a wide range of orchestration tools, databases, and LLM providers.
+OpenRAG is an open-source package for building agentic RAG systems that integrates with a wide range of orchestration tools, vector databases, and LLM providers.
 
 OpenRAG connects and amplifies three popular, proven open-source projects into one powerful platform:
 
-* [Langflow](https://docs.langflow.org): Langflow is a versatile tool for building and deploying AI agents and MCP servers. It supports all major LLMs, popular vector databases, and a growing library of AI tools.
+* [Langflow](https://docs.langflow.org): Langflow is a versatile tool for building and deploying AI agents and MCP servers. It supports all major LLMs, vector databases, and a growing library of AI tools.
 
    OpenRAG uses several built-in flows, and it provides full access to all Langflow features through the embedded Langflow visual editor.
 
@@ -17,7 +17,7 @@ OpenRAG connects and amplifies three popular, proven open-source projects into o
 * [OpenSearch](https://docs.opensearch.org/latest/): OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite that makes it easy to ingest, search, visualize, and analyze data.
 It provides powerful hybrid search capabilities with enterprise-grade security and multi-tenancy support.
 
-   OpenRAG uses OpenSearch as the underlying database for storing and retrieving your documents and associated vector data (embeddings). You can ingest documents from a variety of sources, including your local filesystem and OAuth authenticated connectors to popular cloud storage services.
+   OpenRAG uses OpenSearch as the underlying vector database for storing and retrieving your documents and associated vector data (embeddings). You can ingest documents from a variety of sources, including your local filesystem and OAuth authenticated connectors to popular cloud storage services.
 
 * [Docling](https://docling-project.github.io/docling/): Docling simplifies document processing, supports many file formats and advanced PDF parsing, and provides seamless integrations with the generative AI ecosystem.
 
@@ -59,7 +59,7 @@ flowchart TD
 
 * **OpenRAG backend**: The central orchestration service that coordinates all other components.
 
-* **Langflow**: This container runs a Langflow instance. It provides the embedded Langflow visual editor for editing and creating flow, and it connects to the **OpenSearch** container for document storage and retrieval.
+* **Langflow**: This container runs a Langflow instance. It provides the embedded Langflow visual editor for editing and creating flow, and it connects to the **OpenSearch** container for vector storage and retrieval.
 
 * **Docling Serve**: This is a local document processing service managed by the **OpenRAG backend**.
 

docs/docs/reference/api-sdk-overview.mdx

@@ -1,12 +0,0 @@
----
-title: OpenRAG APIs and SDKs
-slug: /reference/api-sdk-overview
----
-
-You can use OpenRAG's APIs and SDKs to integrate and extend OpenRAG's capabilities:
-
-* [Python SDK](https://github.com/langflow-ai/openrag/tree/main/sdks/python)
-* [TypeScript/JavaScript SDK](https://github.com/langflow-ai/openrag/tree/main/sdks/typescript)
-
-<!-- TBD: MCP: See https://github.com/langflow-ai/openrag/pull/729 -->
-<!-- TBD: API Reference: See https://github.com/langflow-ai/openrag/issues/734 -->

docs/docs/reference/configuration.mdx

@@ -3,127 +3,145 @@ title: Environment variables
 slug: /reference/configuration
 ---
 
-import PartialDockerComposeUp from '@site/docs/_partial-docker-compose-up.mdx';
+import Icon from "@site/src/components/icon/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 
-OpenRAG's `.env` file is the primary configuration file for OpenRAG.
-Environment variables in `.env` always take precedence over other sources.
+OpenRAG recognizes environment variables from the following sources:
 
-For deployments managed with the Terminal User Interface (TUI), this file is located at `~/.openrag/tui`, and it can be created automatically during [installation](/install-options).
+* [Environment variables](#configure-environment-variables): Values set in the `.env` file.
+* [Langflow runtime overrides](#langflow-runtime-overrides): Langflow components can set environment variables at runtime.
+* [Default or fallback values](#default-values-and-fallbacks): These values are default or fallback values if OpenRAG doesn't find a value.
 
-For [self-managed deployments](/docker), this file can be located at the root of your OpenRAG project directory or referenced from another location.
+## Configure environment variables
 
-For an example, see [`.env.example` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/.env.example).
+Environment variables are set in a `.env` file in the root of your OpenRAG project directory.
 
-:::tip
-OpenRAG's Docker Compose files are populated automatically using values from the `.env` file, so you don't need to edit the Docker Compose files manually.
-:::
+For an example `.env` file, see [`.env.example` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/.env.example).
 
-If a variable isn't set, OpenRAG uses default or fallback values where available.
-Not all variables have default values, and errors can occur if required variables aren't set.
-Default values can be found in the reference tables on this page and in [`config_manager.py`](https://github.com/langflow-ai/openrag/blob/main/src/config/config_manager.py), [`settings.py`](https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py), and [`logging_config.py`](https://github.com/langflow-ai/openrag/blob/main/src/utils/logging_config.py).
+The Docker Compose files are populated with values from your `.env`, so you don't need to edit the Docker Compose files manually.
 
-You can [temporarily set Langflow variables at runtime](/agents#modify-a-flow-at-runtime).
-However, these temporary overrides don't overlap with most OpenRAG environment variables.
-The only exceptions are flow-level Langflow settings, such as the language model used in a flow.
+Environment variables always take precedence over other variables.
 
-## Edit the `.env` file {#set-environment-variables}
+### Set environment variables {#set-environment-variables}
 
-During [installation](/install-options), an initial `.env` file is created automatically or manually.
-You can edit this file to change OpenRAG configuration settings after installation.
+After you start OpenRAG, you must [stop and restart OpenRAG containers](/install#tui-container-management) to apply any changes you make to the `.env` file.
 
-Each OpenRAG environment variable is either mutable or immutable.
-This determines the actions you must take to apply changes after editing the `.env` file:
+To set mutable environment variables, do the following:
 
-* **Mutable environment variables**: You can apply changes to mutable environment variables by [stopping and restarting the OpenRAG services](/manage-services) after editing the `.env` file.
+1. Stop OpenRAG with the TUI or Docker Compose.
 
-* **Immutable environment variables**: You must [redeploy OpenRAG](/reinstall) with your modified `.env` file if you change immutable environment variables.
+2. Set the values in the `.env` file:
 
-## Model provider settings {#model-provider-settings}
+   ```bash
+   LOG_LEVEL=DEBUG
+   LOG_FORMAT=json
+   SERVICE_NAME=openrag-dev
+   ```
+
+3. Start OpenRAG with the TUI or Docker Compose.
+
+Certain environment variables that you set during [application onboarding](/install#application-onboarding), such as provider API keys and provider endpoints, require resetting the containers after modifying the `.env` file.
+
+To change immutable variables with TUI-managed containers, you must [reinstall OpenRAG](/install#reinstall) and either delete or modify the `.env` file before you repeat the setup and onboarding process in the TUI.
+
+To change immutable variables with self-managed containers, do the following:
+
+1. Stop OpenRAG with Docker Compose.
+
+2. Remove the containers:
+
+   ```bash
+   docker-compose down
+   ```
+
+3. Update the values in your `.env` file.
+
+4. Start OpenRAG with Docker Compose:
+
+   ```bash
+   docker-compose up -d
+   ```
+
+5. Repeat [application onboarding](/install#application-onboarding). The values in your `.env` file are automatically populated.
+
+## Supported environment variables
+
+All OpenRAG configuration can be controlled through environment variables.
+
+### AI provider settings
 
 Configure which models and providers OpenRAG uses to generate text and embeddings.
-You only need to provide credentials for the providers you are using in OpenRAG.
-
-These variables are initially set during the [application onboarding process](/install#application-onboarding).
-Some of these variables are immutable and can only be changed by redeploying OpenRAG, as explained in [Set environment variables](#set-environment-variables).
+These are initially set during [application onboarding](/install#application-onboarding).
+Some values are immutable and can only be changed by recreating the OpenRAG containers, as explained in [Set environment variables](#set-environment-variables).
 
 | Variable | Default | Description |
 |----------|---------|-------------|
-| `EMBEDDING_MODEL` | `text-embedding-3-small` | Embedding model for generating vector embeddings for documents in the knowledge base and similarity search queries. Can be changed after the application onboarding process. Accepts one or more models. |
+| `EMBEDDING_MODEL` | `text-embedding-3-small` | Embedding model for generating vector embeddings for documents in the knowledge base and similarity search queries. Can be changed after application onboarding. Accepts one or more models. |
 | `LLM_MODEL` | `gpt-4o-mini` | Language model for language processing and text generation in the **Chat** feature. |
-| `MODEL_PROVIDER` | `openai` | Model provider, as one of `openai`, `watsonx`, `ollama`, or `anthropic`. |
-| `ANTHROPIC_API_KEY` | Not set | API key for the Anthropic language model provider. |
-| `OPENAI_API_KEY` | Not set | API key for the OpenAI model provider, which is also the default model provider. |
-| `OLLAMA_ENDPOINT` | Not set | Custom provider endpoint for the Ollama model provider. |
-| `WATSONX_API_KEY` | Not set | API key for the IBM watsonx.ai model provider. |
-| `WATSONX_ENDPOINT` | Not set | Custom provider endpoint for the IBM watsonx.ai model provider. |
-| `WATSONX_PROJECT_ID` | Not set | Project ID for the IBM watsonx.ai model provider. |
+| `MODEL_PROVIDER` | `openai` | Model provider, such as OpenAI or IBM watsonx.ai. |
+| `OPENAI_API_KEY` | Not set | Optional OpenAI API key for the default model. For other providers, use `PROVIDER_API_KEY`. |
+| `PROVIDER_API_KEY` | Not set | API key for the model provider. |
+| `PROVIDER_ENDPOINT` | Not set | Custom provider endpoint for the IBM and Ollama model providers. Leave unset for other model providers. |
+| `PROVIDER_PROJECT_ID` | Not set | Project ID for the IBM watsonx.ai model provider only. Leave unset for other model providers. |
 
-## Document processing settings {#document-processing-settings}
+### Document processing
 
 Control how OpenRAG [processes and ingests documents](/ingestion) into your knowledge base.
 
-Most of these settings can be configured on the OpenRAG **Settings** page or in the `.env` file.
-
 | Variable | Default | Description |
 |----------|---------|-------------|
 | `CHUNK_OVERLAP` | `200` | Overlap between chunks. |
 | `CHUNK_SIZE` | `1000` | Text chunk size for document processing. |
 | `DISABLE_INGEST_WITH_LANGFLOW` | `false` | Disable Langflow ingestion pipeline. |
 | `DOCLING_OCR_ENGINE` | Set by OS | OCR engine for document processing. For macOS, `ocrmac`. For any other OS, `easyocr`. |
-| `DOCLING_SERVE_URL` | `http://**HOST_IP**:5001` | URL for the [Docling Serve instance](/knowledge#select-a-docling-implementation). By default, OpenRAG starts a local `docling serve` process and auto-detects the host. To use your own local or remote Docling Serve instance, set this variable to the full path to the target instance. The service must run on port 5001. |
 | `OCR_ENABLED` | `false` | Enable OCR for image processing. |
-| `OPENRAG_DOCUMENTS_PATH` | `~/.openrag/documents` | The [local documents path](/knowledge#set-the-local-documents-path) for ingestion. |
+| `OPENRAG_DOCUMENTS_PATHS` | `./openrag-documents` | Document paths for ingestion. |
 | `PICTURE_DESCRIPTIONS_ENABLED` | `false` | Enable picture descriptions. |
 
-## Langflow settings {#langflow-settings}
+### Langflow settings
 
-Configure the OpenRAG Langflow server's authentication, contact point, and built-in flow definitions.
-
-:::info
-The `LANGFLOW_SUPERUSER_PASSWORD` is set in your `.env` file, and this value determines the default values for several other Langflow authentication variables.
-
-If the `LANGFLOW_SUPERUSER_PASSWORD` variable isn't set, then the Langflow server starts _without_ authentication enabled.
-
-For better security, it is recommended to set `LANGFLOW_SUPERUSER_PASSWORD` so the [Langflow server starts with authentication enabled](https://docs.langflow.org/api-keys-and-authentication#start-a-langflow-server-with-authentication-enabled).
-:::
+Configure Langflow authentication.
 
 | Variable | Default | Description |
 |----------|---------|-------------|
-| `LANGFLOW_AUTO_LOGIN` | Determined by `LANGFLOW_SUPERUSER_PASSWORD` | Whether to enable [auto-login mode](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login) for the Langflow visual editor and CLI. If `LANGFLOW_SUPERUSER_PASSWORD` isn't set, then `LANGFLOW_AUTO_LOGIN` is `True` and auto-login mode is enabled. If `LANGFLOW_SUPERUSER_PASSWORD` is set, then `LANGFLOW_AUTO_LOGIN` is `False` and auto-login mode is disabled. Langflow API calls always require authentication with a Langflow API key regardless of the auto-login setting. |
-| `LANGFLOW_ENABLE_SUPERUSER_CLI` | Determined by `LANGFLOW_SUPERUSER_PASSWORD` | Whether to enable the [Langflow CLI `langflow superuser` command](https://docs.langflow.org/api-keys-and-authentication#langflow-enable-superuser-cli). If `LANGFLOW_SUPERUSER_PASSWORD` isn't set, then `LANGFLOW_ENABLE_SUPERUSER_CLI` is `True` and superuser accounts can be created with the Langflow CLI. If `LANGFLOW_SUPERUSER_PASSWORD` is set, then `LANGFLOW_ENABLE_SUPERUSER_CLI` is `False` and the `langflow superuser` command is disabled. |
-| `LANGFLOW_NEW_USER_IS_ACTIVE` | Determined by `LANGFLOW_SUPERUSER_PASSWORD` | Whether new [Langflow user accounts are active by default](https://docs.langflow.org/api-keys-and-authentication#langflow-new-user-is-active). If `LANGFLOW_SUPERUSER_PASSWORD` isn't set, then `LANGFLOW_NEW_USER_IS_ACTIVE` is `True` and new user accounts are active by default. If `LANGFLOW_SUPERUSER_PASSWORD` is set, then `LANGFLOW_NEW_USER_IS_ACTIVE` is `False` and new user accounts are inactive by default. |
-| `LANGFLOW_PUBLIC_URL` | `http://localhost:7860` | Public URL for the Langflow instance. Forms the base URL for Langflow API calls and other interfaces with your OpenRAG Langflow instance. |
-| `LANGFLOW_KEY` | Automatically generated | A Langflow API key to run flows with Langflow API calls. Because Langflow API keys are server-specific, allow OpenRAG to generate this key initially. You can create additional Langflow API keys after deploying OpenRAG. |
-| `LANGFLOW_SECRET_KEY` | Automatically generated | Secret encryption key for Langflow internal operations. It is recommended to [generate your own Langflow secret key](https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key) for this variable. If this variable isn't set, then Langflow generates a secret key automatically.  |
-| `LANGFLOW_SUPERUSER` | `admin` | Username for the Langflow administrator user. |
-| `LANGFLOW_SUPERUSER_PASSWORD` | Not set | Langflow administrator password. If this variable isn't set, then the Langflow server starts _without_ authentication enabled. It is recommended to set `LANGFLOW_SUPERUSER_PASSWORD` so the [Langflow server starts with authentication enabled](https://docs.langflow.org/api-keys-and-authentication#start-a-langflow-server-with-authentication-enabled). |
+| `LANGFLOW_AUTO_LOGIN` | `False` | Enable auto-login for Langflow. |
+| `LANGFLOW_CHAT_FLOW_ID` | Built-in flow ID | This value is automatically set to the ID of the chat [flow](/agents). The default value is found in [`.env.example`](https://github.com/langflow-ai/openrag/blob/main/.env.example). Only change this value if you explicitly don't want to use this built-in flow. |
+| `LANGFLOW_ENABLE_SUPERUSER_CLI` | `False` | Enable superuser privileges for Langflow CLI commands. |
+| `LANGFLOW_INGEST_FLOW_ID` | Built-in flow ID | This value is automatically set to the ID of the ingestion [flow](/agents). The default value is found in [`.env.example`](https://github.com/langflow-ai/openrag/blob/main/.env.example). Only change this value if you explicitly don't want to use this built-in flow. |
+| `LANGFLOW_KEY` | Automatically generated | Explicit Langflow API key. |
+| `LANGFLOW_NEW_USER_IS_ACTIVE` | `False` | Whether new Langflow users are active by default. |
+| `LANGFLOW_PUBLIC_URL` | `http://localhost:7860` | Public URL for the Langflow instance. |
+| `LANGFLOW_SECRET_KEY` | Not set | Secret key for Langflow internal operations. |
+| `LANGFLOW_SUPERUSER` | None, must be explicitly set | Langflow admin username. Required. |
+| `LANGFLOW_SUPERUSER_PASSWORD` | None, must be explicitly set | Langflow admin password. Required. |
 | `LANGFLOW_URL` | `http://localhost:7860` | URL for the Langflow instance. |
-| `LANGFLOW_CHAT_FLOW_ID`, `LANGFLOW_INGEST_FLOW_ID`, `NUDGES_FLOW_ID` | Built-in flow IDs | These variables are set automatically to the IDs of the chat, ingestion, and nudges [flows](/agents). The default values are found in [`.env.example`](https://github.com/langflow-ai/openrag/blob/main/.env.example). Only change these values if you want to replace a built-in flow with your own custom flow. The flow JSON must be present in your version of the OpenRAG codebase. For example, if you [deploy self-managed services](/docker), you can add the flow JSON to your local clone of the OpenRAG repository before deploying OpenRAG. |
-| `SYSTEM_PROMPT` | `You are a helpful AI assistant with access to a knowledge base. Answer questions based on the provided context.` | System prompt instructions for the agent driving the **Agent** flow (OpenRAG **Chat**). |
+| `NUDGES_FLOW_ID` | Built-in flow ID | This value is automatically set to the ID of the nudges [flow](/agents). The default value is found in [`.env.example`](https://github.com/langflow-ai/openrag/blob/main/.env.example). Only change this value if you explicitly don't want to use this built-in flow. |
+| `SYSTEM_PROMPT` | `You are a helpful AI assistant with access to a knowledge base. Answer questions based on the provided context.` | System prompt instructions for the agent driving the **Chat** flow. |
 
-## OAuth provider settings
+### OAuth provider settings
 
-Configure [OAuth providers](/ingestion#oauth-ingestion) and external service integrations.
+Configure OAuth providers and external service integrations.
 
 | Variable | Default | Description |
 |----------|---------|-------------|
-| `AWS_ACCESS_KEY_ID`<br/>`AWS_SECRET_ACCESS_KEY` | Not set | Enable access to AWS S3 with an [AWS OAuth app](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html) integration. |
-| `GOOGLE_OAUTH_CLIENT_ID`<br/>`GOOGLE_OAUTH_CLIENT_SECRET` | Not set | Enable the [Google OAuth client](https://developers.google.com/identity/protocols/oauth2) integration. You can generate these values in the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). |
-| `MICROSOFT_GRAPH_OAUTH_CLIENT_ID`<br/>`MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET` | Not set | Enable the [Microsoft Graph OAuth client](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/graph-oauth) integration by providing [Azure application registration credentials for SharePoint and OneDrive](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/app-registration?view=odsp-graph-online). |
-| `WEBHOOK_BASE_URL` | Not set | Base URL for OAuth connector webhook endpoints. If this variable isn't set, a default base URL is used. |
+| `AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY` | - | AWS integrations. |
+| `GOOGLE_OAUTH_CLIENT_ID` / `GOOGLE_OAUTH_CLIENT_SECRET` | - | Google OAuth authentication. |
+| `MICROSOFT_GRAPH_OAUTH_CLIENT_ID` / `MICROSOFT_GRAPH_OAUTH_CLIENT_SECRET` | - | Microsoft OAuth. |
+| `WEBHOOK_BASE_URL` | - | Base URL for webhook endpoints. |
 
-## OpenSearch settings
+### OpenSearch settings
 
 Configure OpenSearch database authentication.
 
 | Variable | Default | Description |
 |----------|---------|-------------|
-| `OPENSEARCH_HOST` | `localhost` | OpenSearch instance host. |
-| `OPENSEARCH_PORT` | `9200` | OpenSearch instance port. |
-| `OPENSEARCH_USERNAME` | `admin` | OpenSearch administrator username. |
-| `OPENSEARCH_PASSWORD` | Must be set at start up | Required. OpenSearch administrator password. Must adhere to the [OpenSearch password complexity requirements](https://docs.opensearch.org/latest/security/configuration/demo-configuration/#setting-up-a-custom-admin-password). You must set this directly in the `.env` or in the TUI's [**Basic/Advanced Setup**](/install#setup). |
+| `OPENSEARCH_HOST` | `localhost` | OpenSearch host. |
+| `OPENSEARCH_PASSWORD` | - | Password for OpenSearch admin user. Required. |
+| `OPENSEARCH_PORT` | `9200` | OpenSearch port. |
+| `OPENSEARCH_USERNAME` | `admin` | OpenSearch username. |
 
-## System settings
+### System settings
 
 Configure general system components, session management, and logging.
 
@@ -132,9 +150,28 @@ Configure general system components, session management, and logging.
 | `LANGFLOW_KEY_RETRIES` | `15` | Number of retries for Langflow key generation. |
 | `LANGFLOW_KEY_RETRY_DELAY` | `2.0` | Delay between retries in seconds. |
 | `LANGFLOW_VERSION` | `OPENRAG_VERSION` | Langflow Docker image version. By default, OpenRAG uses the `OPENRAG_VERSION` for the Langflow Docker image version. |
-| `LOG_FORMAT` | Not set | Set to `json` to enabled JSON-formatted log output. If this variable isn't set, then the default logging format is used. |
-| `LOG_LEVEL` | `INFO` | Logging level. Can be one of `DEBUG`, `INFO`, `WARNING`, or `ERROR`. `DEBUG` provides the most detailed logs but can impact performance. |
+| `LOG_FORMAT` | Disabled | Set to `json` to enabled JSON-formatted log output. |
+| `LOG_LEVEL` | `INFO` | Logging level (DEBUG, INFO, WARNING, ERROR). |
 | `MAX_WORKERS` | `1` | Maximum number of workers for document processing. |
-| `OPENRAG_VERSION` | `latest` | The version of the OpenRAG Docker images to run. For more information, see [Upgrade OpenRAG](/upgrade) |
+| `OPENRAG_VERSION` | `latest` | The version of the OpenRAG Docker images to run. For more information, see [Upgrade OpenRAG](/install#upgrade) |
 | `SERVICE_NAME` | `openrag` | Service name for logging. |
-| `SESSION_SECRET` | Automatically generated | Session management. |
+| `SESSION_SECRET` | Automatically generated | Session management. |
+
+## Langflow runtime overrides
+
+You can modify [flow](/agents) settings at runtime without permanently changing the flow's configuration.
+
+Runtime overrides are implemented through _tweaks_, which are one-time parameter modifications that are passed to specific Langflow components during flow execution.
+
+For more information on tweaks, see the Langflow documentation on [Input schema (tweaks)](https://docs.langflow.org/concepts-publish#input-schema).
+
+## Default values and fallbacks
+
+If a variable isn't set by environment variables or a configuration file, OpenRAG can use a default value if one is defined in the codebase.
+Default values can be found in the OpenRAG repository:
+
+* OpenRAG configuration: [`config_manager.py`](https://github.com/langflow-ai/openrag/blob/main/src/config/config_manager.py)
+
+* System configuration: [`settings.py`](https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py)
+
+* Logging configuration: [`logging_config.py`](https://github.com/langflow-ai/openrag/blob/main/src/utils/logging_config.py)

docs/docs/support/contribute.mdx

@@ -1,155 +0,0 @@
----
-title: Contribute to OpenRAG
-slug: /support/contribute
----
-
-There are several ways you can interact with the OpenRAG community and contribute to the OpenRAG codebase.
-
-## Star OpenRAG on GitHub
-
-If you like OpenRAG, you can star the [OpenRAG GitHub repository](https://github.com/langflow-ai/openrag).
-Stars help other users find OpenRAG more easily, and quickly understand that other users have found it useful.
-
-Because OpenRAG is open-source, the more visible the repository is, the more likely the codebase is to attract contributors.
-
-## Watch the GitHub repository
-
-You can watch the [OpenRAG GitHub repository](https://github.com/langflow-ai/openrag) to get notified about new releases and other repository activity.
-
-To get release notifications only, select **Releases only**.
-
-If you select **Watching**, you will receive notifications about new releases as well as issues, discussions, and pull requests. For information about customizing repository notifications, see the [GitHub documentation on repository subscriptions](https://docs.github.com/en/subscriptions-and-notifications/how-tos/managing-subscriptions-for-activity-on-github/viewing-your-subscriptions).
-
-## Request enhancements and get help through GitHub
-
-You can report bugs, submit feature requests, and get help with OpenRAG through the GitHub repository.
-
-The repository is the best place to report bugs and request enhancements to ensure that they are tracked by OpenRAG maintainers.
-
-### GitHub issues
-
-The [Issues page in the OpenRAG repository](https://github.com/langflow-ai/openrag/issues) is actively updated with bugs and feature requests.
-
-:::tip
-The best way to promote a request or bug is to comment on an existing issue that matches your request.
-
-Before you report a bug or submit a feature request, search for existing similar issues.
-If you find one, add a comment with any relevant details instead of opening a new issue.
-
-Highly active issues are more likely to receive attention from contributors.
-:::
-
-Feature planning for OpenRAG is tracked through the [Projects page in the OpenRAG repository](https://github.com/langflow-ai/openrag/projects).
-
-### GitHub discussions
-
-If you need help with your code or OpenRAG in general, you can visit the [OpenRAG GitHub Discussions page](https://github.com/langflow-ai/openrag/discussions).
-
-The OpenRAG team doesn't provide individual support over email, and the team believes that public discussions help more users by virtue of their discoverability.
-
-## Community guidelines and tips
-
-Because the OpenRAG repository is public, the OpenRAG team asks that you follow these guidelines when submitting questions and issues:
-
-* **Provide as many details as possible**: Simply stating that a feature doesn't work isn't helpful. The OpenRAG team needs details in order to recreate and find the issue.
-* **Explain what exactly went wrong**: Include error messages and descriptions of _how_ your code failed, not just the fact that it failed.
-* **Retrace your steps**: Explain what happened before the error, what you expected to happen instead of the error, and any recent changes you made, such as upgrading OpenRAG or a dependency.
-* **Describe your environment**: Include your operating system, OpenRAG version, Python version, and any other environment-related details that could have contributed to the issue.
-* **Include snippets of the code that failed**: Be sure to omit any sensitive values, and only provide parts that are relevant to the failure rather than the entire script. Providing code snippets makes it much easier to reproduce errors, troubleshoot, and provide specific advice.
-  * If your submission includes long sections of code, logs, or tracebacks, wrap them in [details tags](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) (`<details> PASTE CODE HERE </details>`) to collapse the content and make it easier to read your submission.
-* **Omit sensitive information**: Other than the information available on your public GitHub profile, don't include sensitive or personally identifying data, such as security keys, full names, personal identification numbers, addresses, and phone numbers.
-* **Be kind**: Although bugs can be frustrating with any software, remember that your messages are read by real people who want to help. While you don't have to be saccharine, there's no need to be rude to get support.
-  * Your issues and discussions are attached to your GitHub account, and they can be read by anyone on the internet, including current and potential employers and colleagues.
-  * The OpenRAG repository is a public GitHub repository and, therefore, subject to the [GitHub Code of Conduct](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct).
-
-## Contribute to the codebase
-
-If you want to contribute code to OpenRAG, you can do so by submitting a pull request (PR) to the OpenRAG GitHub repository.
-
-See [CONTRIBUTING.md](https://github.com/langflow-ai/openrag/blob/main/CONTRIBUTING.md) to set up your development environment and learn about the contribution process.
-
-### Tips for successful submissions
-
-* Explain the motivation for your submission in the PR description.
-Clarify how the change benefits the OpenRAG codebase and its users.
-
-* Keep PRs small and focused on a single change or set of related changes.
-
-* If applicable, include tests that verify your changes.
-
-* Add documentation for new features, or edit existing documentation (if needed) when modifying existing code.
-For more information, see [Contribute documentation](#contribute-documentation).
-
-### Use of AI tools in contributions
-
-If you use AI tools to generate significant portions of the code in your PR, the OpenRAG team asks that you do the following:
-
-* Consider disclosing significant use of AI tools in your pull request description, particularly if you are unable to expertly review your own code contributions. For example: `I used an AI tool to generate the code for <function>, but I am not an expert in <language>. There might be some inefficiencies or antipatterns present.`
-* Avoid using AI tools to generate large volumes of code if you don't have personal knowledge of that language and the functionality being implemented. Instead, consider submitting a feature request to the [Issues page in the OpenRAG repository](https://github.com/langflow-ai/openrag/issues).
-* Be critical when reviewing code or documentation generated by AI tools to ensure it is accurate, efficient, and avoids antipatterns and vulnerabilities.
-* Don't flood the repository with AI-generated pull requests.
-Low quality and spam contributions can be closed without review at the discretion of the maintainers.
-Repeated low-quality contributions can lead to a ban on contributions.
-
-## Contribute documentation {#contribute-documentation}
-
-The OpenRAG documentation is built using [Docusaurus](https://docusaurus.io/) and written in [Markdown](https://docusaurus.io/docs/markdown-features).
-For style guidance, see the [Google Developer Documentation Style Guide](https://developers.google.com/style).
-
-1. Install [Node.js](https://nodejs.org/en/download/package-manager).
-
-2. Fork the [OpenRAG GitHub repository](https://github.com/langflow-ai/openrag).
-
-3. Add the new remote to your local repository on your local machine:
-
-    ```bash
-    git remote add FORK_NAME https://github.com/GIT_USERNAME/openrag.git
-    ```
-
-    Replace the following:
-
-    * `FORK_NAME`: A name for your fork of the repository
-    * `GIT_USERNAME`: Your Git username
-
-4. Change to the `/docs` directory in your local repository:
-
-    ```bash
-    cd openrag/docs
-    ```
-
-    If you're running a development container for code contributions, run the documentation build from outside the container on your host terminal.
-    The documentation build might not work properly when run from inside the development container workspace.
-
-5. Install dependencies and start a local Docusaurus static site with hot reload:
-
-    ```bash
-    npm install
-    npm start
-    ```
-
-    The documentation is served at `http://localhost:3000`.
-
-6. To edit and create content, work with the `.mdx` files in the `openrag/docs/docs` directory.
-
-    Create new files in `.mdx` format.
-
-    Navigation is defined in `openrag/docs/sidebars.js`.
-
-    Most pages use a `slug` for shorthand cross-referencing, rather than supplying the full or relative directory path.
-    For example, if a page has a `slug` of `/cool-page`, you can link to it with `[Cool page](/cool-page)` from any other `/docs` page.
-
-7. Recommended: After making some changes, run `npm run build` to build the site locally with more robust logging.
-This can help you find broken links before creating a PR.
-
-8. Create a pull request against the `main` branch of the OpenRAG repository with a clear title and description of your changes:
-
-   * Provide a clear title in the format of `Docs: <summary of change>`. For example, `Docs: fix broken link on contributing page`. Pull request titles appear in OpenRAG's release notes, so they should explain what the PR does as explicitly as possible.
-   * Explain why and how you made the changes in the pull request description.
-   * If the pull request closes an issue, include `Closes #NUMBER` in the description, such as `Closes #1234`.
-   * If you used AI tools to write significant portions of the documentation, consider disclosing this in the PR description.
-
-9. Add the `documentation` label to your pull request.
-
-10. Keep an eye on your pull request in case an OpenRAG maintainer requests changes or asks questions.
-
-   OpenRAG technical writers can directly edit documentation PRs to enforce style guidelines and fix errors.

docs/docs/support/troubleshoot.mdx

@@ -3,21 +3,17 @@ title: Troubleshoot OpenRAG
 slug: /support/troubleshoot
 ---
 
-import PartialOllamaModels from '@site/docs/_partial-ollama-models.mdx';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 
 This page provides troubleshooting advice for issues you might encounter when using OpenRAG or contributing to OpenRAG.
 
-## Installation and start up issues
+## OpenSearch fails to start
 
-The following issues relate to OpenRAG installation and start up.
+Check that `OPENSEARCH_PASSWORD` set in [Environment variables](/reference/configuration) meets requirements.
+The password must contain at least 8 characters, and must contain at least one uppercase letter, one lowercase letter, one digit, and one special character that is strong.
 
-### OpenSearch fails to start
-
-Check that the value of the `OPENSEARCH_PASSWORD` [environment variable](/reference/configuration) meets the [OpenSearch password complexity requirements](https://docs.opensearch.org/latest/security/configuration/demo-configuration/#setting-up-a-custom-admin-password).
-
-If you need to change the password, you must [reset the OpenRAG services](/manage-services).
-
-### OpenRAG fails to start from the TUI with operation not supported
+## OpenRAG fails to start from the TUI with operation not supported
 
 This error occurs when starting OpenRAG with the TUI in [WSL (Windows Subsystem for Linux)](https://learn.microsoft.com/en-us/windows/wsl/install).
 
@@ -25,7 +21,7 @@ The error occurs because OpenRAG is running within a WSL environment, so `webbro
 
 To access the OpenRAG application, open a web browser and enter `http://localhost:3000` in the address bar.
 
-### OpenRAG installation fails with unable to get local issuer certificate
+## OpenRAG installation fails with unable to get local issuer certificate
 
 If you are installing OpenRAG on macOS, and the installation fails with `unable to get local issuer certificate`, run the following command, and then retry the installation:
 
@@ -35,27 +31,18 @@ open "/Applications/Python VERSION/Install Certificates.command"
 
 Replace `VERSION` with your installed Python version, such as `3.13`.
 
-### Application onboarding gets stuck on Google Chrome
-
-If the OpenRAG onboarding process gets stuck when using Google Chrome, try clearing your browser's cache.
-
 ## Langflow connection issues
 
-Verify that the value of the `LANGFLOW_SUPERUSER` environment variable is correct.
-For more information about this variable and how this variable controls Langflow access, see [Langflow settings](/reference/configuration#langflow-settings).
+Verify the `LANGFLOW_SUPERUSER` credentials set in [Environment variables](/reference/configuration) are correct.
 
-## Container out of memory errors {#container-out-of-memory-errors}
+## Container out of memory errors
 
-Increase your container VM's allocated memory, or use a CPU-only deployment to reduce memory usage.
+Increase Docker memory allocation or use [docker-compose-cpu.yml](https://github.com/langflow-ai/openrag/blob/main/docker-compose-cpu.yml) to deploy OpenRAG.
 
-For TUI-managed deployments, you can enable **CPU mode** on the TUI's **Status** page.
-
-For self-managed deployments, CPU-only deployments use the `docker-compose.yml` file that doesn't have GPU overrides.
-
-## Memory issue with Podman on macOS {#memory-issue-with-podman-on-macos}
+## Memory issue with Podman on macOS
 
 If you're using Podman on macOS, you might need to increase VM memory on your Podman machine.
-This example increases the machine size to 8 GB of RAM, which is the minimum recommended RAM for OpenRAG:
+This example increases the machine size to 8 GB of RAM, which should be sufficient to run OpenRAG.
 
 ```bash
 podman machine stop
@@ -66,52 +53,31 @@ podman machine start
 
 ## Port conflicts
 
-With the default [environment variable](/reference/configuration) values, OpenRAG requires the following ports to be available on the host machine:
+Ensure ports 3000, 7860, 8000, 9200, 5601 are available.
 
-* 3000: Langflow application
-* 5001: Docling local ingestion service
-* 5601: OpenSearch Dashboards
-* 7860: Docling UI
-* 8000: Docling API
-* 9200: OpenSearch service
+## OCR ingestion fails (easyocr not installed)
 
-## OCR ingestion fails (easyocr not installed) {#ocr-ingestion-fails-easyocr-not-installed}
+If Docling ingestion fails with an OCR-related error and mentions `easyocr` is missing, this is likely due to a stale `uv` cache.
 
-Docling ingestion can fail with an OCR-related error that mentions `easyocr` is missing.
-This is likely due to a stale `uv` cache when you [install OpenRAG with `uvx`](/install-uvx).
+`easyocr` is already included as a dependency in OpenRAG's `pyproject.toml`. Project-managed installations using `uv sync` and `uv run` always sync dependencies directly from your `pyproject.toml`, so they should have `easyocr` installed.
 
-When you invoke OpenRAG with `uvx openrag`, `uvx` creates a cached, ephemeral environment that doesn't modify your project.
-The location and path of this cache depends on your operating system.
-For example, on macOS, this is typically a user cache directory, such as `~/.cache/uv`.
+If you're running OpenRAG with `uvx openrag`, `uvx` creates a cached, ephemeral environment that doesn't modify your project. This cache can become stale.
 
-This cache can become stale, producing errors like missing dependencies.
+On macOS, this cache directory is typically a user cache directory such as `/Users/USER_NAME/.cache/uv`.
 
-1. If the TUI is open, press <kbd>q</kbd> to exit the TUI.
-
-2. Clear the `uv` cache:
+1. To clear the uv cache, run:
 
    ```bash
    uv cache clean
    ```
 
-   To clear the OpenRAG cache only, run:
-
-   ```bash
-   uv cache clean openrag
-   ```
-
-3. Invoke OpenRAG to restart the TUI:
+2. Start OpenRAG:
 
    ```bash
    uvx openrag
    ```
 
-4. Click **Launch OpenRAG**, and then retry document ingestion.
-
-If you install OpenRAG with `uv`, dependencies are synced directly from your `pyproject.toml` file.
-This should automatically install `easyocr` because `easyocr` is included as a dependency in OpenRAG's `pyproject.toml`.
-
-If you don't need OCR, you can disable OCR-based processing in your [ingestion settings](/knowledge#knowledge-ingestion-settings) to avoid requiring `easyocr`.
+If you don't need OCR, you can disable OCR-based processing in your ingestion settings to avoid requiring `easyocr`.
 
 ## Upgrade fails due to Langflow container already exists {#langflow-container-already-exists-during-upgrade}
 
@@ -121,42 +87,51 @@ To resolve this issue, do the following:
 
 1. Remove only the Langflow container:
 
+   <Tabs groupId="Container software">
+   <TabItem value="Podman" label="Podman">
+
    1. Stop the Langflow container:
 
-      ```bash title="Docker"
-      docker stop langflow
-      ```
-
-      ```bash title="Podman"
+      ```bash
       podman stop langflow
       ```
 
    2. Remove the Langflow container:
 
-      ```bash title="Docker"
-      docker rm langflow --force
-      ```
-
-      ```bash title="Podman"
+      ```bash
       podman rm langflow --force
       ```
 
-2. Retry the [upgrade](/upgrade).
+   </TabItem>
+   <TabItem value="Docker" label="Docker" default>
 
-3. If reinstalling the Langflow container doesn't resolve the issue, then you must [reset all containers](/manage-services) or [reinstall OpenRAG](/reinstall).
+   1. Stop the Langflow container:
 
-4. Retry the [upgrade](/upgrade).
+      ```bash
+      docker stop langflow
+      ```
 
-   If no updates are available after reinstalling OpenRAG, then you reinstalled at the latest version, and your deployment is up to date.
+   2. Remove the Langflow container:
+
+      ```bash
+      docker rm langflow --force
+      ```
+
+   </TabItem>
+   </Tabs>
+
+2. Retry the upgrade:
+
+   * [Upgrade self-managed containers](/docker#upgrade-containers)
+   * [Upgrade TUI-managed containers](/install#upgrade-containers)
+
+3. If reinstalling the Langflow container doesn't resolve the issue, you must reset your OpenRAG deployment:
+
+   * [Reset self-managed containers](/docker#reset-containers)
+   * [Reset TUI-managed containers](/install#reset-containers)
+
+4. Retry the upgrade.
 
 ## Document ingestion or similarity search issues
 
-See [Troubleshoot ingestion](/ingestion#troubleshoot-ingestion).
-
-## Ollama model issues
-
-<PartialOllamaModels />
-
-## Chat issues
-
-See [Troubleshoot chat](/chat#troubleshoot-chat).
+See [Troubleshoot ingestion](/ingestion#troubleshoot-ingestion).

docs/docusaurus.config.js

@@ -8,113 +8,12 @@ import {themes as prismThemes} from 'prism-react-renderer';
 
 // This runs in Node.js - Don't use client-side code here (browser APIs, JSX...)
 
-const isProduction = process.env.NODE_ENV === 'production';
-
 /** @type {import('@docusaurus/types').Config} */
 const config = {
   title: 'OpenRAG',
   tagline: 'Open Source RAG Platform',
   favicon: 'img/favicon.ico',
 
-  headTags: [
-    ...(isProduction
-      ? [
-          // Google Consent Mode - Set defaults before Google tags load
-          {
-            tagName: "script",
-            attributes: {},
-            innerHTML: `
-              window.dataLayer = window.dataLayer || [];
-              function gtag(){dataLayer.push(arguments);}
-
-              // Set default consent to denied
-              gtag('consent', 'default', {
-                'ad_storage': 'denied',
-                'ad_user_data': 'denied',
-                'ad_personalization': 'denied',
-                'analytics_storage': 'denied'
-              });
-            `,
-          },
-          // TrustArc Consent Update Listener
-          {
-            tagName: "script",
-            attributes: {},
-            innerHTML: `
-              (function() {
-                function updateGoogleConsent() {
-                  if (typeof window.truste !== 'undefined' && window.truste.cma) {
-                    var consent = window.truste.cma.callApi('getConsent', window.location.href) || {};
-
-                    // Map TrustArc categories to Google consent types
-                    // Category 0 = Required, 1 = Functional, 2 = Advertising, 3 = Analytics
-                    var hasAdvertising = consent[2] === 1;
-                    var hasAnalytics = consent[3] === 1;
-
-                    gtag('consent', 'update', {
-                      'ad_storage': hasAdvertising ? 'granted' : 'denied',
-                      'ad_user_data': hasAdvertising ? 'granted' : 'denied',
-                      'ad_personalization': hasAdvertising ? 'granted' : 'denied',
-                      'analytics_storage': hasAnalytics ? 'granted' : 'denied'
-                    });
-                  }
-                }
-
-                // Listen for consent changes
-                if (window.addEventListener) {
-                  window.addEventListener('cm_data_subject_consent_changed', updateGoogleConsent);
-                  window.addEventListener('cm_consent_preferences_set', updateGoogleConsent);
-                }
-
-                // Initial check after TrustArc loads
-                if (document.readyState === 'complete') {
-                  updateGoogleConsent();
-                } else {
-                  window.addEventListener('load', updateGoogleConsent);
-                }
-              })();
-            `,
-          },
-          // IBM Analytics Configuration (required for TrustArc)
-          {
-            tagName: "script",
-            attributes: {},
-            innerHTML: `
-              window._ibmAnalytics = {
-                "settings": {
-                  "name": "DataStax",
-                  "tealiumProfileName": "ibm-subsidiary",
-                },
-                "trustarc": {
-                  "privacyPolicyLink": "https://ibm.com/privacy"
-                }
-              };
-              window.digitalData = {
-                "page": {
-                  "pageInfo": {
-                    "ibm": {
-                      "siteId": "IBM_DataStax",
-                    }
-                  },
-                  "category": {
-                    "primaryCategory": "PC230"
-                  }
-                }
-              };
-            `,
-          },
-          // IBM Common Stats Script - loads TrustArc
-          {
-            tagName: "script",
-            attributes: {
-              src: "//1.www.s81c.com/common/stats/ibm-common.js",
-              async: "true",
-            },
-          },
-        ]
-      : []),
-  ],
-
   // Future flags, see https://docusaurus.io/docs/api/docusaurus-config#future
   future: {
     v4: true, // Improve compatibility with the upcoming Docusaurus v4
@@ -127,7 +26,7 @@ const config = {
   baseUrl: process.env.BASE_URL ? process.env.BASE_URL : '/',
 
   // Control search engine indexing - set to true to prevent indexing
-  noIndex: false,
+  noIndex: true,
 
   // GitHub pages deployment config.
   // If you aren't using GitHub pages, you don't need these.
@@ -176,19 +75,6 @@ const config = {
         theme: {
           customCss: './src/css/custom.css',
         },
-        // Use preset-classic sitemap https://docusaurus.io/docs/api/plugins/@docusaurus/plugin-sitemap
-        sitemap: {
-          lastmod: 'date',
-          changefreq: 'weekly',
-          priority: 0.5,
-          ignorePatterns: ['/tags/**'],
-          filename: 'sitemap.xml',
-          createSitemapItems: async (params) => {
-            const {defaultCreateSitemapItems, ...rest} = params;
-            const items = await defaultCreateSitemapItems(rest);
-            return items.filter((item) => !item.url.includes('/page/'));
-          },
-        },
       }),
     ],
   ],
@@ -229,26 +115,15 @@ const config = {
               {
                 html: `<div class="footer-links">
                   <span>© ${new Date().getFullYear()} OpenRAG</span>
-                  <span id="preferenceCenterContainer"> ·&nbsp; <a href="#" onclick="if(typeof window !== 'undefined' && window.truste && window.truste.eu && window.truste.eu.clickListener) { window.truste.eu.clickListener(); } return false;" style="cursor: pointer;">Manage Privacy Choices</a></span>
                   </div>`,
               },
             ],
           },
         ],
       },
-      algolia: {
-        appId: "SMEA51Q5OL",
-        // public key, safe to commit
-        apiKey: "b2ec302e9880e8979ad6a68f0c36271e",
-        indexName: "openrag-algolia",
-        contextualSearch: true,
-        searchParameters: {},
-        searchPagePath: "search",
-      },
       prism: {
         theme: prismThemes.github,
         darkTheme: prismThemes.dracula,
-        additionalLanguages: ['bash', 'docker', 'yaml'],
       },
       mermaid: {
         theme: {light: 'neutral', dark: 'forest'},

docs/package-lock.json

@@ -26,7 +26,7 @@
         "typescript": "~5.9.3"
       },
       "engines": {
-        "node": ">=20.20.0"
+        "node": ">=18.0"
       }
     },
     "node_modules/@ai-sdk/gateway": {
@@ -15975,9 +15975,9 @@
       }
     },
     "node_modules/qs": {
-      "version": "6.14.1",
-      "resolved": "https://registry.npmjs.org/qs/-/qs-6.14.1.tgz",
-      "integrity": "sha512-4EK3+xJl8Ts67nLYNwqw/dsFVnCf+qR7RgXSK9jEEm9unao3njwMDdmsdvoKBKHzxd7tCYz5e5M+SnMjdtXGQQ==",
+      "version": "6.14.0",
+      "resolved": "https://registry.npmjs.org/qs/-/qs-6.14.0.tgz",
+      "integrity": "sha512-YWWTjgABSKcvs/nWBi9PycY/JiPJqOD4JA6o9Sej2AtvSGarXxKC3OQSk4pAarbdQlKAh5D4FCQkJNkW+GAn3w==",
       "license": "BSD-3-Clause",
       "dependencies": {
         "side-channel": "^1.1.0"

docs/package.json

@@ -46,6 +46,6 @@
     ]
   },
   "engines": {
-    "node": ">=20.20.0"
+    "node": ">=18.0"
   }
 }

docs/sidebars.js

@@ -22,39 +22,8 @@ const sidebars = {
       label: "About OpenRAG"
     },
     "get-started/quickstart",
-    {
-      type: "category",
-      label: "Installation",
-      items: [
-        "get-started/install-options",
-        { type: "doc",
-          id:  "get-started/install",
-          label: "Run the installer script",
-        },
-        { type: "doc",
-          id: "get-started/install-uv",
-          label: "Install OpenRAG with uv",
-        },
-        "get-started/install-uvx",
-        { type: "doc",
-          id: "get-started/install-windows",
-          label: "Install OpenRAG on Windows",
-        },
-        { type: "doc",
-          id: "get-started/docker",
-          label: "Deploy self-managed services",
-        },
-        "get-started/upgrade",
-        "get-started/reinstall",
-        "get-started/uninstall",
-      ],
-    },
-    "get-started/tui",
-    {
-      type: "doc",
-      id: "get-started/manage-services",
-      label: "Manage services",
-    },
+    "get-started/install",
+    "get-started/docker",
     {
       type: "doc",
       id: "core-components/agents",
@@ -75,18 +44,7 @@ const sidebars = {
       label: "Chat",
     },
     "reference/configuration",
-    {
-      type: "doc",
-      id: "reference/api-sdk-overview",
-      label: "APIs and SDKs",
-    },
-    "support/contribute",
     "support/troubleshoot",
-    {
-      type: "link",
-      label: "Changelog",
-      href: "https://github.com/langflow-ai/openrag/releases",
-    },
   ],
 };
 

docs/static/robots.txt

@@ -1,8 +1,12 @@
 # Robots.txt for OpenRAG Documentation
 
-# Allow all crawlers
+# Block all crawlers by default
 User-agent: *
-Allow: /
+Disallow: /
 
-# Sitemap location
-Sitemap: https://docs.openr.ag/sitemap.xml
+# Allow specific crawlers if needed (uncomment when ready for launch)
+# User-agent: Googlebot
+# Allow: /
+
+# Sitemap location (uncomment when ready for launch)
+# Sitemap: https://docs.openr.ag/sitemap.xml

flows/components/opensearch_multimodel.py

@@ -12,18 +12,7 @@ from opensearchpy.exceptions import OpenSearchException, RequestError
 
 from lfx.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
 from lfx.base.vectorstores.vector_store_connection_decorator import vector_store_connection
-from lfx.inputs.inputs import DictInput
-from lfx.io import (
-    BoolInput,
-    DropdownInput,
-    HandleInput,
-    IntInput,
-    MultilineInput,
-    Output,
-    SecretStrInput,
-    StrInput,
-    TableInput,
-)
+from lfx.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput, TableInput
 from lfx.log import logger
 from lfx.schema.data import Data
 
@@ -96,32 +85,6 @@ class OpenSearchVectorStoreComponentMultimodalMultiEmbedding(LCVectorStoreCompon
     icon: str = "OpenSearch"
     description: str = (
         "Store and search documents using OpenSearch with multi-model hybrid semantic and keyword search."
-        "To search use the tools search_documents and raw_search. Search documents takes a query for vector search, for example\n"
-        "  {search_query: \"components in openrag\"}"
-        "\n"
-        "you can also override the filter_expression to limit the hybrid query in search_documents by also passing filter_expression\n"
-        "for example:\n"
-        "  {search_query: \"components in openrag\",  filter_expression: {\"data_sources\":[\"my_doc.md\"],\"document_types\":[\"*\"],\"owners\":[\"*\"],\"connector_types\":[\"*\"]},\"limit\":10,\"scoreThreshold\":0}"
-        "\n"
-        "raw_search takes actual opensearch queries for example:"
-        "  {"
-        "    \"size\": 100,"
-        "    \"query\": {"
-        "        \"term\": {\"filename\": \"my_doc.md\"}"
-        "    }"
-        "    \"_source\": [\"filename\", \"text\", \"page\"]"
-        " }"
-        "\n"
-        "or:"
-        "\n"
-        "  {"
-        "     \"size\": 0,"
-        "     \"aggs\": {"
-        "         \"distinct_filenames\": {"
-        "             \"cardinality\": {\"field\": \"filename\"}"
-        "         }"
-        "     },"
-        "  }"
     )
 
     # Keys we consider baseline
@@ -362,55 +325,7 @@ class OpenSearchVectorStoreComponentMultimodalMultiEmbedding(LCVectorStoreCompon
                 "Disable for self-signed certificates in development environments."
             ),
         ),
-        # DictInput(name="query", display_name="Query", input_types=["Data"], is_list=False, tool_mode=True),
     ]
-    outputs = [
-        Output(
-            display_name="Search Results",
-            name="search_results",
-            method="search_documents",
-        ),
-        Output(display_name="DataFrame", name="dataframe", method="as_dataframe"),
-        Output(display_name="Raw Search", name="raw_search", method="raw_search"),
-    ]
-
-    def raw_search(self, query: str | None = None) -> Data:
-        """Execute a raw OpenSearch query against the target index.
-
-        Args:
-            query (dict[str, Any]): The OpenSearch query DSL dictionary.
-
-        Returns:
-            Data: Search results as a Data object.
-
-        Raises:
-            ValueError: If 'query' is not a valid OpenSearch query (must be a non-empty dict).
-        """
-        query = self.search_query
-        if isinstance(query, str):
-            query = json.loads(query)
-        client = self.build_client()
-        logger.info(f"query: {query}")
-        resp = client.search(
-            index=self.index_name,
-            body=query,
-            params={"terminate_after": 0},
-        )
-        # Remove any _source keys whose value is a list of floats (embedding vectors)
-        def is_vector(val):
-            # Accepts if it's a list of numbers (float or int) and has reasonable vector length (>3)
-            return (
-                isinstance(val, list) and len(val) > 100 and all(isinstance(x, (float, int)) for x in val)
-            )
-        if "hits" in resp and "hits" in resp["hits"]:
-            for hit in resp["hits"]["hits"]:
-                source = hit.get("_source")
-                if isinstance(source, dict):
-                    keys_to_remove = [k for k, v in source.items() if is_vector(v)]
-                    for k in keys_to_remove:
-                        source.pop(k)
-        logger.info(f"Raw search response (all embedding vectors removed): {resp}")
-        return Data(**resp)
 
     def _get_embedding_model_name(self, embedding_obj=None) -> str:
         """Get the embedding model name from component config or embedding object.
@@ -950,58 +865,99 @@ class OpenSearchVectorStoreComponentMultimodalMultiEmbedding(LCVectorStoreCompon
             metadatas.append(data_copy)
         self.log(metadatas)
 
-        # Generate embeddings (threaded for concurrency) with retries
-        def embed_chunk(chunk_text: str) -> list[float]:
-            return selected_embedding.embed_documents([chunk_text])[0]
+        # Generate embeddings with rate-limit-aware retry logic using tenacity
+        from tenacity import (
+            retry,
+            retry_if_exception,
+            stop_after_attempt,
+            wait_exponential,
+        )
 
-        vectors: list[list[float]] | None = None
-        last_exception: Exception | None = None
-        delay = 1.0
-        attempts = 0
-        max_attempts = 3
+        def is_rate_limit_error(exception: Exception) -> bool:
+            """Check if exception is a rate limit error (429)."""
+            error_str = str(exception).lower()
+            return "429" in error_str or "rate_limit" in error_str or "rate limit" in error_str
+
+        def is_other_retryable_error(exception: Exception) -> bool:
+            """Check if exception is retryable but not a rate limit error."""
+            # Retry on most exceptions except for specific non-retryable ones
+            # Add other non-retryable exceptions here if needed
+            return not is_rate_limit_error(exception)
+
+        # Create retry decorator for rate limit errors (longer backoff)
+        retry_on_rate_limit = retry(
+            retry=retry_if_exception(is_rate_limit_error),
+            stop=stop_after_attempt(5),
+            wait=wait_exponential(multiplier=2, min=2, max=30),
+            reraise=True,
+            before_sleep=lambda retry_state: logger.warning(
+                f"Rate limit hit for chunk (attempt {retry_state.attempt_number}/5), "
+                f"backing off for {retry_state.next_action.sleep:.1f}s"
+            ),
+        )
+
+        # Create retry decorator for other errors (shorter backoff)
+        retry_on_other_errors = retry(
+            retry=retry_if_exception(is_other_retryable_error),
+            stop=stop_after_attempt(3),
+            wait=wait_exponential(multiplier=1, min=1, max=8),
+            reraise=True,
+            before_sleep=lambda retry_state: logger.warning(
+                f"Error embedding chunk (attempt {retry_state.attempt_number}/3), "
+                f"retrying in {retry_state.next_action.sleep:.1f}s: {retry_state.outcome.exception()}"
+            ),
+        )
+
+        def embed_chunk_with_retry(chunk_text: str, chunk_idx: int) -> list[float]:
+            """Embed a single chunk with rate-limit-aware retry logic."""
+
+            @retry_on_rate_limit
+            @retry_on_other_errors
+            def _embed(text: str) -> list[float]:
+                return selected_embedding.embed_documents([text])[0]
 
-        while attempts < max_attempts:
-            attempts += 1
             try:
-                # Restrict concurrency for IBM/Watsonx models to avoid rate limits
-                is_ibm = (embedding_model and "ibm" in str(embedding_model).lower()) or (
-                    selected_embedding and "watsonx" in type(selected_embedding).__name__.lower()
+                return _embed(chunk_text)
+            except Exception as e:
+                logger.error(
+                    f"Failed to embed chunk {chunk_idx} after all retries: {e}",
+                    error=str(e),
                 )
-                logger.debug(f"Is IBM: {is_ibm}")
-                max_workers = 1 if is_ibm else min(max(len(texts), 1), 8)
+                raise
 
-                with ThreadPoolExecutor(max_workers=max_workers) as executor:
-                    futures = {executor.submit(embed_chunk, chunk): idx for idx, chunk in enumerate(texts)}
-                    vectors = [None] * len(texts)
-                    for future in as_completed(futures):
-                        idx = futures[future]
-                        vectors[idx] = future.result()
-                break
-            except Exception as exc:
-                last_exception = exc
-                if attempts >= max_attempts:
-                    logger.error(
-                        f"Embedding generation failed for model {embedding_model} after retries",
-                        error=str(exc),
-                    )
-                    raise
-                logger.warning(
-                    "Threaded embedding generation failed for model %s (attempt %s/%s), retrying in %.1fs",
-                    embedding_model,
-                    attempts,
-                    max_attempts,
-                    delay,
-                )
-                time.sleep(delay)
-                delay = min(delay * 2, 8.0)
+        # Restrict concurrency for IBM/Watsonx models to avoid rate limits
+        is_ibm = (embedding_model and "ibm" in str(embedding_model).lower()) or (
+            selected_embedding and "watsonx" in type(selected_embedding).__name__.lower()
+        )
+        logger.debug(f"Is IBM: {is_ibm}")
 
-        if vectors is None:
-            raise RuntimeError(
-                f"Embedding generation failed for {embedding_model}: {last_exception}"
-                if last_exception
-                else f"Embedding generation failed for {embedding_model}"
+        # For IBM models, use sequential processing with rate limiting
+        # For other models, use parallel processing
+        vectors: list[list[float]] = [None] * len(texts)
+
+        if is_ibm:
+            # Sequential processing with inter-request delay for IBM models
+            inter_request_delay = 0.6  # ~1.67 req/s, safely under 2 req/s limit
+            logger.info(
+                f"Using sequential processing for IBM model with {inter_request_delay}s delay between requests"
             )
 
+            for idx, chunk in enumerate(texts):
+                if idx > 0:
+                    # Add delay between requests (but not before the first one)
+                    time.sleep(inter_request_delay)
+                vectors[idx] = embed_chunk_with_retry(chunk, idx)
+        else:
+            # Parallel processing for non-IBM models
+            max_workers = min(max(len(texts), 1), 8)
+            logger.debug(f"Using parallel processing with {max_workers} workers")
+
+            with ThreadPoolExecutor(max_workers=max_workers) as executor:
+                futures = {executor.submit(embed_chunk_with_retry, chunk, idx): idx for idx, chunk in enumerate(texts)}
+                for future in as_completed(futures):
+                    idx = futures[future]
+                    vectors[idx] = future.result()
+
         if not vectors:
             self.log(f"No vectors generated from documents for model {embedding_model}.")
             return

frontend/app/api/mutations/useCreateApiKeyMutation.ts

@@ -1,57 +0,0 @@
-import {
-  type UseMutationOptions,
-  useMutation,
-  useQueryClient,
-} from "@tanstack/react-query";
-
-export interface CreateApiKeyRequest {
-  name: string;
-}
-
-export interface CreateApiKeyResponse {
-  key_id: string;
-  api_key: string;
-  name: string;
-  key_prefix: string;
-  created_at: string;
-}
-
-export const useCreateApiKeyMutation = (
-  options?: Omit<
-    UseMutationOptions<CreateApiKeyResponse, Error, CreateApiKeyRequest>,
-    "mutationFn"
-  >,
-) => {
-  const queryClient = useQueryClient();
-
-  async function createApiKey(
-    variables: CreateApiKeyRequest,
-  ): Promise<CreateApiKeyResponse> {
-    const response = await fetch("/api/keys", {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify(variables),
-    });
-
-    if (!response.ok) {
-      const errorData = await response.json().catch(() => ({}));
-      throw new Error(errorData.error || "Failed to create API key");
-    }
-
-    return response.json();
-  }
-
-  return useMutation({
-    mutationFn: createApiKey,
-    onSuccess: (...args) => {
-      queryClient.invalidateQueries({
-        queryKey: ["api-keys"],
-      });
-      options?.onSuccess?.(...args);
-    },
-    onError: options?.onError,
-    onSettled: options?.onSettled,
-  });
-};

frontend/app/api/mutations/useRevokeApiKeyMutation.ts

@@ -1,49 +0,0 @@
-import {
-  type UseMutationOptions,
-  useMutation,
-  useQueryClient,
-} from "@tanstack/react-query";
-
-export interface RevokeApiKeyRequest {
-  key_id: string;
-}
-
-export interface RevokeApiKeyResponse {
-  success: boolean;
-}
-
-export const useRevokeApiKeyMutation = (
-  options?: Omit<
-    UseMutationOptions<RevokeApiKeyResponse, Error, RevokeApiKeyRequest>,
-    "mutationFn"
-  >,
-) => {
-  const queryClient = useQueryClient();
-
-  async function revokeApiKey(
-    variables: RevokeApiKeyRequest,
-  ): Promise<RevokeApiKeyResponse> {
-    const response = await fetch(`/api/keys/${variables.key_id}`, {
-      method: "DELETE",
-    });
-
-    if (!response.ok) {
-      const errorData = await response.json().catch(() => ({}));
-      throw new Error(errorData.error || "Failed to revoke API key");
-    }
-
-    return response.json();
-  }
-
-  return useMutation({
-    mutationFn: revokeApiKey,
-    onSuccess: (...args) => {
-      queryClient.invalidateQueries({
-        queryKey: ["api-keys"],
-      });
-      options?.onSuccess?.(...args);
-    },
-    onError: options?.onError,
-    onSettled: options?.onSettled,
-  });
-};

frontend/app/api/queries/useGetAllFiltersQuery.ts

@@ -1,36 +0,0 @@
-import {
-	type UseQueryOptions,
-	useQuery,
-	useQueryClient,
-} from "@tanstack/react-query";
-import type { KnowledgeFilter } from "./useGetFiltersSearchQuery";
-
-export const useGetAllFiltersQuery = (
-	options?: Omit<UseQueryOptions<KnowledgeFilter[]>, "queryKey" | "queryFn">,
-) => {
-	const queryClient = useQueryClient();
-
-	async function getAllFilters(): Promise<KnowledgeFilter[]> {
-		const response = await fetch("/api/knowledge-filter/search", {
-			method: "POST",
-			headers: { "Content-Type": "application/json" },
-			body: JSON.stringify({ query: "", limit: 1000 }), // Fetch all filters
-		});
-
-		const json = await response.json();
-		if (!response.ok || !json.success) {
-			// ensure we always return a KnowledgeFilter[] to satisfy the return type
-			return [];
-		}
-		return (json.filters || []) as KnowledgeFilter[];
-	}
-
-	return useQuery<KnowledgeFilter[]>(
-		{
-			queryKey: ["knowledge-filters", "all"],
-			queryFn: getAllFilters,
-			...options,
-		},
-		queryClient,
-	);
-};

frontend/app/api/queries/useGetApiKeysQuery.ts

@@ -1,31 +0,0 @@
-import { type UseQueryOptions, useQuery } from "@tanstack/react-query";
-
-export interface ApiKey {
-  key_id: string;
-  name: string;
-  key_prefix: string;
-  created_at: string;
-  last_used_at: string | null;
-}
-
-export interface GetApiKeysResponse {
-  keys: ApiKey[];
-}
-
-export const useGetApiKeysQuery = (
-  options?: Omit<UseQueryOptions<GetApiKeysResponse>, "queryKey" | "queryFn">,
-) => {
-  async function getApiKeys(): Promise<GetApiKeysResponse> {
-    const response = await fetch("/api/keys");
-    if (response.ok) {
-      return await response.json();
-    }
-    throw new Error("Failed to fetch API keys");
-  }
-
-  return useQuery({
-    queryKey: ["api-keys"],
-    queryFn: getApiKeys,
-    ...options,
-  });
-};

frontend/app/settings/page.tsx

@@ -1,6 +1,6 @@
 "use client";
 
-import { ArrowUpRight, Copy, Key, Loader2, Minus, PlugZap, Plus, Trash2 } from "lucide-react";
+import { ArrowUpRight, Loader2, Minus, PlugZap, Plus } from "lucide-react";
 import Link from "next/link";
 import { useRouter, useSearchParams } from "next/navigation";
 import { Suspense, useCallback, useEffect, useState } from "react";
@@ -11,19 +11,8 @@ import {
 	useGetOllamaModelsQuery,
 	useGetOpenAIModelsQuery,
 } from "@/app/api/queries/useGetModelsQuery";
-import { useGetApiKeysQuery } from "@/app/api/queries/useGetApiKeysQuery";
-import { useCreateApiKeyMutation } from "@/app/api/mutations/useCreateApiKeyMutation";
-import { useRevokeApiKeyMutation } from "@/app/api/mutations/useRevokeApiKeyMutation";
 import { useGetSettingsQuery } from "@/app/api/queries/useGetSettingsQuery";
 import { ConfirmationDialog } from "@/components/confirmation-dialog";
-import {
-	Dialog,
-	DialogContent,
-	DialogDescription,
-	DialogFooter,
-	DialogHeader,
-	DialogTitle,
-} from "@/components/ui/dialog";
 import { LabelWrapper } from "@/components/label-wrapper";
 import { ProtectedRoute } from "@/components/protected-route";
 import { Button } from "@/components/ui/button";
@@ -133,45 +122,11 @@ function KnowledgeSourcesPage() {
 	const [pictureDescriptions, setPictureDescriptions] =
 		useState<boolean>(false);
 
-	// API Keys state
-	const [createKeyDialogOpen, setCreateKeyDialogOpen] = useState(false);
-	const [newKeyName, setNewKeyName] = useState("");
-	const [newlyCreatedKey, setNewlyCreatedKey] = useState<string | null>(null);
-	const [showKeyDialogOpen, setShowKeyDialogOpen] = useState(false);
-
 	// Fetch settings using React Query
 	const { data: settings = {} } = useGetSettingsQuery({
 		enabled: isAuthenticated || isNoAuthMode,
 	});
 
-	// Fetch API keys
-	const { data: apiKeysData, isLoading: apiKeysLoading } = useGetApiKeysQuery({
-		enabled: isAuthenticated || isNoAuthMode,
-	});
-
-	// API key mutations
-	const createApiKeyMutation = useCreateApiKeyMutation({
-		onSuccess: (data) => {
-			setNewlyCreatedKey(data.api_key);
-			setCreateKeyDialogOpen(false);
-			setShowKeyDialogOpen(true);
-			setNewKeyName("");
-			toast.success("API key created");
-		},
-		onError: (error) => {
-			toast.error("Failed to create API key", { description: error.message });
-		},
-	});
-
-	const revokeApiKeyMutation = useRevokeApiKeyMutation({
-		onSuccess: () => {
-			toast.success("API key revoked");
-		},
-		onError: (error) => {
-			toast.error("Failed to revoke API key", { description: error.message });
-		},
-	});
-
 	// Fetch models for each provider
 	const { data: openaiModels, isLoading: openaiLoading } =
 		useGetOpenAIModelsQuery(
@@ -432,36 +387,6 @@ function KnowledgeSourcesPage() {
 		updateSettingsMutation.mutate({ picture_descriptions: checked });
 	};
 
-	// API Keys handlers
-	const handleCreateApiKey = () => {
-		if (!newKeyName.trim()) {
-			toast.error("Please enter a name for the API key");
-			return;
-		}
-		createApiKeyMutation.mutate({ name: newKeyName.trim() });
-	};
-
-	const handleRevokeApiKey = (keyId: string) => {
-		revokeApiKeyMutation.mutate({ key_id: keyId });
-	};
-
-	const handleCopyApiKey = async () => {
-		if (newlyCreatedKey) {
-			await navigator.clipboard.writeText(newlyCreatedKey);
-			toast.success("API key copied to clipboard");
-		}
-	};
-
-	const formatDate = (dateString: string | null) => {
-		if (!dateString) return "Never";
-		const date = new Date(dateString);
-		return date.toLocaleDateString(undefined, {
-			year: "numeric",
-			month: "short",
-			day: "numeric",
-		});
-	};
-
 	// Helper function to get connector icon
 	const getConnectorIcon = useCallback((iconName: string) => {
 		const iconMap: { [key: string]: React.ReactElement } = {
@@ -1390,209 +1315,6 @@ function KnowledgeSourcesPage() {
 					</div>
 				</CardContent>
 			</Card>
-
-			{/* API Keys Section */}
-			{(isAuthenticated || isNoAuthMode) && (
-				<Card>
-					<CardHeader>
-						<div className="flex items-center justify-between mb-3">
-							<CardTitle className="text-lg">API Keys</CardTitle>
-							<Button
-								onClick={() => setCreateKeyDialogOpen(true)}
-								size="sm"
-							>
-								<Plus className="h-4 w-4 mr-2" />
-								Create Key
-							</Button>
-						</div>
-						<CardDescription>
-							API keys allow programmatic access to OpenRAG via the public API.
-							Keep your keys secure and never share them publicly.
-						</CardDescription>
-					</CardHeader>
-					<CardContent>
-						{apiKeysLoading ? (
-							<div className="flex items-center justify-center py-8">
-								<Loader2 className="h-6 w-6 animate-spin text-muted-foreground" />
-							</div>
-						) : apiKeysData?.keys && apiKeysData.keys.length > 0 ? (
-							<div className="border rounded-lg overflow-hidden">
-								<table className="w-full">
-									<thead className="bg-muted/50">
-										<tr>
-											<th className="text-left text-sm font-medium text-muted-foreground px-4 py-3">
-												Name
-											</th>
-											<th className="text-left text-sm font-medium text-muted-foreground px-4 py-3">
-												Key
-											</th>
-											<th className="text-left text-sm font-medium text-muted-foreground px-4 py-3">
-												Created
-											</th>
-											<th className="text-left text-sm font-medium text-muted-foreground px-4 py-3">
-												Last Used
-											</th>
-											<th className="text-right text-sm font-medium text-muted-foreground px-4 py-3">
-												Actions
-											</th>
-										</tr>
-									</thead>
-									<tbody>
-										{apiKeysData.keys.map((key) => (
-											<tr key={key.key_id} className="border-t">
-												<td className="px-4 py-3 text-sm font-medium">
-													{key.name}
-												</td>
-												<td className="px-4 py-3">
-													<code className="text-sm bg-muted px-2 py-1 rounded">
-														{key.key_prefix}...
-													</code>
-												</td>
-												<td className="px-4 py-3 text-sm text-muted-foreground">
-													{formatDate(key.created_at)}
-												</td>
-												<td className="px-4 py-3 text-sm text-muted-foreground">
-													{formatDate(key.last_used_at)}
-												</td>
-												<td className="px-4 py-3 text-right">
-													<ConfirmationDialog
-														trigger={
-															<Button
-																variant="ghost"
-																size="sm"
-																className="text-destructive hover:text-destructive hover:bg-destructive/10"
-															>
-																<Trash2 className="h-4 w-4" />
-															</Button>
-														}
-														title="Revoke API Key"
-														description={
-															<>
-																Are you sure you want to revoke the API key{" "}
-																<strong>{key.name}</strong>? This action cannot
-																be undone and any applications using this key
-																will stop working.
-															</>
-														}
-														confirmText="Revoke"
-														variant="destructive"
-														onConfirm={(closeDialog) => {
-															handleRevokeApiKey(key.key_id);
-															closeDialog();
-														}}
-													/>
-												</td>
-											</tr>
-										))}
-									</tbody>
-								</table>
-							</div>
-						) : (
-							<div className="text-center py-8">
-								<Key className="h-12 w-12 mx-auto text-muted-foreground/50 mb-4" />
-								<p className="text-muted-foreground mb-4">
-									No API keys yet. Create one to get started.
-								</p>
-								<Button
-									variant="outline"
-									onClick={() => setCreateKeyDialogOpen(true)}
-									size="sm"
-								>
-									<Plus className="h-4 w-4 mr-2" />
-									Create your first API key
-								</Button>
-							</div>
-						)}
-					</CardContent>
-				</Card>
-			)}
-
-			{/* Create API Key Dialog */}
-			<Dialog open={createKeyDialogOpen} onOpenChange={setCreateKeyDialogOpen}>
-				<DialogContent>
-					<DialogHeader>
-						<DialogTitle>Create API Key</DialogTitle>
-						<DialogDescription>
-							Give your API key a name to help you identify it later.
-						</DialogDescription>
-					</DialogHeader>
-					<div className="py-4">
-						<LabelWrapper label="Name" id="api-key-name">
-							<Input
-								id="api-key-name"
-								placeholder="e.g., Production App, Development"
-								value={newKeyName}
-								onChange={(e) => setNewKeyName(e.target.value)}
-								onKeyDown={(e) => {
-									if (e.key === "Enter") {
-										handleCreateApiKey();
-									}
-								}}
-							/>
-						</LabelWrapper>
-					</div>
-					<DialogFooter>
-						<Button
-							variant="ghost"
-							onClick={() => {
-								setCreateKeyDialogOpen(false);
-								setNewKeyName("");
-							}}
-							size="sm"
-						>
-							Cancel
-						</Button>
-						<Button
-							onClick={handleCreateApiKey}
-							disabled={createApiKeyMutation.isPending || !newKeyName.trim()}
-							size="sm"
-						>
-							{createApiKeyMutation.isPending ? (
-								<>
-									<Loader2 className="h-4 w-4 mr-2 animate-spin" />
-									Creating...
-								</>
-							) : (
-								"Create Key"
-							)}
-						</Button>
-					</DialogFooter>
-				</DialogContent>
-			</Dialog>
-
-			{/* Show Created API Key Dialog */}
-			<Dialog
-				open={showKeyDialogOpen}
-				onOpenChange={(open) => {
-					setShowKeyDialogOpen(open);
-					if (!open) {
-						setNewlyCreatedKey(null);
-					}
-				}}
-			>
-				<DialogContent>
-					<DialogHeader>
-						<DialogTitle>API Key Created</DialogTitle>
-						<DialogDescription>
-							Copy your API key now. You won&apos;t be able to see it again.
-						</DialogDescription>
-					</DialogHeader>
-					<div className="py-4">
-						<div className="bg-muted rounded-lg p-4 font-mono text-sm break-all">
-							{newlyCreatedKey}
-						</div>
-					</div>
-					<DialogFooter>
-						<Button variant="ghost" onClick={() => setShowKeyDialogOpen(false)} size="sm">
-							Close
-						</Button>
-						<Button onClick={handleCopyApiKey} size="sm">
-							<Copy className="h-4 w-4 mr-2" />
-							Copy Key
-						</Button>
-					</DialogFooter>
-				</DialogContent>
-			</Dialog>
 		</div>
 	);
 }

frontend/components/discord-link.tsx

@@ -0,0 +1,47 @@
+"use client";
+
+import * as React from "react";
+import { cn } from "@/lib/utils";
+import { useDiscordMembers } from "@/hooks/use-discord-members";
+import { formatCount } from "@/lib/format-count";
+
+interface DiscordLinkProps {
+  inviteCode?: string;
+  className?: string;
+}
+
+const DiscordLink = React.forwardRef<HTMLAnchorElement, DiscordLinkProps>(
+  ({ inviteCode = "EqksyE2EX9", className }, ref) => {
+    const { data, isLoading, error } = useDiscordMembers(inviteCode);
+
+    return (
+      <a
+        ref={ref}
+        href={`https://discord.gg/${inviteCode}`}
+        target="_blank"
+        rel="noopener noreferrer"
+        className={cn(
+          "inline-flex h-8 items-center justify-center rounded-md px-2 text-sm font-medium text-muted-foreground shadow-sm transition-colors hover:bg-accent hover:text-accent-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2",
+          className,
+        )}
+      >
+        <svg className="h-4 w-4" viewBox="0 0 24 24" fill="currentColor">
+          <path d="M20.317 4.37a19.791 19.791 0 0 0-4.885-1.515.074.074 0 0 0-.079.037c-.21.375-.444.864-.608 1.25a18.27 18.27 0 0 0-5.487 0 12.64 12.64 0 0 0-.617-1.25.077.077 0 0 0-.079-.037A19.736 19.736 0 0 0 3.677 4.37a.07.07 0 0 0-.032.027C.533 9.046-.32 13.58.099 18.057a.082.082 0 0 0 .031.057 19.9 19.9 0 0 0 5.993 3.03.078.078 0 0 0 .084-.028c.462-.63.874-1.295 1.226-1.994a.076.076 0 0 0-.041-.106 13.107 13.107 0 0 1-1.872-.892.077.077 0 0 1-.008-.128 10.2 10.2 0 0 0 .372-.292.074.074 0 0 1 .077-.01c3.928 1.793 8.18 1.793 12.062 0a.074.074 0 0 1 .078.01c.120.098.246.198.373.292a.077.077 0 0 1-.006.127 12.299 12.299 0 0 1-1.873.892.077.077 0 0 0-.041.107c.36.698.772 1.362 1.225 1.993a.076.076 0 0 0 .084.028 19.839 19.839 0 0 0 6.002-3.03.077.077 0 0 0 .032-.054c.5-5.177-.838-9.674-3.549-13.66a.061.061 0 0 0-.031-.03zM8.02 15.33c-1.183 0-2.157-1.085-2.157-2.419 0-1.333.956-2.419 2.157-2.419 1.21 0 2.176 1.096 2.157 2.42 0 1.333-.956 2.418-2.157 2.418zm7.975 0c-1.183 0-2.157-1.085-2.157-2.419 0-1.333.955-2.419 2.157-2.419 1.21 0 2.176 1.096 2.157 2.42 0 1.333-.946 2.418-2.157 2.418z" />
+        </svg>
+        <span className="hidden sm:inline ml-2">
+          {isLoading
+            ? "..."
+            : error
+              ? "--"
+              : data
+                ? formatCount(data.approximate_member_count)
+                : "--"}
+        </span>
+      </a>
+    );
+  },
+);
+
+DiscordLink.displayName = "DiscordLink";
+
+export { DiscordLink };

frontend/components/file-upload-area.tsx

@@ -0,0 +1,103 @@
+"use client";
+
+import * as React from "react";
+import { cn } from "@/lib/utils";
+import { Button } from "@/components/ui/button";
+import { Loader2 } from "lucide-react";
+
+interface FileUploadAreaProps {
+  onFileSelected?: (file: File) => void;
+  isLoading?: boolean;
+  className?: string;
+}
+
+const FileUploadArea = React.forwardRef<HTMLDivElement, FileUploadAreaProps>(
+  ({ onFileSelected, isLoading = false, className }, ref) => {
+    const [isDragging, setIsDragging] = React.useState(false);
+    const fileInputRef = React.useRef<HTMLInputElement>(null);
+
+    const handleDragOver = (e: React.DragEvent) => {
+      e.preventDefault();
+      setIsDragging(true);
+    };
+
+    const handleDragLeave = (e: React.DragEvent) => {
+      e.preventDefault();
+      setIsDragging(false);
+    };
+
+    const handleDrop = (e: React.DragEvent) => {
+      e.preventDefault();
+      setIsDragging(false);
+
+      const files = Array.from(e.dataTransfer.files);
+      if (files.length > 0 && onFileSelected) {
+        onFileSelected(files[0]);
+      }
+    };
+
+    const handleFileSelect = (e: React.ChangeEvent<HTMLInputElement>) => {
+      const files = Array.from(e.target.files || []);
+      if (files.length > 0 && onFileSelected) {
+        onFileSelected(files[0]);
+      }
+    };
+
+    const handleClick = () => {
+      if (!isLoading) {
+        fileInputRef.current?.click();
+      }
+    };
+
+    return (
+      <div
+        ref={ref}
+        className={cn(
+          "relative flex min-h-[150px] w-full cursor-pointer flex-col items-center justify-center rounded-lg border-2 border-dashed border-border bg-background p-6 text-center transition-colors hover:bg-muted/50",
+          isDragging && "border-primary bg-primary/5",
+          isLoading && "cursor-not-allowed opacity-50",
+          className,
+        )}
+        onDragOver={handleDragOver}
+        onDragLeave={handleDragLeave}
+        onDrop={handleDrop}
+        onClick={handleClick}
+      >
+        <input
+          ref={fileInputRef}
+          type="file"
+          onChange={handleFileSelect}
+          className="hidden"
+          disabled={isLoading}
+        />
+
+        <div className="flex flex-col items-center gap-4">
+          {isLoading && (
+            <div className="rounded-full bg-muted p-4">
+              <Loader2 className="h-8 w-8 animate-spin text-muted-foreground" />
+            </div>
+          )}
+
+          <div className="space-y-2">
+            <h3 className="text-lg font-medium text-foreground">
+              {isLoading
+                ? "Processing file..."
+                : "Drop files here or click to upload"}
+            </h3>
+            <p className="text-sm text-muted-foreground">
+              {isLoading
+                ? "Please wait while your file is being processed"
+                : ""}
+            </p>
+          </div>
+
+          {!isLoading && <Button size="sm">+ Upload</Button>}
+        </div>
+      </div>
+    );
+  },
+);
+
+FileUploadArea.displayName = "FileUploadArea";
+
+export { FileUploadArea };

frontend/components/github-star-button.tsx

@@ -0,0 +1,47 @@
+"use client";
+
+import * as React from "react";
+import { cn } from "@/lib/utils";
+import { Github } from "lucide-react";
+import { useGitHubStars } from "@/hooks/use-github-stars";
+import { formatCount } from "@/lib/format-count";
+
+interface GitHubStarButtonProps {
+  repo?: string;
+  className?: string;
+}
+
+const GitHubStarButton = React.forwardRef<
+  HTMLAnchorElement,
+  GitHubStarButtonProps
+>(({ repo = "phact/openrag", className }, ref) => {
+  const { data, isLoading, error } = useGitHubStars(repo);
+
+  return (
+    <a
+      ref={ref}
+      href={`https://github.com/${repo}`}
+      target="_blank"
+      rel="noopener noreferrer"
+      className={cn(
+        "inline-flex h-8 items-center justify-center rounded-md px-2 text-sm font-medium text-muted-foreground shadow-sm transition-colors hover:bg-accent hover:text-accent-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2",
+        className,
+      )}
+    >
+      <Github className="h-4 w-4" />
+      <span className="hidden sm:inline ml-2">
+        {isLoading
+          ? "..."
+          : error
+            ? "--"
+            : data
+              ? formatCount(data.stargazers_count)
+              : "--"}
+      </span>
+    </a>
+  );
+});
+
+GitHubStarButton.displayName = "GitHubStarButton";
+
+export { GitHubStarButton };

frontend/components/knowledge-filter-dropdown.tsx

@@ -0,0 +1,458 @@
+"use client";
+
+import { useState, useEffect, useRef } from "react";
+import { Button } from "@/components/ui/button";
+import { Input } from "@/components/ui/input";
+import { Card, CardContent } from "@/components/ui/card";
+
+import { Label } from "@/components/ui/label";
+import { Textarea } from "@/components/ui/textarea";
+import {
+  ChevronDown,
+  Filter,
+  Search,
+  X,
+  Loader2,
+  Plus,
+  Save,
+} from "lucide-react";
+import { cn } from "@/lib/utils";
+
+interface KnowledgeFilter {
+  id: string;
+  name: string;
+  description: string;
+  query_data: string;
+  owner: string;
+  created_at: string;
+  updated_at: string;
+}
+
+interface ParsedQueryData {
+  query: string;
+  filters: {
+    data_sources: string[];
+    document_types: string[];
+    owners: string[];
+  };
+  limit: number;
+  scoreThreshold: number;
+}
+
+interface KnowledgeFilterDropdownProps {
+  selectedFilter: KnowledgeFilter | null;
+  onFilterSelect: (filter: KnowledgeFilter | null) => void;
+}
+
+export function KnowledgeFilterDropdown({
+  selectedFilter,
+  onFilterSelect,
+}: KnowledgeFilterDropdownProps) {
+  const [isOpen, setIsOpen] = useState(false);
+  const [filters, setFilters] = useState<KnowledgeFilter[]>([]);
+  const [loading, setLoading] = useState(false);
+  const [searchQuery, setSearchQuery] = useState("");
+  const [showCreateModal, setShowCreateModal] = useState(false);
+  const [createName, setCreateName] = useState("");
+  const [createDescription, setCreateDescription] = useState("");
+  const [creating, setCreating] = useState(false);
+  const dropdownRef = useRef<HTMLDivElement>(null);
+
+  const loadFilters = async (query = "") => {
+    setLoading(true);
+    try {
+      const response = await fetch("/api/knowledge-filter/search", {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify({
+          query,
+          limit: 20, // Limit for dropdown
+        }),
+      });
+
+      const result = await response.json();
+      if (response.ok && result.success) {
+        setFilters(result.filters);
+      } else {
+        console.error("Failed to load knowledge filters:", result.error);
+        setFilters([]);
+      }
+    } catch (error) {
+      console.error("Error loading knowledge filters:", error);
+      setFilters([]);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const deleteFilter = async (filterId: string, e: React.MouseEvent) => {
+    e.stopPropagation();
+
+    try {
+      const response = await fetch(`/api/knowledge-filter/${filterId}`, {
+        method: "DELETE",
+      });
+
+      if (response.ok) {
+        // Remove from local state
+        setFilters((prev) => prev.filter((f) => f.id !== filterId));
+
+        // If this was the selected filter, clear selection
+        if (selectedFilter?.id === filterId) {
+          onFilterSelect(null);
+        }
+      } else {
+        console.error("Failed to delete knowledge filter");
+      }
+    } catch (error) {
+      console.error("Error deleting knowledge filter:", error);
+    }
+  };
+
+  const handleFilterSelect = (filter: KnowledgeFilter) => {
+    onFilterSelect(filter);
+    setIsOpen(false);
+  };
+
+  const handleClearFilter = () => {
+    onFilterSelect(null);
+    setIsOpen(false);
+  };
+
+  const handleCreateNew = () => {
+    setIsOpen(false);
+    setShowCreateModal(true);
+  };
+
+  const handleCreateFilter = async () => {
+    if (!createName.trim()) return;
+
+    setCreating(true);
+    try {
+      // Create a basic filter with wildcards (match everything by default)
+      const defaultFilterData = {
+        query: "",
+        filters: {
+          data_sources: ["*"],
+          document_types: ["*"],
+          owners: ["*"],
+        },
+        limit: 10,
+        scoreThreshold: 0,
+      };
+
+      const response = await fetch("/api/knowledge-filter", {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify({
+          name: createName.trim(),
+          description: createDescription.trim(),
+          queryData: JSON.stringify(defaultFilterData),
+        }),
+      });
+
+      const result = await response.json();
+      if (response.ok && result.success) {
+        // Create the new filter object
+        const newFilter: KnowledgeFilter = {
+          id: result.filter.id,
+          name: createName.trim(),
+          description: createDescription.trim(),
+          query_data: JSON.stringify(defaultFilterData),
+          owner: result.filter.owner,
+          created_at: result.filter.created_at,
+          updated_at: result.filter.updated_at,
+        };
+
+        // Add to local filters list
+        setFilters((prev) => [newFilter, ...prev]);
+
+        // Select the new filter
+        onFilterSelect(newFilter);
+
+        // Close modal and reset form
+        setShowCreateModal(false);
+        setCreateName("");
+        setCreateDescription("");
+      } else {
+        console.error("Failed to create knowledge filter:", result.error);
+      }
+    } catch (error) {
+      console.error("Error creating knowledge filter:", error);
+    } finally {
+      setCreating(false);
+    }
+  };
+
+  const handleCancelCreate = () => {
+    setShowCreateModal(false);
+    setCreateName("");
+    setCreateDescription("");
+  };
+
+  const getFilterSummary = (filter: KnowledgeFilter): string => {
+    try {
+      const parsed = JSON.parse(filter.query_data) as ParsedQueryData;
+      const parts = [];
+
+      if (parsed.query) parts.push(`"${parsed.query}"`);
+      if (parsed.filters.data_sources.length > 0)
+        parts.push(`${parsed.filters.data_sources.length} sources`);
+      if (parsed.filters.document_types.length > 0)
+        parts.push(`${parsed.filters.document_types.length} types`);
+      if (parsed.filters.owners.length > 0)
+        parts.push(`${parsed.filters.owners.length} owners`);
+
+      return parts.join(" • ") || "No filters";
+    } catch {
+      return "Invalid filter";
+    }
+  };
+
+  useEffect(() => {
+    if (isOpen) {
+      loadFilters();
+    }
+  }, [isOpen]);
+
+  useEffect(() => {
+    const timeoutId = setTimeout(() => {
+      if (isOpen) {
+        loadFilters(searchQuery);
+      }
+    }, 300);
+
+    return () => clearTimeout(timeoutId);
+  }, [searchQuery, isOpen]);
+
+  // Close dropdown when clicking outside
+  useEffect(() => {
+    const handleClickOutside = (event: MouseEvent) => {
+      if (
+        dropdownRef.current &&
+        !dropdownRef.current.contains(event.target as Node)
+      ) {
+        setIsOpen(false);
+      }
+    };
+
+    document.addEventListener("mousedown", handleClickOutside);
+    return () => document.removeEventListener("mousedown", handleClickOutside);
+  }, []);
+
+  return (
+    <div className="relative" ref={dropdownRef}>
+      <Button
+        variant={selectedFilter ? "default" : "outline"}
+        size="sm"
+        onClick={() => setIsOpen(!isOpen)}
+        className={cn(
+          "flex items-center gap-2 h-8 px-3",
+          selectedFilter
+            ? "hover:bg-primary hover:text-primary-foreground"
+            : "hover:bg-transparent hover:text-foreground hover:border-border",
+        )}
+      >
+        <Filter className="h-3 w-3" />
+        {selectedFilter ? (
+          <span className="max-w-32 truncate">{selectedFilter.name}</span>
+        ) : (
+          <span>All Knowledge</span>
+        )}
+        <ChevronDown
+          className={cn("h-3 w-3 transition-transform", isOpen && "rotate-180")}
+        />
+      </Button>
+
+      {isOpen && (
+        <Card className="absolute right-0 top-full mt-1 w-80 max-h-96 overflow-hidden z-50 shadow-lg border-border/50 bg-card/95 backdrop-blur-sm">
+          <CardContent className="p-0">
+            {/* Search Header */}
+            <div className="p-3 border-b border-border/50">
+              <div className="relative">
+                <Search className="absolute left-3 top-1/2 transform -translate-y-1/2 h-3 w-3 text-muted-foreground" />
+                <Input
+                  placeholder="Search filters..."
+                  value={searchQuery}
+                  onChange={(e) => setSearchQuery(e.target.value)}
+                  className="pl-9 h-8 text-sm"
+                />
+              </div>
+            </div>
+
+            {/* Filter List */}
+            <div className="max-h-64 overflow-y-auto">
+              {/* Clear filter option */}
+              <div
+                onClick={handleClearFilter}
+                className={cn(
+                  "flex items-center gap-3 p-3 hover:bg-accent hover:text-accent-foreground cursor-pointer border-b border-border/30 transition-colors",
+                  !selectedFilter && "bg-accent text-accent-foreground",
+                )}
+              >
+                <div className="flex items-center gap-2 flex-1">
+                  <Filter className="h-4 w-4 text-muted-foreground" />
+                  <div>
+                    <div className="text-sm font-medium">All Knowledge</div>
+                    <div className="text-xs text-muted-foreground">
+                      No filters applied
+                    </div>
+                  </div>
+                </div>
+              </div>
+
+              {loading ? (
+                <div className="flex items-center justify-center p-4">
+                  <Loader2 className="h-4 w-4 animate-spin" />
+                  <span className="ml-2 text-sm text-muted-foreground">
+                    Loading...
+                  </span>
+                </div>
+              ) : filters.length === 0 ? (
+                <div className="p-4 text-center text-sm text-muted-foreground">
+                  {searchQuery ? "No filters found" : "No saved filters"}
+                </div>
+              ) : (
+                filters.map((filter) => (
+                  <div
+                    key={filter.id}
+                    onClick={() => handleFilterSelect(filter)}
+                    className={cn(
+                      "flex items-center gap-3 p-3 hover:bg-accent hover:text-accent-foreground cursor-pointer group transition-colors",
+                      selectedFilter?.id === filter.id &&
+                        "bg-accent text-accent-foreground",
+                    )}
+                  >
+                    <div className="flex items-center gap-2 flex-1 min-w-0">
+                      <Filter className="h-4 w-4 text-muted-foreground group-hover:text-accent-foreground flex-shrink-0" />
+                      <div className="min-w-0 flex-1">
+                        <div className="text-sm font-medium truncate group-hover:text-accent-foreground">
+                          {filter.name}
+                        </div>
+                        <div className="text-xs text-muted-foreground group-hover:text-accent-foreground/70 truncate">
+                          {getFilterSummary(filter)}
+                        </div>
+                      </div>
+                    </div>
+                    <Button
+                      variant="ghost"
+                      size="sm"
+                      onClick={(e) => deleteFilter(filter.id, e)}
+                      className="opacity-0 group-hover:opacity-100 h-6 w-6 p-0 bg-transparent hover:bg-gray-700 hover:text-white transition-all duration-200 border border-transparent hover:border-gray-600"
+                    >
+                      <X className="h-3 w-3 text-gray-400 hover:text-white" />
+                    </Button>
+                  </div>
+                ))
+              )}
+            </div>
+
+            {/* Create New Filter Option */}
+            <div className="border-t border-border/50">
+              <div
+                onClick={handleCreateNew}
+                className="flex items-center gap-3 p-3 hover:bg-accent hover:text-accent-foreground cursor-pointer transition-colors"
+              >
+                <Plus className="h-4 w-4 text-green-500" />
+                <div>
+                  <div className="text-sm font-medium text-green-600">
+                    Create New Filter
+                  </div>
+                  <div className="text-xs text-muted-foreground">
+                    Save current search as filter
+                  </div>
+                </div>
+              </div>
+            </div>
+
+            {/* Selected Filter Details */}
+            {selectedFilter && (
+              <div className="border-t border-border/50 p-3 bg-muted/20">
+                <div className="text-xs text-muted-foreground">
+                  <strong>Selected:</strong> {selectedFilter.name}
+                </div>
+                {selectedFilter.description && (
+                  <div className="text-xs text-muted-foreground mt-1 line-clamp-2">
+                    {selectedFilter.description}
+                  </div>
+                )}
+              </div>
+            )}
+          </CardContent>
+        </Card>
+      )}
+
+      {/* Create Filter Modal */}
+      {showCreateModal && (
+        <div className="fixed inset-0 bg-black/50 flex items-center justify-center z-50">
+          <div className="bg-card border border-border rounded-lg p-6 w-full max-w-md mx-4">
+            <h3 className="text-lg font-semibold mb-4">
+              Create New Knowledge Filter
+            </h3>
+
+            <div className="space-y-4">
+              <div>
+                <Label htmlFor="filter-name" className="font-medium">
+                  Name <span className="text-red-400">*</span>
+                </Label>
+                <Input
+                  id="filter-name"
+                  type="text"
+                  placeholder="Enter filter name"
+                  value={createName}
+                  onChange={(e) => setCreateName(e.target.value)}
+                  className="mt-1"
+                />
+              </div>
+
+              <div>
+                <Label htmlFor="filter-description" className="font-medium">
+                  Description (optional)
+                </Label>
+                <Textarea
+                  id="filter-description"
+                  placeholder="Brief description of this filter"
+                  value={createDescription}
+                  onChange={(e) => setCreateDescription(e.target.value)}
+                  className="mt-1"
+                  rows={3}
+                />
+              </div>
+            </div>
+
+            <div className="flex justify-end gap-2 mt-6">
+              <Button
+                variant="outline"
+                onClick={handleCancelCreate}
+                disabled={creating}
+              >
+                Cancel
+              </Button>
+              <Button
+                onClick={handleCreateFilter}
+                disabled={!createName.trim() || creating}
+                className="flex items-center gap-2"
+              >
+                {creating ? (
+                  <>
+                    <Loader2 className="h-4 w-4 animate-spin" />
+                    Creating...
+                  </>
+                ) : (
+                  <>
+                    <Save className="h-4 w-4" />
+                    Create Filter
+                  </>
+                )}
+              </Button>
+            </div>
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}

frontend/components/knowledge-filter-list.tsx

@@ -1,171 +1,165 @@
 "use client";
 
 import { Plus } from "lucide-react";
-import { useGetAllFiltersQuery } from "@/app/api/queries/useGetAllFiltersQuery";
-import type { KnowledgeFilter } from "@/app/api/queries/useGetFiltersSearchQuery";
-import { useKnowledgeFilter } from "@/contexts/knowledge-filter-context";
-import { cn } from "@/lib/utils";
+import { useState } from "react";
 import {
-	type FilterColor,
-	type IconKey,
-	iconKeyToComponent,
+  type KnowledgeFilter,
+  useGetFiltersSearchQuery,
+} from "@/app/api/queries/useGetFiltersSearchQuery";
+import { cn } from "@/lib/utils";
+import { useKnowledgeFilter } from "@/contexts/knowledge-filter-context";
+import {
+  type FilterColor,
+  type IconKey,
+  iconKeyToComponent,
 } from "./filter-icon-popover";
 import { filterAccentClasses } from "./knowledge-filter-panel";
 
 interface ParsedQueryData {
-	query: string;
-	filters: {
-		data_sources: string[];
-		document_types: string[];
-		owners: string[];
-	};
-	limit: number;
-	scoreThreshold: number;
-	color: FilterColor;
-	icon: IconKey;
+  query: string;
+  filters: {
+    data_sources: string[];
+    document_types: string[];
+    owners: string[];
+  };
+  limit: number;
+  scoreThreshold: number;
+  color: FilterColor;
+  icon: IconKey;
 }
 
 interface KnowledgeFilterListProps {
-	selectedFilter: KnowledgeFilter | null;
-	onFilterSelect: (filter: KnowledgeFilter | null) => void;
+  selectedFilter: KnowledgeFilter | null;
+  onFilterSelect: (filter: KnowledgeFilter | null) => void;
 }
 
 export function KnowledgeFilterList({
-	selectedFilter,
-	onFilterSelect,
+  selectedFilter,
+  onFilterSelect,
 }: KnowledgeFilterListProps) {
-	const { startCreateMode } = useKnowledgeFilter();
+  const [searchQuery] = useState("");
+  const { startCreateMode } = useKnowledgeFilter();
 
-	const { data, isFetching: loading } = useGetAllFiltersQuery();
+  const { data, isFetching: loading } = useGetFiltersSearchQuery(
+    searchQuery,
+    20,
+  );
 
-	const filters = data || [];
+  const filters = data || [];
 
-	const handleFilterSelect = (filter: KnowledgeFilter) => {
-		if (filter.id === selectedFilter?.id) {
-			onFilterSelect(null);
-			return;
-		}
-		onFilterSelect(filter);
-	};
+  const handleFilterSelect = (filter: KnowledgeFilter) => {
+    if (filter.id === selectedFilter?.id) {
+      onFilterSelect(null);
+      return;
+    }
+    onFilterSelect(filter);
+  };
 
-	const handleCreateNew = () => {
-		startCreateMode();
-	};
+  const handleCreateNew = () => {
+    startCreateMode();
+  };
 
-	const parseQueryData = (queryData: string): ParsedQueryData => {
-		const parsed = JSON.parse(queryData);
-		// Provide defaults for missing fields to handle API-created filters
-		return {
-			query: parsed.query ?? "",
-			filters: {
-				data_sources: parsed.filters?.data_sources ?? ["*"],
-				document_types: parsed.filters?.document_types ?? ["*"],
-				owners: parsed.filters?.owners ?? ["*"],
-			},
-			limit: parsed.limit ?? 10,
-			scoreThreshold: parsed.scoreThreshold ?? 0,
-			color: parsed.color ?? "zinc",
-			icon: parsed.icon ?? "filter",
-		};
-	};
+  const parseQueryData = (queryData: string): ParsedQueryData => {
+    return JSON.parse(queryData) as ParsedQueryData;
+  };
 
-	return (
-		<div className="flex-1 min-h-0 flex flex-col">
-			<div className="px-3 flex-1 min-h-0 flex flex-col">
-				<div className="flex-shrink-0">
-					<div className="flex items-center justify-between mb-3 mr-2 ml-4">
-						<h3 className="text-xs font-medium text-muted-foreground">
-							Knowledge Filters
-						</h3>
-						<button
-							type="button"
-							className="p-1 hover:bg-accent rounded"
-							onClick={handleCreateNew}
-							title="Create New Filter"
-						>
-							<Plus className="h-4 w-4 text-muted-foreground" />
-						</button>
-					</div>
-					<div className="overflow-y-auto scrollbar-hide space-y-1">
-						{loading ? (
-							<div className="text-[13px] text-muted-foreground p-2 ml-2">
-								Loading...
-							</div>
-						) : filters.length === 0 ? (
-							<div className="text-[13px] text-muted-foreground pb-2 pt-3 ml-4">
-								No saved filters
-							</div>
-						) : (
-							filters.map((filter) => (
-								<div
-									key={filter.id}
-									onClick={() => handleFilterSelect(filter)}
-									className={cn(
-										"flex items-center gap-3 px-3 py-2 w-full rounded-lg hover:bg-accent hover:text-accent-foreground cursor-pointer group transition-colors",
-										selectedFilter?.id === filter.id &&
-											"active bg-accent text-accent-foreground",
-									)}
-								>
-									<div className="flex flex-col gap-1 flex-1 min-w-0">
-										<div className="flex items-center gap-2">
-											{(() => {
-												const parsed = parseQueryData(
-													filter.query_data,
-												) as ParsedQueryData;
-												const Icon = iconKeyToComponent(parsed.icon);
-												return (
-													<div
-														className={cn(
-															"flex items-center justify-center w-5 h-5 rounded transition-colors",
-															filterAccentClasses[parsed.color],
-															parsed.color === "zinc" &&
-																"group-hover:bg-background group-[.active]:bg-background",
-														)}
-													>
-														{Icon && <Icon className="h-3 w-3" />}
-													</div>
-												);
-											})()}
-											<div className="text-sm font-medium truncate group-hover:text-accent-foreground">
-												{filter.name}
-											</div>
-										</div>
-										{filter.description && (
-											<div className="text-xs text-muted-foreground line-clamp-2">
-												{filter.description}
-											</div>
-										)}
-										<div className="flex items-center gap-2">
-											<div className="text-xs text-muted-foreground">
-												{new Date(filter.created_at).toLocaleDateString(
-													undefined,
-													{
-														month: "short",
-														day: "numeric",
-														year: "numeric",
-													},
-												)}
-											</div>
-											<span className="text-xs bg-muted text-muted-foreground px-1 py-0.5 rounded-sm group-hover:bg-background group-[.active]:bg-background transition-colors">
-												{(() => {
-													const dataSources = parseQueryData(filter.query_data)
-														.filters.data_sources;
-													if (dataSources[0] === "*") return "All sources";
-													const count = dataSources.length;
-													return `${count} ${
-														count === 1 ? "source" : "sources"
-													}`;
-												})()}
-											</span>
-										</div>
-									</div>
-								</div>
-							))
-						)}
-					</div>
-				</div>
-				{/* Create flow moved to panel create mode */}
-			</div>
-		</div>
-	);
+  return (
+    <div className="flex-1 min-h-0 flex flex-col">
+      <div className="px-3 flex-1 min-h-0 flex flex-col">
+        <div className="flex-shrink-0">
+          <div className="flex items-center justify-between mb-3 mr-2 ml-4">
+            <h3 className="text-xs font-medium text-muted-foreground">
+              Knowledge Filters
+            </h3>
+            <button
+              type="button"
+              className="p-1 hover:bg-accent rounded"
+              onClick={handleCreateNew}
+              title="Create New Filter"
+            >
+              <Plus className="h-4 w-4 text-muted-foreground" />
+            </button>
+          </div>
+          <div className="overflow-y-auto scrollbar-hide space-y-1">
+            {loading ? (
+              <div className="text-[13px] text-muted-foreground p-2 ml-2">
+                Loading...
+              </div>
+            ) : filters.length === 0 ? (
+              <div className="text-[13px] text-muted-foreground pb-2 pt-3 ml-4">
+                {searchQuery ? "No filters found" : "No saved filters"}
+              </div>
+            ) : (
+              filters.map((filter) => (
+                <div
+                  key={filter.id}
+                  onClick={() => handleFilterSelect(filter)}
+                  className={cn(
+                    "flex items-center gap-3 px-3 py-2 w-full rounded-lg hover:bg-accent hover:text-accent-foreground cursor-pointer group transition-colors",
+                    selectedFilter?.id === filter.id &&
+                      "active bg-accent text-accent-foreground",
+                  )}
+                >
+                  <div className="flex flex-col gap-1 flex-1 min-w-0">
+                    <div className="flex items-center gap-2">
+                      {(() => {
+                        const parsed = parseQueryData(
+                          filter.query_data,
+                        ) as ParsedQueryData;
+                        const Icon = iconKeyToComponent(parsed.icon);
+                        return (
+                          <div
+                            className={cn(
+                              "flex items-center justify-center w-5 h-5 rounded transition-colors",
+                              filterAccentClasses[parsed.color],
+                              parsed.color === "zinc" &&
+                                "group-hover:bg-background group-[.active]:bg-background",
+                            )}
+                          >
+                            {Icon && <Icon className="h-3 w-3" />}
+                          </div>
+                        );
+                      })()}
+                      <div className="text-sm font-medium truncate group-hover:text-accent-foreground">
+                        {filter.name}
+                      </div>
+                    </div>
+                    {filter.description && (
+                      <div className="text-xs text-muted-foreground line-clamp-2">
+                        {filter.description}
+                      </div>
+                    )}
+                    <div className="flex items-center gap-2">
+                      <div className="text-xs text-muted-foreground">
+                        {new Date(filter.created_at).toLocaleDateString(
+                          undefined,
+                          {
+                            month: "short",
+                            day: "numeric",
+                            year: "numeric",
+                          },
+                        )}
+                      </div>
+                      <span className="text-xs bg-muted text-muted-foreground px-1 py-0.5 rounded-sm group-hover:bg-background group-[.active]:bg-background transition-colors">
+                        {(() => {
+                          const dataSources = parseQueryData(filter.query_data)
+                            .filters.data_sources;
+                          if (dataSources[0] === "*") return "All sources";
+                          const count = dataSources.length;
+                          return `${count} ${
+                            count === 1 ? "source" : "sources"
+                          }`;
+                        })()}
+                      </span>
+                    </div>
+                  </div>
+                </div>
+              ))
+            )}
+          </div>
+        </div>
+        {/* Create flow moved to panel create mode */}
+      </div>
+    </div>
+  );
 }

frontend/components/knowledge-filter-panel.tsx

@@ -96,16 +96,15 @@ export function KnowledgeFilterPanel() {
       setQuery(parsedFilterData.query || "");
 
       // Set the actual filter selections from the saved knowledge filter
-      const filters = parsedFilterData.filters || {};
+      const filters = parsedFilterData.filters;
 
       // Use the exact selections from the saved filter
       // Empty arrays mean "none selected" not "all selected"
-      // Provide defaults for missing fields to handle API-created filters
       const processedFilters = {
-        data_sources: filters.data_sources ?? ["*"],
-        document_types: filters.document_types ?? ["*"],
-        owners: filters.owners ?? ["*"],
-        connector_types: filters.connector_types ?? ["*"],
+        data_sources: filters.data_sources,
+        document_types: filters.document_types,
+        owners: filters.owners,
+        connector_types: filters.connector_types || ["*"],
       };
 
       console.log("[DEBUG] Loading filter selections:", processedFilters);
@@ -115,8 +114,8 @@ export function KnowledgeFilterPanel() {
       setScoreThreshold(parsedFilterData.scoreThreshold || 0);
       setName(selectedFilter.name);
       setDescription(selectedFilter.description || "");
-      setColor(parsedFilterData.color ?? "zinc");
-      setIconKey(parsedFilterData.icon ?? "filter");
+      setColor(parsedFilterData.color);
+      setIconKey(parsedFilterData.icon);
     }
   }, [selectedFilter, parsedFilterData]);
 
@@ -124,20 +123,13 @@ export function KnowledgeFilterPanel() {
   useEffect(() => {
     if (createMode && parsedFilterData) {
       setQuery(parsedFilterData.query || "");
-      // Provide defaults for missing filter fields
-      const filters = parsedFilterData.filters || {};
-      setSelectedFilters({
-        data_sources: filters.data_sources ?? ["*"],
-        document_types: filters.document_types ?? ["*"],
-        owners: filters.owners ?? ["*"],
-        connector_types: filters.connector_types ?? ["*"],
-      });
+      setSelectedFilters(parsedFilterData.filters);
       setResultLimit(parsedFilterData.limit || 10);
       setScoreThreshold(parsedFilterData.scoreThreshold || 0);
       setName("");
       setDescription("");
-      setColor(parsedFilterData.color ?? "zinc");
-      setIconKey(parsedFilterData.icon ?? "filter");
+      setColor(parsedFilterData.color);
+      setIconKey(parsedFilterData.icon);
     }
   }, [createMode, parsedFilterData]);
 

frontend/components/layout-wrapper.tsx

@@ -15,7 +15,6 @@ import {
 } from "@/components/provider-health-banner";
 import { TaskNotificationMenu } from "@/components/task-notification-menu";
 import { useAuth } from "@/contexts/auth-context";
-import { useChat } from "@/contexts/chat-context";
 import { useKnowledgeFilter } from "@/contexts/knowledge-filter-context";
 import { useTask } from "@/contexts/task-context";
 import { cn } from "@/lib/utils";
@@ -28,7 +27,6 @@ export function LayoutWrapper({ children }: { children: React.ReactNode }) {
   const { isMenuOpen } = useTask();
   const { isPanelOpen } = useKnowledgeFilter();
   const { isLoading, isAuthenticated, isNoAuthMode } = useAuth();
-  const { isOnboardingComplete } = useChat();
 
   // List of paths that should not show navigation
   const authPaths = ["/login", "/auth/callback"];
@@ -93,17 +91,17 @@ export function LayoutWrapper({ children }: { children: React.ReactNode }) {
             isOpen={isDoclingUnhealthy}
             className="w-full"
           >
-          <DoclingHealthBanner />
-        </AnimatedConditional>
-        {settings?.edited && isOnboardingComplete && (
-          <AnimatedConditional
-            vertical
-            isOpen={isProviderUnhealthy}
-            className="w-full"
-          >
-            <ProviderHealthBanner />
+            <DoclingHealthBanner />
           </AnimatedConditional>
-        )}
+          {settings?.edited && (
+            <AnimatedConditional
+              vertical
+              isOpen={isProviderUnhealthy}
+              className="w-full"
+            >
+              <ProviderHealthBanner />
+            </AnimatedConditional>
+          )}
         </div>
 
         <ChatRenderer settings={settings}>{children}</ChatRenderer>

frontend/components/login-required.tsx

@@ -0,0 +1,48 @@
+"use client";
+
+import {
+  Card,
+  CardContent,
+  CardDescription,
+  CardHeader,
+  CardTitle,
+} from "@/components/ui/card";
+import { Button } from "@/components/ui/button";
+import { useAuth } from "@/contexts/auth-context";
+import { Lock, LogIn } from "lucide-react";
+
+interface LoginRequiredProps {
+  title?: string;
+  description?: string;
+  feature?: string;
+}
+
+export function LoginRequired({
+  title = "Authentication Required",
+  description = "You need to sign in to access this feature",
+  feature,
+}: LoginRequiredProps) {
+  const { login } = useAuth();
+
+  return (
+    <div className="flex items-center justify-center min-h-[400px]">
+      <Card className="max-w-md mx-auto">
+        <CardHeader className="text-center">
+          <div className="flex items-center justify-center w-12 h-12 bg-primary/10 rounded-full mx-auto mb-4">
+            <Lock className="h-6 w-6 text-primary" />
+          </div>
+          <CardTitle>{title}</CardTitle>
+          <CardDescription>
+            {feature ? `You need to sign in to access ${feature}` : description}
+          </CardDescription>
+        </CardHeader>
+        <CardContent className="text-center">
+          <Button onClick={login} className="w-full">
+            <LogIn className="h-4 w-4 mr-2" />
+            Sign In with Google
+          </Button>
+        </CardContent>
+      </Card>
+    </div>
+  );
+}

frontend/components/mode-toggle.tsx

@@ -0,0 +1,23 @@
+"use client";
+
+import * as React from "react";
+import { Moon, Sun } from "lucide-react";
+import { useTheme } from "next-themes";
+
+import { Button } from "@/components/ui/button";
+
+export function ModeToggle() {
+  const { theme, setTheme } = useTheme();
+
+  return (
+    <Button
+      variant="ghost"
+      size="icon"
+      onClick={() => setTheme(theme === "light" ? "dark" : "light")}
+    >
+      <Sun className="h-[1.2rem] w-[1.2rem] rotate-0 scale-100 transition-all dark:-rotate-90 dark:scale-0" />
+      <Moon className="absolute h-[1.2rem] w-[1.2rem] rotate-90 scale-0 transition-all dark:rotate-0 dark:scale-100" />
+      <span className="sr-only">Toggle theme</span>
+    </Button>
+  );
+}

frontend/components/navigation-layout.tsx

@@ -0,0 +1,81 @@
+"use client";
+
+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 {
+  children: React.ReactNode;
+}
+
+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
+          conversations={conversations}
+          isConversationsLoading={isConversationsLoading}
+          onNewConversation={handleNewConversation}
+        />
+      </div>
+      <main className="md:pl-72">
+        <div className="flex flex-col min-h-screen">
+          <header className="sticky top-0 z-40 w-full border-b border-border/40 bg-background">
+            <div className="container flex h-14 max-w-screen-2xl items-center">
+              <div className="mr-4 hidden md:flex">
+                <h1 className="text-lg font-semibold tracking-tight">
+                  OpenRAG
+                </h1>
+              </div>
+              <div className="flex flex-1 items-center justify-between space-x-2 md:justify-end">
+                <div className="w-full flex-1 md:w-auto md:flex-none">
+                  {/* Search component could go here */}
+                </div>
+                <nav className="flex items-center space-x-2">
+                  <KnowledgeFilterDropdown
+                    selectedFilter={selectedFilter}
+                    onFilterSelect={setSelectedFilter}
+                  />
+                  <ModeToggle />
+                </nav>
+              </div>
+            </div>
+          </header>
+          <div className="flex-1">
+            <div className="container py-6 lg:py-8">{children}</div>
+          </div>
+        </div>
+      </main>
+    </div>
+  );
+}

frontend/components/ui/checkbox.tsx

@@ -0,0 +1,30 @@
+"use client";
+
+import * as React from "react";
+import * as CheckboxPrimitive from "@radix-ui/react-checkbox";
+import { Check } from "lucide-react";
+
+import { cn } from "@/lib/utils";
+
+const Checkbox = React.forwardRef<
+  React.ElementRef<typeof CheckboxPrimitive.Root>,
+  React.ComponentPropsWithoutRef<typeof CheckboxPrimitive.Root>
+>(({ className, ...props }, ref) => (
+  <CheckboxPrimitive.Root
+    ref={ref}
+    className={cn(
+      "peer h-4 w-4 shrink-0 rounded-sm border border-primary ring-offset-background focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-primary data-[state=checked]:text-primary-foreground",
+      className,
+    )}
+    {...props}
+  >
+    <CheckboxPrimitive.Indicator
+      className={cn("flex items-center justify-center text-current")}
+    >
+      <Check className="h-4 w-4" />
+    </CheckboxPrimitive.Indicator>
+  </CheckboxPrimitive.Root>
+));
+Checkbox.displayName = CheckboxPrimitive.Root.displayName;
+
+export { Checkbox };