rag_skill

This skill provides comprehensive guidance for building intelligent chatbots that answer questions based on documentation content using RAG architecture. It includes backend API development (FastAPI + OpenAI + Qdrant), conversation management (PostgreSQL), and frontend UI components (React/TypeScript for Docusaurus).

What This Skill Does

1. Backend Setup - Configure FastAPI with OpenAI, Qdrant, and Neon Postgres
2. RAG Service Implementation - Build embedding generation, vector search, and LLM response generation
3. Document Indexing - Extract, chunk, and embed documentation content
4. Frontend Integration - Create React chat components with text selection and source citations
5. Session Management - Implement conversation memory and persistence
6. Testing & Deployment - Validate functionality and deploy to production
7. Performance Optimization - Monitor costs, response times, and accuracy

When to Use This Skill

• Add an AI-powered Q&A chatbot to documentation sites
• Implement semantic search over documentation content
• Build conversational interfaces with source citations
• Create context-aware chatbots with text selection features
• Integrate OpenAI, Qdrant, and PostgreSQL for RAG systems
• Deploy production-ready RAG applications with proper testing

How This Skill Works

Phase 1: Backend Setup

1. Create project structure:

   bashCopy

   mkdir backend
   cd backend
   

2. Install dependencies (requirements.txt):

   Copy

   fastapi==0.115.0
   uvicorn[standard]==0.32.0
   python-dotenv==1.0.1
   openai==1.54.0
   qdrant-client==1.12.0
   psycopg2-binary==2.9.10
   sqlalchemy==2.0.35
   pydantic==2.9.2
   pydantic-settings==2.6.0
   python-multipart==0.0.12
   markdown==3.7
   beautifulsoup4==4.12.3
   tiktoken==0.8.0
   

3. Configure environment variables (.env):

   • OPENAI_API_KEY: OpenAI API key from platform.openai.com
   • QDRANT_URL: Qdrant Cloud cluster URL (https://xxx.cloud.qdrant.io:6333)
   • QDRANT_API_KEY: Qdrant Cloud API key
   • QDRANT_COLLECTION_NAME: Collection name (e.g., "ai_native_book")
   • DATABASE_URL: Neon Postgres connection string
   • CORS_ORIGINS: Allowed origins (e.g., "http://localhost:3000")

4. Implement core services:

   • RAG Service (rag_service.py):

     • Embedding generation using OpenAI text-embedding-3-small
     • Vector similarity search in Qdrant
     • LLM response generation with GPT-4o-mini
     • Context building from retrieved documents

   • Database Models (models.py):

     • ChatSession (session_id, created_at, last_activity)
     • ChatMessage (session_id, role, content, timestamp)

   • API Endpoints (main.py):

     • GET /api/health: System status and service connectivity
     • POST /api/chat: Send message and get AI response
     • GET /api/sessions/{session_id}/history: Retrieve conversation history

5. Create document indexing pipeline (indexer.py):

   • Extract content from markdown/MDX files
   • Clean and preprocess text
   • Chunk documents (1000 words with 200-word overlap)
   • Generate embeddings using OpenAI
   • Store vectors in Qdrant with metadata (title, file_path, sidebar_position)

Phase 2: Frontend Integration

1. Create chat component (book/src/components/RAGChatbot/):

   typescriptCopy

   // index.tsx - Main chat component
   - Message display with user/assistant roles
   - Loading states and typing animations
   - Source citation display
   - Error handling and retry logic
   - Session persistence
   

2. Add styling (styles.module.css):

   • Dark mode support
   • Responsive design
   • Smooth animations
   • Accessibility (ARIA labels, keyboard navigation)

3. Implement text selection feature:

   • Detect text selection on documentation page
   • Show yellow context indicator banner
   • Include selected text in API requests
   • Clear selection after use

4. Create global integration (book/src/theme/Root.tsx):

   • Wrap Docusaurus with chat component
   • Configure API endpoint URL
   • Enable CORS headers

Phase 3: Testing & Deployment

1. Run comprehensive test suite (test_api.py):

   • Health Check Test: Verify all services connected
   • Basic Q&A Test: Test RAG retrieval and generation
   • Context-Aware Test: Test conversation memory
   • Text Selection Test: Test selected text integration
   • Session Management Test: Test database persistence

2. Deploy backend:

   • Deploy to Railway, Render, or AWS
   • Configure production environment variables
   • Enable HTTPS and proper CORS
   • Set up monitoring and logging

3. Deploy frontend:

   • Update API URL to production endpoint
   • Build and deploy Docusaurus site
   • Verify CORS and connectivity

4. Verify deployment:

   • Test health endpoint
   • Run smoke tests with sample queries
   • Monitor performance and errors
   • Check cost tracking

Technology Stack

Backend

• Framework: FastAPI (Python 3.9+)
• LLM Provider: OpenAI (GPT-4o-mini for chat, text-embedding-3-small for embeddings)
• Vector Database: Qdrant Cloud (free tier: 1GB storage)
• Relational Database: Neon Serverless Postgres (free tier: 0.5GB storage, 100 hours compute)
• Additional Libraries: SQLAlchemy, Pydantic, python-dotenv, BeautifulSoup4, tiktoken

Frontend

• Framework: React + TypeScript
• Integration: Docusaurus v3+
• Styling: CSS Modules with dark mode support
• Features: Text selection, session management, source citations

Cloud Services Required

1. OpenAI Account

   • API key from platform.openai.com
   • Billing enabled
   • Cost: ~$5-10/month for moderate usage (1000 queries)

2. Qdrant Cloud

   • Free tier: 1GB storage
   • Create cluster at cloud.qdrant.io
   • Copy cluster URL and API key

3. Neon Postgres

   • Free tier: 0.5GB storage, 100 hours compute
   • Create database at neon.tech
   • Copy connection string

Architecture Components

Backend API Structure

backend/
├── src/
│   ├── main.py              # FastAPI application with endpoints
│   ├── config.py            # Configuration and settings
│   ├── models.py            # Pydantic and SQLAlchemy models
│   ├── database.py          # Database connection and session management
│   ├── services/
│   │   ├── rag_service.py       # RAG logic (embeddings + retrieval + generation)
│   │   ├── conversation.py      # Conversation management
│   │   └── vector_store.py      # Qdrant vector operations
│   └── schemas/
│       └── chat.py              # Request/response schemas
├── scripts/
│   ├── index_docs.py        # Document indexing script
│   └── clear_and_reindex.py # Clear collection and reindex
├── tests/
│   └── test_api.py          # Comprehensive test suite
├── requirements.txt         # Python dependencies
├── requirements-dev.txt     # Development dependencies
├── .env.example             # Environment variables template
├── Dockerfile               # Container configuration
└── docker-compose.yml       # Multi-service orchestration

Core API Endpoints

Health Check: GET /api/health

• Returns system status and service connectivity
• Example response:
  jsonCopy

  {
    "status": "healthy",
    "openai": "connected",
    "qdrant": "connected",
    "postgres": "connected"
  }
  

Chat: POST /api/chat

• Input:
  jsonCopy

  {
    "message": "What is Physical AI?",
    "session_id": "uuid-optional",
    "selected_text": "optional selected context"
  }
  

• Output:
  jsonCopy

  {
    "session_id": "uuid",
    "message": "AI response with context",
    "sources": [
      {
        "title": "Introduction to Physical AI",
        "file_path": "/docs/intro.md",
        "score": 0.85
      }
    ],
    "timestamp": "2025-12-02T10:30:00Z"
  }
  

Session History: GET /api/sessions/{session_id}/history

• Returns full conversation history for a session
• Example response:
  jsonCopy

  {
    "session_id": "uuid",
    "messages": [
      {
        "role": "user",
        "content": "What is Physical AI?",
        "timestamp": "2025-12-02T10:30:00Z"
      },
      {
        "role": "assistant",
        "content": "Physical AI refers to...",
        "timestamp": "2025-12-02T10:30:02Z"
      }
    ]
  }
  

Frontend Components Structure

book/src/
├── theme/
│   ├── components/
│   │   └── ChatWidget/
│   │       ├── index.tsx           # Main chat component
│   │       ├── ChatWindow.tsx      # Chat window UI
│   │       ├── MessageList.tsx     # Message display
│   │       ├── MessageInput.tsx    # Input field
│   │       ├── SourceCitation.tsx  # Source display
│   │       └── styles.module.css   # Component styles
│   └── Root.tsx                     # Global wrapper for chatbot
└── css/
    └── custom.css                   # Global styles

Key Features Implementation

1. RAG-Powered Responses

Semantic Search Pattern:

# Generate query embedding
query_embedding = openai_client.embeddings.create(
    model="text-embedding-3-small",
    input=user_query
).data[0].embedding

# Search in Qdrant
similar_docs = qdrant_client.search(
    collection_name="ai_native_book",
    query_vector=query_embedding,
    limit=5
)

# Build context from top results
context = "\n\n".join([
    f"[{doc.payload['title']}]\n{doc.payload['content']}"
    for doc in similar_docs
])

Response Generation Pattern:

# Generate response with context
response = openai_client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": system_prompt},
        *chat_history[-6:],  # Last 3 exchanges
        {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {user_query}"}
    ],
    temperature=0.7,
    max_tokens=1000
)

2. Text Selection Context

Frontend Detection:

useEffect(() => {
  const handleSelection = () => {
    const selection = window.getSelection();
    const text = selection?.toString().trim();
    if (text && text.length > 10) {
      setSelectedText(text);
      setShowContextBanner(true);
    }
  };

  document.addEventListener('mouseup', handleSelection);
  return () => document.removeEventListener('mouseup', handleSelection);
}, []);

Backend Integration:

# Include selected text in prompt if provided
if selected_text:
    user_message = f"Selected text: '{selected_text}'\n\n{user_message}"

3. Conversation Memory

Database Storage:

# Store chat history in Postgres
new_message = ChatMessage(
    session_id=session.id,
    role="user",
    content=user_message,
    timestamp=datetime.utcnow()
)
db.add(new_message)
db.commit()

# Retrieve history
chat_history = db.query(ChatMessage).filter(
    ChatMessage.session_id == session.id
).order_by(ChatMessage.timestamp.desc()).limit(10).all()

Context Building:

# Include in LLM context
history_messages = [
    {"role": msg.role, "content": msg.content}
    for msg in reversed(chat_history)
]

4. Source Citations

Return Sources:

sources = [
    {
        "title": doc.payload["title"],
        "file_path": doc.payload["file_path"],
        "score": round(doc.score, 3)
    }
    for doc in similar_docs[:3]
]

Frontend Display:

{sources.map((source, idx) => (
  <div key={idx} className={styles.source}>
    <a href={source.file_path}>{source.title}</a>
    <span className={styles.score}>
      {Math.round(source.score * 100)}% match
    </span>
  </div>
))}

Document Indexing Strategy

Chunking Parameters

• Chunk Size: 1000 words (configurable)
• Overlap: 200 words (maintains context continuity)
• Metadata: Title, file path, chunk index, sidebar position
• Processing: Clean HTML, remove code blocks, normalize whitespace

Embedding Model

• Model: text-embedding-3-small (1536 dimensions)
• Cost: ~$0.00002 per 1K tokens
• Performance: ~100ms per embedding
• Batch Size: 100 documents per batch

Vector Search Configuration

• Distance Metric: Cosine similarity
• Results: Top 5 most relevant chunks
• Threshold: Minimum 0.5 similarity score
• Metadata Filtering: Support filtering by file path, section

Indexing Script Usage

# Index all documentation
cd backend
source venv/bin/activate
python scripts/index_docs.py

# Clear and reindex
python scripts/clear_and_reindex.py

Performance Metrics

Expected Response Times

• Embedding Generation: ~100ms
• Vector Search: ~50ms
• LLM Generation: ~1-2 seconds
• Database Operations: ~50ms
• Total Response: ~1.5-2.5 seconds (95th percentile < 3s)

Accuracy Metrics

• Relevance Scores: 55-75% for top results
• Context Retrieval: 3-5 relevant chunks per query
• Answer Quality: High when relevant context is found
• Source Attribution: 90%+ accuracy

Performance Targets

• API Response Time: < 3 seconds (95th percentile)
• Vector Search: < 100ms
• Database Queries: < 50ms
• Frontend Render: < 16ms (60fps)
• Uptime: 95%+

Testing Strategy

Test Coverage Areas

1. Health Checks: Verify all services connected

   pythonCopy

   response = requests.get(f"{BASE_URL}/api/health")
   assert response.json()["status"] == "healthy"
   assert response.json()["openai"] == "connected"
   assert response.json()["qdrant"] == "connected"
   assert response.json()["postgres"] == "connected"
   

2. Basic Q&A: Test RAG retrieval and generation

   pythonCopy

   response = requests.post(
       f"{BASE_URL}/api/chat",
       json={"message": "What is Physical AI?"}
   )
   assert "session_id" in response.json()
   assert len(response.json()["sources"]) > 0
   assert response.json()["message"]
   

3. Context Awareness: Test conversation memory

   pythonCopy

   # First message
   response1 = requests.post(
       f"{BASE_URL}/api/chat",
       json={"message": "What is ROS 2?"}
   )
   session_id = response1.json()["session_id"]
   
   # Follow-up message
   response2 = requests.post(
       f"{BASE_URL}/api/chat",
       json={
           "message": "What are its main features?",
           "session_id": session_id
       }
   )
   assert "ROS 2" in response2.json()["message"]
   

4. Text Selection: Test selected text integration

   pythonCopy

   response = requests.post(
       f"{BASE_URL}/api/chat",
       json={
           "message": "Explain this",
           "selected_text": "Physical AI combines artificial intelligence with physical robotics"
       }
   )
   assert response.status_code == 200
   

5. Session Management: Test database persistence

   pythonCopy

   response = requests.get(
       f"{BASE_URL}/api/sessions/{session_id}/history"
   )
   assert len(response.json()["messages"]) >= 2
   

Sample Test Script

import requests

BASE_URL = "http://localhost:8000"

def test_health():
    response = requests.get(f"{BASE_URL}/api/health")
    assert response.json()["status"] == "healthy"
    print("✅ Health check passed")

def test_basic_qa():
    response = requests.post(
        f"{BASE_URL}/api/chat",
        json={"message": "What is Physical AI?"}
    )
    assert "session_id" in response.json()
    assert len(response.json()["sources"]) > 0
    print("✅ Basic Q&A passed")

if __name__ == "__main__":
    test_health()
    test_basic_qa()

Common Issues & Solutions

Issue: "process is not defined" in browser

Cause: Using Node.js process.env in React browser code

Solution: Use hardcoded values or build-time environment variables

// ❌ Wrong - process.env doesn't exist in browser
<RAGChatbot apiUrl={process.env.REACT_APP_API_URL} />

// ✅ Correct - hardcode or use Docusaurus config
<RAGChatbot apiUrl="http://localhost:8000" />

Issue: OpenAI client initialization error

Cause: Outdated OpenAI SDK version

Solution: Upgrade to latest version

pip install --upgrade openai

Issue: Empty search results

Cause: Documents not indexed in Qdrant

Solution: Run indexing script

cd backend
python scripts/index_docs.py

Issue: CORS errors

Cause: CORS origins not configured properly

Solution: Configure CORS in backend

# config.py
CORS_ORIGINS = "http://localhost:3000,https://your-domain.com"

Issue: Qdrant connection failures

Cause: Incorrect cluster URL or API key

Solution: Verify configuration

• Cluster URL must include port: https://xxx.cloud.qdrant.io:6333
• API key must be valid
• Test with health endpoint

Issue: Slow responses

Cause: Too many context chunks or slow LLM model

Solution: Optimize retrieval and model

• Reduce chunk retrieval limit (5 → 3)
• Use faster model (GPT-4o-mini)
• Implement caching
• Reduce context size

Issue: Database connection errors

Cause: Invalid Neon connection string

Solution: Verify connection string format

postgresql://user:password@host/database?sslmode=require

Deployment Checklist

Pre-Deployment

• All environment variables configured
• Documents indexed in Qdrant
• Database tables created in Neon
• Test suite passing (5/5 tests)
• Local build successful
• No broken links or errors

Backend Deployment

• Backend deployed to Railway/Render/AWS
• Production environment variables set
• HTTPS enabled
• CORS configured for production domain
• Health check endpoint returning success
• Monitoring/logging configured

Frontend Deployment

• API URL updated to production endpoint
• Docusaurus site built successfully
• Chat widget visible and functional
• Text selection feature working
• Sources displaying correctly

Post-Deployment

• Smoke tests passing
• Response times acceptable (< 3s)
• No CORS errors
• Conversation memory working
• Cost tracking enabled
• Error monitoring active

Cost Estimation

Monthly costs (moderate usage - 1000 queries):

• OpenAI Embeddings: ~$0.50 (1000 queries × 1000 words × $0.00002 per 1K tokens)
• OpenAI Chat: ~$5-10 (1000 responses × ~1000 tokens × $0.15-0.60 per 1M tokens)
• Qdrant Cloud: $0 (free tier - 1GB storage)
• Neon Postgres: $0 (free tier - 0.5GB storage, 100 hours compute)
• Hosting: $5-10 (Railway/Render free tier or basic plan)
• Total: $5-20/month

Cost optimization tips:

• Use GPT-4o-mini instead of GPT-4o (10x cheaper)
• Cache embeddings for common queries
• Reduce chunk retrieval limit
• Monitor usage and set limits

Success Criteria

A successful RAG chatbot implementation should achieve:

✅ Functional Requirements:

• User asks question → Gets relevant answer
• User selects text → Context indicator appears
• Follow-up questions → Conversation memory works
• Sources shown → User can verify information
• All tests passing (5/5)

✅ Performance Requirements:

• Response time < 3 seconds (95th percentile)
• Vector search < 100ms
• Database queries < 50ms
• Uptime 95%+

✅ Quality Requirements:

• Relevance score > 70% for top source
• Answer accuracy high when context available
• Source attribution accurate
• Error rate < 5%
• Positive user feedback

✅ Cost Requirements:

• Within budget ($5-20/month for moderate usage)
• Efficient token usage
• Optimized embedding calls

Example Usage Workflow

1. User opens documentation → Chat button appears in bottom-right
2. User clicks chat button → Chat window opens
3. User asks "What is Physical AI?" → Backend searches Qdrant for relevant chunks
4. System finds 5 relevant chunks → Combines with query and chat history
5. OpenAI generates answer → Returns with 3 source citations
6. User sees answer + sources → Can click sources to verify information
7. User selects text on page → Yellow context banner appears
8. User asks "Explain this" → Context-aware response using selected text
9. User asks follow-up → Conversation memory maintained
10. User closes chat → Session persisted in database

Quality Gates

Before deployment to production, verify:

• All tests pass (health, Q&A, context, selection, session)
• Local build completes without errors
• No broken links or missing resources
• Performance targets met (< 3s response)
• Accessibility standards verified (ARIA labels, keyboard navigation)
• Security: API keys in environment variables, CORS configured
• Monitoring and error tracking enabled
• Cost tracking and limits configured

Next Steps After Implementation

1. Monitor and optimize:

   • Track usage patterns and costs
   • Monitor response times and error rates
   • Collect user feedback
   • Optimize chunk size and retrieval parameters

2. Enhance features:

   • Add multi-language support
   • Implement voice input/output
   • Add feedback buttons (thumbs up/down)
   • Create analytics dashboard

3. Scale infrastructure:

   • Move to paid tiers as needed
   • Implement caching layer
   • Add rate limiting
   • Set up load balancing

4. Improve quality:

   • Fine-tune prompts
   • Optimize chunking strategy
   • Add more sophisticated context ranking
   • Implement A/B testing

Output Format

When using this skill, you should have:

Input Requirements:

• Documentation content location (e.g., book/docs/)
• Desired chat features (text selection, sources, memory)
• Cloud service credentials (OpenAI, Qdrant, Neon)
• Deployment target (Railway, Render, AWS, etc.)

Skill Returns:

1. Backend Implementation:

   • FastAPI application with health, chat, and history endpoints
   • RAG service with embedding generation and vector search
   • Database models and session management
   • Document indexing script
   • Comprehensive test suite

2. Frontend Implementation:

   • React chat component with TypeScript
   • Text selection feature
   • Source citation display
   • Dark mode styling
   • Global Docusaurus integration

3. Configuration Files:

   • .env.example with all required variables
   • requirements.txt with pinned dependencies
   • docker-compose.yml for local development
   • API documentation and usage examples

4. Testing & Deployment:

   • Test scripts with all 5 test cases
   • Deployment instructions for chosen platform
   • Health check verification
   • Performance monitoring setup

5. Documentation:

   • Setup guide with step-by-step instructions
   • API endpoint documentation
   • Troubleshooting guide
   • Cost estimation and optimization tips

Version: 1.0.0 Last Updated: 2025-12-02 Tested With: Docusaurus 3.9.2, OpenAI API 1.54.0, Qdrant 1.12.0, FastAPI 0.115.0