Deploy DeveloperDocs-AI-Copilot-RAG to Hugging Face Space

Dockerfile

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+FROM python:3.12-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create non-root user (required by HF Spaces)
+RUN useradd -m -u 1000 user
+ENV HOME=/home/user
+ENV PATH=/home/user/.local/bin:$PATH
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Download sentence-transformers model at build time to avoid runtime delays
+RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
+
+# Copy application code
+COPY . .
+
+# Create necessary directories and set ownership
+RUN mkdir -p data/vectordb data/raw data/processed evals/results \
+    && chown -R user:user /app
+
+# Switch to non-root user
+USER user
+
+# Expose port
+EXPOSE 7860
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD curl -f http://localhost:7860/health || exit 1
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV GRADIO_SERVER_NAME=0.0.0.0
+ENV GRADIO_SERVER_PORT=7860
+
+# Run the application
+CMD ["python", "app.py"]

README.md

@@ -1,11 +1,244 @@
----
-title: DeveloperDocs RAG
-emoji: 🌍
-colorFrom: yellow
-colorTo: green
-sdk: docker
-pinned: false
-short_description: Q&A RAG for developer docs understanding
+# 🤖 Developer Docs Copilot
+
+> Production-grade RAG system that answers questions using official techstack documentation (eg:fastapi)
+
+[![Deployed on HuggingFace](https://img.shields.io/badge/🤗-HuggingFace%20Spaces-blue)](https://huggingface.co/spaces)
+[![Docker](https://img.shields.io/badge/Docker-Ready-2496ED?logo=docker&logoColor=white)](https://www.docker.com/)
+[![Python 3.10+](https://img.shields.io/badge/Python-3.10+-3776AB?logo=python&logoColor=white)](https://www.python.org/)
+
+## 🎯 What This Project Demonstrates
+
+This is a **production-style RAG (Retrieval-Augmented Generation)** system that showcases:
+
+- ✅ **Professional documentation ingestion pipeline** with chunking strategies
+- ✅ **Semantic search** using vector embeddings (ChromaDB)
+- ✅ **Source attribution** with clickable citations
+- ✅ **RAG evaluation metrics** (RAGAS framework)
+- ✅ **Dockerized deployment** ready for cloud platforms
+- ✅ **Production-grade error handling** and logging
+
+## 🏗️ Architecture
+
+```
+┌─────────────┐
+│   User      │
+│  Question   │
+└──────┬──────┘
+       │
+       ▼
+┌─────────────────────────────────────┐
+│  1. Query Embedding                 │
+│     (sentence-transformers)         │
+└──────────┬──────────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────────┐
+│  2. Vector Search (ChromaDB)        │
+│     - Top 5 relevant chunks         │
+│     - Metadata: source, section     │
+└──────────┬──────────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────────┐
+│  3. Context Assembly                │
+│     - Format chunks                 │
+│     - Add instructions              │
+└──────────┬──────────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────────┐
+│  4. LLM Generation (HF Inference)   │
+│     - Answer with citations         │
+│     - Code examples preserved       │
+└──────────┬──────────────────────────┘
+           │
+           ▼
+┌─────────────────────────────────────┐
+│  5. Response + Source Links         │
+└─────────────────────────────────────┘
+```
+
+### Local Setup
+
+```bash
+# Clone the repository
+git clone https://github.com/aishwarya30998/DeveloperDocs-AI-Copilot-RAG.git
+cd DeveloperDocs-AI-Copilot-RAG
+
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate
+# On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+
+
+# create .env and add your HF_TOKEN
+
+
+# Run the application
+python app.py
+```
+
+Visit `http://localhost:7860` in your browser.
+
+## 📦 Project Structure
+
+```
+fastapi-docs-copilot/
+├── app.py                      # Gradio UI application
+├── Dockerfile                  # Container configuration
+├── docker-compose.yml          # Local container orchestration
+├── requirements.txt            # Python dependencies
+├── .env.example               # Environment variables template
+│
+├── src/
+│   ├── __init__.py
+│   ├── config.py              # Configuration management
+│   ├── chunking.py            # Document chunking strategies
+│   ├── embeddings.py          # Embedding generation
+│   ├── retriever.py           # Vector search logic
+│   ├── rag_pipeline.py        # Main RAG orchestration
+│   └── prompts.py             # Prompt templates
+│
+├── scripts/
+│   ├── ingest_docs.py         # Documentation ingestion
+│   ├── evaluate_rag.py        # RAG metrics evaluation
+│   └── test_retrieval.py      # Test retrieval quality
+│
+├── data/
+│   ├── raw/                   # Downloaded documentation
+│   ├── processed/             # Chunked documents
+│   └── vectordb/              # ChromaDB storage
+│
+├── tests/
+│   ├── test_chunking.py
+│   ├── test_retriever.py
+│   └── test_rag_pipeline.py
+│
+��── evals/
+    ├── test_queries.json      # Evaluation dataset
+    └── results/               # Evaluation outputs
+```
+
+## 🎯 Key Features
+
+### 1. Smart Chunking
+
+- **Semantic chunking** with overlap for context preservation
+- **Metadata enrichment** (section titles, URLs, code blocks)
+- **Configurable chunk sizes** (300-800 tokens)
+
+### 2. Retrieval Quality
+
+- **Hybrid search** (semantic + keyword)
+- **Reranking** for improved relevance
+- **Source attribution** with confidence scores
+
+### 3. Answer Generation
+
+- **Code-aware formatting** (preserves indentation)
+- **Inline citations** with source links
+- **Fallback handling** for low-confidence results
+
+### 4. Production Features
+
+- **Health check endpoint** (`/health`)
+- **Query logging** for analytics
+- **Rate limiting** (basic throttling)
+- **Error recovery** with graceful degradation
+
+## 📊 RAG Evaluation
+
+We use **RAGAS** framework to measure:
+
+| Metric                | Description                 | Target Score |
+| --------------------- | --------------------------- | ------------ |
+| **Faithfulness**      | Answer accuracy vs. context | > 0.8        |
+| **Answer Relevancy**  | Response relevance to query | > 0.7        |
+| **Context Precision** | Retrieval accuracy          | > 0.75       |
+| **Context Recall**    | Context completeness        | > 0.8        |
+
+Run evaluations:
+
+```bash
+python evaluate_rag.py
+```
+
+## 🐳 Docker Deployment
+
+### Build and run locally:
+
+```bash
+docker build -t developerdocs-rag
+docker run -p 7860:7860 --name developerdocs-rag-container developerdocs-rag
+```
+
+### Deploy to HuggingFace Spaces:
+
+1. Create a new Space on HuggingFace
+2. Enable Docker SDK
+3. Push this repository
+4. Add `HF_TOKEN` as a Space secret
+5. Deploy automatically
+
+## 🧪 Testing
+
+```bash
+# Run all tests
+
+
+# Test chunking strategy
+pytest test_chunking.py -v
+
+# Test retrieval quality
+python test_retrieval.py
+```
+
+## 📈 Performance Benchmarks
+
+On HuggingFace Spaces (free tier):
+
+- **Query latency**: ~2-3 seconds
+- **Vector DB size**: ~150MB (FastAPI docs)
+- **Memory usage**: ~800MB
+- **Concurrent users**: 5-10
+
+## 🛠️ Technology Stack
+
+| Component      | Technology                               | Why?                               |
+| -------------- | ---------------------------------------- | ---------------------------------- |
+| **Embeddings** | `sentence-transformers/all-MiniLM-L6-v2` | Fast, lightweight, good quality    |
+| **Vector DB**  | ChromaDB                                 | Easy setup, persistent storage     |
+| **LLM**        | HuggingFace Inference API (Mistral-7B)   | Free tier, good code understanding |
+| **Framework**  | LangChain                                | Industry standard, modular         |
+| **UI**         | Gradio                                   | Rapid prototyping, HF integration  |
+| **Deployment** | Docker + HF Spaces                       | Free, scalable, shareable          |
+
+## 🔮 Future Enhancements
+
+- [ ] Multi-documentation support (React, Django, etc.)
+- [ ] Conversation memory for follow-up questions
+- [ ] Advanced retrieval (HyDE, Multi-Query)
+- [ ] User feedback loop for continuous improvement
+- [ ] Analytics dashboard for query patterns
+
+## 📝 License
+
+MIT License - feel free to use for your portfolio!
+
+## 🤝 Contributing
+
+This is a portfolio project, but suggestions are welcome via issues.
+
+## 📧 Contact
+
+Built by Aishwarya as a portfolio demonstration of production RAG systems.
+
+- Portfolio: https://aishwarya30998.github.io/projects.html
+- LinkedIn: https://www.linkedin.com/in/aishwarya-pentyala/
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+⭐ If this helped you understand production RAG, give it a star!

__init__.py

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+"""
+Developer Docs AI Copilot - RAG System
+"""
+__version__ = "1.0.0"

app.py

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+"""
+Developer Docs AI Copilot - Gradio UI Application
+
+Production-grade RAG chatbot interface for any developer documentation.
+
+Two-tab UI:
+  Setup tab  — enter a docs URL, trigger ingestion/embedding
+  Chat tab   — ask questions, get answers with source citations
+"""
+import logging
+import sys
+import queue
+import threading
+from pathlib import Path
+from typing import List, Tuple, Optional
+import gradio as gr
+from datetime import datetime
+import json
+from urllib.parse import urlparse
+
+from src import create_rag_pipeline, settings
+from src.config import RESULTS_DIR
+from ingest_docs import run_ingestion
+
+logging.basicConfig(
+    level=getattr(logging, settings.log_level),
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+# Global pipeline state
+rag_pipeline = None
+pipeline_stats: dict = {}
+current_docs_name: str = settings.docs_name  # may be updated after ingestion
+
+
+def _try_load_pipeline():
+    """Attempt to load the RAG pipeline from an existing vector DB."""
+    global rag_pipeline, pipeline_stats
+    try:
+        rag_pipeline = create_rag_pipeline()
+        pipeline_stats = rag_pipeline.get_stats()
+        logger.info(f"Pipeline loaded. {pipeline_stats.get('total_chunks', 0)} chunks indexed.")
+    except Exception as e:
+        logger.warning(f"Could not load pipeline on startup (run Setup first): {e}")
+        rag_pipeline = None
+        pipeline_stats = {}
+
+
+_try_load_pipeline()
+
+
+# Query logging
+
+QUERY_LOG_FILE = RESULTS_DIR / "query_log.jsonl"
+
+
+def log_query(question: str, response: dict):
+    try:
+        entry = {
+            "timestamp": datetime.now().isoformat(),
+            "docs_name": current_docs_name,
+            "question": question,
+            "answer": response.get("answer", ""),
+            "source_count": response.get("source_count", 0),
+            "confidence": response.get("confidence", "unknown"),
+            "chunks_retrieved": response.get("chunks_retrieved", 0),
+        }
+        with open(QUERY_LOG_FILE, "a", encoding="utf-8") as f:
+            f.write(json.dumps(entry) + "\n")
+    except Exception as e:
+        logger.error(f"Failed to log query: {e}")
+
+
+# Chat helpers
+
+def format_sources(sources: List[dict]) -> str:
+    if not sources:
+        return "No sources available."
+    formatted = "### Sources\n\n"
+    for i, source in enumerate(sources, 1):
+        title = source.get("title", "Unknown")
+        section = source.get("section", "")
+        url = source.get("url", "#")
+        score = source.get("score", 0.0)
+        formatted += f"{i}. **{title}**"
+        if section:
+            formatted += f" ({section})"
+        formatted += f"\n   - Relevance: {score:.2%}\n"
+        if url and url != "#":
+            formatted += f"   - [View Documentation]({url})\n"
+        formatted += "\n"
+    return formatted
+
+
+def process_query(question: str, history: List[Tuple[str, str]]) -> Tuple[str, str]:
+    if not rag_pipeline:
+        return (
+            "Pipeline not ready. Please go to the **Setup** tab and ingest documentation first.",
+            "No sources available.",
+        )
+    if not question or not question.strip():
+        return "Please enter a question.", ""
+
+    try:
+        logger.info(f"Processing query: {question[:100]}...")
+        response = rag_pipeline.query(question, top_k=5)
+        log_query(question, response)
+
+        answer = response["answer"]
+        confidence = response.get("confidence", "unknown")
+        chunks_retrieved = response.get("chunks_retrieved", 0)
+        answer += f"\n\n---\n*Confidence: {confidence.upper()} | Retrieved {chunks_retrieved} chunks*"
+
+        sources_text = format_sources(response.get("sources", []))
+        return answer, sources_text
+
+    except Exception as e:
+        logger.error(f"Error processing query: {e}", exc_info=True)
+        return f"Error: {str(e)}", "No sources available."
+
+
+# Ingestion helper — runs in a background thread, streams log lines via queue
+def _derive_docs_name(url: str) -> str:
+    hostname = urlparse(url).hostname or ""
+    return hostname.split(".")[0].replace("-", " ").title()
+
+
+def ingest_and_stream(docs_url: str, docs_name: str, url_patterns_raw: str):
+    """
+    Generator function: runs ingestion in a background thread and streams
+    status lines to the Gradio Textbox.
+    """
+    global rag_pipeline, pipeline_stats, current_docs_name
+
+    docs_url = docs_url.strip().rstrip("/")
+    docs_name = docs_name.strip() or _derive_docs_name(docs_url)
+    url_patterns = [p.strip() for p in url_patterns_raw.split(",") if p.strip()]
+
+    if not docs_url:
+        yield "Please enter a documentation URL."
+        return
+
+    # Queue used to pass log lines from the worker thread to the generator
+    log_q: queue.Queue = queue.Queue()
+    result_holder: dict = {}
+    error_holder: dict = {}
+
+    def worker():
+        try:
+            stats = run_ingestion(
+                docs_url=docs_url,
+                docs_name=docs_name,
+                url_patterns=url_patterns or None,
+                progress_callback=lambda msg: log_q.put(msg),
+            )
+            result_holder["stats"] = stats
+        except Exception as exc:
+            error_holder["error"] = str(exc)
+            logger.error(f"Ingestion failed: {exc}", exc_info=True)
+        finally:
+            log_q.put(None)  # sentinel
+
+    thread = threading.Thread(target=worker, daemon=True)
+    thread.start()
+
+    # Stream log lines as they arrive
+    accumulated = ""
+    while True:
+        try:
+            line = log_q.get(timeout=120)
+        except queue.Empty:
+            yield accumulated + "\n[Timed out waiting for ingestion]"
+            return
+
+        if line is None:  # sentinel → done
+            break
+
+        accumulated += line + "\n"
+        yield accumulated
+
+    thread.join(timeout=5)
+
+    if "error" in error_holder:
+        yield accumulated + f"\n\nIngestion failed: {error_holder['error']}"
+        return
+
+    # Reload the RAG pipeline with the newly ingested docs
+    accumulated += "\nReloading RAG pipeline..."
+    yield accumulated
+
+    try:
+        # Update settings so the pipeline and prompts use the new docs name
+        settings.docs_url = docs_url
+        settings.docs_name = docs_name
+        current_docs_name = docs_name
+
+        rag_pipeline = create_rag_pipeline()
+        pipeline_stats = rag_pipeline.get_stats()
+
+        accumulated += f"\nPipeline ready — {pipeline_stats.get('total_chunks', 0)} chunks indexed."
+        accumulated += f"\n\nSwitch to the Chat tab and start asking questions about {docs_name}!"
+        yield accumulated
+
+    except Exception as e:
+        accumulated += f"\n\nPipeline reload failed: {e}"
+        yield accumulated
+
+
+# UI
+def create_ui():
+    custom_css = """
+    .stats-box {
+        background: #e3f2fd;
+        padding: 10px;
+        border-radius: 5px;
+        margin: 10px 0;
+    }
+    """
+
+    with gr.Blocks(
+        title="Developer Docs AI Copilot",
+        theme=gr.themes.Soft(),
+        css=custom_css,
+    ) as app:
+
+        gr.Markdown("# Developer Docs AI Copilot")
+        gr.Markdown(
+            "Ingest any developer documentation and ask questions answered directly from the source."
+        )
+
+        with gr.Tabs() as tabs:
+
+            # TAB 1 — Setup
+
+            with gr.Tab("⚙️ Setup — Ingest Docs", id="setup"):
+                gr.Markdown(
+                    "Enter the URL of any developer documentation site. "
+                    "The system will scrape, chunk, embed, and index it for Q&A."
+                )
+
+                with gr.Row():
+                    docs_url_input = gr.Textbox(
+                        label="Documentation URL",
+                        placeholder="e.g. https://docs.djangoproject.com/en/stable/",
+                        scale=3,
+                    )
+                    docs_name_input = gr.Textbox(
+                        label="Docs Name (optional — auto-derived if empty)",
+                        placeholder="e.g. Django",
+                        scale=1,
+                    )
+
+                url_patterns_input = gr.Textbox(
+                    label="URL Path Patterns to include (optional, comma-separated)",
+                    placeholder="e.g. /topics,/ref,/howto   — leave empty to include all pages",
+                )
+
+                ingest_btn = gr.Button("Ingest Documentation", variant="primary")
+
+                ingest_status = gr.Textbox(
+                    label="Ingestion Log",
+                    lines=20,
+                    interactive=False,
+                    placeholder="Status will appear here when you click Ingest...",
+                )
+
+                # Wire up the button to the streaming generator
+                ingest_btn.click(
+                    fn=ingest_and_stream,
+                    inputs=[docs_url_input, docs_name_input, url_patterns_input],
+                    outputs=ingest_status,
+                )
+
+                gr.Markdown("""
+                **Tips:**
+                - Most documentation sites (FastAPI, Django, React, Stripe, etc.) work out of the box
+                - Use URL patterns to ingest only a specific section (faster)
+                - Re-run ingestion any time to switch to a different documentation source
+                - Default page cap is **50 pages** — sufficient for most demos
+                """)
+
+            # TAB 2 — Chat
+            with gr.Tab("💬 Chat", id="chat"):
+
+                # Live status bar
+                status_text = (
+                    f"Ready — {pipeline_stats.get('total_chunks', 0)} chunks indexed "
+                    f"({current_docs_name})"
+                    if rag_pipeline
+                    else "Not ready — please ingest documentation in the Setup tab first."
+                )
+                status_md = gr.Markdown(f"**Status:** {status_text}")
+
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        chatbot = gr.Chatbot(
+                            label="Conversation",
+                            height=420,
+                            show_copy_button=True,
+                        )
+
+                        with gr.Row():
+                            question_input = gr.Textbox(
+                                label="Ask a question",
+                                placeholder="e.g. How do I get started?",
+                                lines=2,
+                                scale=4,
+                            )
+                            submit_btn = gr.Button("Ask", variant="primary", scale=1)
+
+                        gr.Examples(
+                            examples=[
+                                "How do I get started?",
+                                "What are the core concepts?",
+                                "Show me a basic example",
+                                "How do I handle authentication?",
+                                "What is the recommended project structure?",
+                            ],
+                            inputs=question_input,
+                            label="Example Questions",
+                        )
+
+                    with gr.Column(scale=1):
+                        sources_display = gr.Markdown(
+                            value="Sources will appear here after asking a question."
+                        )
+
+                clear_btn = gr.Button("Clear Conversation")
+
+                def respond(message, chat_history):
+                    answer, sources = process_query(message, chat_history)
+                    chat_history.append((message, answer))
+                    return "", chat_history, sources
+
+                submit_btn.click(
+                    respond,
+                    inputs=[question_input, chatbot],
+                    outputs=[question_input, chatbot, sources_display],
+                )
+                question_input.submit(
+                    respond,
+                    inputs=[question_input, chatbot],
+                    outputs=[question_input, chatbot, sources_display],
+                )
+                clear_btn.click(
+                    lambda: ([], "Sources will appear here after asking a question."),
+                    outputs=[chatbot, sources_display],
+                )
+
+        gr.Markdown(
+            "---\n*Built with: ChromaDB · Sentence Transformers · HuggingFace · Gradio*"
+        )
+
+    return app
+
+
+def health_check():
+    return {"status": "healthy", "pipeline_ready": rag_pipeline is not None}
+
+
+if __name__ == "__main__":
+    logger.info("Starting Developer Docs AI Copilot...")
+    app = create_ui()
+    logger.info(f"Launching on port {settings.app_port}")
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=settings.app_port,
+        share=False,
+        show_error=True,
+    )

ci.yml

@@ -0,0 +1,66 @@
+name: CI/CD Pipeline
+
+on:
+  push:
+    branches: [ main, develop ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - uses: actions/checkout@v3
+    
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    
+    - name: Cache dependencies
+      uses: actions/cache@v3
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+    
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        pip install pytest pytest-cov
+    
+    - name: Run tests
+      run: |
+        pytest tests/ -v --cov=src --cov-report=term-missing
+    
+    - name: Lint code
+      run: |
+        pip install flake8
+        flake8 src/ --max-line-length=100 --ignore=E501,W503
+
+  build:
+    runs-on: ubuntu-latest
+    needs: test
+    if: github.ref == 'refs/heads/main'
+    
+    steps:
+    - uses: actions/checkout@v3
+    
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v2
+    
+    - name: Build Docker image
+      run: |
+        docker build -t fastapi-copilot:latest .
+    
+    - name: Test Docker image
+      run: |
+        docker run -d --name test-container -p 7860:7860 \
+          -e HF_TOKEN=${{ secrets.HF_TOKEN }} \
+          fastapi-copilot:latest
+        sleep 10
+        curl -f http://localhost:7860/health || exit 1
+        docker stop test-container

docker-compose.yml

@@ -0,0 +1,25 @@
+version: '3.8'
+
+services:
+  developerdocs-copilot:
+    build: .
+    container_name: developer-docs-copilot
+    ports:
+      - "7860:7860"
+    env_file:
+      - .env
+    volumes:
+      # Mount vector DB for persistence
+      - ./data/vectordb:/app/data/vectordb
+      # Mount for live code changes during development
+      - ./src:/app/src
+      - ./app.py:/app/app.py
+    environment:
+      - PYTHONUNBUFFERED=1
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:7860/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 60s

evaluate_rag.py

@@ -0,0 +1,229 @@
+"""
+Evaluate RAG pipeline using RAGAS framework.
+
+Measures:
+- Faithfulness: Answer accuracy vs. retrieved context
+- Answer Relevancy: How relevant the answer is to the question
+- Context Precision: How precise the retrieved context is
+- Context Recall: Coverage of relevant information
+"""
+import logging
+import sys
+from pathlib import Path
+import json
+from typing import List, Dict, Any
+from datetime import datetime
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from src import create_rag_pipeline, settings
+from src.config import EVALS_DIR, RESULTS_DIR
+
+try:
+    from datasets import Dataset
+    from ragas import evaluate
+    from ragas.metrics import (
+        faithfulness,
+        answer_relevancy,
+        context_precision,
+        context_recall,
+    )
+    RAGAS_AVAILABLE = True
+except ImportError:
+    RAGAS_AVAILABLE = False
+    print("WARNING: RAGAS not installed. Install with: pip install ragas")
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+# Evaluation dataset
+TEST_QUERIES = [
+    {
+        "question": "How do I create a FastAPI application?",
+        "ground_truth": "You create a FastAPI application by importing FastAPI and creating an instance: from fastapi import FastAPI; app = FastAPI()"
+    },
+    {
+        "question": "What are path parameters in FastAPI?",
+        "ground_truth": "Path parameters are variables in the URL path that FastAPI can extract and pass to your endpoint function."
+    },
+    {
+        "question": "How do I add request validation?",
+        "ground_truth": "FastAPI uses Pydantic models for request validation. You define a model with type hints and use it as a parameter type."
+    },
+    {
+        "question": "What is dependency injection in FastAPI?",
+        "ground_truth": "Dependency injection allows you to declare dependencies that FastAPI will resolve and inject into your endpoint functions."
+    },
+    {
+        "question": "How do I handle authentication in FastAPI?",
+        "ground_truth": "FastAPI provides security utilities for OAuth2, JWT tokens, and API keys. You can use dependencies to protect endpoints."
+    },
+]
+
+
+def run_evaluation():
+    """Run RAGAS evaluation on the RAG pipeline."""
+    
+    if not RAGAS_AVAILABLE:
+        logger.error("RAGAS not available. Please install it.")
+        return
+    
+    logger.info("=" * 60)
+    logger.info("RAG Evaluation with RAGAS")
+    logger.info("=" * 60)
+    
+    # Initialize pipeline
+    logger.info("Initializing RAG pipeline...")
+    pipeline = create_rag_pipeline()
+    
+    # Prepare evaluation data
+    logger.info(f"\nRunning evaluation on {len(TEST_QUERIES)} queries...")
+    
+    evaluation_data = {
+        "question": [],
+        "answer": [],
+        "contexts": [],
+        "ground_truth": []
+    }
+    
+    for item in TEST_QUERIES:
+        question = item["question"]
+        logger.info(f"\nProcessing: {question}")
+        
+        # Get response from pipeline
+        response = pipeline.query(question, top_k=5)
+        
+        # Extract data for RAGAS
+        evaluation_data["question"].append(question)
+        evaluation_data["answer"].append(response["answer"])
+        evaluation_data["ground_truth"].append(item["ground_truth"])
+        
+        # Get context from retrieved chunks
+        contexts = []
+        retrieved_chunks = pipeline.retriever.retrieve(question, top_k=5)
+        for chunk in retrieved_chunks:
+            contexts.append(chunk["content"])
+        evaluation_data["contexts"].append(contexts)
+        
+        logger.info(f"  Answer length: {len(response['answer'])} chars")
+        logger.info(f"  Contexts retrieved: {len(contexts)}")
+    
+    # Create dataset
+    dataset = Dataset.from_dict(evaluation_data)
+    
+    # Run evaluation
+    logger.info("\n" + "=" * 60)
+    logger.info("Running RAGAS metrics...")
+    logger.info("=" * 60)
+    
+    try:
+        results = evaluate(
+            dataset,
+            metrics=[
+                faithfulness,
+                answer_relevancy,
+                context_precision,
+                context_recall,
+            ],
+        )
+        
+        # Display results
+        logger.info("\n" + "=" * 60)
+        logger.info("Evaluation Results")
+        logger.info("=" * 60)
+        
+        metrics = {
+            "faithfulness": results["faithfulness"],
+            "answer_relevancy": results["answer_relevancy"],
+            "context_precision": results["context_precision"],
+            "context_recall": results["context_recall"],
+        }
+        
+        for metric_name, score in metrics.items():
+            logger.info(f"{metric_name.replace('_', ' ').title()}: {score:.4f}")
+        
+        # Overall score
+        overall_score = sum(metrics.values()) / len(metrics)
+        logger.info(f"\nOverall Score: {overall_score:.4f}")
+        
+        # Interpretation
+        logger.info("\n" + "=" * 60)
+        logger.info("Interpretation")
+        logger.info("=" * 60)
+        logger.info("Scores range from 0 to 1 (higher is better)")
+        logger.info("Target scores for production:")
+        logger.info("  • Faithfulness: > 0.80 (answers are accurate)")
+        logger.info("  • Answer Relevancy: > 0.70 (answers address the question)")
+        logger.info("  • Context Precision: > 0.75 (retrieved context is relevant)")
+        logger.info("  • Context Recall: > 0.80 (all relevant info is retrieved)")
+        
+        # Save results
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        results_file = RESULTS_DIR / f"ragas_eval_{timestamp}.json"
+        
+        results_dict = {
+            "timestamp": timestamp,
+            "metrics": metrics,
+            "overall_score": overall_score,
+            "test_queries": TEST_QUERIES,
+            "settings": {
+                "chunk_size": settings.chunk_size,
+                "chunk_overlap": settings.chunk_overlap,
+                "top_k": 5,
+                "embedding_model": settings.embedding_model,
+                "llm_model": settings.llm_model
+            }
+        }
+        
+        with open(results_file, 'w') as f:
+            json.dump(results_dict, f, indent=2)
+        
+        logger.info(f"\nResults saved to: {results_file}")
+        
+    except Exception as e:
+        logger.error(f"Evaluation failed: {e}", exc_info=True)
+
+
+def simple_accuracy_test():
+    """Simple accuracy test without RAGAS."""
+    logger.info("Running simple accuracy test...")
+    
+    pipeline = create_rag_pipeline()
+    
+    correct = 0
+    total = len(TEST_QUERIES)
+    
+    for item in TEST_QUERIES:
+        question = item["question"]
+        response = pipeline.query(question)
+        
+        # Simple check: does answer contain key terms?
+        answer_lower = response["answer"].lower()
+        ground_truth_lower = item["ground_truth"].lower()
+        
+        # Extract key terms from ground truth
+        key_terms = [term for term in ground_truth_lower.split() if len(term) > 4]
+        
+        # Check if at least 50% of key terms are in answer
+        matches = sum(1 for term in key_terms if term in answer_lower)
+        if matches / len(key_terms) >= 0.5:
+            correct += 1
+            logger.info(f"✓ {question}")
+        else:
+            logger.info(f"✗ {question}")
+    
+    accuracy = correct / total
+    logger.info(f"\nSimple Accuracy: {accuracy:.2%} ({correct}/{total})")
+
+
+if __name__ == "__main__":
+    if RAGAS_AVAILABLE:
+        run_evaluation()
+    else:
+        logger.warning("RAGAS not available. Running simple test instead.")
+        simple_accuracy_test()

ingest_docs.py

@@ -0,0 +1,360 @@
+"""
+Ingest developer documentation into the vector database.
+
+This script:
+1. Scrapes documentation from any URL (via sitemap or recursive crawl)
+2. Chunks the content semantically
+3. Generates embeddings
+4. Stores in ChromaDB
+
+Usage:
+    python ingest_docs.py
+
+Configure via environment variables (or .env):
+    DOCS_URL          - Base URL of the documentation (required)
+    DOCS_NAME         - auto-derived if empty
+    DOCS_URL_PATTERNS - Comma-separated path patterns to include, e.g. "/tutorial,/guide"
+                        Leave empty to include all pages under the base URL.
+    COLLECTION_NAME   - ChromaDB collection name
+"""
+import logging
+import re
+from pathlib import Path
+from urllib.parse import urlparse, urljoin
+import requests
+from bs4 import BeautifulSoup
+from typing import List, Dict, Any, Optional
+from tqdm import tqdm
+import json
+
+from src.config import settings, RAW_DATA_DIR, PROCESSED_DATA_DIR
+from src.chunking import create_chunker
+from src.retriever import create_retriever
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+class DocsScraper:
+    """
+    Generic documentation scraper that works with any documentation site.
+
+    Discovers pages via sitemap.xml first; falls back to recursive same-domain
+    crawling if no sitemap is available.
+    """
+
+    def __init__(
+        self,
+        base_url: str,
+        url_patterns: Optional[List[str]] = None,
+        max_pages: int = 200,
+    ):
+        """
+        Args:
+            base_url:     Root URL of the documentation site.
+            url_patterns: Optional list of path substrings to include
+                          (e.g. ["/tutorial", "/guide"]).  When empty/None,
+                          all pages whose URL starts with base_url are included.
+            max_pages:    Safety cap on the number of pages to scrape.
+        """
+        self.base_url = base_url.rstrip("/")
+        self.url_patterns = url_patterns or []
+        self.max_pages = max_pages
+
+        parsed = urlparse(self.base_url)
+        self.base_domain = parsed.netloc
+
+    # URL discovery
+    def get_doc_urls(self) -> List[str]:
+        """Return a deduplicated list of documentation page URLs."""
+        urls = self._urls_from_sitemap()
+        if not urls:
+            logger.warning("No sitemap found or empty — falling back to recursive crawl")
+            urls = self._urls_from_crawl()
+
+        urls = self._filter_urls(urls)
+        logger.info(f"Discovered {len(urls)} documentation pages")
+        return urls[: self.max_pages]
+
+    def _urls_from_sitemap(self) -> List[str]:
+        """Try to fetch all URLs from sitemap.xml."""
+        sitemap_url = f"{self.base_url}/sitemap.xml"
+        logger.info(f"Fetching sitemap: {sitemap_url}")
+        try:
+            resp = requests.get(sitemap_url, timeout=10)
+            resp.raise_for_status()
+            soup = BeautifulSoup(resp.content, "xml")
+            urls = [loc.text.strip() for loc in soup.find_all("loc")]
+            logger.info(f"Found {len(urls)} URLs in sitemap")
+            return urls
+        except Exception as e:
+            logger.warning(f"Could not load sitemap: {e}")
+            return []
+
+    def _urls_from_crawl(self, start_url: Optional[str] = None) -> List[str]:
+        """
+        Recursively crawl same-domain links starting from base_url.
+        Limited to self.max_pages pages to avoid runaway crawls.
+        """
+        start = start_url or self.base_url
+        visited: set = set()
+        queue: List[str] = [start]
+        found: List[str] = []
+
+        while queue and len(found) < self.max_pages * 2:
+            url = queue.pop(0)
+            if url in visited:
+                continue
+            visited.add(url)
+
+            try:
+                resp = requests.get(url, timeout=10)
+                if resp.status_code != 200:
+                    continue
+                soup = BeautifulSoup(resp.content, "html.parser")
+                found.append(url)
+
+                for tag in soup.find_all("a", href=True):
+                    href = tag["href"].strip()
+                    absolute = urljoin(url, href).split("#")[0]
+                    if (
+                        absolute not in visited
+                        and urlparse(absolute).netloc == self.base_domain
+                        and absolute.startswith("http")
+                    ):
+                        queue.append(absolute)
+            except Exception as e:
+                logger.debug(f"Crawl error for {url}: {e}")
+
+        return found
+
+    def _filter_urls(self, urls: List[str]) -> List[str]:
+        """
+        Keep only URLs that belong to the same domain and, if url_patterns
+        is set, match at least one pattern.
+        """
+        filtered = []
+        for url in urls:
+            parsed = urlparse(url)
+            if parsed.netloc != self.base_domain:
+                continue
+            if self.url_patterns:
+                if not any(p in parsed.path for p in self.url_patterns):
+                    continue
+            filtered.append(url)
+        seen = set()
+        unique = []
+        for u in filtered:
+            if u not in seen:
+                seen.add(u)
+                unique.append(u)
+        return unique
+
+    # Page scraping
+    def scrape_page(self, url: str) -> Dict[str, Any]:
+        """
+        Scrape a single documentation page.
+
+        Returns a dict with keys: url, title, section, content, success.
+        """
+        try:
+            resp = requests.get(url, timeout=10)
+            resp.raise_for_status()
+            soup = BeautifulSoup(resp.content, "html.parser")
+
+            main_content = (
+                soup.find("main")
+                or soup.find("article")
+                or soup.find(attrs={"role": "main"})
+                or soup.find("div", class_=re.compile(r"content|doc|page|main", re.I))
+                or soup.find("body")
+            )
+
+            if not main_content:
+                logger.warning(f"No content container found for {url}")
+                return {"url": url, "success": False}
+
+            # Strip navigation / chrome elements
+            for unwanted in main_content.find_all(
+                ["nav", "header", "footer", "script", "style", "aside"]
+            ):
+                unwanted.decompose()
+
+            text = main_content.get_text(separator="\n", strip=True)
+
+            h1 = soup.find("h1")
+            if h1:
+                title_text = h1.get_text(strip=True)
+            elif soup.title:
+                title_text = soup.title.get_text(strip=True)
+            else:
+                parts = [p for p in urlparse(url).path.split("/") if p]
+                title_text = parts[-1].replace("-", " ").replace("_", " ").title() if parts else url
+
+            path_parts = [p for p in urlparse(url).path.strip("/").split("/") if p]
+            section = path_parts[0].replace("-", " ").replace("_", " ").title() if path_parts else "General"
+
+            return {
+                "url": url,
+                "title": title_text,
+                "section": section,
+                "content": text,
+                "success": True,
+            }
+
+        except Exception as e:
+            logger.error(f"Error scraping {url}: {e}")
+            return {"url": url, "success": False, "error": str(e)}
+
+
+# Helpers
+
+def _safe_filename(name: str) -> str:
+    """Convert a docs name into a safe filename prefix."""
+    return re.sub(r"[^a-zA-Z0-9_-]", "_", name).lower()
+
+
+# Programmatic ingestion API (used by app.py UI)
+def run_ingestion(
+    docs_url: str,
+    docs_name: str,
+    url_patterns: Optional[List[str]] = None,
+    max_pages: int = 50,
+    progress_callback=None,
+) -> dict:
+    """
+    Run the full ingestion pipeline programmatically.
+
+    Args:
+        docs_url:          Base URL of the documentation site.
+        docs_name:         Human-readable name.
+        url_patterns:      Optional list of path substrings to filter pages.
+        max_pages:         Maximum number of pages to scrape.
+        progress_callback: Optional callable(message: str) for live status updates.
+
+    Returns:
+        Stats dict with keys: total_chunks, collection_name, embedding_dimension,
+        metadata_fields, pages_scraped.
+    """
+    def emit(msg: str):
+        logger.info(msg)
+        if progress_callback:
+            progress_callback(msg)
+
+    safe_name = _safe_filename(docs_name)
+    url_patterns = url_patterns or []
+
+    emit("=" * 50)
+    emit(f"Ingestion Pipeline: {docs_name}")
+    emit(f"Source: {docs_url}")
+    if url_patterns:
+        emit(f"URL patterns: {url_patterns}")
+    emit("=" * 50)
+
+    # Step 1: Scrape
+    emit(f"\n[1/4] Discovering and scraping {docs_name} documentation...")
+    scraper = DocsScraper(
+        base_url=docs_url,
+        url_patterns=url_patterns,
+        max_pages=max_pages * 4,
+    )
+    urls = scraper.get_doc_urls()
+    urls = urls[:max_pages]
+    emit(f"      Scraping {len(urls)} pages...")
+
+    documents = []
+    for i, url in enumerate(urls, 1):
+        doc = scraper.scrape_page(url)
+        if doc.get("success"):
+            documents.append(doc)
+        if i % 10 == 0 or i == len(urls):
+            emit(f"      Scraped {i}/{len(urls)} pages ({len(documents)} succeeded)")
+
+    emit(f"[1/4] Done — {len(documents)} pages scraped successfully")
+
+    # Save raw documents
+    raw_file = RAW_DATA_DIR / f"{safe_name}_docs_raw.json"
+    with open(raw_file, "w", encoding="utf-8") as f:
+        json.dump(documents, f, indent=2, ensure_ascii=False)
+
+    # Step 2: Chunk
+    emit(f"\n[2/4] Chunking {len(documents)} documents...")
+    chunker = create_chunker(
+        chunk_size=settings.chunk_size,
+        chunk_overlap=settings.chunk_overlap,
+    )
+    all_chunks = []
+    for doc in documents:
+        metadata = {
+            "source": doc["url"],
+            "title": doc["title"],
+            "section": doc["section"],
+            "url": doc["url"],
+            "docs_name": docs_name,
+        }
+        chunks = chunker.chunk_document(text=doc["content"], metadata=metadata)
+        all_chunks.extend(chunks)
+
+    emit(f"[2/4] Done — {len(all_chunks)} chunks created")
+
+    processed_file = PROCESSED_DATA_DIR / f"{safe_name}_docs_chunks.json"
+    with open(processed_file, "w", encoding="utf-8") as f:
+        json.dump(
+            [chunk.to_dict() for chunk in all_chunks],
+            f,
+            indent=2,
+            ensure_ascii=False,
+        )
+
+    # Step 3: Embed + store
+    emit(f"\n[3/4] Generating embeddings and storing in ChromaDB...")
+    emit(f"      This may take a few minutes for large doc sets...")
+    retriever = create_retriever()
+
+    try:
+        retriever.reset_collection()
+    except Exception:
+        pass
+
+    batch_size = 100
+    total_batches = (len(all_chunks) + batch_size - 1) // batch_size
+    for idx, i in enumerate(range(0, len(all_chunks), batch_size), 1):
+        batch = all_chunks[i : i + batch_size]
+        retriever.add_documents(batch)
+        emit(f"      Stored batch {idx}/{total_batches}")
+
+    # Step 4: Verify
+    emit(f"\n[4/4] Verifying ingestion...")
+    stats = retriever.get_collection_stats()
+    stats["pages_scraped"] = len(documents)
+
+    emit("\n" + "=" * 50)
+    emit("Ingestion Complete!")
+    emit(f"  Pages scraped  : {len(documents)}")
+    emit(f"  Chunks indexed : {stats['total_chunks']}")
+    emit(f"  Collection     : {stats['collection_name']}")
+    emit(f"  Embedding dim  : {stats['embedding_dimension']}")
+    emit("=" * 50)
+
+    return stats
+
+# CLI entry point
+def main():
+    """CLI entry point — reads config from settings / .env."""
+    url_patterns: List[str] = []
+    if settings.docs_url_patterns.strip():
+        url_patterns = [p.strip() for p in settings.docs_url_patterns.split(",") if p.strip()]
+
+    run_ingestion(
+        docs_url=settings.docs_url,
+        docs_name=settings.docs_name,
+        url_patterns=url_patterns,
+    )
+    logger.info("Ready to use! Run 'python app.py' to start the UI")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,40 @@
+# Core Dependencies
+python-dotenv==1.0.0
+gradio==4.44.0
+langchain==0.1.20
+langchain-community==0.0.38
+langchain-huggingface==0.0.1
+
+# Vector Store & Embeddings
+chromadb==0.4.22
+sentence-transformers==2.6.0
+
+# Document Processing
+beautifulsoup4==4.12.3
+lxml==5.1.0
+markdownify==0.11.6
+pypdf==3.17.4
+
+# RAG Evaluation
+ragas==0.1.7
+datasets==2.16.1
+
+# API & Monitoring
+fastapi==0.109.2
+uvicorn==0.27.1
+pydantic==2.6.1
+pydantic-settings==2.1.0
+
+# Utilities
+requests==2.31.0
+tqdm==4.66.1
+python-multipart==0.0.9
+
+# Testing
+pytest==7.4.4
+pytest-asyncio==0.23.4
+pytest-cov==4.1.0
+
+# Hugging Face
+huggingface-hub==0.27.0
+transformers==4.40.0

src/__init__.py

@@ -0,0 +1,21 @@
+"""
+Developer Docs AI Copilot - src package
+"""
+from src.config import settings
+from src.chunking import SemanticChunker, DocumentChunk, create_chunker
+from src.embeddings import EmbeddingGenerator, create_embedding_generator
+from src.retriever import DocumentRetriever, create_retriever
+from src.rag_pipeline import RAGPipeline, create_rag_pipeline
+
+__all__ = [
+    "settings",
+    "SemanticChunker",
+    "DocumentChunk",
+    "create_chunker",
+    "EmbeddingGenerator",
+    "create_embedding_generator",
+    "DocumentRetriever",
+    "create_retriever",
+    "RAGPipeline",
+    "create_rag_pipeline",
+]

src/chunking.py

@@ -0,0 +1,265 @@
+"""
+Document chunking strategies for RAG.
+
+Implements semantic chunking with overlap, metadata enrichment,
+and configurable strategies for different content types.
+"""
+import re
+from typing import List, Dict, Any, Optional
+from dataclasses import dataclass
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DocumentChunk:
+    """Represents a single document chunk with metadata."""
+    content: str
+    metadata: Dict[str, Any]
+    chunk_id: str
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for storage."""
+        return {
+            "content": self.content,
+            "metadata": self.metadata,
+            "chunk_id": self.chunk_id
+        }
+
+
+class SemanticChunker:
+    """
+    Smart chunking that preserves semantic meaning.
+    
+    Features:
+    - Splits on natural boundaries (paragraphs, sentences)
+    - Maintains context with overlap
+    - Preserves code blocks intact
+    - Enriches chunks with metadata
+    """
+    
+    def __init__(
+        self,
+        chunk_size: int = 600,
+        chunk_overlap: int = 100,
+        preserve_code_blocks: bool = True
+    ):
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+        self.preserve_code_blocks = preserve_code_blocks
+    
+    def chunk_document(
+        self,
+        text: str,
+        metadata: Optional[Dict[str, Any]] = None
+    ) -> List[DocumentChunk]:
+        """
+        Split document into semantically meaningful chunks.
+        
+        Args:
+            text: Document text to chunk
+            metadata: Optional metadata to attach to all chunks
+            
+        Returns:
+            List of DocumentChunk objects
+        """
+        if not text or not text.strip():
+            logger.warning("Empty text provided for chunking")
+            return []
+        
+        metadata = metadata or {}
+        
+        # Extract and preserve code blocks
+        code_blocks = []
+        if self.preserve_code_blocks:
+            text, code_blocks = self._extract_code_blocks(text)
+        
+        # Split into paragraphs first
+        paragraphs = self._split_paragraphs(text)
+        
+        # Create chunks
+        chunks = []
+        current_chunk = []
+        current_size = 0
+        
+        for i, para in enumerate(paragraphs):
+            para_tokens = self._estimate_tokens(para)
+            
+            # If single paragraph exceeds chunk size, split by sentences
+            if para_tokens > self.chunk_size:
+                if current_chunk:
+                    chunks.append(self._create_chunk(
+                        current_chunk,
+                        metadata,
+                        len(chunks)
+                    ))
+                    current_chunk = []
+                    current_size = 0
+                
+                # Split long paragraph
+                sentence_chunks = self._split_long_paragraph(para, metadata, len(chunks))
+                chunks.extend(sentence_chunks)
+                continue
+            
+            # Add paragraph to current chunk
+            if current_size + para_tokens <= self.chunk_size:
+                current_chunk.append(para)
+                current_size += para_tokens
+            else:
+                # Save current chunk
+                if current_chunk:
+                    chunks.append(self._create_chunk(
+                        current_chunk,
+                        metadata,
+                        len(chunks)
+                    ))
+                
+                # Start new chunk with overlap
+                overlap_text = self._get_overlap_text(current_chunk)
+                current_chunk = [overlap_text, para] if overlap_text else [para]
+                current_size = self._estimate_tokens(overlap_text) + para_tokens
+        
+        # Add remaining chunk
+        if current_chunk:
+            chunks.append(self._create_chunk(
+                current_chunk,
+                metadata,
+                len(chunks)
+            ))
+        
+        # Reinsert code blocks
+        if code_blocks:
+            chunks = self._reinsert_code_blocks(chunks, code_blocks)
+        
+        logger.info(f"Created {len(chunks)} chunks from document")
+        return chunks
+    
+    def _extract_code_blocks(self, text: str) -> tuple[str, List[Dict[str, str]]]:
+        """Extract code blocks to preserve them intact."""
+        code_pattern = r'```[\s\S]*?```|`[^`]+`'
+        code_blocks = []
+        
+        def replace_code(match):
+            placeholder = f"__CODE_BLOCK_{len(code_blocks)}__"
+            code_blocks.append({
+                "placeholder": placeholder,
+                "content": match.group(0)
+            })
+            return placeholder
+        
+        text_without_code = re.sub(code_pattern, replace_code, text)
+        return text_without_code, code_blocks
+    
+    def _reinsert_code_blocks(
+        self,
+        chunks: List[DocumentChunk],
+        code_blocks: List[Dict[str, str]]
+    ) -> List[DocumentChunk]:
+        """Reinsert code blocks into chunks."""
+        for chunk in chunks:
+            for code_block in code_blocks:
+                chunk.content = chunk.content.replace(
+                    code_block["placeholder"],
+                    code_block["content"]
+                )
+        return chunks
+    
+    def _split_paragraphs(self, text: str) -> List[str]:
+        """Split text into paragraphs."""
+        # Split on double newlines or more
+        paragraphs = re.split(r'\n\s*\n', text)
+        return [p.strip() for p in paragraphs if p.strip()]
+    
+    def _split_long_paragraph(
+        self,
+        paragraph: str,
+        metadata: Dict[str, Any],
+        start_idx: int
+    ) -> List[DocumentChunk]:
+        """Split a long paragraph by sentences."""
+        sentences = re.split(r'(?<=[.!?])\s+', paragraph)
+        
+        chunks = []
+        current_chunk = []
+        current_size = 0
+        
+        for sentence in sentences:
+            sentence_tokens = self._estimate_tokens(sentence)
+            
+            if current_size + sentence_tokens <= self.chunk_size:
+                current_chunk.append(sentence)
+                current_size += sentence_tokens
+            else:
+                if current_chunk:
+                    chunks.append(self._create_chunk(
+                        current_chunk,
+                        metadata,
+                        start_idx + len(chunks)
+                    ))
+                current_chunk = [sentence]
+                current_size = sentence_tokens
+        
+        if current_chunk:
+            chunks.append(self._create_chunk(
+                current_chunk,
+                metadata,
+                start_idx + len(chunks)
+            ))
+        
+        return chunks
+    
+    def _create_chunk(
+        self,
+        text_segments: List[str],
+        metadata: Dict[str, Any],
+        chunk_idx: int
+    ) -> DocumentChunk:
+        """Create a DocumentChunk from text segments."""
+        content = "\n\n".join(text_segments)
+        
+        # Enrich metadata
+        enriched_metadata = {
+            **metadata,
+            "chunk_index": chunk_idx,
+            "chunk_size": len(content),
+            "has_code": "```" in content or "`" in content,
+        }
+        
+        chunk_id = f"{metadata.get('source', 'unknown')}_{chunk_idx}"
+        
+        return DocumentChunk(
+            content=content,
+            metadata=enriched_metadata,
+            chunk_id=chunk_id
+        )
+    
+    def _get_overlap_text(self, chunks: List[str]) -> str:
+        """Get overlap text from previous chunks."""
+        if not chunks:
+            return ""
+        
+        combined = " ".join(chunks[-2:])
+        tokens = self._estimate_tokens(combined)
+        
+        if tokens <= self.chunk_overlap:
+            return combined
+        
+        # Truncate to overlap size
+        words = combined.split()
+        overlap_words = words[-(self.chunk_overlap // 4):]
+        return " ".join(overlap_words)
+    
+    @staticmethod
+    def _estimate_tokens(text: str) -> int:
+        """Rough token estimation (1 token ≈ 4 characters)."""
+        return len(text) // 4
+
+
+def create_chunker(chunk_size: int = 600, chunk_overlap: int = 100) -> SemanticChunker:
+    """Factory function to create a chunker instance."""
+    return SemanticChunker(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        preserve_code_blocks=True
+    )

src/config.py

@@ -0,0 +1,95 @@
+"""
+Configuration management for Developer Docs AI Copilot.
+"""
+import os
+from pathlib import Path
+from typing import Optional
+from urllib.parse import urlparse
+from pydantic_settings import BaseSettings
+from pydantic import Field, model_validator
+
+
+class Settings(BaseSettings):
+    """Application settings loaded from environment variables."""
+
+    # API Keys
+    hf_token: str = Field(default="", alias="HF_TOKEN")
+
+    # Model Configuration
+    llm_model: str = Field(
+        default="meta-llama/Llama-3.2-3B-Instruct",
+        alias="LLM_MODEL"
+    )
+    llm_max_tokens: int = Field(default=512, alias="LLM_MAX_TOKENS")
+    llm_temperature: float = Field(default=0.1, alias="LLM_TEMPERATURE")
+
+    embedding_model: str = Field(
+        default="sentence-transformers/all-MiniLM-L6-v2",
+        alias="EMBEDDING_MODEL"
+    )
+
+    # Vector Database
+    chroma_persist_dir: str = Field(
+        default="./data/vectordb",
+        alias="CHROMA_PERSIST_DIR"
+    )
+    collection_name: str = Field(
+        default="developer_docs",
+        alias="COLLECTION_NAME"
+    )
+
+    # Chunking Configuration
+    chunk_size: int = Field(default=600, alias="CHUNK_SIZE")
+    chunk_overlap: int = Field(default=100, alias="CHUNK_OVERLAP")
+
+    # Retrieval Configuration
+    top_k_retrieval: int = Field(default=5, alias="TOP_K_RETRIEVAL")
+    min_similarity_score: float = Field(
+        default=0.2,
+        alias="MIN_SIMILARITY_SCORE"
+    )
+
+    # Application Settings
+    app_port: int = Field(default=7860, alias="APP_PORT")
+    log_level: str = Field(default="INFO", alias="LOG_LEVEL")
+
+    # Documentation Source
+    docs_url: str = Field(
+        default="https://fastapi.tiangolo.com",
+        alias="DOCS_URL"
+    )
+    # Human-readable name for the docs. it is auto-derived from URL if not set
+    docs_name: str = Field(default="", alias="DOCS_NAME")
+    
+    docs_url_patterns: str = Field(default="", alias="DOCS_URL_PATTERNS")
+
+    @model_validator(mode="after")
+    def set_docs_name(self) -> "Settings":
+        if not self.docs_name:
+            hostname = urlparse(self.docs_url).hostname or ""
+            name = hostname.split(".")[0].replace("-", " ").title()
+            self.docs_name = name
+        return self
+
+    class Config:
+        env_file = ".env"
+        env_file_encoding = "utf-8"
+        case_sensitive = False
+
+
+# Global settings instance
+settings = Settings()
+
+
+# Directory paths
+PROJECT_ROOT = Path(__file__).parent.parent
+DATA_DIR = PROJECT_ROOT / "data"
+RAW_DATA_DIR = DATA_DIR / "raw"
+PROCESSED_DATA_DIR = DATA_DIR / "processed"
+VECTORDB_DIR = DATA_DIR / "vectordb"
+EVALS_DIR = PROJECT_ROOT / "evals"
+RESULTS_DIR = EVALS_DIR / "results"
+
+# Ensure directories exist
+for directory in [RAW_DATA_DIR, PROCESSED_DATA_DIR, VECTORDB_DIR, RESULTS_DIR]:
+    directory.mkdir(parents=True, exist_ok=True)

src/embeddings.py

@@ -0,0 +1,113 @@
+"""
+Embedding generation for RAG system.
+
+Handles text-to-vector conversion using sentence-transformers.
+"""
+from typing import List, Union
+import logging
+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingGenerator:
+    """
+    Generates embeddings for text using sentence-transformers.
+    
+    Features:
+    - Batch processing for efficiency
+    - Caching of model
+    - Normalized embeddings for cosine similarity
+    """
+    
+    def __init__(self, model_name: str = "sentence-transformers/all-MiniLM-L6-v2"):
+        """
+        Initialize embedding generator.
+        
+        Args:
+            model_name: HuggingFace model identifier
+        """
+        self.model_name = model_name
+        logger.info(f"Loading embedding model: {model_name}")
+        
+        try:
+            self.model = SentenceTransformer(model_name)
+            self.embedding_dim = self.model.get_sentence_embedding_dimension()
+            logger.info(f"Model loaded. Embedding dimension: {self.embedding_dim}")
+        except Exception as e:
+            logger.error(f"Failed to load embedding model: {e}")
+            raise
+    
+    def embed_text(self, text: Union[str, List[str]]) -> np.ndarray:
+        """
+        Generate embeddings for text.
+        
+        Args:
+            text: Single text string or list of strings
+            
+        Returns:
+            Numpy array of embeddings (shape: [n_texts, embedding_dim])
+        """
+        if isinstance(text, str):
+            text = [text]
+        
+        if not text:
+            raise ValueError("No text provided for embedding")
+        
+        try:
+            # Generate embeddings
+            embeddings = self.model.encode(
+                text,
+                normalize_embeddings=True,  # For cosine similarity
+                show_progress_bar=len(text) > 10,
+                batch_size=32
+            )
+            
+            logger.debug(f"Generated embeddings for {len(text)} texts")
+            return embeddings
+            
+        except Exception as e:
+            logger.error(f"Embedding generation failed: {e}")
+            raise
+    
+    def embed_query(self, query: str) -> np.ndarray:
+        """
+        Generate embedding for a single query.
+        
+        Args:
+            query: Query text
+            
+        Returns:
+            1D numpy array of embedding
+        """
+        embedding = self.embed_text(query)
+        return embedding[0]  # Return single embedding
+    
+    def embed_documents(self, documents: List[str]) -> np.ndarray:
+        """
+        Generate embeddings for a batch of documents.
+        
+        Args:
+            documents: List of document texts
+            
+        Returns:
+            2D numpy array of embeddings
+        """
+        return self.embed_text(documents)
+
+
+def create_embedding_generator(model_name: str = None) -> EmbeddingGenerator:
+    """
+    Factory function to create embedding generator.
+    
+    Args:
+        model_name: Optional model name override
+        
+    Returns:
+        EmbeddingGenerator instance
+    """
+    from src.config import settings
+    
+    model = model_name or settings.embedding_model
+    return EmbeddingGenerator(model_name=model)

src/prompts.py

@@ -0,0 +1,140 @@
+"""
+Prompt templates for the RAG system.
+"""
+from typing import List, Dict, Any
+
+from src.config import settings
+
+_DOCS_NAME = settings.docs_name
+
+
+def _build_system_prompt(docs_name: str) -> str:
+    return f"""You are a helpful assistant specialized in {docs_name} documentation.
+
+Your role is to answer questions ONLY using the provided context from the official {docs_name} documentation.
+
+Guidelines:
+1. Answer based ONLY on the provided context
+2. If the context doesn't contain the answer, say "I don't have enough information in the documentation to answer that"
+3. Preserve code formatting and indentation
+4. Include code examples when available in the context
+5. Cite sources by mentioning the section (e.g., "According to the Routing section...")
+6. Be concise but complete
+7. Use technical language appropriate for developers
+
+If you're unsure, it's better to admit it than to make up information."""
+
+
+SYSTEM_PROMPT = _build_system_prompt(_DOCS_NAME)
+
+
+def create_rag_prompt(query: str, context_chunks: List[Dict[str, Any]]) -> str:
+    """
+    Create the full RAG prompt with context and query.
+    
+    Args:
+        query: User's question
+        context_chunks: Retrieved document chunks with metadata
+        
+    Returns:
+        Formatted prompt string
+    """
+    # Build context section
+    context_parts = []
+    for i, chunk in enumerate(context_chunks, 1):
+        source = chunk["metadata"].get("source", "Unknown")
+        section = chunk["metadata"].get("section", "")
+        
+        context_header = f"[Context {i}"
+        if section:
+            context_header += f" - {section}"
+        context_header += f" from {source}]"
+        
+        context_parts.append(f"{context_header}\n{chunk['content']}\n")
+    
+    context_text = "\n".join(context_parts)
+    
+    # Create full prompt
+    prompt = f"""{SYSTEM_PROMPT}
+
+---
+
+CONTEXT FROM DOCUMENTATION:
+
+{context_text}
+
+---
+
+USER QUESTION: {query}
+
+ANSWER (based only on the context above):"""
+    
+    return prompt
+
+
+def create_no_context_prompt(query: str) -> str:
+    """
+    Create prompt when no relevant context is found.
+
+    Args:
+        query: User's question
+
+    Returns:
+        Formatted prompt string
+    """
+    prompt = f"""{SYSTEM_PROMPT}
+
+USER QUESTION: {query}
+
+Unfortunately, I couldn't find relevant information in the {_DOCS_NAME} documentation to answer this question.
+
+This could mean:
+1. The question is about a topic not covered in the documentation I have access to
+2. The question might need to be rephrased
+3. The topic might be covered in a different section
+
+Can you rephrase your question or provide more context?"""
+
+    return prompt
+
+
+def format_response_with_sources(
+    answer: str,
+    sources: List[Dict[str, Any]]
+) -> Dict[str, Any]:
+    """
+    Format the final response with sources.
+    
+    Args:
+        answer: Generated answer
+        sources: Retrieved source chunks
+        
+    Returns:
+        Formatted response dictionary
+    """
+    # Extract unique sources
+    unique_sources = {}
+    for source in sources:
+        metadata = source["metadata"]
+        source_key = metadata.get("url", metadata.get("source", "Unknown"))
+        
+        if source_key not in unique_sources:
+            unique_sources[source_key] = {
+                "url": metadata.get("url", ""),
+                "title": metadata.get("title", ""),
+                "section": metadata.get("section", ""),
+                "score": source.get("score", 0.0)
+            }
+    
+    # Sort by relevance score
+    sorted_sources = sorted(
+        unique_sources.values(),
+        key=lambda x: x["score"],
+        reverse=True
+    )
+    
+    return {
+        "answer": answer,
+        "sources": sorted_sources,
+        "source_count": len(sorted_sources)
+    }

src/rag_pipeline.py

@@ -0,0 +1,219 @@
+"""
+Main RAG pipeline orchestration.
+
+Coordinates retrieval and generation for question answering.
+"""
+import logging
+import requests
+from typing import Dict, Any, Optional, List
+
+from src.retriever import DocumentRetriever
+from src.prompts import create_rag_prompt, create_no_context_prompt, format_response_with_sources
+from src.config import settings
+
+# HuggingFace router — OpenAI-compatible chat completions endpoint
+_HF_API_URL = "https://router.huggingface.co/v1/chat/completions"
+
+logger = logging.getLogger(__name__)
+
+
+class RAGPipeline:
+    """
+    Orchestrates the RAG pipeline: retrieve → generate → format.
+    
+    Features:
+    - Smart retrieval with filtering
+    - LLM generation via HuggingFace Inference API
+    - Source attribution
+    - Error handling with graceful degradation
+    """
+    
+    def __init__(
+        self,
+        retriever: DocumentRetriever,
+        llm_model: Optional[str] = None,
+        min_similarity_score: float = 0.5
+    ):
+        """
+        Initialize RAG pipeline.
+        
+        Args:
+            retriever: Document retriever instance
+            llm_model: Optional LLM model name override
+            min_similarity_score: Minimum score for relevant results
+        """
+        self.retriever = retriever
+        self.llm_model = llm_model or settings.llm_model
+        self.min_similarity_score = min_similarity_score
+        
+        self._api_url = _HF_API_URL
+        self._headers = {
+            "Authorization": f"Bearer {settings.hf_token}",
+            "Content-Type": "application/json",
+        }
+        logger.info(f"LLM endpoint: {self._api_url} model={self.llm_model}")
+    
+    def query(
+        self,
+        question: str,
+        top_k: int = 5,
+        filter_metadata: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """
+        Process a user query through the RAG pipeline.
+        
+        Args:
+            question: User's question
+            top_k: Number of chunks to retrieve
+            filter_metadata: Optional metadata filters
+            
+        Returns:
+            Dictionary with answer, sources, and metadata
+        """
+        try:
+            logger.info(f"Processing query: {question[:100]}...")
+            
+            # Step 1: Retrieve relevant context
+            retrieved_chunks = self.retriever.retrieve(
+                query=question,
+                top_k=top_k,
+                filter_metadata=filter_metadata
+            )
+            
+            # Log raw scores for diagnostics
+            scores = [round(c["score"], 4) for c in retrieved_chunks]
+            logger.info(f"Raw chunk scores: {scores}")
+
+            # Filter by minimum similarity score
+            relevant_chunks = [
+                chunk for chunk in retrieved_chunks
+                if chunk["score"] >= self.min_similarity_score
+            ]
+
+            logger.info(f"Found {len(relevant_chunks)} relevant chunks (threshold: {self.min_similarity_score})")
+            
+            # Step 2: Generate answer
+            if not relevant_chunks:
+                answer = f"I couldn't find relevant information in the {settings.docs_name} documentation to answer this question. Could you rephrase or ask about a different topic?"
+                return {
+                    "answer": answer,
+                    "sources": [],
+                    "source_count": 0,
+                    "confidence": "low",
+                    "chunks_retrieved": 0
+                }
+            
+            # Create prompt
+            prompt = create_rag_prompt(question, relevant_chunks)
+            
+            # Generate answer
+            answer = self._generate_answer(prompt)
+            
+            # Step 3: Format response
+            response = format_response_with_sources(answer, relevant_chunks)
+            
+            # Add metadata
+            response["confidence"] = self._estimate_confidence(relevant_chunks)
+            response["chunks_retrieved"] = len(relevant_chunks)
+            
+            logger.info("Query processed successfully")
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error processing query: {e}", exc_info=True)
+            return {
+                "answer": f"An error occurred while processing your question: {str(e)}",
+                "sources": [],
+                "source_count": 0,
+                "confidence": "error",
+                "chunks_retrieved": 0
+            }
+    
+    def _generate_answer(self, prompt: str) -> str:
+        """
+        Generate answer using LLM.
+        
+        Args:
+            prompt: Formatted prompt with context
+            
+        Returns:
+            Generated answer text
+        """
+        try:
+            # Use OpenAI-compatible chat completions endpoint
+            payload = {
+                "model": f"{self.llm_model}:fastest",
+                "messages": [{"role": "user", "content": prompt}],
+                "max_tokens": settings.llm_max_tokens,
+                "temperature": settings.llm_temperature,
+                "top_p": 0.9,
+            }
+            response = requests.post(
+                self._api_url,
+                headers=self._headers,
+                json=payload,
+                timeout=60
+            )
+            response.raise_for_status()
+            result = response.json()
+            answer = result["choices"][0]["message"]["content"].strip()
+            logger.debug(f"Generated answer ({len(answer)} chars)")
+
+            return answer
+            
+        except Exception as e:
+            logger.error(f"LLM generation failed: {e}")
+            raise
+    
+    def _estimate_confidence(self, chunks: List[Dict[str, Any]]) -> str:
+        """
+        Estimate confidence based on retrieval scores.
+        
+        Args:
+            chunks: Retrieved chunks with scores
+            
+        Returns:
+            Confidence level: "high", "medium", or "low"
+        """
+        if not chunks:
+            return "low"
+        
+        avg_score = sum(chunk["score"] for chunk in chunks) / len(chunks)
+        
+        if avg_score >= 0.75:
+            return "high"
+        elif avg_score >= 0.6:
+            return "medium"
+        else:
+            return "low"
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """Get pipeline statistics."""
+        return {
+            "llm_model": self.llm_model,
+            "min_similarity_score": self.min_similarity_score,
+            **self.retriever.get_collection_stats()
+        }
+
+
+def create_rag_pipeline(
+    retriever: Optional[DocumentRetriever] = None
+) -> RAGPipeline:
+    """
+    Factory function to create RAG pipeline.
+    
+    Args:
+        retriever: Optional retriever override
+        
+    Returns:
+        RAGPipeline instance
+    """
+    from src.retriever import create_retriever
+    
+    if retriever is None:
+        retriever = create_retriever()
+    
+    return RAGPipeline(
+        retriever=retriever,
+        min_similarity_score=settings.min_similarity_score
+    )

src/retriever.py

@@ -0,0 +1,224 @@
+"""
+Vector retrieval system using ChromaDB.
+
+Handles document storage, indexing, and semantic search.
+"""
+import logging
+from typing import List, Dict, Any, Optional
+from pathlib import Path
+import chromadb
+from chromadb.config import Settings as ChromaSettings
+from chromadb.utils import embedding_functions
+
+from src.embeddings import EmbeddingGenerator
+from src.chunking import DocumentChunk
+
+logger = logging.getLogger(__name__)
+
+
+class DocumentRetriever:
+    """
+    Manages document storage and retrieval using ChromaDB.
+    
+    Features:
+    - Persistent vector storage
+    - Semantic similarity search
+    - Metadata filtering
+    - Source attribution
+    """
+    
+    def __init__(
+        self,
+        persist_directory: str,
+        collection_name: str,
+        embedding_generator: EmbeddingGenerator
+    ):
+        """
+        Initialize retriever.
+        
+        Args:
+            persist_directory: Path to ChromaDB storage
+            collection_name: Name of the collection
+            embedding_generator: Embedding generator instance
+        """
+        self.persist_directory = Path(persist_directory)
+        self.persist_directory.mkdir(parents=True, exist_ok=True)
+        
+        self.collection_name = collection_name
+        self.embedding_generator = embedding_generator
+        
+        # Initialize ChromaDB client
+        logger.info(f"Initializing ChromaDB at {persist_directory}")
+        self.client = chromadb.PersistentClient(
+            path=str(self.persist_directory),
+            settings=ChromaSettings(
+                anonymized_telemetry=False,
+                allow_reset=True
+            )
+        )
+        
+        # Get or create collection (cosine distance for proper similarity scores)
+        self.collection = self._get_or_create_collection()
+        coll_meta = self.collection.metadata or {}
+        self._use_cosine = coll_meta.get("hnsw:space") == "cosine"
+        logger.info(f"Collection '{collection_name}' ready. Count: {self.collection.count()}. Distance: {'cosine' if self._use_cosine else 'l2'}")
+    
+    def _get_or_create_collection(self):
+        """Get existing collection or create new one."""
+        try:
+            # Try to get existing collection
+            collection = self.client.get_collection(
+                name=self.collection_name
+            )
+            logger.info(f"Loaded existing collection: {self.collection_name}")
+        except Exception:
+            # Create new collection with cosine distance so scores stay in [0, 1]
+            collection = self.client.create_collection(
+                name=self.collection_name,
+                metadata={"hnsw:space": "cosine", "description": "Developer documentation chunks"}
+            )
+            logger.info(f"Created new collection: {self.collection_name}")
+
+        return collection
+    
+    def add_documents(self, chunks: List[DocumentChunk]) -> None:
+        """
+        Add document chunks to the vector store.
+        
+        Args:
+            chunks: List of DocumentChunk objects
+        """
+        if not chunks:
+            logger.warning("No chunks to add")
+            return
+        
+        logger.info(f"Adding {len(chunks)} chunks to collection")
+        
+        # Prepare data for ChromaDB
+        documents = [chunk.content for chunk in chunks]
+        metadatas = [chunk.metadata for chunk in chunks]
+        ids = [chunk.chunk_id for chunk in chunks]
+        
+        # Generate embeddings
+        embeddings = self.embedding_generator.embed_documents(documents)
+        
+        # Add to collection in batches
+        batch_size = 100
+        for i in range(0, len(chunks), batch_size):
+            batch_end = min(i + batch_size, len(chunks))
+            
+            self.collection.add(
+                embeddings=embeddings[i:batch_end].tolist(),
+                documents=documents[i:batch_end],
+                metadatas=metadatas[i:batch_end],
+                ids=ids[i:batch_end]
+            )
+            
+            logger.debug(f"Added batch {i//batch_size + 1}")
+        
+        logger.info(f"Successfully added {len(chunks)} chunks. Total: {self.collection.count()}")
+    
+    def retrieve(
+        self,
+        query: str,
+        top_k: int = 5,
+        filter_metadata: Optional[Dict[str, Any]] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        Retrieve relevant documents for a query.
+        
+        Args:
+            query: Search query
+            top_k: Number of results to return
+            filter_metadata: Optional metadata filters
+            
+        Returns:
+            List of results with content, metadata, and scores
+        """
+        logger.debug(f"Retrieving top {top_k} results for query: {query[:100]}...")
+        
+        # Generate query embedding
+        query_embedding = self.embedding_generator.embed_query(query)
+        
+        # Search
+        results = self.collection.query(
+            query_embeddings=[query_embedding.tolist()],
+            n_results=top_k,
+            where=filter_metadata,
+            include=["documents", "metadatas", "distances"]
+        )
+        
+        # Format results
+        formatted_results = []
+        if results["documents"] and results["documents"][0]:
+            for i in range(len(results["documents"][0])):
+                d = results["distances"][0][i]
+                score = max(0.0, 1 - d) if self._use_cosine else max(0.0, 1 - d ** 2 / 2)
+                formatted_results.append({
+                    "content": results["documents"][0][i],
+                    "metadata": results["metadatas"][0][i],
+                    "score": score,
+                    "id": results["ids"][0][i] if "ids" in results else None
+                })
+        
+        logger.info(f"Retrieved {len(formatted_results)} results")
+        return formatted_results
+    
+    def get_collection_stats(self) -> Dict[str, Any]:
+        """Get statistics about the collection."""
+        count = self.collection.count()
+        
+        # Sample a document to get metadata fields
+        sample = self.collection.peek(limit=1)
+        metadata_fields = list(sample["metadatas"][0].keys()) if sample["metadatas"] else []
+        
+        return {
+            "total_chunks": count,
+            "collection_name": self.collection_name,
+            "metadata_fields": metadata_fields,
+            "embedding_dimension": self.embedding_generator.embedding_dim
+        }
+    
+    def delete_collection(self) -> None:
+        """Delete the entire collection."""
+        logger.warning(f"Deleting collection: {self.collection_name}")
+        self.client.delete_collection(name=self.collection_name)
+    
+    def reset_collection(self) -> None:
+        """Reset collection (delete and recreate)."""
+        logger.warning("Resetting collection")
+        try:
+            self.delete_collection()
+        except Exception:
+            pass
+        self.collection = self._get_or_create_collection()
+
+
+def create_retriever(
+    persist_directory: Optional[str] = None,
+    collection_name: Optional[str] = None,
+    embedding_generator: Optional[EmbeddingGenerator] = None
+) -> DocumentRetriever:
+    """
+    Factory function to create retriever.
+    
+    Args:
+        persist_directory: Optional directory override
+        collection_name: Optional collection name override
+        embedding_generator: Optional embedding generator override
+        
+    Returns:
+        DocumentRetriever instance
+    """
+    from src.config import settings
+    from src.embeddings import create_embedding_generator
+    
+    persist_dir = persist_directory or settings.chroma_persist_dir
+    coll_name = collection_name or settings.collection_name
+    emb_gen = embedding_generator or create_embedding_generator()
+    
+    return DocumentRetriever(
+        persist_directory=persist_dir,
+        collection_name=coll_name,
+        embedding_generator=emb_gen
+    )

test_chunking.py

@@ -0,0 +1,97 @@
+"""
+Tests for document chunking functionality.
+"""
+import pytest
+from src.chunking import SemanticChunker, DocumentChunk
+
+
+@pytest.fixture
+def chunker():
+    """Create a chunker instance for testing."""
+    return SemanticChunker(chunk_size=200, chunk_overlap=50)
+
+
+def test_basic_chunking(chunker):
+    """Test basic document chunking."""
+    text = """
+    FastAPI is a modern, fast (high-performance) web framework.
+    
+    It is based on standard Python type hints.
+    
+    The key features are:
+    - Fast: Very high performance
+    - Fast to code: Increase development speed
+    - Fewer bugs: Reduce human errors
+    """
+    
+    chunks = chunker.chunk_document(text)
+    
+    assert len(chunks) > 0
+    assert all(isinstance(chunk, DocumentChunk) for chunk in chunks)
+    assert all(chunk.content for chunk in chunks)
+
+
+def test_chunk_metadata(chunker):
+    """Test that metadata is properly attached."""
+    text = "FastAPI is awesome."
+    metadata = {
+        "source": "test.md",
+        "title": "Test Document",
+        "url": "https://example.com"
+    }
+    
+    chunks = chunker.chunk_document(text, metadata=metadata)
+    
+    assert len(chunks) > 0
+    chunk = chunks[0]
+    
+    assert chunk.metadata["source"] == "test.md"
+    assert chunk.metadata["title"] == "Test Document"
+    assert chunk.metadata["url"] == "https://example.com"
+    assert "chunk_index" in chunk.metadata
+
+
+def test_code_block_preservation(chunker):
+    """Test that code blocks are preserved."""
+    text = """
+    Here's an example:
+    
+    ```python
+    from fastapi import FastAPI
+    app = FastAPI()
+    ```
+    
+    This creates an app.
+    """
+    
+    chunks = chunker.chunk_document(text)
+    
+    # Code block should be preserved
+    combined_content = " ".join(chunk.content for chunk in chunks)
+    assert "```python" in combined_content
+    assert "FastAPI" in combined_content
+
+
+def test_empty_text(chunker):
+    """Test handling of empty text."""
+    chunks = chunker.chunk_document("")
+    assert chunks == []
+    
+    chunks = chunker.chunk_document("   ")
+    assert chunks == []
+
+
+def test_to_dict(chunker):
+    """Test DocumentChunk serialization."""
+    text = "Test content"
+    metadata = {"source": "test"}
+    
+    chunks = chunker.chunk_document(text, metadata=metadata)
+    chunk = chunks[0]
+    
+    chunk_dict = chunk.to_dict()
+    
+    assert "content" in chunk_dict
+    assert "metadata" in chunk_dict
+    assert "chunk_id" in chunk_dict
+    assert chunk_dict["content"] == chunk.content

test_retrieval.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+"""
+Test retrieval quality independently.
+
+Useful for debugging and tuning retrieval parameters.
+"""
+import logging
+import sys
+from pathlib import Path
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from src import create_retriever
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+TEST_QUERIES = [
+    "How do I create a router in FastAPI?",
+    "What are dependencies?",
+    "How do I handle errors?",
+    "Show me authentication examples",
+    "How do I validate request bodies?",
+]
+
+
+def test_retrieval():
+    """Test retrieval with various queries."""
+    
+    logger.info("=" * 60)
+    logger.info("Retrieval Quality Test")
+    logger.info("=" * 60)
+    
+    # Initialize retriever
+    retriever = create_retriever()
+    stats = retriever.get_collection_stats()
+    
+    logger.info(f"\nVector Database Stats:")
+    logger.info(f"  Total chunks: {stats['total_chunks']}")
+    logger.info(f"  Collection: {stats['collection_name']}")
+    logger.info(f"  Embedding dim: {stats['embedding_dimension']}")
+    
+    # Test each query
+    for i, query in enumerate(TEST_QUERIES, 1):
+        logger.info("\n" + "=" * 60)
+        logger.info(f"Test {i}/{len(TEST_QUERIES)}")
+        logger.info("=" * 60)
+        logger.info(f"Query: {query}")
+        
+        # Retrieve
+        results = retriever.retrieve(query, top_k=3)
+        
+        logger.info(f"\nFound {len(results)} results:")
+        
+        for j, result in enumerate(results, 1):
+            logger.info(f"\n--- Result {j} ---")
+            logger.info(f"Score: {result['score']:.4f}")
+            logger.info(f"Source: {result['metadata'].get('title', 'Unknown')}")
+            logger.info(f"Section: {result['metadata'].get('section', 'Unknown')}")
+            logger.info(f"Content preview:")
+            logger.info(f"{result['content'][:200]}...")
+        
+        # Quality check
+        avg_score = sum(r['score'] for r in results) / len(results) if results else 0
+        logger.info(f"\nAverage relevance score: {avg_score:.4f}")
+        
+        if avg_score >= 0.75:
+            logger.info("✓ High quality results")
+        elif avg_score >= 0.6:
+            logger.info("⚠ Medium quality results")
+        else:
+            logger.info("✗ Low quality results - consider tuning")
+    
+    logger.info("\n" + "=" * 60)
+    logger.info("Retrieval test complete")
+    logger.info("=" * 60)
+
+
+if __name__ == "__main__":
+    test_retrieval()