📚 Doc-MCP: Documentation RAG System

Transform GitHub documentation repositories into intelligent, queryable knowledge bases using RAG and MCP.

✨ Features

• Semantic Search - Find answers across documentation using natural language
• 🤖 AI-Powered Q&A - Get intelligent responses with source citations
• 📚 Batch Processing - Ingest entire repositories with progress tracking
• 🔄 Incremental Updates - Detect and sync only changed files
• 🗂️ Repository Management - Complete CRUD operations for ingested docs

🚀 Quick Start

Prerequisites

• Python 3.13+
• MongoDB Atlas with Vector Search enabled
• Nebius API key for embeddings and LLM
• GitHub token (optional, for private repos and higher rate limits)

Installation

# Clone and setup
git clone https://github.com/md-abid-hussain/doc-mcp.git
cd doc-mcp
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# .venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

Configuration

# Setup environment
cp .env.example .env

Edit .env with your credentials:

NEBIUS_API_KEY=your_nebius_api_key_here
MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/
GITHUB_API_KEY=your_github_token_here  # Optional

Launch

# Setup database
python scripts/db_setup.py setup

# Start application
python main.py

Visit http://localhost:7860 to access the web interface.

Access MCP at http://127.0.0.1:7860/gradio_api/mcp/sse

Usage

1. Ingest Documentation

• Navigate to "📥 Documentation Ingestion" tab
• Enter GitHub repository URL (e.g., owner/repo)
• Select markdown files to process
• Execute two-step pipeline: Load files → Generate embeddings

2. Query Documentation

• Go to "🤖 AI Documentation Assistant" tab
• Select your repository
• Ask natural language questions
• Get AI responses with source citations

3. Manage Repositories

• Use "�️ Repository Management" tab
• View statistics and file counts
• Delete repositories when needed

🔧 Configuration

Environment Variables

# Required
NEBIUS_API_KEY=your_nebius_api_key_here
MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/

# Optional
GITHUB_API_KEY=your_github_token_here
CHUNK_SIZE=3072
SIMILARITY_TOP_K=5
GITHUB_CONCURRENT_REQUESTS=10

MongoDB Atlas Setup

1. Create cluster with Vector Search enabled
2. Database structure auto-created:
   • doc_rag - documents with embeddings
   • ingested_repos - repository metadata

🐛 Troubleshooting

Common Issues:

• Rate Limits: Add GitHub token for 5000 requests/hour (vs 60)
• Memory Issues: Reduce CHUNK_SIZE in .env
• Connection Errors: Verify MongoDB Atlas Vector Search is enabled
• Database Issues: Run python scripts/db_setup.py status

📖 Documentation

For detailed guides see:

• Advanced configuration options
• Development and contribution guide
• API reference and examples

💻 Author

Md Abid Hussain

• GitHub: @md-abid-hussain
• LinkedIn: md-abid-hussain

📄 License

MIT License - see LICENSE file for details.

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Built with ❤️ using Python, LlamaIndex, Nebius, MongoDB Atlas, and Gradio