README.md

metadata

title: chat-with-avd-doc
app_file: app.py
sdk: gradio
sdk_version: 5.30.0

Network Fabric Documentation Chat Assistant

Queries network documentation with natural languague. Recommend interface ports to users based on network fabric documentation.

🚀 Key Features

• Natural Language Queries: Ask questions about your network in plain English
• Semantic Search: Uses embeddings to find relevant information beyond simple keyword matching
• Device-Aware Context: Distinguishes between global and device-specific information
• Interactive UI: User-friendly Gradio web interface
• Vector Database: Persistent storage of document embeddings using ChromaDB
• LLM Integration: Powered by Google's Gemini Flash model
• Customizable Prompts: Configure system and user prompts through YAML

📋 Requirements

• Python 3.8+
• Dependencies listed in requirements.txt

🔍 Working with the Vector Database

The application uses FAISS to store document embeddings. The database is created in the faiss_index directory.

• Documents are organized by "fabric" collection
• Metadata includes source information that can identify device-specific documentation
• The system distinguishes between global network information and device-specific details

🚀 Usage

1. Start the application:

   uv run app.py
   

2. Access the UI: Open your browser to http://127.0.0.1:7860

3. Query your network documentation:

   • Ask natural language questions about your network
   • Example: "What is the loopback Pool address used by the fabric?"
   • Example: "How many IP addresses are in use in the management network?"
   • Example: "I need unused ports to connect 6 servers. provide me a pair of connections per server."
   • Example: "What is the name server in this network? -> 8.8.8.8"

📂 Project Structure

chat-with-avd-doc/
├── app.py              # Main application with Retriever Tool and Gradio UI
├── prompts.yaml        # Configuration for AI prompts and behavior
├── requirements.txt    # Python dependencies
└── faiss_index/        # FAISS database files
    ├── faiss.index     # FAISS index file

📄 License

MIT License

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request