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 ports to users

🚀 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

🛠️ Technical Stack

• Vector Search: ChromaDB for efficient semantic search
• Embeddings: Nomic AI's text embeddings (nomic-embed-text-v1)
• LLM Orchestration: SmolagentsCodeAgent for tool use and reasoning
• UI Framework: Gradio for the web interface
• Model: Google's Gemini 2.0 Flash via LiteLLM

📋 Requirements

• Python 3.8+
• Dependencies listed in requirements.txt

🔧 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/chat-with-avd-doc.git
   cd chat-with-avd-doc
   

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Configure environment variables (if needed):

   • For LiteLLM to access Gemini, you may need to set API keys
   • Create a .env file or set them directly in your environment

🔍 Working with the Vector Database

The application uses ChromaDB to store document embeddings. The database is created in the vector_db 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

📝 Configuration

Prompt Templates

Edit prompts.yaml to customize:

• System instructions for the AI agent
• Formatting of responses
• Behavior and capabilities

Model Configuration

The application uses Gemini Flash by default. To modify:

# In app.py
model = LiteLLMModel("gemini/gemini-2.0-flash")  # Change model here

🚀 Usage

1. Start the application:

   python 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?"

📂 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
└── vector_db/          # ChromaDB database files
    ├── chroma.sqlite3  # SQLite database for ChromaDB
    └── [UUID folders]  # Vector data storage

🧪 How It Works

1. User Query: User enters a natural language question through the Gradio UI
2. Semantic Encoding: The query is converted to a vector embedding
3. Vector Search: ChromaDB searches for similar document vectors
4. Context Assembly: Top matches are assembled with source metadata
5. LLM Processing: The Gemini model processes the query with retrieved context
6. Response Generation: The system returns a natural language answer

📄 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

Test Questions

1. What is the name server in this network? 8.8.8.8