QueryVault

A robust, production-ready Retrieval-Augmented Generation (RAG) application leveraging FastAPI, Typer, ChromaDB, and Google Gemini. Designed for scalable, maintainable, and secure knowledge retrieval and question answering.

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🚀 Features

• FastAPI Backend: High-performance, asynchronous API server for real-time Q&A.
• Modular Architecture: Clean separation of web, business logic, data pipeline, and configuration.
• Typer CLI: Powerful command-line interface for database management and server operations.
• ChromaDB Integration: Efficient vector database for semantic search and retrieval.
• Google Gemini LLM: State-of-the-art language model for accurate, context-aware answers.
• Pydantic Settings: Type-safe, environment-driven configuration management.
• Production Hardening: In-memory rate limiting, robust error handling, and secure secret management.
• Modern Frontend: Responsive, minimal HTML interface for end users.

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🗂️ Project Structure

QueryVault/
├── .env                  # Environment variables (API keys, secrets)
├── requirements.txt      # Python dependencies
├── README.md             # Project documentation
│
├── app/                  # Main application package
│   ├── main.py           # FastAPI app, endpoints, startup logic
│   ├── cli.py            # Typer CLI commands
│   ├── config.py         # Pydantic settings/configuration
│   ├── core.py           # Core RAG pipeline logic
│   ├── data_builder.py   # Database ingestion/building logic
│   ├── dependencies.py   # Shared dependencies (e.g., rate limiter)
│   └── templates/
│       └── index.html    # HTML frontend
│
└── data/                 # Input Q&A JSON files
    └── my_dataset.json

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⚙️ Setup & Installation

1. Clone the Repository

   git clone https://github.com/Ibnuawf/QueryVault
   cd QueryVault

2. Create a Virtual Environment

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

3. Configure Environment Variables Create a .env file in the project root:

   GEMINI_API_KEY=your-google-ai-api-key

4. Install Dependencies

   pip install -r requirements.txt

5. Add Your Data Place your Q&A JSON files (e.g., my_dataset.json) in the data/ directory.

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🏗️ Building & Running

1. Build the Vector Database

Process your data and create the ChromaDB vector store:

python -m app.cli build-db

2. Start the Web Application

Launch the FastAPI server:

python -m app.cli run-app

• Access the app at: http://127.0.0.1:8000
• API documentation: http://127.0.0.1:8000/docs

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📝 Best Practices & Notes

• Security: Never commit real API keys or secrets to public repositories. Use .env for sensitive config.
• Data Format: Ensure your Q&A JSON files are well-structured and validated before ingestion.
• Extensibility: The modular design allows easy extension for new data sources, models, or endpoints.
• Error Handling: All major operations include robust error handling and logging for production reliability.
• Rate Limiting: The API enforces per-client rate limits to prevent abuse.

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📚 License & Attribution

This project is provided for educational and research purposes. For commercial use, review all dependencies and comply with their respective licenses.

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🙏 Acknowledgements

• FastAPI
• ChromaDB
• Google Gemini
• Sentence Transformers
• Typer
• Pydantic