A Simple Full Stack RAG-bot for Enterprises using React, Qdrant, Langchain, Cohere and FastAPI

This backend API server is a core component of an AI-powered document chat application, designed to interpret and respond to user queries based on the content of uploaded documents. Leveraging the capabilities of LangChain, Cohere, and Qdrant, it offers a robust and scalable solution for semantic document processing.

Features

• Multitenancy Support: Efficiently handles multiple users by segregating their data using unique group IDs.
• Binary Quantization: Utilizes Qdrant's binary quantization for optimal storage and fast retrieval of embeddings.
• Natural Language Processing: Empowered by Cohere's LLM, it provides accurate and context-aware responses.
• Scalable Vector Search: Integrates with Qdrant for scalable and efficient vector search capabilities.

How to Use

1. Set Up Your Environment:
   • Ensure Python is installed.
   • Clone the repository and navigate to the project directory.
2. Configuration:
   • Set environment variables for Cohere API key, Qdrant URL, and Qdrant API key in a .env file.
3. Installation:

    pip install -r requirements.txt
   

4. Start the Server:

   python main.py
   

This will launch the FastAPI server, which will be accessible locally.

Tools and Technologies

• LLM Orchestration: Langchain
• Vector DB and Similarity Search: Qdrant
• API Framework: FastAPI
• LLM Integration: Cohere API

Frontend UI Code

https://github.com/AI-ANK/qdrant-langchain-cohere-ragbot-ui

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support, please open an issue in the GitHub issue tracker.

Live Demo

Try the demo here

Author

Developed by Harshad Suryawanshi If you find this project useful, consider giving it a ⭐ on GitHub!