Advanced RAG control flow with LangGraph🦜🕸:

Implementation of Reflective RAG, Self-RAG & Adaptive RAG tailored towards production-oriented applications with LangGraph🦜🕸️.

Features

• Refactored Notebooks: The original LangChain notebooks have been refactored to enhance readability, maintainability, and usability for developers.
• Production-Oriented: The codebase is designed with a focus on production readiness, allowing developers to seamlessly transition from experimentation to deployment.
• Test Coverage: Comprehensive test coverage ensures the reliability and stability of the application, enabling developers to validate their implementations effectively.
• Documentation: Detailed documentation and branches guides developers through setting up the environment, understanding the codebase, and utilizing LangGraph effectively.

Environment Variables

To run this project, you will need to add the following environment variables to your .env file

PYTHONPATH=/{YOUR_PATH_TO_PROJECT}/Advanced-RAG-LangChain

OPENAI_API_KEY=<your-openai-api-key>

How to get you OpenAI API Key https://platform.openai.com/account/api-keys

TAVILY_API_KEY=<your-tavily-api-key>

How to get you Tavily API Key https://docs.mindmac.app/how-to.../internet-browsing/get-tavily-key

Run Locally

Clone the project

git clone https://github.com/PrMestizo/Advanced-RAG-LangChain.git

Go to the project directory

cd Advanced-RAG-LangChain

Install dependencies

poetry install

Start the flask server

poetry run app.py