Advanced RAG control flow with LangGraph🦜🕸:

Implementation of Reflective RAG, Self-RAG & Adaptive RAG tailored towards developers and production-oriented applications for learning LangGraph🦜🕸️.

This repository contains a refactored version of the original LangChain's Cookbook,

See Original YouTube video:Advance RAG control flow with Mistral and LangChain

of Sophia Young from Mistral & Lance Martin from LangChain

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}/langgraph-course

OPENAI_API_KEY

TAVILY_API_KEY

Run Locally

Clone the project

  git clone https://github.com/emarco177/langgraph-course.git

Go to the project directory

  cd langgraph-course

Install dependencies

  poetry install

Start the flask server

  poetry run main.py

Running Tests

To run tests, run the following command

  poetry run pytest . -s -v

Acknowledgements

Original LangChain repository: LangChain Cookbook By Sophia Young from Mistral & Lance Martin from LangChain

🔗 Links