Grecil / Corrective-RAG

Implementation of Corrective RAG using LangChain and LangGraph.

Corrective-RAG

An Implementation of this paper - https://arxiv.org/pdf/2401.15884 using LangChain and LangGraph

Requirements

• Large Language Model (Change it in models/LLM. By default it uses gemini-1.5-flash by Google. You can use any model supported by langchain.)
• Embeddings Model (Change it in models/EM. By default it uses text-embeddings-004 by Google. You can use any model supported by langchain.)
• Python 3.10 (use other versions at your own risk) (skip if using docker)
• Python libraries mentioned in requirements.txt (skip if using docker)

Setup

• Copy the repository
• Change LLM and EM in models.
• Use indexer.ipynb to create FAISS index of your files.
• Run the app
  • Locally

    streamlit run streamlit_app.py
    

  • Docker

    docker build -t corrective-rag .
    docker run -p 8501:8501 corrective-rag
    

You can check this app out here - https://corrective-rag.streamlit.app