Corrective RAG (CRAG)

Corrective RAG (CRAG) is a strategy for Retrieval-Augmented Generation (RAG) that integrates self-reflection and self-grading mechanisms to enhance the accuracy of responses by evaluating the relevance of retrieved documents.

Reference & Inspiration:
Learn more about CRAG in LangGraph

Prerequisites

To get started with this project, ensure you have access to the following:

• OpenAI API – For language model operations.
• Qdrant Vector Database – For vector storage and retrieval.
• Tavily API Key – Required for specific integrations.

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Getting Started

Follow these steps to set up and run the project:

1. Set up a virtual environment

   python -m venv env
   source env/bin/activate  # For Linux/macOS
   env\Scripts\activate     # For Windows

2. Install dependencies
   Navigate to the project directory and run:

   pip install -r my-agents/requirements.txt

3. Open in LangGraph Studio

   • Launch the LangGraph Studio desktop application.
   • Open the project folder within the application.