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rag_tutorials/corrective_rag/README.md
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This project demonstrates Corrective RAG (Retrieval Augmented Generation), an advanced approach to RAG that incorporates self-reflection / self-grading on retrieved documents - document relevance checking, query transformation, and web search fallback mechanisms to improve the quality of responses by far. Complete explanation of CRAG down below. This project demonstrates Corrective RAG (Retrieval Augmented Generation), an advanced approach to RAG that incorporates self-reflection / self-grading on retrieved documents - document relevance checking, query transformation, and web search fallback mechanisms to improve the quality of responses by far. Complete explanation of CRAG down below.
## Demo
## Features ## Features
- **Smart Document Retrieval**: Uses Qdrant vector store for efficient document retrieval - **Smart Document Retrieval**: Uses Qdrant vector store for efficient document retrieval
- **Document Relevance Grading**: Employs Claude 3 to assess document relevance - **Document Relevance Grading**: Employs Claude 3.5 sonnet to assess document relevance
- **Query Transformation**: Improves search results by optimizing queries when needed - **Query Transformation**: Improves search results by optimizing queries when needed
- **Web Search Fallback**: Uses Tavily API for web search when local documents aren't sufficient - **Web Search Fallback**: Uses Tavily API for web search when local documents aren't sufficient
- **Multi-Model Approach**: Combines OpenAI embeddings and Claude 3 for different tasks - **Multi-Model Approach**: Combines OpenAI embeddings and Claude 3.5 sonnet for different tasks
- **Interactive UI**: Built with Streamlit for easy document upload and querying - **Interactive UI**: Built with Streamlit for easy document upload and querying
## How to Run? ## How to Run?