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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.
## Demo
## Features
- **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
- **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
## How to Run?