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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.
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## Demo
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## Features
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- **Smart Document Retrieval**: Uses Qdrant vector store for efficient document retrieval
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- **Document Relevance Grading**: Employs Claude 3 to assess document relevance
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- **Document Relevance Grading**: Employs Claude 3.5 sonnet to assess document relevance
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- **Query Transformation**: Improves search results by optimizing queries when needed
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- **Web Search Fallback**: Uses Tavily API for web search when local documents aren't sufficient
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- **Multi-Model Approach**: Combines OpenAI embeddings and Claude 3 for different tasks
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- **Multi-Model Approach**: Combines OpenAI embeddings and Claude 3.5 sonnet for different tasks
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- **Interactive UI**: Built with Streamlit for easy document upload and querying
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## How to Run?
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