Add initial implementation of Retrieval-Augmented Generation (RAG) system with comprehensive documentation. Introduce notebooks for building healthcare customer support and corrective RAG systems, along with necessary dependencies and knowledge base integration. Include visual aids and reference links for enhanced understanding.

Add new notebooks for querying healthcare knowledge graphs and vector indexing with LangChain. Enhance the end-to-end RAG system for the Roman Empire by integrating entity extraction, hybrid retrieval, and full-text search functionalities.

Update .gitignore to include venv, .vscode, .idea directories, and .env file for improved project environment management.

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Enhance the 2-reranking notebook with detailed output messages for each step of the re-ranking process, including user query handling, document retrieval, and final ranking results. Improve clarity and user experience by providing feedback on library imports, document loading, and LLM initialization.

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Enhance query enhancement techniques across multiple notebooks by adding detailed explanations, learning objectives, and practical examples for query expansion, decomposition, and HyDE. Improve user understanding of RAG systems and retrieval strategies.

Enhance hybrid search strategies with detailed explanations, learning objectives, and implementation steps across multiple notebooks. Introduce MMR for diverse retrieval, re-ranking techniques, and query expansion methods, improving overall retrieval quality and user experience.

Add new notebooks for document splitting, chunking, hybrid search strategies, query enhancement, and RAG system evaluation in LangChain

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Add notebooks for embedding models, vector databases, and retrieval strategies in LangChain

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