This project uses a Retrieval-Augmented Generation (RAG) pipeline to extract relevant information from PDFs. Users can upload PDFs, submit queries, and get accurate responses based on the document content. It combines a retriever model and generative model for intelligent, real-time query handling.
nlp data-retrieval word-embedding rag vector-search embedding-vectors vector-database llm langchain query-search
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Updated
Oct 12, 2024 - Python