Pdf Semantic Retrieval Using Langchain And Faiss

Abstract

In Today’s Information-driven Environment, PDF Documents Serve As A Primary Medium For Storing Academic, Corporate, Legal, And Technical Knowledge. However, Retrieving Specific And Meaningful Information From Large PDF Files Remains A Major Challenge, Especially When Relying On Traditional Keyword-based Search Methods That Fail To Capture Deeper Semantic Meaning. This Project, “PDF Semantic Retrieval System Using LangChain And FAISS”, Addresses This Challenge By Developing An Intelligent, Context-aware Retrieval System Capable Of Understanding User Queries And Locating The Most Relevant Sections Within PDF Documents. The Proposed System Extracts Text From PDF Files, Segments It Into Context-preserving Chunks, And Generates High-dimensional Semantic Embeddings Using Transformer-based Models. These Embeddings Are Stored In FAISS, A High-performance Vector Search Library Optimized For Large-scale Similarity Search.

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