PDF query tool using LLMs: Enhancing document interaction and analysis

This is an advanced AI-driven tool for querying and extracting meaningful information from PDF documents using Large Language Models and Retrieval-Augmented Generation techniques. The tool enables upload of multiple PDFs, interaction with users conversationally to extract contextually relevant information around their queries, overcoming the limits of traditional keyword-based search. Text extraction is performed with PyPDF2 and followed by context-aware chunking using RecursiveCharacter-TextSplitter to preserve semantic relationships in documents. Semantic vectorization is possible using Google Generative AI embeddings and similarity search with FAISS, which serves the purpose of efficient information retrieval. Google Gemini AI processes conversational queries to return responses that are precise and contextually correct. The tool applies to many different purposes, such as reviewing legal documents, summarizing articles, extraction of medical policy, or analyzing financial agreements. The future version will develop and include the integration of OCR into scanned PDFs, improve ambiguity resolution, and scale operations using cloud-based solutions as well as voice-assisted search capabilities. This solution fills the gap between static document formats and dynamic user interaction, providing a seamless blend of natural language processing and machine learning for efficient and intuitive document analysis.