Abstract: This Document centers on the development of an advanced chatbot system that seamlessly integrates with PDF documents, significantly enhancing users’ ability to extract information using natural language queries. It addresses the growing need for efficient information retrieval from textual content, particularly in academic and professional contexts. The system provides a user-friendly platform designed to quickly and accurately extract relevant information from PDFs. To achieve this, it incorporates several modern technologies. Streamlit is used to build an intuitive and interactive user interface. For PDF parsing and text extraction, the system employs PyPDF2. LangChain is responsible for text processing and generating semantic embeddings, which improve the efficiency and relevance of indexed data. Google’s Generative AI powers the chatbot, enabling it to understand complex user queries and generate accurate, context-aware responses. Additionally, FAISS is integrated to support similarity-based search, ensuring fast and precise information retrieval from the vectorized content. The system workflow begins with users uploading PDF files, which are then parsed, processed, and indexed. The chatbot interacts with users by understanding their queries and providing targeted responses based on the indexed content. The primary aim of this project is to offer a highly interactive and user-centric experience, simplifying how users engage with and extract insights from PDF documents. Future enhancements may include support for more complex queries, broader document format compatibility, and advanced features to improve user engagement. Ultimately, this project contributes to the advancement of natural language processing and intelligent information retrieval, offering value to a wide range of domains requiring effective document analysis.
Keywords: LangChain, Vector Database, Multi-PDF Chat, AI, FAISS, OCR, Streamlit, GPT.
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DOI:
10.17148/IJARCCE.2025.14449
