Intelligent Document Interaction with Advanced Vector Embeddings and FAISS-CPU Indexing

Our research project aims to introduce an artificial intelligence (AI) conversational model that can be used to efficiently communicate with multiple PDF documents. Our solution uses natural language processing algorithms to enable users to have lively conversations with PDFs that have been uploaded. We have built a comprehensive platform that can interpret user queries and provide pertinent responses based on the content of uploaded documents by integrating Python with Streamlit for interface development, FAISSCPU for storage and similarity search, PyPDF2 for text extraction, ChromaDB, Langchain, and Google Gemini Generative AI for enhanced functionality. Our goals include developing a UI that is easy to use, processing documents correctly, and having strong conversational capabilities.