Intelligent Document Query System using Retrieval-Augmented Generation (RAG)

The RAG Chatbot can be considered an AI document-interactive system that integrates Retrieval-Augmented Generation (RAG) technology, thus allowing the owner to interact with their archival documents. It uses Streamlit for its user interface with the backend employing Ollama for the local running of the Large Language Model (LLM) and FAISS for fast vector-based similarity search. The users can upload their documents in formats such as PDF, DOCX, and TXT which are then directly subjected to the text extraction, chunking, and embedding generation process. The embeddings that are produced are indexed by FAISS which allows the chatbot to retrieve the document parts relevant to its answer. The system architecture assures that the response generated by the LLM is always tied to the user's document improving precision and enabling the customer to identify the information source. Besides, the chatbot has features such as document management in real-time, debug mode for response tracing, and local data persistence that not only augment its functionalities but also guarantee privacy. This project illustrates a complete end-to-end realization of a local RAG pipeline integrating language comprehension, retrieval speed, and user engagement in a seamless conversational interface.