This project demonstrates the development of an AI-powered chatbot capable of answering user queries based on the content of uploaded PDF documents. Built using Retrieval-Augmented Generation (RAG), it combines LangChain, FAISS, and HuggingFace Transformers to retrieve and generate contextually accurate responses.

Users can upload one or more PDF files, ask natural language questions, and receive intelligent answers extracted from the document contents. The project showcases practical applications of agentic AI, embeddings-based retrieval, and open-source LLMs to enable domain-specific conversational intelligence.

Key components include:

LangChain for chaining retrieval and generation logic

HuggingFace sentence-transformers (MiniLM) for text embeddings

FAISS for efficient vector similarity search

Flan-T5 model for text generation

Streamlit for an interactive user interface

This project lays the foundation for document-grounded conversational agents that can power enterprise knowledge assistants, research bots, or intelligent PDF readers—all running locally without API dependencies.