RAGbot---Generative_AI-RAG-Application

RAGbot - Generative AI RAG Application A powerful chatbot assistant leveraging Retrieval-Augmented Generation (RAG) to answer questions from multiple PDF documents. RAGbot generates accurate responses and provides source references, making it an ideal assistant for working with domain-specific PDFs. Table of Contents Project Structure Installation Backend Setup Frontend Setup Usage Running the Backend Running the Frontend Video Demo License Project Structure You will find a helpful readme files in the backend and frontend directories. Backend (rag-app) Core Files pyproject.toml and poetry.lock: Dependency management files for the backend. rag-data-loader/: Module for loading and processing PDF documents. pdf-documents/: Directory to store the input PDF files. app/: Main backend logic, including: - server.py: API entry point for the backend server. - ragchain.py: Logic for connecting LangChain with PDF data. Utilities .env: Environment variable configurations , put "OPENAIAPIKEY" and Langchain tracing keys . Dockerfile: Containerization support for the backend. Frontend (frontend) Core Files src/: React source code for the RAGbot user interface.