Chat with file using RAG

This project is a web-based application that allows users to upload files and interact with their content using an AI-powered chatbot. The system utilizes Retrieval-Augmented Generation (RAG) to enhance responses by retrieving relevant information from the uploaded files before generating AI-driven answers. The goal is to provide accurate, context-aware responses, making it easier for users to extract insights from their documents efficiently.

Key Features

File Upload & Processing: Users can upload various file formats (PDF, TXT, DOCX, etc.), and the system extracts relevant content for querying.

Intelligent Question Answering: Users can ask questions related to the file’s content, and the chatbot provides precise answers based on retrieved document segments.

RAG-Based Approach: Combines document retrieval techniques with generative AI to enhance response accuracy and relevance.

Efficient Search & Summarization: Enables fast content searching and summarization, saving users time in analyzing lengthy documents.

Web-Based Interface: A user-friendly UI that allows easy interaction without technical knowledge.

Technology Stack

Backend: Python (FastAPI/Flask)

LLM Integration: OpenAI API / LangChain

Vector Database: FAISS / ChromaDB for efficient document retrieval

Frontend: Streamlit / React for a seamless user experience

File Handling: PyMuPDF, PDFplumber, docx2txt for text extraction

Use Cases

Students & Researchers – Quickly extract insights from academic papers and textbooks.

Legal & Compliance Teams – Navigate contracts and policies with ease.

Business Professionals – Summarize reports and find key information in seconds.

This project aims to revolutionize how users interact with documents, making information retrieval seamless and intuitive.