metadata

title: RAG Document Summarizer
emoji: 📄
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
short_description: AI-powered document summarization and Q&A using RAG

RAG Document Summarizer

A modern, production-ready Retrieval-Augmented Generation (RAG) application for document summarization and question answering. Built with FastAPI, LangChain, and Mistral AI API, this project enables advanced document processing, chunking, vector search, and AI-powered summarization and querying.

Features

Document Upload: Supports PDF, DOCX, PPTX, and TXT files
OCR Support: Extracts text from scanned PDFs using Tesseract OCR
Chunking: Splits documents into manageable chunks for efficient processing
Vector Store: Embeds and stores chunks for fast similarity search
AI Summarization: Uses Mistral AI API for high-quality summaries and answers
Modern UI: Clean, responsive web interface

How to Use

Upload your documents (PDF, DOCX, PPTX, TXT)
The system will automatically process and chunk your documents
Ask questions about your documents using the query interface
Get AI-powered summaries and answers based on your document content

Technical Stack

Backend: FastAPI, Python 3.10+
AI/ML: LangChain, Mistral AI API, Sentence Transformers
Vector Database: ChromaDB
Document Processing: PyPDF2, pdfplumber, unstructured, pytesseract
Frontend: Modern HTML/CSS/JavaScript with Tailwind CSS

Environment Variables

MISTRAL_API_KEY: Required for AI-powered features (get from Mistral AI)

License

MIT License