File size: 1,310 Bytes
d2c2d7f 2934401 d2c2d7f 221a95c d2c2d7f 2934401 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
---
title: RAG MVP - Document Q&A
emoji: 📚
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.44.1
app_file: app.py
pinned: false
---
# RAG MVP - Document Q&A System
A simple Retrieval-Augmented Generation (RAG) system that allows you to upload PDF documents and ask questions about their content.
## Features
- **PDF Upload**: Upload any PDF document
- **Text Processing**: Automatic text extraction and chunking
- **Semantic Search**: Find relevant information using sentence embeddings
- **Q&A Interface**: Ask questions and get answers based on document content
## How to Use
1. Upload a PDF document using the file upload interface
2. Click "Process Document" and wait for processing to complete
3. Ask questions about the document in the question box
4. Get relevant answers based on the document content
## Technical Stack
- **Gradio**: Web interface
- **sentence-transformers**: Text embeddings (all-MiniLM-L6-v2)
- **PyPDF2**: PDF text extraction
- **scikit-learn**: Similarity search
- **NumPy**: Numerical operations
## Example Questions
- What is the main topic of this document?
- Can you summarize the key points?
- What are the important details mentioned?
This is a minimal viable product (MVP) demonstrating core RAG functionality with a simple, user-friendly interface.
|