| title: Pythonic Rag Fastapi React | |
| emoji: 📊 | |
| colorFrom: indigo | |
| colorTo: pink | |
| sdk: docker | |
| pinned: false | |
| short_description: A bare bones fast api based React QA application | |
| # Document Q&A Chat | |
| This is a RAG (Retrieval Augmented Generation) application that allows you to: | |
| 1. Upload documents (PDF/TXT) | |
| 2. Ask questions about the content | |
| 3. Get AI-powered responses based on the document content | |
| ## Features | |
| - Document upload support (PDF, TXT) | |
| - Real-time chat interface | |
| - Context-aware responses | |
| - Modern, responsive UI | |
| ## Technology Stack | |
| - Frontend: React with TypeScript | |
| - Backend: FastAPI | |
| - Vector Database: For document embeddings | |
| - LLM: OpenAI for generating responses | |
| - Docker for deployment | |
| ## Usage | |
| 1. Upload your document using the upload interface | |
| 2. Wait for the document to be processed | |
| 3. Start asking questions about your document | |
| 4. Get AI-generated responses based on the document content | |