https://github.com/en-atul/ask-docs
A Retrieval-Augmented Generation (RAG) app that lets you query your documents (PDF, TXT, MD, etc.) using LangChain, Chroma, and OpenAI API.
https://github.com/en-atul/ask-docs
chromadb docker-compose fastapi langchain nextjs openai-api python rag
Last synced: 3 months ago
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A Retrieval-Augmented Generation (RAG) app that lets you query your documents (PDF, TXT, MD, etc.) using LangChain, Chroma, and OpenAI API.
- Host: GitHub
- URL: https://github.com/en-atul/ask-docs
- Owner: en-atul
- Created: 2025-08-23T18:15:36.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-24T21:09:15.000Z (10 months ago)
- Last Synced: 2025-08-24T21:10:58.647Z (10 months ago)
- Topics: chromadb, docker-compose, fastapi, langchain, nextjs, openai-api, python, rag
- Language: Python
- Homepage:
- Size: 70.3 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# RAG with FastAPI & LangChain
A modern document Q&A system built with FastAPI, LangChain, and Next.js that allows users to upload documents and ask questions about their content using Retrieval-Augmented Generation (RAG).
## 🚀 Features
- **Document Upload**: Support for PDF and TXT files
- **Smart Search**: Document-specific search with global fallback capabilities
- **Real-time Streaming**: Server-Sent Events for live query responses
- **Contextual Compression**: Automatic result sanitization and deduplication
- **Modern UI**: Beautiful, responsive interface built with Next.js and Tailwind CSS
- **Vector Database**: ChromaDB for efficient document storage and retrieval
- **OpenAI Integration**: Human-readable answer formatting
## 📸 Screenshots
## 🎥 Demo Video
[Watch Demo](assets/recording.mov)
## 🏗️ Architecture
- **Backend**: FastAPI with LangChain for RAG implementation
- **Frontend**: Next.js 15 with TypeScript and Tailwind CSS
- **Vector Database**: ChromaDB for document embeddings
- **AI Provider**: OpenAI for text generation and embeddings
## 📋 Prerequisites
- Python 3.8+
- Node.js 18+
- Docker and Docker Compose
- OpenAI API key
## 🛠️ Installation & Setup
### 1. Clone the Repository
```bash
git clone
cd ask-docs
```
### 2. Start ChromaDB
First, start the ChromaDB vector database using Docker:
```bash
cd server
docker-compose up -d
```
This will start ChromaDB on port 8001.
### 3. Set Up the Backend
```bash
cd server
pip install -r requirements.txt
```
Create a `.env` file in the server directory:
```bash
OPENAI_API_KEY=your_openai_api_key_here
```
### 4. Set Up the Frontend
```bash
cd client
npm install
```
## 🚀 Running the Application
### 1. Start the Server
```bash
cd server
python main.py
```
The FastAPI server will start on `http://localhost:8000`
### 2. Start the Client
```bash
cd client
npm run dev
```
The Next.js application will start on `http://localhost:3000`
## 🔧 API Endpoints
- `POST /api/documents/upload` - Upload a document
- `POST /api/documents/query` - Query documents
- `POST /api/documents/query/stream` - Stream query responses
- `GET /api/documents/stats` - Get document statistics
- `GET /api/documents/health` - Health check
- `GET /docs` - API documentation (Swagger UI)
## 🏃♂️ Quick Start
1. **Start ChromaDB**: `docker-compose up -d`
2. **Start Server**: `python main.py` (from server directory)
3. **Start Client**: `npm run dev` (from client directory)
4. **Open Browser**: Navigate to `http://localhost:3000`
5. **Upload Document**: Click the upload button and select a PDF or TXT file
6. **Ask Questions**: Start asking questions about your uploaded document!
## 🛠️ Development
### Backend Development
The backend is built with FastAPI and includes:
- Document processing with LangChain
- Vector embeddings with OpenAI
- ChromaDB integration for document storage
- Streaming responses with Server-Sent Events
### Frontend Development
The frontend is built with Next.js 15 and includes:
- Modern React with TypeScript
- Tailwind CSS for styling
- Real-time chat interface
- File upload functionality
- Responsive design
## 📁 Project Structure
```
ask-docs/
├── client/ # Next.js frontend
│ ├── src/app/ # App router components
│ ├── public/ # Static assets
│ └── package.json # Frontend dependencies
├── server/ # FastAPI backend
│ ├── controller/ # API controllers
│ ├── service/ # Business logic
│ ├── config/ # Configuration files
│ ├── main.py # FastAPI application
│ └── requirements.txt # Python dependencies
├── assets/ # Media files
│ ├── photo.png # Screenshot
│ └── recording.mov # Demo video
└── README.md # This file
```
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request
## 📄 License
This project is created for **learning purposes** and is open source. Feel free to use, modify, and learn from this code for educational purposes.
**Note**: This is a learning project demonstrating RAG (Retrieval-Augmented Generation) implementation with FastAPI, LangChain, and Next.js. The code is provided as-is for educational use.
## 🙏 Acknowledgments
- [LangChain](https://langchain.com/) for RAG implementation
- [FastAPI](https://fastapi.tiangolo.com/) for the backend framework
- [Next.js](https://nextjs.org/) for the frontend framework
- [ChromaDB](https://www.trychroma.com/) for vector storage
- [OpenAI](https://openai.com/) for AI capabilities