A full-stack RAG (Retrieval-Augmented Generation) assistant that helps users with LangChain, LangGraph, and LangSmith documentation.

• 🤖 AI-Powered Q&A - Ask questions about LangChain, LangGraph, and LangSmith
• 📚 Documentation RAG - Retrieves relevant documentation snippets for accurate answers
• 🎯 Service Filtering - Filter answers to specific services
• 📖 Source Citations - See exactly which documentation was used
• 🌙 Modern Dark UI - Beautiful glassmorphism design with animations

LangChainAssist/
├── backend/                 # FastAPI backend
│   ├── main.py             # Application entry point
│   ├── config.py           # Configuration settings
│   ├── requirements.txt    # Python dependencies
│   ├── routers/            # API routes
│   ├── services/           # Business logic (RAG, doc loading)
│   └── models/             # Pydantic schemas
│
└── frontend/               # Next.js frontend
    ├── src/
    │   ├── app/            # Next.js app router
    │   ├── components/     # React components
    │   └── lib/            # API client
    └── package.json

• Python 3.10+
• Node.js 18+
• OpenAI API key

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create .env file
cp .env.example .env
# Edit .env and add your OPENAI_API_KEY

# Start the server
python main.py

The API will be available at http://localhost:8000

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

The frontend will be available at http://localhost:3000

The easiest way to run the application is using Docker Compose.

1. Ensure you have Docker and Docker Compose installed.
2. Create the environment files:

   # Backend
   cp backend/.env.example backend/.env
   # Edit backend/.env and add your OPENAI_API_KEY
   
   # Frontend
   cp frontend/.env.local.example frontend/.env.local

3. Run with Docker Compose:

   docker-compose up --build

The application will be available at:

• Frontend: http://localhost:3006
• Backend docs: http://localhost:3005/docs

If you prefer to run it manually ("regularly") on your computer:

Terminal 1: Backend

cd backend
# Install python dependencies
pip install -r requirements.txt
# Run the server
python main.py

Terminal 2: Frontend

cd frontend
# Install node dependencies
npm install
# Run the development server
npm run dev

curl -X POST http://localhost:8000/api/chat \
  -H "Content-Type: application/json" \
  -d '{"question": "How do I create a chain in LangChain?", "service_filter": "langchain"}'

Create a .env.local file:

NEXT_PUBLIC_API_URL=http://localhost:8000

• Backend: FastAPI, LangChain, ChromaDB, OpenAI
• Frontend: Next.js 14, React, Tailwind CSS, TypeScript
• RAG Pipeline: Document loading, chunking, embedding, vector search

Since this application uses OpenAI for LLM and Embeddings, the local resource requirements are lightweight.

Recommended Configuration:

• CPU: 2 vCPUs (sufficient for web server and vector search)
• RAM: 4GB (Next.js build/runtime + FastAPI + ChromaDB)
• GPU: None Required (All heavy AI lifting is done via OpenAI API)
• Storage: 10GB+ SSD (for Docker images and vector database)

Why no GPU? The generic "GPU" requirement for AI apps usually applies when running local LLMs (like Llama 2, Mistral). This app is "Cloud Native AI" - it sends text to OpenAI and gets answers back. Your server just acts as a coordinator.

MIT