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Quick Start

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Prerequisites

  • Node.js 18+
  • Nebius API key
  • Qdrant Cloud account and API key

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Setup

  1. Clone and setup environment: 
    git clone https://github.com/nebius/ai-studio-cookbook
cd rag/content-gen-pipeline-qdrant
cp backend/env.example backend/.env
cp frontend/env.example frontend/.env
  2. Configure environment variables: 
    # Backend (.env)
PORT=3001
NODE_ENV=development
QDRANT_URL=https://your-cluster-id.us-east-1-0.aws.cloud.qdrant.io:6333
QDRANT_API_KEY=your_qdrant_cloud_api_key_here
NEBIUS_API_KEY=your_nebius_api_key_here
CORS_ORIGIN=http://localhost:3000

# Frontend (.env)
REACT_APP_API_BASE_URL=http://localhost:3001
REACT_APP_API_TIMEOUT=30000
  3. Start the application: 
    cd backend && npm install && npm start
cd frontend && npm install && npm start

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Current Workflows

The platform supports two main workflows for content generation:

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1. Content Generation Workflow

Purpose: Generate AI-powered content suggestions for various content types with optional document context. Content Types:
  • Social Media Posts: Generate engaging posts for LinkedIn, Twitter, Instagram, etc.
  • Articles: Create blog posts and articles with structured content
  • Demo Applications: Generate demo ideas and application concepts
Process:
  1. Select content type (social media, article, or demo)
  2. Add optional goals or requirements
  3. Optionally provide document context for RAG-enhanced generation
  4. AI generates multiple suggestions with:
    • Titles and descriptions
    • Key points or features
    • Target audience
    • Platform recommendations
    • Engagement strategies
Features:
  • Uses Nebius AI Studio (Llama-3.3-70B-Instruct model for generation, Qwen/Qwen3-Embedding-8B for embeddings)
  • RAG-enhanced generation using uploaded document context
  • Provides formatted, ready-to-use content
  • Stores generation history

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2. Document Upload & Processing Workflow

Purpose: Upload and process documents to provide context for RAG-enhanced content generation. Supported Formats:
  • Text files (.txt, .md, .docx)
  • URLs (web pages)
Process:
  1. Upload documents through the web interface
  2. Automatic content extraction and processing
  3. Document chunking (1000 words with 200-word overlap)
  4. Vector embedding generation using Nebius AI Studio (Qwen/Qwen3-Embedding-8B)
  5. Storage in Qdrant Cloud vector database
  6. Context retrieval for content generation
Features:
  • Automatic content extraction from URLs
  • Intelligent document chunking
  • Vector embedding for semantic search using Nebius Embedding Qwen/Qwen3-Embedding-8B model
  • UUID-based point IDs for reliability
  • Context-aware content generation

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Local Development

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Development Setup

  1. Clone the repository: 
    git clone <repository-url>
cd nebius-qdrant
  2. Install dependencies: 
    # Backend dependencies
cd backend
npm install

# Frontend dependencies
cd ../frontend
npm install
  3. Configure environment: 
    # Copy environment files
cp backend/env.example backend/.env
cp frontend/env.example frontend/.env

# Edit backend/.env with your API keys
NEBIUS_API_KEY=your_nebius_api_key
QDRANT_URL=https://your-cluster-id.us-east-1-0.aws.cloud.qdrant.io:6333
QDRANT_API_KEY=your_qdrant_cloud_api_key
  4. Start development servers: 
    # Terminal 1: Backend
cd backend
npm start

# Terminal 2: Frontend
cd frontend
npm start

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Development URLs

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Development Features

  • Hot reloading for both frontend and backend
  • Real-time API testing
  • Vector database management via Qdrant Cloud
  • Content generation history
  • Document processing logs

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Project Structure

nebius-qdrant/
├── backend/                 # Node.js API server
│   ├── src/
│   │   ├── controllers/     # API endpoints
│   │   │   ├── contentController.js    # Content generation logic
│   │   │   ├── dataController.js       # Document upload & management
│   │   │   └── feedbackController.js   # User feedback handling
│   │   ├── services/        # Business logic
│   │   │   ├── nebiusService.js        # Nebius AI integration
│   │   │   ├── qdrantService.js        # Qdrant Cloud operations
│   │   │   ├── embeddingService.js     # Nebius AI embeddings
│   │   │   └── documentService.js      # Document processing
│   │   └── utils/          # Utilities
│   │       └── formatter.js            # Response formatting
│   ├── examples/           # Usage examples
│   │   └── rag-usage-example.js        # RAG workflow demo
│   └── uploads/            # Temporary file storage
├── frontend/               # React application
│   ├── src/
│   │   ├── components/     # React components
│   │   │   ├── DocumentUpload.js       # File upload interface
│   │   │   ├── Header.js               # Navigation header
│   │   │   └── Sidebar.js              # Side navigation
│   │   ├── pages/         # Page components
│   │   │   ├── Dashboard.js            # Main dashboard
│   │   │   ├── ContentGenerator.js     # Content generation UI
│   │   │   ├── DataUpload.js           # Document management
│   │   │   ├── Analytics.js            # Usage analytics
│   │   │   └── History.js              # Generation history
│   │   └── services/      # API client
│   │       └── api.js                  # API integration
│   └── public/            # Static assets
└── docker-compose.yml     # Docker configuration (backend + frontend only)

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API Endpoints

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Content Generation

  • POST /api/content/suggest - Generate content suggestions with optional RAG context
  • GET /api/content/stats - Get content statistics
  • GET /api/content/history - Get generation history

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Data Management

  • POST /api/data/company - Upload company data
  • POST /api/data/files - Upload documents
  • POST /api/data/links - Upload URLs
  • GET /api/data/documents - Get uploaded documents
  • GET /api/data/stats - Get data statistics

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Feedback

  • POST /api/feedback - Submit user feedback

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Features

  • AI-Powered Content: Uses Nebius AI Studio (Llama-3.3-70B-Instruct for generation, Qwen/Qwen3-Embedding-8B for embeddings) for high-quality content generation
  • RAG-Enhanced Generation: Context-aware content creation using uploaded documents
  • Document Processing: Automatic chunking, embedding, and storage
  • Vector Search: Qdrant Cloud integration for semantic search and context retrieval
  • Modern UI: Clean, responsive React interface with Tailwind CSS
  • Real-time Processing: Live content generation and document processing
  • Multi-format Support: Text files, URLs, JSON, and CSV processing
  • History Tracking: Complete generation history and analytics