EchoWrite AI - Setup and Usage Guide

🎯 Overview

EchoWrite AI is a sophisticated content repurposing system that transforms any URL (YouTube video or webpage) into multiple content formats using AI agents orchestrated with LangGraph.

Output Formats:

• 📰 Blog Post (800-1200 words)
• 🐦 Twitter Thread (5-8 tweets)
• 💼 LinkedIn Post (150-300 words)

🏗️ Architecture

The system uses a multi-agent pipeline with LangGraph:

1. Content Extraction Router - Intelligently chooses YouTube or web scraping
2. RAG Pipeline - Creates vector embeddings using ChromaDB
3. Insights Analyst - Extracts key themes, statistics, and arguments
4. Content Generation Suite - Three parallel agents create different formats

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Environment Setup

Create a .env file in the root directory:

GROQ_API_KEY=your_groq_api_key_here

3. Run the API Server

# Development server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# Or using Python directly
python -m app.main

4. Access the API

• API Documentation: http://localhost:8000/docs
• Health Check: http://localhost:8000/health
• Main Endpoint: POST http://localhost:8000/repurpose

📡 API Usage

Request Format

{
  "url": "https://www.youtube.com/watch?v=example"
}

Response Format

{
  "success": true,
  "url": "https://www.youtube.com/watch?v=example",
  "extraction_method": "youtube",
  "word_count": 1500,
  "processing_time": 45.2,
  "insights": {
    "main_themes": ["AI development", "automation"],
    "key_statistics": ["50% increase", "10x faster"],
    "main_arguments": ["AI improves productivity"],
    "target_audience": "developers",
    "content_type": "educational",
    "tone": "informative"
  },
  "content": {
    "blog_post": "# How AI is Transforming...",
    "twitter_thread": [
      "🧵 Thread: AI in development",
      "1/ Traditional workflows are evolving..."
    ],
    "linkedin_post": "The future of development is here..."
  },
  "errors": [],
  "current_step": "completed"
}

🧪 Testing

Run the end-to-end test:

python test_system.py

📁 Project Structure

EchoWrite/
├── app/
│   ├── __init__.py
│   ├── main.py              # FastAPI application
│   ├── schemas.py           # Pydantic models
│   ├── ai_core/
│   │   ├── graph.py         # LangGraph workflow
│   │   ├── nodes.py         # Pipeline nodes
│   │   └── prompts.py       # AI agent prompts
│   └── services/
│       ├── content_extractor.py  # Extraction tools
│       └── rag_pipeline.py       # ChromaDB integration
├── data/                    # ChromaDB storage
├── requirements.txt
├── test_system.py
└── .env                     # Environment variables

🔧 Configuration

Supported URLs

• YouTube: youtube.com, youtu.be, m.youtube.com
• Websites: Any public webpage with readable content

Content Lengths

• Blog Post: 800-1200 words
• Twitter Thread: 5-8 tweets (~280 chars each)
• LinkedIn Post: 150-300 words

🛠️ Development

Adding New Content Formats

1. Update ContentState in graph.py
2. Add new prompt in prompts.py
3. Extend generate_content_suite node in nodes.py
4. Update response schemas in schemas.py

Customizing AI Behavior

Edit prompts in app/ai_core/prompts.py to change:

• Content tone and style
• Output structure
• Analysis depth

🐛 Troubleshooting

Common Issues

1. Import Errors: Install dependencies with pip install -r requirements.txt
2. API Key Error: Ensure GROQ_API_KEY is set in .env
3. ChromaDB Issues: Check that data/ directory is writable
4. URL Extraction Fails: Verify URL is publicly accessible

Error Responses

The API returns detailed error information in the errors array:

{
  "success": false,
  "errors": ["Content extraction failed: Invalid URL"],
  "current_step": "extracting_content"
}

🚀 Production Deployment

Environment Variables

GROQ_API_KEY=your_production_key
LOG_LEVEL=INFO
CORS_ORIGINS=https://yourdomain.com

Docker Deployment

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

📊 Performance

• Typical Processing Time: 30-60 seconds
• Memory Usage: ~500MB with ChromaDB
• Concurrent Requests: Limited by Groq API rate limits

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

📄 License

This project is available under the MIT License.