Content Creation & SEO Agent

A production-ready AI agent for automated content creation, SEO optimization, and multi-platform content repurposing using FastAPI, Ollama, and RAG (Retrieval-Augmented Generation).

Features

• 🚀 Multi-platform Content Generation - Blog posts, LinkedIn, Twitter, Medium
• 🔍 SEO Optimization - Keyword research, meta descriptions, content scoring
• ♻️ Content Repurposing - Convert blog posts to threads, newsletters, social media
• 🧠 RAG-Powered Research - Uses vector embeddings for fact-based content
• 🎨 Image Prompt Generation - AI-generated image descriptions for content
• 📊 Analytics Integration - Track content performance metrics

Tech Stack

• FastAPI - High-performance async API framework
• Ollama - Local LLM inference (llama3.2, mistral)
• ChromaDB - Vector database for RAG
• LangChain - LLM orchestration and RAG pipeline
• BeautifulSoup4 - Web scraping for competitor analysis
• Sentence Transformers - Text embeddings

Architecture

content-seo-agent/
├── src/
│   ├── agent/
│   │   ├── content_generator.py    # Core content generation logic
│   │   ├── seo_optimizer.py        # SEO analysis and optimization
│   │   ├── repurposer.py           # Content format conversion
│   │   └── rag_engine.py           # RAG implementation
│   ├── api/
│   │   ├── main.py                 # FastAPI application
│   │   └── routes.py               # API endpoints
│   ├── models/
│   │   └── schemas.py              # Pydantic models
│   └── utils/
│       ├── embeddings.py           # Vector embedding utilities
│       └── scraper.py              # Web scraping utilities
├── data/
│   └── knowledge_base/             # RAG knowledge base storage
├── tests/
│   └── test_agent.py
├── requirements.txt
├── .env.example
└── docker-compose.yml

Installation

Prerequisites

• Python 3.10+
• Ollama installed locally (ollama.ai)
• Docker (optional, for deployment)

Setup

# Clone repository
cd content-seo-agent

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

# Install dependencies
pip install -r requirements.txt

# Pull Ollama models
ollama pull llama3.2
ollama pull mistral

# Setup environment
cp .env.example .env
# Edit .env with your configuration

# Initialize knowledge base
python scripts/init_knowledge_base.py

Usage

Start the API Server

# Development mode with auto-reload
uvicorn src.api.main:app --reload --port 8000

# Production mode
uvicorn src.api.main:app --host 0.0.0.0 --port 8000 --workers 4

API Endpoints

Generate Blog Post

POST /api/v1/generate/blog
{
  "topic": "Introduction to AI Agents",
  "keywords": ["AI", "agents", "automation"],
  "tone": "professional",
  "length": 1500
}

SEO Analysis

POST /api/v1/seo/analyze
{
  "content": "Your article content here...",
  "target_keywords": ["AI agents", "automation"]
}

Repurpose Content

POST /api/v1/repurpose
{
  "source_content": "Blog post content...",
  "target_format": "twitter_thread",
  "max_tweets": 10
}

Python SDK

from content_agent import ContentAgent

# Initialize agent
agent = ContentAgent(model="llama3.2")

# Generate content with RAG
blog_post = agent.generate_blog(
    topic="AI in Healthcare",
    keywords=["AI", "healthcare", "diagnosis"],
    use_rag=True,
    tone="educational"
)

# SEO optimize
optimized = agent.optimize_seo(
    content=blog_post,
    target_keywords=["AI healthcare", "medical AI"]
)

# Repurpose to social media
thread = agent.repurpose(
    content=blog_post,
    format="twitter_thread"
)

RAG Knowledge Base

The agent uses RAG to ground content in factual information:

1. Add documents to knowledge base:

python scripts/add_to_kb.py --file research_paper.pdf

2. Query knowledge base:

python scripts/query_kb.py --query "What are the benefits of AI agents?"

Docker Deployment

# Build and run with Docker Compose
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Configuration

Edit .env file:

# Ollama Configuration
OLLAMA_HOST=http://localhost:11434
OLLAMA_MODEL=llama3.2

# ChromaDB Configuration
CHROMA_HOST=localhost
CHROMA_PORT=8001

# API Configuration
API_HOST=0.0.0.0
API_PORT=8000

# Content Settings
DEFAULT_TONE=professional
MAX_CONTENT_LENGTH=3000

Performance

• Average generation time: 15-30 seconds for 1000-word blog post
• SEO analysis: < 2 seconds
• Repurposing: 5-10 seconds
• RAG retrieval: < 1 second

Testing

# Run all tests
pytest tests/

# Run with coverage
pytest --cov=src tests/

# Run specific test
pytest tests/test_agent.py::test_content_generation

Roadmap

• Multi-language support (Spanish, French, German)
• Image generation integration (DALL-E, Stable Diffusion)
• WordPress/Medium auto-publishing
• A/B testing for content variations
• Content calendar scheduling
• Analytics dashboard

Contributing

Contributions welcome! Please read CONTRIBUTING.md first.

License

MIT License - see LICENSE file for details.

Support

• Documentation: useagenticai.in
• Issues: GitHub Issues
• Email: info@useagenticai.in

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