Product Review Agent

A complex structured output agent demonstrating advanced Pydantic schemas for product review analysis.

🎯 What This Demonstrates

• Complex Schemas: Multi-field Pydantic models with various data types
• List Fields: Arrays of strings for pros/cons analysis
• Boolean Logic: Recommendation decisions based on review content
• Sentiment Analysis: Automated sentiment classification

🚀 Quick Start

1. Install OpenAI Agents SDK:

   pip install openai-agents
   

2. Set up environment:

   cp ../env.example .env
   # Edit .env and add your OpenAI API key
   

3. Run the agent:

   from agents import Runner
   from agent import root_agent
   
   review_text = "This laptop is amazing! Great performance, long battery life, but a bit heavy."
   result = Runner.run_sync(root_agent, f"Analyze this review: {review_text}")
   print(result.final_output)  # Returns ProductReview object
   

💡 Key Concepts

• Rating Validation: Integer constraints (1-5 stars)
• Sentiment Enum: Automatic positive/negative/neutral classification
• List Processing: Extracting multiple pros and cons
• Optional Fields: Handling missing reviewer information

🧪 Example Output

{
    "product_name": "Gaming Laptop XYZ",
    "rating": 4,
    "summary": "Great performance but heavy design",
    "sentiment": "positive",
    "pros": ["Great performance", "Long battery life", "Good display"],
    "cons": ["Heavy weight", "Expensive price"],
    "recommend": true,
    "reviewer_name": "TechEnthusiast123"
}

🔗 Next Steps

• Support Ticket Agent - Basic structured output
• Tutorial 3: Tool Using Agent - Adding tools to agents