#!/usr/bin/env python3
"""
Lineage Graph Extractor - Integration Example

This script demonstrates how to use the Lineage Graph Extractor agent
programmatically with the Anthropic API.

Usage:
    python integration_example.py
"""

import os
from anthropic import Anthropic
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

def load_agent_config():
    """Load the agent configuration from memories/agent.md"""
    config_path = os.path.join(os.path.dirname(__file__), "memories", "agent.md")
    
    with open(config_path, "r") as f:
        return f.read()

def extract_lineage(client, system_prompt, user_message):
    """
    Send a lineage extraction request to the agent.
    
    Args:
        client: Anthropic client instance
        system_prompt: Agent system prompt
        user_message: User's lineage extraction request
    
    Returns:
        Agent's response text
    """
    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=4000,
        system=system_prompt,
        messages=[{
            "role": "user",
            "content": user_message
        }]
    )
    
    return response.content[0].text

def main():
    """Main function demonstrating agent usage"""
    
    # Initialize Anthropic client
    api_key = os.getenv("ANTHROPIC_API_KEY")
    if not api_key:
        print("Error: ANTHROPIC_API_KEY not found in environment variables.")
        print("Please set it in your .env file.")
        return
    
    client = Anthropic(api_key=api_key)
    
    # Load agent configuration
    print("Loading agent configuration...")
    system_prompt = load_agent_config()
    print("✓ Agent configuration loaded\n")
    
    # Example 1: Simple greeting to test agent
    print("=" * 60)
    print("Example 1: Testing agent connection")
    print("=" * 60)
    response = extract_lineage(
        client,
        system_prompt,
        "Hello! What can you help me with?"
    )
    print(response)
    print()
    
    # Example 2: Extract lineage from sample metadata
    print("=" * 60)
    print("Example 2: Extract lineage from sample metadata")
    print("=" * 60)
    
    sample_metadata = """
    {
        "tables": [
            {
                "name": "raw_orders",
                "type": "source",
                "description": "Raw order data from API"
            },
            {
                "name": "raw_customers",
                "type": "source",
                "description": "Raw customer data from database"
            },
            {
                "name": "stg_orders",
                "type": "staging",
                "description": "Cleaned and standardized orders",
                "depends_on": ["raw_orders"]
            },
            {
                "name": "stg_customers",
                "type": "staging",
                "description": "Cleaned and standardized customers",
                "depends_on": ["raw_customers"]
            },
            {
                "name": "fct_orders",
                "type": "fact",
                "description": "Order facts with customer data",
                "depends_on": ["stg_orders", "stg_customers"]
            }
        ]
    }
    """
    
    response = extract_lineage(
        client,
        system_prompt,
        f"Extract lineage from this metadata and create a Mermaid diagram:\n\n{sample_metadata}"
    )
    print(response)
    print()
    
    # Example 3: BigQuery extraction (requires credentials)
    if os.getenv("GOOGLE_CLOUD_PROJECT"):
        print("=" * 60)
        print("Example 3: BigQuery lineage extraction")
        print("=" * 60)
        
        project_id = os.getenv("GOOGLE_CLOUD_PROJECT")
        response = extract_lineage(
            client,
            system_prompt,
            f"Extract lineage from BigQuery project: {project_id}, dataset: analytics"
        )
        print(response)
    else:
        print("Skipping BigQuery example (GOOGLE_CLOUD_PROJECT not set)")
    
    print("\n" + "=" * 60)
    print("Examples complete!")
    print("=" * 60)

if __name__ == "__main__":
    main()