Tech Stack Advisor - Code Viewer

"""Test the complete LangGraph workflow orchestration."""
import asyncio
import json
from backend.src.orchestration import TechStackOrchestrator
from backend.src.core.logging import setup_logging


async def test_workflow() -> None:
    """Test the workflow with various queries."""
    setup_logging()

    print("🚀 Testing Tech Stack Advisor - LangGraph Workflow\n")
    print("=" * 70)

    # Initialize orchestrator
    print("\n📊 Initializing TechStackOrchestrator...")
    orchestrator = TechStackOrchestrator()
    print("   ✓ Orchestrator initialized")
    print(f"   - Agents loaded: 4 (Database, Infrastructure, Cost, Security)")
    print(f"   - Workflow nodes: 6 (parse, 4 agents, synthesize)")

    # Test queries
    test_queries = [
        {
            "name": "Real-time Chat App",
            "query": "I'm building a real-time chat application expecting 100K daily active users. What tech stack should I use?",
        },
        {
            "name": "E-commerce Platform",
            "query": "Need a database and infrastructure for an e-commerce site with 50k DAU, payment processing, and GDPR compliance",
        },
        {
            "name": "Healthcare App",
            "query": "Building a healthcare application with HIPAA compliance requirements for 10,000 daily users",
        },
    ]

    # Run first query in detail
    print("\n" + "=" * 70)
    print(f"\n🔍 Test Query 1: {test_queries[0]['name']}")
    print(f"   Query: \"{test_queries[0]['query']}\"\n")

    result = await orchestrator.process_query(test_queries[0]["query"])

    if result.get("status") == "success":
        print("✅ Workflow completed successfully!\n")

        # Show parsed context
        print("📋 Parsed Context:")
        context = result.get("parsed_context", {})
        print(f"   - DAU: {context.get('dau', 0):,}")
        print(f"   - QPS: {context.get('qps', 0)}")
        print(f"   - Data Type: {context.get('data_type', 'N/A')}")
        print(f"   - Workload: {context.get('workload_type', 'N/A')}")
        print(f"   - Sensitivity: {context.get('data_sensitivity', 'N/A')}")

        # Show agent results summary
        print("\n🤖 Agent Results:")
        recommendations = result.get("recommendations", {})

        # Database
        db_result = recommendations.get("database", {})
        if db_result:
            scale_info = db_result.get("scale_info", {})
            print(f"\n   1. Database Agent:")
            print(f"      - Scale Tier: {scale_info.get('tier', 'N/A')}")
            print(f"      - Cache Recommended: {scale_info.get('cache_recommended', False)}")
            print(f"      - Recommendation: {scale_info.get('recommendation', 'N/A')[:60]}...")

        # Infrastructure
        infra_result = recommendations.get("infrastructure", {})
        if infra_result:
            scale_info = infra_result.get("scale_info", {})
            print(f"\n   2. Infrastructure Agent:")
            print(f"      - Scale Tier: {scale_info.get('tier', 'N/A')}")
            print(f"      - Architecture: {scale_info.get('suggested_architecture', 'N/A')}")
            print(f"      - Deployment: {scale_info.get('deployment_strategy', 'N/A')[:60]}...")

        # Cost
        cost_result = recommendations.get("cost", {})
        if cost_result:
            comparisons = cost_result.get("cost_comparisons", [])
            if comparisons:
                cheapest = comparisons[0]
                print(f"\n   3. Cost Agent:")
                print(f"      - Recommended: {cheapest.get('provider', 'N/A').upper()}")
                print(f"      - Monthly Cost: ${cheapest.get('monthly_cost', 0):.2f}")
                print(f"      - Annual Cost: ${cheapest.get('monthly_cost', 0) * 12:.2f}")

        # Security
        security_result = recommendations.get("security", {})
        if security_result:
            threat = security_result.get("threat_assessment", {})
            print(f"\n   4. Security Agent:")
            print(f"      - Risk Priority: {threat.get('priority', 'N/A')}")
            print(f"      - Risk Multiplier: {threat.get('risk_multiplier', 0)}x")
            print(f"      - Architecture: {threat.get('architecture', 'N/A')}")

        print("\n" + "=" * 70)

    else:
        print(f"❌ Workflow failed: {result.get('error', 'Unknown error')}")

    # Quick test remaining queries
    print("\n\n📝 Quick Tests for Other Queries:\n")
    for i, test_query in enumerate(test_queries[1:], start=2):
        print(f"   {i}. {test_query['name']}...", end=" ")
        result = await orchestrator.process_query(test_query["query"])
        if result.get("status") == "success":
            context = result.get("parsed_context", {})
            print(f"✓ (DAU: {context.get('dau', 0):,}, Compliance: {len(context.get('compliance', []))} rules)")
        else:
            print(f"✗ Error: {result.get('error', 'Unknown')}")

    print("\n" + "=" * 70)
    print("\n✅ LangGraph Workflow Testing Complete!")
    print("\n🎯 Workflow Features Demonstrated:")
    print("   ✓ Query parsing and context extraction")
    print("   ✓ Sequential agent coordination (Database → Infra → Cost → Security)")
    print("   ✓ State management across agents")
    print("   ✓ Error handling and logging")
    print("   ✓ Result synthesis and formatting")
    print("\n📦 Next Steps:")
    print("   - Create FastAPI endpoints to expose this workflow")
    print("   - Add RAG integration with Qdrant")
    print("   - Build Streamlit frontend")
    print()


if __name__ == "__main__":
    asyncio.run(test_workflow())