API_TEST_RESULTS.md

API End-to-End Test Results

Test Summary

Date: Checkpoint Task 10 Status: ✅ PASSED API Endpoint: http://localhost:8000

Tests Performed

1. Health Check Endpoint ✅

Request: GET /health Response:

{
  "status": "healthy",
  "rag_system": {
    "total_chunks": 11,
    "total_articles": 5
  }
}

Result: PASSED - API is healthy and RAG system initialized correctly

2. Root Endpoint ✅

Request: GET / Response:

{
  "name": "Smart Escalation API",
  "version": "1.0.0",
  "status": "operational",
  "endpoints": {
    "ask": "/ask (POST)"
  }
}

Result: PASSED - Root endpoint returns API information

3. Valid Question - Billing ✅

Request: POST /ask

{
  "question": "What subscription plans are available?"
}

Response:

{
  "response_type": "answer",
  "message": "Hello there!\n\nTaskFlow offers three subscription plans:\n\n*   **Free Plan**\n*   **Pro Plan**\n*   **Enterprise Plan**\n\nLet me know if you have any other questions!",
  "confidence_explanation": "High confidence - retrieved relevant content from 2 help articles with best similarity score 0.68",
  "sources": ["billing.md", "integrations.md"]
}

Result: PASSED - API returns answer with high confidence

4. Valid Question - Integrations ✅

Request: POST /ask

{
  "question": "How do I integrate with Slack?"
}

Response:

{
  "response_type": "answer",
  "message": "Hello there! I'd be happy to help you with that.\n\nTo integrate with Slack:\n\n1.  Go to Settings > Integrations.\n2.  Click \"Connect\" next to Slack.\n3.  Follow the authorization prompts.\n\nPlease note that integrations, including Slack, are available on Pro and Enterprise plans only.\n\nLet me know if you have any other questions!",
  "confidence_explanation": "High confidence - retrieved relevant content from 2 help articles with best similarity score 0.67",
  "sources": ["integrations.md", "troubleshooting.md"]
}

Result: PASSED - API returns detailed answer with sources

5. Out-of-Scope Question ✅

Request: POST /ask

{
  "question": "What is the weather like today?"
}

Response:

{
  "response_type": "escalation",
  "message": "I'm not certain I can answer this accurately. Let me connect you with a human support agent who can help.",
  "confidence_explanation": "Best retrieval score was 0.05, below confidence threshold of 0.5",
  "sources": null
}

Result: PASSED - API correctly escalates when question is out of scope

6. Invalid Request - Empty Question ✅

Request: POST /ask

{
  "question": ""
}

Response: HTTP 422

{
  "detail": [{
    "type": "string_too_short",
    "loc": ["body", "question"],
    "msg": "String should have at least 1 character",
    "input": "",
    "ctx": {"min_length": 1}
  }]
}

Result: PASSED - API validates input and returns appropriate error

Issues Found and Fixed

Issue 1: Response Type Mismatch

Problem: EscalationDecision used "escalate" but API expected "escalation" Fix: Updated all instances in src/escalation.py to use "escalation" Status: ✅ FIXED

Issue 2: Incorrect Model Name

Problem: Configuration used gemini-1.5-flash which doesn't exist Fix: Updated to gemini-2.5-flash in .env and .env.example Status: ✅ FIXED

System Configuration

• Embedding Model: all-MiniLM-L6-v2
• LLM Model: gemini-2.5-flash
• Relevance Threshold: 0.5
• Top-K Retrieval: 3
• Total Articles: 5
• Total Chunks: 11

Conclusion

✅ All tests passed successfully!

The API is functioning correctly with:

• Proper request validation
• Accurate answer generation for in-scope questions
• Intelligent escalation for out-of-scope questions
• Appropriate error handling
• Correct response schema
• Working health check endpoints

The system is ready to proceed to Phase 3 (Frontend implementation).