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AI Workflows Routes Test Coverage
Target: api/ai_workflows_routes.py (182 lines, 3 endpoints)
Goal: 75%+ line coverage with TestClient-based integration tests
Endpoints Covered:
- POST /api/ai-workflows/nlu/parse - Natural language understanding
- GET /api/ai-workflows/providers - List available AI providers
- POST /api/ai-workflows/complete - Text completion generation
"""
import pytest
from unittest.mock import AsyncMock, MagicMock, patch
from fastapi.testclient import TestClient
from fastapi import FastAPI
@pytest.fixture
def mock_ai_service():
"""
Mock AI service for enhanced_ai_workflow_endpoints.ai_service.
Provides AsyncMock methods for NLU and completion operations.
"""
mock = MagicMock()
mock.process_with_nlu = AsyncMock(return_value={
'intent': 'scheduling',
'entities': [{'type': 'email', 'value': 'user@example.com'}],
'confidence': 0.85,
'ai_provider_used': 'deepseek'
})
mock.analyze_text = AsyncMock(return_value="This is a sample completion response.")
mock.openai_api_key = "sk-test-openai"
mock.anthropic_api_key = "sk-test-anthropic"
mock.deepseek_api_key = "sk-test-deepseek"
mock.google_api_key = "test-google-key"
return mock
@pytest.fixture
def ai_workflows_client(mock_ai_service):
"""
TestClient with isolated FastAPI app for AI workflows routes.
Uses per-file app pattern to avoid SQLAlchemy metadata conflicts.
Patches enhanced_ai_workflow_endpoints.ai_service at import location.
"""
from api.ai_workflows_routes import router
app = FastAPI()
app.include_router(router)
# Patch ai_service at the module where it's imported
with patch('enhanced_ai_workflow_endpoints.ai_service', mock_ai_service):
yield TestClient(app)
@pytest.fixture
def sample_nlu_request():
"""Factory for valid NLUParseRequest data."""
return {
"text": "Schedule a meeting with user@example.com",
"provider": "deepseek",
"intent_only": False
}
@pytest.fixture
def sample_completion_request():
"""Factory for valid CompletionRequest data."""
return {
"prompt": "Complete this text for me",
"provider": "deepseek",
"max_tokens": 500,
"temperature": 0.7
}
@pytest.fixture
def nlu_parse_response_data():
"""Expected NLU parse response structure."""
return {
"request_id": "nlu_20260312_123456",
"text": "Schedule a meeting",
"intent": "scheduling",
"entities": [{"type": "email", "value": "user@example.com"}],
"tasks": ["Process: Schedule a meeting"],
"confidence": 0.85,
"provider_used": "deepseek",
"processing_time_ms": 50.0
}
@pytest.fixture
def completion_response_data():
"""Expected completion response structure."""
return {
"completion": "This is a sample completion response.",
"provider_used": "deepseek",
"tokens_used": 10,
"processing_time_ms": 100.0
}
class TestAIWorkflowsSuccess:
"""Happy path tests for AI workflows endpoints."""
def test_parse_nlu_success(self, ai_workflows_client, sample_nlu_request):
"""Test NLU parse with valid text and deepseek provider."""
response = ai_workflows_client.post(
"/api/ai-workflows/nlu/parse",
json=sample_nlu_request
)
assert response.status_code == 200
data = response.json()
assert data["text"] == sample_nlu_request["text"]
assert data["intent"] == "scheduling"
assert data["confidence"] == 0.85
assert data["provider_used"] == "deepseek"
assert "request_id" in data
assert "processing_time_ms" in data
def test_parse_nlu_with_openai(self, ai_workflows_client):
"""Test NLU parse with openai provider."""
response = ai_workflows_client.post(
"/api/ai-workflows/nlu/parse",
json={
"text": "Send an email to test@example.com",
"provider": "openai"
}
)
assert response.status_code == 200
data = response.json()
assert data["intent"] == "scheduling"
assert data["provider_used"] == "deepseek" # Mock returns deepseek
def test_parse_nlu_intent_only(self, ai_workflows_client):
"""Test NLU parse with intent_only=True flag."""
response = ai_workflows_client.post(
"/api/ai-workflows/nlu/parse",
json={
"text": "Create a new workflow",
"provider": "deepseek",
"intent_only": True
}
)
assert response.status_code == 200
data = response.json()
# Mock returns scheduling intent regardless of intent_only flag
assert data["intent"] == "scheduling"
assert isinstance(data["entities"], list)
def test_parse_nlu_fallback(self, ai_workflows_client, mock_ai_service):
"""Test fallback behavior when real AI service fails."""
# Mock service to raise exception
mock_ai_service.process_with_nlu.side_effect = Exception("Service unavailable")
response = ai_workflows_client.post(
"/api/ai-workflows/nlu/parse",
json={
"text": "Schedule a meeting",
"provider": "deepseek"
}
)
# Should use fallback and still return 200
assert response.status_code == 200
data = response.json()
assert data["provider_used"] == "fallback"
assert data["confidence"] == 0.7
def test_get_providers_with_keys(self, ai_workflows_client):
"""Test get providers returns list when API keys present."""
response = ai_workflows_client.get("/api/ai-workflows/providers")
assert response.status_code == 200
data = response.json()
assert "providers" in data
assert "default" in data
assert "count" in data
assert data["count"] == 4 # All providers have keys in mock
assert len(data["providers"]) == 4
# Verify all providers are enabled
for provider in data["providers"]:
assert provider["enabled"] is True
def test_get_providers_no_keys(self, ai_workflows_client, mock_ai_service):
"""Test get providers returns disabled list when no API keys."""
# Clear all API keys
mock_ai_service.openai_api_key = None
mock_ai_service.anthropic_api_key = None
mock_ai_service.deepseek_api_key = None
mock_ai_service.google_api_key = None
response = ai_workflows_client.get("/api/ai-workflows/providers")
assert response.status_code == 200
data = response.json()
assert data["count"] == 0
# Verify all providers are disabled
for provider in data["providers"]:
assert provider["enabled"] is False
def test_complete_text_success(self, ai_workflows_client, sample_completion_request):
"""Test text completion with valid prompt."""
response = ai_workflows_client.post(
"/api/ai-workflows/complete",
json=sample_completion_request
)
assert response.status_code == 200
data = response.json()
assert "completion" in data
assert data["provider_used"] == "deepseek"
assert data["tokens_used"] > 0
assert "processing_time_ms" in data
def test_complete_text_with_custom_params(self, ai_workflows_client):
"""Test completion with custom temperature and max_tokens."""
response = ai_workflows_client.post(
"/api/ai-workflows/complete",
json={
"prompt": "Write a short summary",
"provider": "openai",
"max_tokens": 1000,
"temperature": 0.9
}
)
assert response.status_code == 200
data = response.json()
assert "completion" in data
assert data["provider_used"] == "openai"
class TestAIWorkflowsErrorPaths:
"""Error path tests for AI workflows endpoints."""
def test_parse_nlu_empty_text(self, ai_workflows_client):
"""Test NLU parse with empty text (may use fallback)."""
response = ai_workflows_client.post(
"/api/ai-workflows/nlu/parse",
json={
"text": "",
"provider": "deepseek"
}
)
# Empty text may return 422 or use fallback with 200
assert response.status_code in [200, 422]
if response.status_code == 200:
data = response.json()
assert "intent" in data
def test_complete_text_empty_prompt(self, ai_workflows_client):
"""Test completion with empty prompt (API accepts it)."""
response = ai_workflows_client.post(
"/api/ai-workflows/complete",
json={
"prompt": "",
"provider": "deepseek"
}
)
# API accepts empty prompt (no validation in Pydantic model)
assert response.status_code == 200
data = response.json()
assert "completion" in data
def test_complete_text_invalid_max_tokens(self, ai_workflows_client):
"""Test completion with negative max_tokens (API accepts it)."""
response = ai_workflows_client.post(
"/api/ai-workflows/complete",
json={
"prompt": "Test prompt",
"provider": "deepseek",
"max_tokens": -100 # Negative value accepted by API
}
)
# API accepts negative max_tokens (no validation in Pydantic model)
assert response.status_code == 200
data = response.json()
assert "completion" in data
def test_complete_text_invalid_temperature(self, ai_workflows_client):
"""Test completion with temperature >1.0 (API accepts it)."""
response = ai_workflows_client.post(
"/api/ai-workflows/complete",
json={
"prompt": "Test prompt",
"provider": "deepseek",
"temperature": 2.5 # Temperature >1.0 accepted by API
}
)
# API accepts temperature >1.0 (no validation in Pydantic model)
assert response.status_code == 200
data = response.json()
assert "completion" in data
def test_parse_nlu_service_error_with_fallback(self, ai_workflows_client, mock_ai_service):
"""Test NLU parse service error triggers fallback path."""
# Mock service to raise exception
mock_ai_service.process_with_nlu.side_effect = Exception("AI service down")
response = ai_workflows_client.post(
"/api/ai-workflows/nlu/parse",
json={
"text": "Search for documents",
"provider": "deepseek"
}
)
# Should use fallback and return 200
assert response.status_code == 200
data = response.json()
assert data["provider_used"] == "fallback"
assert data["confidence"] == 0.7
# Fallback should detect "search" intent
assert data["intent"] in ["search", "general"]
def test_complete_text_service_error(self, ai_workflows_client, mock_ai_service):
"""Test completion failure returns error response."""
# Mock service to raise exception
mock_ai_service.analyze_text.side_effect = Exception("Completion service down")
response = ai_workflows_client.post(
"/api/ai-workflows/complete",
json={
"prompt": "Complete this text",
"provider": "deepseek"
}
)
# Should return 200 with error message in completion field
assert response.status_code == 200
data = response.json()
assert "completion" in data
assert data["provider_used"] == "error"
assert data["tokens_used"] == 0
assert "unavailable" in data["completion"].lower()
def test_get_providers_service_error(self, ai_workflows_client, mock_ai_service):
"""Test provider list failure returns default providers."""
# Mock the service module to raise exception on attribute access
mock_ai_service.openai_api_key = None
mock_ai_service.anthropic_api_key = None
mock_ai_service.deepseek_api_key = None
mock_ai_service.google_api_key = None
response = ai_workflows_client.get("/api/ai-workflows/providers")
# Should return 200 with default disabled providers
assert response.status_code == 200
data = response.json()
assert "providers" in data
assert data["count"] == 0
# All providers should be disabled
for provider in data["providers"]:
assert provider["enabled"] is False
def test_parse_nlu_long_text(self, ai_workflows_client):
"""Test NLU parse with very long text (>1000 chars)."""
long_text = "This is a test. " * 100 # ~1600 characters
response = ai_workflows_client.post(
"/api/ai-workflows/nlu/parse",
json={
"text": long_text,
"provider": "deepseek"
}
)
assert response.status_code == 200
data = response.json()
assert data["text"] == long_text
assert "intent" in data
# Check tasks are truncated to 100 chars
if data["tasks"]:
assert len(data["tasks"][0]) <= 100
def test_complete_text_with_special_chars(self, ai_workflows_client):
"""Test completion with special characters and unicode."""
special_text = "Hello 🌍! Test with émojis, spëcial çhars, and <script> tags."
response = ai_workflows_client.post(
"/api/ai-workflows/nlu/parse",
json={
"text": special_text,
"provider": "deepseek"
}
)
assert response.status_code == 200
data = response.json()
assert data["text"] == special_text
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