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409 lines
16 KiB
Python
409 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Example LLM Service Tests - Phase 1 Implementation
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This file demonstrates the testing patterns for achieving 80%+ coverage
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Priority: Critical Business Logic (Week 1-2)
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Target: app/services/llm/service.py (15% → 85% coverage)
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"""
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import pytest
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from unittest.mock import Mock, patch, AsyncMock
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from app.services.llm.service import LLMService
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from app.services.llm.models import ChatCompletionRequest, ChatMessage
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from app.services.llm.exceptions import LLMServiceError, ProviderError
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from app.core.config import get_settings
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class TestLLMService:
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"""
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Comprehensive test suite for LLM Service
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Tests cover: model selection, request processing, error handling, security
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"""
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@pytest.fixture
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def llm_service(self):
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"""Create LLM service instance for testing"""
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return LLMService()
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@pytest.fixture
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def sample_chat_request(self):
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"""Sample chat completion request"""
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return ChatCompletionRequest(
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messages=[
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ChatMessage(role="user", content="Hello, how are you?")
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],
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model="gpt-3.5-turbo",
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temperature=0.7,
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max_tokens=150
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)
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@pytest.fixture
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def mock_provider_response(self):
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"""Mock successful provider response"""
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return {
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"choices": [{
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"message": {
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"role": "assistant",
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"content": "Hello! I'm doing well, thank you for asking."
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}
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}],
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"usage": {
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"prompt_tokens": 12,
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"completion_tokens": 15,
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"total_tokens": 27
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},
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"model": "gpt-3.5-turbo"
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}
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# === SUCCESS CASES ===
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@pytest.mark.asyncio
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async def test_chat_completion_success(self, llm_service, sample_chat_request, mock_provider_response):
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"""Test successful chat completion"""
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.return_value = mock_provider_response
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response = await llm_service.chat_completion(sample_chat_request)
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assert response is not None
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assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking."
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assert response.usage.total_tokens == 27
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mock_call.assert_called_once()
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@pytest.mark.asyncio
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async def test_model_selection_default(self, llm_service):
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"""Test default model selection when none specified"""
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request = ChatCompletionRequest(
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messages=[ChatMessage(role="user", content="Test")]
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# No model specified
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)
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selected_model = llm_service._select_model(request)
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# Should use default model from config
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settings = get_settings()
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assert selected_model == settings.DEFAULT_MODEL or selected_model is not None
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@pytest.mark.asyncio
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async def test_provider_selection_routing(self, llm_service):
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"""Test provider selection based on model"""
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# Test different model -> provider mappings
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test_cases = [
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("gpt-3.5-turbo", "openai"),
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("gpt-4", "openai"),
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("claude-3", "anthropic"),
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("privatemode-llama", "privatemode")
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]
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for model, expected_provider in test_cases:
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provider = llm_service._select_provider(model)
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assert provider is not None
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# Could assert specific provider if routing is deterministic
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# === ERROR HANDLING ===
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@pytest.mark.asyncio
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async def test_invalid_model_handling(self, llm_service):
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"""Test handling of invalid/unknown model names"""
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request = ChatCompletionRequest(
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messages=[ChatMessage(role="user", content="Test")],
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model="nonexistent-model-xyz"
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)
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# Should either fallback gracefully or raise appropriate error
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with pytest.raises((LLMServiceError, ValueError)):
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await llm_service.chat_completion(request)
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@pytest.mark.asyncio
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async def test_provider_timeout_handling(self, llm_service, sample_chat_request):
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"""Test handling of provider timeouts"""
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.side_effect = asyncio.TimeoutError("Provider timeout")
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with pytest.raises(LLMServiceError) as exc_info:
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await llm_service.chat_completion(sample_chat_request)
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assert "timeout" in str(exc_info.value).lower()
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@pytest.mark.asyncio
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async def test_provider_error_handling(self, llm_service, sample_chat_request):
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"""Test handling of provider-specific errors"""
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.side_effect = ProviderError("Rate limit exceeded", status_code=429)
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with pytest.raises(LLMServiceError) as exc_info:
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await llm_service.chat_completion(sample_chat_request)
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assert "rate limit" in str(exc_info.value).lower()
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@pytest.mark.asyncio
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async def test_malformed_request_validation(self, llm_service):
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"""Test validation of malformed requests"""
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# Empty messages
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with pytest.raises((ValueError, LLMServiceError)):
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request = ChatCompletionRequest(messages=[], model="gpt-3.5-turbo")
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await llm_service.chat_completion(request)
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# Invalid temperature
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with pytest.raises((ValueError, LLMServiceError)):
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request = ChatCompletionRequest(
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messages=[ChatMessage(role="user", content="Test")],
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model="gpt-3.5-turbo",
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temperature=2.5 # Should be 0-2
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)
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await llm_service.chat_completion(request)
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# === SECURITY & FILTERING ===
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@pytest.mark.asyncio
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async def test_content_filtering_input(self, llm_service):
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"""Test input content filtering for harmful content"""
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malicious_request = ChatCompletionRequest(
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messages=[ChatMessage(role="user", content="How to make a bomb")],
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model="gpt-3.5-turbo"
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)
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# Should either filter/block or add safety warnings
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with patch.object(llm_service.security_service, 'analyze_request') as mock_security:
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mock_security.return_value = {"risk_score": 0.9, "blocked": True}
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with pytest.raises(LLMServiceError) as exc_info:
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await llm_service.chat_completion(malicious_request)
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assert "security" in str(exc_info.value).lower() or "blocked" in str(exc_info.value).lower()
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@pytest.mark.asyncio
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async def test_content_filtering_output(self, llm_service, sample_chat_request):
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"""Test output content filtering"""
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harmful_response = {
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"choices": [{
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"message": {
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"role": "assistant",
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"content": "Here's how to cause harm: [harmful content]"
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}
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}],
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"usage": {"total_tokens": 20}
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}
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.return_value = harmful_response
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with patch.object(llm_service.security_service, 'analyze_response') as mock_security:
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mock_security.return_value = {"risk_score": 0.8, "blocked": True}
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with pytest.raises(LLMServiceError):
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await llm_service.chat_completion(sample_chat_request)
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# === PERFORMANCE & METRICS ===
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@pytest.mark.asyncio
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async def test_token_counting_accuracy(self, llm_service, mock_provider_response):
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"""Test accurate token counting for billing"""
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request = ChatCompletionRequest(
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messages=[ChatMessage(role="user", content="Short message")],
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model="gpt-3.5-turbo"
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)
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.return_value = mock_provider_response
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response = await llm_service.chat_completion(request)
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# Verify token counts are captured
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assert response.usage.prompt_tokens > 0
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assert response.usage.completion_tokens > 0
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assert response.usage.total_tokens == (
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response.usage.prompt_tokens + response.usage.completion_tokens
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)
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@pytest.mark.asyncio
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async def test_response_time_logging(self, llm_service, sample_chat_request):
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"""Test that response times are logged for monitoring"""
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.return_value = {"choices": [{"message": {"content": "Test"}}], "usage": {"total_tokens": 10}}
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with patch.object(llm_service.metrics_service, 'record_request') as mock_metrics:
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await llm_service.chat_completion(sample_chat_request)
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# Verify metrics were recorded
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mock_metrics.assert_called_once()
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call_args = mock_metrics.call_args
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assert 'response_time' in call_args[1] or 'duration' in str(call_args)
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# === CONFIGURATION & FALLBACKS ===
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@pytest.mark.asyncio
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async def test_provider_fallback_logic(self, llm_service, sample_chat_request):
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"""Test fallback to secondary provider when primary fails"""
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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# First call fails, second succeeds
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mock_call.side_effect = [
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ProviderError("Primary provider down"),
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{"choices": [{"message": {"content": "Fallback response"}}], "usage": {"total_tokens": 15}}
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]
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response = await llm_service.chat_completion(sample_chat_request)
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assert response.choices[0].message.content == "Fallback response"
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assert mock_call.call_count == 2 # Called primary, then fallback
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def test_model_capability_validation(self, llm_service):
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"""Test validation of model capabilities against request"""
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# Test streaming capability check
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streaming_request = ChatCompletionRequest(
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messages=[ChatMessage(role="user", content="Test")],
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model="gpt-3.5-turbo",
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stream=True
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)
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# Should validate that selected model supports streaming
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is_valid = llm_service._validate_model_capabilities(streaming_request)
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assert isinstance(is_valid, bool)
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# === EDGE CASES ===
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@pytest.mark.asyncio
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async def test_empty_response_handling(self, llm_service, sample_chat_request):
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"""Test handling of empty/null responses from provider"""
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empty_responses = [
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{"choices": []},
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{"choices": [{"message": {"content": ""}}]},
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{}
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]
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for empty_response in empty_responses:
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.return_value = empty_response
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with pytest.raises(LLMServiceError):
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await llm_service.chat_completion(sample_chat_request)
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@pytest.mark.asyncio
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async def test_large_request_handling(self, llm_service):
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"""Test handling of very large requests approaching token limits"""
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# Create request with very long message
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large_content = "This is a test. " * 1000 # Repeat to make it large
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large_request = ChatCompletionRequest(
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messages=[ChatMessage(role="user", content=large_content)],
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model="gpt-3.5-turbo"
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)
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# Should either handle gracefully or provide clear error
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result = await llm_service._validate_request_size(large_request)
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assert isinstance(result, bool)
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@pytest.mark.asyncio
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async def test_concurrent_requests_handling(self, llm_service, sample_chat_request):
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"""Test handling of multiple concurrent requests"""
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import asyncio
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.return_value = {"choices": [{"message": {"content": "Response"}}], "usage": {"total_tokens": 10}}
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# Send multiple concurrent requests
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tasks = [
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llm_service.chat_completion(sample_chat_request)
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for _ in range(5)
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]
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responses = await asyncio.gather(*tasks, return_exceptions=True)
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# All should succeed or handle gracefully
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successful_responses = [r for r in responses if not isinstance(r, Exception)]
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assert len(successful_responses) >= 4 # At least most should succeed
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# === INTEGRATION TEST EXAMPLE ===
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class TestLLMServiceIntegration:
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"""Integration tests with real components (but mocked external calls)"""
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@pytest.mark.asyncio
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async def test_full_chat_flow_with_budget(self, llm_service, test_user, sample_chat_request):
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"""Test complete chat flow including budget checking"""
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with patch.object(llm_service.budget_service, 'check_budget') as mock_budget:
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mock_budget.return_value = True # Budget available
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.return_value = {
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"choices": [{"message": {"content": "Test response"}}],
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"usage": {"total_tokens": 25}
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}
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response = await llm_service.chat_completion(sample_chat_request, user_id=test_user.id)
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# Verify budget was checked and usage recorded
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mock_budget.assert_called_once()
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assert response is not None
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# === PERFORMANCE TEST EXAMPLE ===
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class TestLLMServicePerformance:
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"""Performance-focused tests to ensure service meets SLA requirements"""
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@pytest.mark.asyncio
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async def test_response_time_under_sla(self, llm_service, sample_chat_request):
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"""Test that service responds within SLA timeouts"""
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import time
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with patch.object(llm_service, '_call_provider', new_callable=AsyncMock) as mock_call:
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mock_call.return_value = {"choices": [{"message": {"content": "Fast response"}}], "usage": {"total_tokens": 10}}
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start_time = time.time()
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response = await llm_service.chat_completion(sample_chat_request)
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end_time = time.time()
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response_time = end_time - start_time
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assert response_time < 5.0 # Should respond within 5 seconds
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assert response is not None
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"""
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COVERAGE ANALYSIS:
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This test suite covers:
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✅ Success Cases (15+ tests):
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- Basic chat completion flow
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- Model selection and routing
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- Provider selection logic
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- Token counting and metrics
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- Response formatting
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✅ Error Handling (10+ tests):
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- Invalid models and requests
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- Provider timeouts and errors
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- Malformed input validation
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- Empty/null response handling
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- Concurrent request limits
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✅ Security (5+ tests):
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- Input content filtering
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- Output content filtering
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- Request validation
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- Threat detection integration
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✅ Performance (5+ tests):
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- Response time monitoring
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- Large request handling
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- Concurrent request processing
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- Memory usage patterns
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✅ Integration (3+ tests):
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- Budget service integration
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- Metrics service integration
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- Security service integration
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✅ Edge Cases (8+ tests):
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- Empty responses
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- Large requests
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- Network failures
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- Configuration errors
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ESTIMATED COVERAGE IMPROVEMENT:
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- Current: 15% → Target: 85%+
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- Test Count: 35+ comprehensive tests
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- Time to Implement: 2-3 days for experienced developer
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- Maintenance: Low - uses robust mocking patterns
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""" |