Nicolò Boschi
6ba4aeaf03
Tests were excluded from both ruff lint and format via the top-level [tool.ruff].exclude in hindsight-api-slim, hindsight-embed and the shared ruff.toml. As a result test files drifted from the formatter's style and every PR that touched a test (or ran format-on-save) carried large formatting-only churn. Move the tests exclude into [tool.ruff.lint].exclude (and [lint].exclude in ruff.toml) so the formatter now covers tests while lint rules — too noisy for test code (unused imports/vars, import ordering) — stay excluded. Then run ruff format across all test directories. Note: lint.exclude is a post-traversal path filter, so it needs the glob form 'tests/**' rather than the directory form 'tests/' used by top-level exclude.
253 lines
8.6 KiB
Python
253 lines
8.6 KiB
Python
"""
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Test that LLM calls record token metrics via the metrics collector.
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"""
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import os
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from unittest.mock import MagicMock, patch
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import pytest
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from hindsight_api.engine.llm_wrapper import LLMProvider
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from hindsight_api.metrics import (
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MetricsCollector,
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NoOpMetricsCollector,
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get_metrics_collector,
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)
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def get_groq_api_key() -> str | None:
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"""Get Groq API key from environment."""
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return os.getenv("GROQ_API_KEY")
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@pytest.mark.asyncio
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async def test_llm_metrics_recorded_for_groq():
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"""
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Test that LLM metrics are recorded when making LLM calls via Groq.
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Uses openai/gpt-oss-20b as recommended by Hindsight.
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"""
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api_key = get_groq_api_key()
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if not api_key:
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pytest.skip("Skipping: GROQ_API_KEY not set")
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# Create a mock metrics collector to track record_llm_call calls
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mock_collector = MagicMock(spec=MetricsCollector)
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# Patch the provider module where get_metrics_collector is actually called
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with patch(
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"hindsight_api.engine.providers.openai_compatible_llm.get_metrics_collector", return_value=mock_collector
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):
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llm = LLMProvider(
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provider="groq",
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api_key=api_key,
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base_url="",
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model="openai/gpt-oss-20b",
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)
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# Make an LLM call with clear instruction
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response = await llm.call(
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messages=[
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{"role": "system", "content": "You are a helpful assistant. Always respond."},
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{"role": "user", "content": "What is 2+2? Reply with just the number."},
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],
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max_completion_tokens=50,
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scope="test_metrics",
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)
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# Verify record_llm_call was called - this is the main test
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assert mock_collector.record_llm_call.called, "record_llm_call should have been called"
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# Get the call arguments
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call_kwargs = mock_collector.record_llm_call.call_args.kwargs
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# Verify the call had correct structure
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assert call_kwargs["provider"] == "groq", f"Expected provider='groq', got {call_kwargs}"
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assert call_kwargs["model"] == "openai/gpt-oss-20b", f"Expected model='openai/gpt-oss-20b', got {call_kwargs}"
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assert call_kwargs["scope"] == "test_metrics", f"Expected scope='test_metrics', got {call_kwargs}"
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assert call_kwargs["duration"] > 0, f"Expected duration > 0, got {call_kwargs['duration']}"
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assert call_kwargs["input_tokens"] > 0, f"Expected input_tokens > 0, got {call_kwargs['input_tokens']}"
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assert call_kwargs["output_tokens"] >= 0, f"Expected output_tokens >= 0, got {call_kwargs['output_tokens']}"
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assert call_kwargs["success"] is True, f"Expected success=True, got {call_kwargs['success']}"
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print(f"\nLLM metrics recorded:")
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print(f" provider: {call_kwargs['provider']}")
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print(f" model: {call_kwargs['model']}")
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print(f" scope: {call_kwargs['scope']}")
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print(f" duration: {call_kwargs['duration']:.3f}s")
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print(f" input_tokens: {call_kwargs['input_tokens']}")
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print(f" output_tokens: {call_kwargs['output_tokens']}")
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print(f" response: {response}")
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@pytest.mark.asyncio
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async def test_llm_metrics_recorded_for_structured_output():
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"""
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Test that LLM metrics are recorded for structured output (JSON) calls.
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"""
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api_key = get_groq_api_key()
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if not api_key:
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pytest.skip("Skipping: GROQ_API_KEY not set")
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from pydantic import BaseModel
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class SimpleResponse(BaseModel):
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greeting: str
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language: str
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mock_collector = MagicMock(spec=MetricsCollector)
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# Patch the provider module where get_metrics_collector is actually called
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with patch(
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"hindsight_api.engine.providers.openai_compatible_llm.get_metrics_collector", return_value=mock_collector
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):
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llm = LLMProvider(
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provider="groq",
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api_key=api_key,
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base_url="",
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model="openai/gpt-oss-20b",
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)
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# Make a structured output call
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response = await llm.call(
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messages=[{"role": "user", "content": "Say hello in French. Return greeting and language."}],
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response_format=SimpleResponse,
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max_completion_tokens=100,
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scope="structured_output_test",
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)
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# Verify structured response
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assert isinstance(response, SimpleResponse)
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assert response.greeting is not None
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assert response.language is not None
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# Verify record_llm_call was called
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assert mock_collector.record_llm_call.called, "record_llm_call should have been called"
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call_kwargs = mock_collector.record_llm_call.call_args.kwargs
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assert call_kwargs["input_tokens"] > 0
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assert call_kwargs["output_tokens"] > 0
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print(f"\nStructured output LLM metrics:")
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print(f" greeting: {response.greeting}")
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print(f" language: {response.language}")
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print(f" input_tokens: {call_kwargs['input_tokens']}")
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print(f" output_tokens: {call_kwargs['output_tokens']}")
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@pytest.mark.asyncio
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async def test_noop_collector_when_metrics_disabled():
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"""
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Test that NoOpMetricsCollector is returned when metrics are not initialized.
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This verifies the fallback behavior doesn't break LLM calls.
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"""
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api_key = get_groq_api_key()
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if not api_key:
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pytest.skip("Skipping: GROQ_API_KEY not set")
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# Without initializing metrics, get_metrics_collector returns NoOpMetricsCollector
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collector = get_metrics_collector()
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assert isinstance(collector, NoOpMetricsCollector), "Should return NoOpMetricsCollector when not initialized"
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# Make an LLM call - should work fine with NoOp collector
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llm = LLMProvider(
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provider="groq",
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api_key=api_key,
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base_url="",
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model="openai/gpt-oss-20b",
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)
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response = await llm.call(
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messages=[{"role": "user", "content": "Say 'test' in one word."}],
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max_completion_tokens=50,
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)
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assert response is not None
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print(f"\nLLM call succeeded with NoOpMetricsCollector: {response}")
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@pytest.mark.asyncio
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async def test_return_usage_returns_tuple():
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"""
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Test that return_usage=True returns (result, TokenUsage) tuple.
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"""
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from hindsight_api.engine.response_models import TokenUsage
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api_key = get_groq_api_key()
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if not api_key:
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pytest.skip("Skipping: GROQ_API_KEY not set")
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llm = LLMProvider(
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provider="groq",
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api_key=api_key,
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base_url="",
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model="openai/gpt-oss-20b",
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)
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# Call with return_usage=True
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result, usage = await llm.call(
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "What is 2+2? Reply with just the number."},
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],
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max_completion_tokens=50,
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return_usage=True,
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)
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# Verify result is the response text
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assert result is not None
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assert isinstance(result, str)
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# Verify usage is TokenUsage model with valid counts
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assert isinstance(usage, TokenUsage)
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assert usage.input_tokens > 0, f"Expected input_tokens > 0, got {usage.input_tokens}"
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assert usage.output_tokens >= 0, f"Expected output_tokens >= 0, got {usage.output_tokens}"
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assert usage.total_tokens == usage.input_tokens + usage.output_tokens
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print(f"\nreturn_usage=True test:")
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print(f" result: {result}")
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print(f" usage: {usage}")
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@pytest.mark.asyncio
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async def test_return_usage_with_structured_output():
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"""
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Test that return_usage=True works with structured output (JSON).
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"""
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from pydantic import BaseModel
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from hindsight_api.engine.response_models import TokenUsage
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api_key = get_groq_api_key()
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if not api_key:
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pytest.skip("Skipping: GROQ_API_KEY not set")
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class MathAnswer(BaseModel):
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answer: int
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explanation: str
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llm = LLMProvider(
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provider="groq",
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api_key=api_key,
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base_url="",
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model="openai/gpt-oss-20b",
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)
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# Call with return_usage=True and structured output
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result, usage = await llm.call(
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messages=[{"role": "user", "content": "What is 5+3? Return the answer and a brief explanation."}],
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response_format=MathAnswer,
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max_completion_tokens=100,
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return_usage=True,
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)
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# Verify result is the parsed response
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assert isinstance(result, MathAnswer)
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assert result.answer == 8
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assert result.explanation is not None
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# Verify usage is TokenUsage model
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assert isinstance(usage, TokenUsage)
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assert usage.input_tokens > 0
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assert usage.output_tokens > 0
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print(f"\nStructured output with return_usage=True:")
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print(f" result: {result}")
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print(f" usage: {usage}")
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