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Tests cover:
1. ToolCompressionMetrics - Dataclass for tool compression metrics
2. ToolMetricsCollector - Collector for compression metrics
3. HeadroomToolWrapper - Wrapper for LangChain tools with compression
4. wrap_tools_with_headroom - Convenience function for wrapping multiple tools
5. get_tool_metrics / reset_tool_metrics - Global metrics access
"""
from datetime import datetime
from unittest.mock import MagicMock, patch
import pytest
# Check if LangChain is available
try:
from langchain_core.tools import BaseTool, StructuredTool
LANGCHAIN_AVAILABLE = True
except ImportError:
LANGCHAIN_AVAILABLE = False
# Skip all tests if LangChain not installed
pytestmark = pytest.mark.skipif(not LANGCHAIN_AVAILABLE, reason="LangChain not installed")
@pytest.fixture
def mock_tool():
"""Create a mock LangChain tool."""
mock = MagicMock(spec=BaseTool)
mock.name = "test_tool"
mock.description = "A test tool"
mock.invoke = MagicMock(return_value="Tool result")
return mock
@pytest.fixture
def mock_tool_with_large_output():
"""Create a mock tool that returns large output."""
mock = MagicMock(spec=BaseTool)
mock.name = "search_tool"
mock.description = "Search tool with large results"
# Return > 1000 chars to trigger compression
large_output = '{"items": [' + ",".join(f'{{"id": {i}}}' for i in range(200)) + "]}"
mock.invoke = MagicMock(return_value=large_output)
return mock
class TestToolCompressionMetrics:
"""Tests for ToolCompressionMetrics dataclass."""
def test_create_metrics(self):
"""Create metrics with all fields."""
from headroom.integrations.langchain.agents import ToolCompressionMetrics
metrics = ToolCompressionMetrics(
tool_name="search",
timestamp=datetime.now(),
chars_before=5000,
chars_after=2000,
chars_saved=3000,
compression_ratio=0.4,
was_compressed=True,
)
assert metrics.tool_name == "search"
assert metrics.chars_before == 5000
assert metrics.chars_after == 2000
assert metrics.chars_saved == 3000
assert metrics.compression_ratio == 0.4
assert metrics.was_compressed is True
def test_metrics_defaults(self):
"""Verify no default values (all required)."""
from headroom.integrations.langchain.agents import ToolCompressionMetrics
# All fields are required, should raise TypeError if missing
with pytest.raises(TypeError):
ToolCompressionMetrics() # type: ignore[call-arg]
class TestToolMetricsCollector:
"""Tests for ToolMetricsCollector."""
def test_init_empty(self):
"""Initialize with empty metrics list."""
from headroom.integrations.langchain.agents import ToolMetricsCollector
collector = ToolMetricsCollector()
assert collector.metrics == []
def test_add_metric(self):
"""Add a metric to the collector."""
from headroom.integrations.langchain.agents import (
ToolCompressionMetrics,
ToolMetricsCollector,
)
collector = ToolMetricsCollector()
metric = ToolCompressionMetrics(
tool_name="test",
timestamp=datetime.now(),
chars_before=100,
chars_after=80,
chars_saved=20,
compression_ratio=0.8,
was_compressed=True,
)
collector.add(metric)
assert len(collector.metrics) == 1
assert collector.metrics[0] is metric
def test_add_metric_limits_to_1000(self):
"""Metrics list is limited to 1000 entries."""
from headroom.integrations.langchain.agents import (
ToolCompressionMetrics,
ToolMetricsCollector,
)
collector = ToolMetricsCollector()
# Add 1100 metrics
for i in range(1100):
metric = ToolCompressionMetrics(
tool_name=f"tool_{i}",
timestamp=datetime.now(),
chars_before=100,
chars_after=80,
chars_saved=20,
compression_ratio=0.8,
was_compressed=True,
)
collector.add(metric)
assert len(collector.metrics) == 1000
# Should keep the last 1000 (most recent)
assert collector.metrics[0].tool_name == "tool_100"
assert collector.metrics[-1].tool_name == "tool_1099"
def test_get_summary_empty(self):
"""Get summary with no metrics."""
from headroom.integrations.langchain.agents import ToolMetricsCollector
collector = ToolMetricsCollector()
summary = collector.get_summary()
assert summary["total_invocations"] == 0
assert summary["total_compressions"] == 0
assert summary["total_chars_saved"] == 0
def test_get_summary_with_data(self):
"""Get summary with metrics."""
from headroom.integrations.langchain.agents import (
ToolCompressionMetrics,
ToolMetricsCollector,
)
collector = ToolMetricsCollector()
# Add compressed metric
collector.add(
ToolCompressionMetrics(
tool_name="search",
timestamp=datetime.now(),
chars_before=5000,
chars_after=2000,
chars_saved=3000,
compression_ratio=0.4,
was_compressed=True,
)
)
# Add uncompressed metric
collector.add(
ToolCompressionMetrics(
tool_name="simple",
timestamp=datetime.now(),
chars_before=100,
chars_after=100,
chars_saved=0,
compression_ratio=1.0,
was_compressed=False,
)
)
summary = collector.get_summary()
assert summary["total_invocations"] == 2
assert summary["total_compressions"] == 1
assert summary["total_chars_saved"] == 3000
assert summary["average_compression_ratio"] == 0.4 # Only compressed
def test_get_summary_by_tool(self):
"""Get per-tool statistics."""
from headroom.integrations.langchain.agents import (
ToolCompressionMetrics,
ToolMetricsCollector,
)
collector = ToolMetricsCollector()
# Add metrics for different tools
for _i in range(3):
collector.add(
ToolCompressionMetrics(
tool_name="search",
timestamp=datetime.now(),
chars_before=1000,
chars_after=500,
chars_saved=500,
compression_ratio=0.5,
was_compressed=True,
)
)
for _i in range(2):
collector.add(
ToolCompressionMetrics(
tool_name="database",
timestamp=datetime.now(),
chars_before=100,
chars_after=100,
chars_saved=0,
compression_ratio=1.0,
was_compressed=False,
)
)
summary = collector.get_summary()
assert "by_tool" in summary
assert summary["by_tool"]["search"]["invocations"] == 3
assert summary["by_tool"]["search"]["compressions"] == 3
assert summary["by_tool"]["search"]["chars_saved"] == 1500
assert summary["by_tool"]["database"]["invocations"] == 2
assert summary["by_tool"]["database"]["compressions"] == 0
class TestHeadroomToolWrapper:
"""Tests for HeadroomToolWrapper."""
def test_init_defaults(self, mock_tool):
"""Initialize with default settings."""
from headroom.integrations.langchain.agents import HeadroomToolWrapper
wrapper = HeadroomToolWrapper(mock_tool)
assert wrapper.tool is mock_tool
assert wrapper.name == "test_tool"
assert wrapper.description == "A test tool"
assert wrapper.min_chars_to_compress == 1000
def test_init_custom_threshold(self, mock_tool):
"""Initialize with custom compression threshold."""
from headroom.integrations.langchain.agents import (
HeadroomToolWrapper,
ToolMetricsCollector,
)
collector = ToolMetricsCollector()
wrapper = HeadroomToolWrapper(
mock_tool,
min_chars_to_compress=500,
metrics_collector=collector,
)
assert wrapper.min_chars_to_compress == 500
assert wrapper._metrics is collector
def test_call_small_output_no_compression(self, mock_tool):
"""Small outputs are not compressed."""
from headroom.integrations.langchain.agents import (
HeadroomToolWrapper,
ToolMetricsCollector,
)
collector = ToolMetricsCollector()
wrapper = HeadroomToolWrapper(
mock_tool,
min_chars_to_compress=1000,
metrics_collector=collector,
)
result = wrapper("input")
assert result == "Tool result"
assert len(collector.metrics) == 1
assert collector.metrics[0].was_compressed is False
def test_call_large_output_triggers_compression(self, mock_tool_with_large_output):
"""Large outputs trigger compression."""
from headroom.integrations.langchain.agents import (
HeadroomToolWrapper,
ToolMetricsCollector,
)
collector = ToolMetricsCollector()
wrapper = HeadroomToolWrapper(
mock_tool_with_large_output,
min_chars_to_compress=100,
metrics_collector=collector,
)
# Mock compress_tool_result to return compressed output
with patch("headroom.integrations.langchain.agents.compress_tool_result") as mock_compress:
mock_compress.return_value = '{"items": [...compressed...]}'
wrapper("query")
mock_compress.assert_called_once()
assert len(collector.metrics) == 1
assert collector.metrics[0].was_compressed is True
def test_call_converts_non_string_result(self, mock_tool):
"""Non-string results are converted to strings."""
from headroom.integrations.langchain.agents import HeadroomToolWrapper
mock_tool.invoke.return_value = {"key": "value"}
wrapper = HeadroomToolWrapper(mock_tool)
result = wrapper("input")
assert isinstance(result, str)
assert "key" in result
def test_invoke_alias(self, mock_tool):
"""invoke() is an alias for __call__()."""
from headroom.integrations.langchain.agents import HeadroomToolWrapper
wrapper = HeadroomToolWrapper(mock_tool)
result1 = wrapper("input")
mock_tool.invoke.reset_mock()
result2 = wrapper.invoke("input")
assert result1 == result2
def test_compression_failure_returns_original(self, mock_tool_with_large_output):
"""Compression failure returns original output."""
from headroom.integrations.langchain.agents import HeadroomToolWrapper
wrapper = HeadroomToolWrapper(
mock_tool_with_large_output,
min_chars_to_compress=100,
)
with patch("headroom.integrations.langchain.agents.compress_tool_result") as mock_compress:
mock_compress.side_effect = Exception("Compression error")
result = wrapper("query")
# Should return original output
assert "items" in result
assert "id" in result
def test_as_langchain_tool(self, mock_tool):
"""Convert wrapper to LangChain StructuredTool."""
from headroom.integrations.langchain.agents import HeadroomToolWrapper
wrapper = HeadroomToolWrapper(mock_tool)
lc_tool = wrapper.as_langchain_tool()
assert isinstance(lc_tool, StructuredTool)
assert lc_tool.name == "test_tool"
assert lc_tool.description == "A test tool"
def test_metrics_recorded_correctly(self, mock_tool_with_large_output):
"""Verify metrics are recorded correctly."""
from headroom.integrations.langchain.agents import (
HeadroomToolWrapper,
ToolMetricsCollector,
)
collector = ToolMetricsCollector()
wrapper = HeadroomToolWrapper(
mock_tool_with_large_output,
min_chars_to_compress=100,
metrics_collector=collector,
)
original_len = len(mock_tool_with_large_output.invoke.return_value)
with patch("headroom.integrations.langchain.agents.compress_tool_result") as mock_compress:
compressed_result = '{"items": [...]}'
mock_compress.return_value = compressed_result
wrapper("query")
metric = collector.metrics[0]
assert metric.tool_name == "search_tool"
assert metric.chars_before == original_len
assert metric.chars_after == len(compressed_result)
assert metric.chars_saved == original_len - len(compressed_result)
class TestWrapToolsWithHeadroom:
"""Tests for wrap_tools_with_headroom function."""
def test_wrap_single_tool(self, mock_tool):
"""Wrap a single tool."""
from headroom.integrations.langchain.agents import wrap_tools_with_headroom
wrapped = wrap_tools_with_headroom([mock_tool])
assert len(wrapped) == 1
assert isinstance(wrapped[0], StructuredTool)
assert wrapped[0].name == "test_tool"
def test_wrap_multiple_tools(self, mock_tool):
"""Wrap multiple tools."""
from headroom.integrations.langchain.agents import wrap_tools_with_headroom
tool2 = MagicMock(spec=BaseTool)
tool2.name = "tool_2"
tool2.description = "Second tool"
tool2.invoke = MagicMock(return_value="Result 2")
wrapped = wrap_tools_with_headroom([mock_tool, tool2])
assert len(wrapped) == 2
assert wrapped[0].name == "test_tool"
assert wrapped[1].name == "tool_2"
def test_wrap_with_custom_threshold(self, mock_tool):
"""Wrap with custom compression threshold."""
from headroom.integrations.langchain.agents import wrap_tools_with_headroom
wrapped = wrap_tools_with_headroom([mock_tool], min_chars_to_compress=500)
assert len(wrapped) == 1
# Invoke to verify wrapper is configured
# The wrapper should be invoked through the StructuredTool
assert wrapped[0].name == "test_tool"
def test_wrap_with_shared_collector(self, mock_tool):
"""Wrap with shared metrics collector."""
from headroom.integrations.langchain.agents import (
ToolMetricsCollector,
wrap_tools_with_headroom,
)
collector = ToolMetricsCollector()
tool2 = MagicMock(spec=BaseTool)
tool2.name = "tool_2"
tool2.description = "Second tool"
tool2.invoke = MagicMock(return_value="Result 2")
wrapped = wrap_tools_with_headroom(
[mock_tool, tool2],
metrics_collector=collector,
)
# Invoke both tools
wrapped[0].func("input1")
wrapped[1].func("input2")
# Both should use the same collector
assert len(collector.metrics) == 2
def test_wrap_empty_list(self):
"""Wrap empty list returns empty list."""
from headroom.integrations.langchain.agents import wrap_tools_with_headroom
wrapped = wrap_tools_with_headroom([])
assert wrapped == []
class TestGlobalMetrics:
"""Tests for global metrics functions."""
def test_get_tool_metrics(self):
"""get_tool_metrics returns the global collector."""
from headroom.integrations.langchain.agents import (
ToolMetricsCollector,
get_tool_metrics,
)
collector = get_tool_metrics()
assert isinstance(collector, ToolMetricsCollector)
def test_reset_tool_metrics(self):
"""reset_tool_metrics creates new collector."""
from headroom.integrations.langchain.agents import (
ToolCompressionMetrics,
get_tool_metrics,
reset_tool_metrics,
)
# Add a metric to the global collector
collector = get_tool_metrics()
collector.add(
ToolCompressionMetrics(
tool_name="test",
timestamp=datetime.now(),
chars_before=100,
chars_after=100,
chars_saved=0,
compression_ratio=1.0,
was_compressed=False,
)
)
# Reset
reset_tool_metrics()
# New collector should be empty
new_collector = get_tool_metrics()
assert len(new_collector.metrics) == 0
def test_wrapper_uses_global_metrics_by_default(self, mock_tool):
"""HeadroomToolWrapper uses global metrics by default."""
from headroom.integrations.langchain.agents import (
HeadroomToolWrapper,
get_tool_metrics,
reset_tool_metrics,
)
# Reset to start fresh
reset_tool_metrics()
wrapper = HeadroomToolWrapper(mock_tool)
wrapper("input")
global_collector = get_tool_metrics()
assert len(global_collector.metrics) == 1
class TestLangChainNotAvailable:
"""Tests for behavior when LangChain is not available."""
def test_check_raises_import_error(self):
"""_check_langchain_available raises ImportError when not available."""
from headroom.integrations.langchain.agents import _check_langchain_available
# When LangChain IS available, should not raise
try:
_check_langchain_available()
except ImportError:
pytest.fail("Should not raise when LangChain is available")
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