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Tests cover:
1. HeadroomChatMessageHistory - Wrapper for chat message history with compression
2. Message conversion to/from OpenAI format
3. Rolling window compression behavior
4. Token counting and threshold detection
5. Compression statistics tracking
"""
from unittest.mock import MagicMock, patch
import pytest
# Check if LangChain is available
try:
from langchain_core.messages import (
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
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_base_history():
"""Create a mock BaseChatMessageHistory."""
mock = MagicMock()
mock.messages = []
return mock
@pytest.fixture
def mock_provider():
"""Create a mock provider with token counter."""
mock = MagicMock()
mock_counter = MagicMock()
mock_counter.count_text = MagicMock(side_effect=lambda text: len(text.split()))
mock.get_token_counter = MagicMock(return_value=mock_counter)
return mock
@pytest.fixture
def sample_langchain_messages():
"""Sample LangChain messages for testing."""
return [
SystemMessage(content="You are a helpful assistant."),
HumanMessage(content="Hello, how are you?"),
AIMessage(content="I am doing well, thank you!"),
HumanMessage(content="What is the weather today?"),
AIMessage(content="I don't have access to weather data."),
]
class TestHeadroomChatMessageHistoryInit:
"""Tests for HeadroomChatMessageHistory initialization."""
def test_init_defaults(self, mock_base_history):
"""Initialize with default settings."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
with patch("headroom.integrations.langchain.memory.OpenAIProvider"):
history = HeadroomChatMessageHistory(mock_base_history)
assert history._base is mock_base_history
assert history._threshold == 4000
assert history._keep_recent_turns == 5
assert history._model == "gpt-4o"
assert history._compression_count == 0
assert history._total_tokens_saved == 0
def test_init_custom_threshold(self, mock_base_history, mock_provider):
"""Initialize with custom compression threshold."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(
mock_base_history,
compress_threshold_tokens=8000,
keep_recent_turns=10,
model="gpt-4-turbo",
provider=mock_provider,
)
assert history._threshold == 8000
assert history._keep_recent_turns == 10
assert history._model == "gpt-4-turbo"
assert history._provider is mock_provider
class TestHeadroomChatMessageHistoryMessages:
"""Tests for message access and compression."""
def test_messages_returns_empty_when_no_messages(self, mock_base_history, mock_provider):
"""messages property returns empty list when no messages."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
mock_base_history.messages = []
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
messages = history.messages
assert messages == []
def test_messages_returns_uncompressed_when_below_threshold(
self, mock_base_history, mock_provider, sample_langchain_messages
):
"""messages returns uncompressed when below token threshold."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
mock_base_history.messages = sample_langchain_messages
history = HeadroomChatMessageHistory(
mock_base_history,
compress_threshold_tokens=10000, # High threshold
provider=mock_provider,
)
messages = history.messages
# Should return all messages unchanged
assert len(messages) == len(sample_langchain_messages)
assert history._compression_count == 0
def test_messages_compresses_when_over_threshold(self, mock_base_history, mock_provider):
"""messages applies compression when over token threshold."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
# Create messages that exceed threshold
mock_base_history.messages = [
SystemMessage(content="System " * 100),
HumanMessage(content="User " * 100),
AIMessage(content="Assistant " * 100),
]
history = HeadroomChatMessageHistory(
mock_base_history,
compress_threshold_tokens=10, # Very low threshold
provider=mock_provider,
)
# Mock _apply_rolling_window to return fewer messages
with patch.object(history, "_apply_rolling_window") as mock_apply:
mock_apply.return_value = [
SystemMessage(content="Compressed"),
]
_ = history.messages
mock_apply.assert_called_once()
assert history._compression_count == 1
def test_messages_tracks_tokens_saved(self, mock_base_history, mock_provider):
"""Compression tracks tokens saved."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
# Create messages that exceed threshold
mock_base_history.messages = [
SystemMessage(content="Word " * 50),
HumanMessage(content="Word " * 50),
]
history = HeadroomChatMessageHistory(
mock_base_history,
compress_threshold_tokens=10, # Very low threshold
provider=mock_provider,
)
# Mock _apply_rolling_window to return fewer messages
with patch.object(history, "_apply_rolling_window") as mock_apply:
mock_apply.return_value = [
SystemMessage(content="Short"),
]
_ = history.messages
# tokens_saved should increase
assert history._total_tokens_saved > 0
class TestHeadroomChatMessageHistoryAddMessage:
"""Tests for add_message methods."""
def test_add_message(self, mock_base_history, mock_provider):
"""add_message delegates to base history."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
msg = HumanMessage(content="Hello")
history.add_message(msg)
mock_base_history.add_message.assert_called_once_with(msg)
def test_add_user_message(self, mock_base_history, mock_provider):
"""add_user_message delegates to base history."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
history.add_user_message("Hello")
mock_base_history.add_user_message.assert_called_once_with("Hello")
def test_add_ai_message(self, mock_base_history, mock_provider):
"""add_ai_message delegates to base history."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
history.add_ai_message("Response")
mock_base_history.add_ai_message.assert_called_once_with("Response")
def test_clear(self, mock_base_history, mock_provider):
"""clear delegates to base history."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
history.clear()
mock_base_history.clear.assert_called_once()
class TestHeadroomChatMessageHistoryConversion:
"""Tests for message format conversion."""
def test_convert_to_openai_system_message(self, mock_base_history, mock_provider):
"""Convert SystemMessage to OpenAI format."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
messages = [SystemMessage(content="You are helpful.")]
result = history._convert_to_openai(messages)
assert len(result) == 1
assert result[0]["role"] == "system"
assert result[0]["content"] == "You are helpful."
def test_convert_to_openai_human_message(self, mock_base_history, mock_provider):
"""Convert HumanMessage to OpenAI format."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
messages = [HumanMessage(content="Hello")]
result = history._convert_to_openai(messages)
assert result[0]["role"] == "user"
assert result[0]["content"] == "Hello"
def test_convert_to_openai_ai_message(self, mock_base_history, mock_provider):
"""Convert AIMessage to OpenAI format."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
messages = [AIMessage(content="I can help.")]
result = history._convert_to_openai(messages)
assert result[0]["role"] == "assistant"
assert result[0]["content"] == "I can help."
def test_convert_to_openai_ai_message_with_tool_calls(self, mock_base_history, mock_provider):
"""Convert AIMessage with tool_calls to OpenAI format."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
messages = [
AIMessage(
content="Calling tool...",
tool_calls=[{"id": "call_1", "name": "search", "args": {"q": "test"}}],
)
]
result = history._convert_to_openai(messages)
assert result[0]["role"] == "assistant"
assert "tool_calls" in result[0]
assert result[0]["tool_calls"][0]["id"] == "call_1"
def test_convert_to_openai_tool_message(self, mock_base_history, mock_provider):
"""Convert ToolMessage to OpenAI format."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
messages = [ToolMessage(content='{"result": "data"}', tool_call_id="call_1")]
result = history._convert_to_openai(messages)
assert result[0]["role"] == "tool"
assert result[0]["tool_call_id"] == "call_1"
assert result[0]["content"] == '{"result": "data"}'
def test_convert_from_openai_system(self, mock_base_history, mock_provider):
"""Convert OpenAI system message back to LangChain."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
openai_msgs = [{"role": "system", "content": "System prompt"}]
result = history._convert_from_openai(openai_msgs)
assert len(result) == 1
assert isinstance(result[0], SystemMessage)
assert result[0].content == "System prompt"
def test_convert_from_openai_user(self, mock_base_history, mock_provider):
"""Convert OpenAI user message back to LangChain."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
openai_msgs = [{"role": "user", "content": "Hello"}]
result = history._convert_from_openai(openai_msgs)
assert isinstance(result[0], HumanMessage)
assert result[0].content == "Hello"
def test_convert_from_openai_assistant(self, mock_base_history, mock_provider):
"""Convert OpenAI assistant message back to LangChain."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
openai_msgs = [{"role": "assistant", "content": "Response"}]
result = history._convert_from_openai(openai_msgs)
assert isinstance(result[0], AIMessage)
assert result[0].content == "Response"
def test_convert_from_openai_assistant_with_tool_calls(self, mock_base_history, mock_provider):
"""Convert OpenAI assistant message with tool_calls back to LangChain."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
openai_msgs = [
{
"role": "assistant",
"content": "",
"tool_calls": [{"id": "call_1", "name": "search", "args": {}}],
}
]
result = history._convert_from_openai(openai_msgs)
assert isinstance(result[0], AIMessage)
# LangChain may add a 'type' field to tool_calls, so just check key fields
assert len(result[0].tool_calls) == 1
assert result[0].tool_calls[0]["id"] == "call_1"
assert result[0].tool_calls[0]["name"] == "search"
assert result[0].tool_calls[0]["args"] == {}
def test_convert_from_openai_tool(self, mock_base_history, mock_provider):
"""Convert OpenAI tool message back to LangChain."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(mock_base_history, provider=mock_provider)
openai_msgs = [{"role": "tool", "tool_call_id": "call_1", "content": '{"data": 1}'}]
result = history._convert_from_openai(openai_msgs)
assert isinstance(result[0], ToolMessage)
assert result[0].tool_call_id == "call_1"
assert result[0].content == '{"data": 1}'
class TestHeadroomChatMessageHistoryTokenCounting:
"""Tests for token counting."""
def test_count_tokens(self, mock_base_history, mock_provider):
"""Count tokens using provider's tokenizer."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(
mock_base_history,
provider=mock_provider,
model="gpt-4o",
)
messages = [
HumanMessage(content="Hello world"),
AIMessage(content="Hi there"),
]
count = history._count_tokens(messages)
# Mock counts words, so "Hello world" = 2, "Hi there" = 2
assert count == 4
mock_provider.get_token_counter.assert_called_with("gpt-4o")
class TestHeadroomChatMessageHistoryStats:
"""Tests for compression statistics."""
def test_get_compression_stats_initial(self, mock_base_history, mock_provider):
"""Get initial compression stats."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(
mock_base_history,
compress_threshold_tokens=4000,
keep_recent_turns=5,
provider=mock_provider,
)
stats = history.get_compression_stats()
assert stats["compression_count"] == 0
assert stats["total_tokens_saved"] == 0
assert stats["threshold_tokens"] == 4000
assert stats["keep_recent_turns"] == 5
def test_get_compression_stats_after_compression(self, mock_base_history, mock_provider):
"""Get compression stats after compression."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
mock_base_history.messages = [
SystemMessage(content="Word " * 100),
HumanMessage(content="Word " * 100),
]
history = HeadroomChatMessageHistory(
mock_base_history,
compress_threshold_tokens=10,
provider=mock_provider,
)
# Mock _apply_rolling_window
with patch.object(history, "_apply_rolling_window") as mock_apply:
mock_apply.return_value = [SystemMessage(content="Short")]
_ = history.messages
stats = history.get_compression_stats()
assert stats["compression_count"] == 1
assert stats["total_tokens_saved"] > 0
class TestHeadroomChatMessageHistoryRollingWindow:
"""Tests for rolling window compression."""
def test_apply_rolling_window_calls_pipeline(self, mock_base_history, mock_provider):
"""_apply_rolling_window uses TransformPipeline."""
from headroom.integrations.langchain.memory import HeadroomChatMessageHistory
history = HeadroomChatMessageHistory(
mock_base_history,
compress_threshold_tokens=1000,
keep_recent_turns=5,
provider=mock_provider,
)
messages = [
HumanMessage(content="Hello"),
AIMessage(content="Hi there"),
]
with patch("headroom.integrations.langchain.memory.TransformPipeline") as MockPipeline:
mock_instance = MagicMock()
mock_result = MagicMock()
mock_result.messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there"},
]
mock_instance.apply.return_value = mock_result
MockPipeline.return_value = mock_instance
result = history._apply_rolling_window(messages)
MockPipeline.assert_called_once()
mock_instance.apply.assert_called_once()
# Result should be converted back to LangChain messages
assert all(isinstance(m, BaseMessage) for m in result)
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.memory 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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