85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
from unittest.mock import Mock
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import pytest
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.llms.langchain import LangchainLLM
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# Add the import for BaseChatModel
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try:
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from langchain.chat_models.base import BaseChatModel
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except ImportError:
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from unittest.mock import MagicMock
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BaseChatModel = MagicMock
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@pytest.fixture
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def mock_langchain_model():
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"""Mock a Langchain model for testing."""
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mock_model = Mock(spec=BaseChatModel)
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mock_model.invoke.return_value = Mock(content="This is a test response")
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return mock_model
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def test_langchain_initialization(mock_langchain_model):
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"""Test that LangchainLLM initializes correctly with a valid model."""
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# Create a config with the model instance directly
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config = BaseLlmConfig(model=mock_langchain_model, temperature=0.7, max_tokens=100, api_key="test-api-key")
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# Initialize the LangchainLLM
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llm = LangchainLLM(config)
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# Verify the model was correctly assigned
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assert llm.langchain_model == mock_langchain_model
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def test_generate_response(mock_langchain_model):
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"""Test that generate_response correctly processes messages and returns a response."""
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# Create a config with the model instance
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config = BaseLlmConfig(model=mock_langchain_model, temperature=0.7, max_tokens=100, api_key="test-api-key")
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# Initialize the LangchainLLM
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llm = LangchainLLM(config)
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# Create test messages
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello, how are you?"},
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{"role": "assistant", "content": "I'm doing well! How can I help you?"},
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{"role": "user", "content": "Tell me a joke."},
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]
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# Get response
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response = llm.generate_response(messages)
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# Verify the correct message format was passed to the model
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expected_langchain_messages = [
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("system", "You are a helpful assistant."),
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("human", "Hello, how are you?"),
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("ai", "I'm doing well! How can I help you?"),
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("human", "Tell me a joke."),
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]
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mock_langchain_model.invoke.assert_called_once()
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# Extract the first argument of the first call
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actual_messages = mock_langchain_model.invoke.call_args[0][0]
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assert actual_messages == expected_langchain_messages
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assert response == "This is a test response"
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def test_invalid_model():
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"""Test that LangchainLLM raises an error with an invalid model."""
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config = BaseLlmConfig(model="not-a-valid-model-instance", temperature=0.7, max_tokens=100, api_key="test-api-key")
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with pytest.raises(ValueError, match="`model` must be an instance of BaseChatModel"):
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LangchainLLM(config)
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def test_missing_model():
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"""Test that LangchainLLM raises an error when model is None."""
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config = BaseLlmConfig(model=None, temperature=0.7, max_tokens=100, api_key="test-api-key")
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with pytest.raises(ValueError, match="`model` parameter is required"):
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LangchainLLM(config)
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