Add langchain embedding, update langchain LLM and version bump -> 0.1.84 (#2510)

tests/llms/test_langchain.py

@@ -4,97 +4,99 @@ import pytest
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.langchain import LangchainLLM
 
+# Add the import for BaseChatModel
+try:
+    from langchain.chat_models.base import BaseChatModel
+except ImportError:
+    from unittest.mock import MagicMock
+    BaseChatModel = MagicMock
+
 
 @pytest.fixture
 def mock_langchain_model():
     """Mock a Langchain model for testing."""
-    with patch("langchain_openai.ChatOpenAI") as mock_chat_model:
-        mock_model = Mock()
-        mock_model.invoke.return_value = Mock(content="This is a test response")
-        mock_chat_model.return_value = mock_model
-        yield mock_model
+    mock_model = Mock(spec=BaseChatModel)
+    mock_model.invoke.return_value = Mock(content="This is a test response")
+    return mock_model
 
 
-def test_langchain_initialization():
-    """Test that LangchainLLM initializes correctly with a valid provider."""
-    with patch("langchain_openai.ChatOpenAI") as mock_chat_model:
-        # Setup the mock model
-        mock_model = Mock()
-        mock_chat_model.return_value = mock_model
-        
-        # Create a config with OpenAI provider
-        config = BaseLlmConfig(
-            model="gpt-3.5-turbo",
-            temperature=0.7,
-            max_tokens=100,
-            api_key="test-api-key",
-            langchain_provider="OpenAI"
-        )
-        
-        # Initialize the LangchainLLM
-        llm = LangchainLLM(config)
-        
-        # Verify the model was initialized with correct parameters
-        mock_chat_model.assert_called_once_with(
-            model="gpt-3.5-turbo",
-            temperature=0.7,
-            max_tokens=100,
-            api_key="test-api-key"
-        )
-        
-        assert llm.langchain_model == mock_model
+def test_langchain_initialization(mock_langchain_model):
+    """Test that LangchainLLM initializes correctly with a valid model."""
+    # Create a config with the model instance directly
+    config = BaseLlmConfig(
+        model=mock_langchain_model,
+        temperature=0.7,
+        max_tokens=100,
+        api_key="test-api-key"
+    )
+    
+    # Initialize the LangchainLLM
+    llm = LangchainLLM(config)
+    
+    # Verify the model was correctly assigned
+    assert llm.langchain_model == mock_langchain_model
 
 
 def test_generate_response(mock_langchain_model):
     """Test that generate_response correctly processes messages and returns a response."""
-    # Create a config with OpenAI provider
+    # Create a config with the model instance
     config = BaseLlmConfig(
-        model="gpt-3.5-turbo",
+        model=mock_langchain_model,
         temperature=0.7,
         max_tokens=100,
-        api_key="test-api-key",
-        langchain_provider="OpenAI"
+        api_key="test-api-key"
     )
     
     # Initialize the LangchainLLM
-    with patch("langchain_openai.ChatOpenAI", return_value=mock_langchain_model):
-        llm = LangchainLLM(config)
-        
-        # Create test messages
-        messages = [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": "Hello, how are you?"},
-            {"role": "assistant", "content": "I'm doing well! How can I help you?"},
-            {"role": "user", "content": "Tell me a joke."}
-        ]
-        
-        # Get response
-        response = llm.generate_response(messages)
-        
-        # Verify the correct message format was passed to the model
-        expected_langchain_messages = [
-            ("system", "You are a helpful assistant."),
-            ("human", "Hello, how are you?"),
-            ("ai", "I'm doing well! How can I help you?"),
-            ("human", "Tell me a joke.")
-        ]
-        
-        mock_langchain_model.invoke.assert_called_once()
-        # Extract the first argument of the first call
-        actual_messages = mock_langchain_model.invoke.call_args[0][0]
-        assert actual_messages == expected_langchain_messages
-        assert response == "This is a test response"
+    llm = LangchainLLM(config)
+    
+    # Create test messages
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello, how are you?"},
+        {"role": "assistant", "content": "I'm doing well! How can I help you?"},
+        {"role": "user", "content": "Tell me a joke."}
+    ]
+    
+    # Get response
+    response = llm.generate_response(messages)
+    
+    # Verify the correct message format was passed to the model
+    expected_langchain_messages = [
+        ("system", "You are a helpful assistant."),
+        ("human", "Hello, how are you?"),
+        ("ai", "I'm doing well! How can I help you?"),
+        ("human", "Tell me a joke.")
+    ]
+    
+    mock_langchain_model.invoke.assert_called_once()
+    # Extract the first argument of the first call
+    actual_messages = mock_langchain_model.invoke.call_args[0][0]
+    assert actual_messages == expected_langchain_messages
+    assert response == "This is a test response"
 
 
-def test_invalid_provider():
-    """Test that LangchainLLM raises an error with an invalid provider."""
+def test_invalid_model():
+    """Test that LangchainLLM raises an error with an invalid model."""
     config = BaseLlmConfig(
-        model="test-model",
+        model="not-a-valid-model-instance",
         temperature=0.7,
         max_tokens=100,
-        api_key="test-api-key",
-        langchain_provider="InvalidProvider"
+        api_key="test-api-key"
     )
     
-    with pytest.raises(ValueError, match="Invalid provider: InvalidProvider"):
+    with pytest.raises(ValueError, match="`model` must be an instance of BaseChatModel"):
+        LangchainLLM(config)
+
+
+def test_missing_model():
+    """Test that LangchainLLM raises an error when model is None."""
+    config = BaseLlmConfig(
+        model=None,
+        temperature=0.7,
+        max_tokens=100,
+        api_key="test-api-key"
+    )
+    
+    with pytest.raises(ValueError, match="`model` parameter is required"):
         LangchainLLM(config)