Support for langchain LLMs (#2506)

tests/llms/test_langchain.py

@@ -0,0 +1,100 @@
+from unittest.mock import Mock, patch
+import pytest
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.langchain import LangchainLLM
+
+
+@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
+
+
+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_generate_response(mock_langchain_model):
+    """Test that generate_response correctly processes messages and returns a response."""
+    # 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
+    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"
+
+
+def test_invalid_provider():
+    """Test that LangchainLLM raises an error with an invalid provider."""
+    config = BaseLlmConfig(
+        model="test-model",
+        temperature=0.7,
+        max_tokens=100,
+        api_key="test-api-key",
+        langchain_provider="InvalidProvider"
+    )
+    
+    with pytest.raises(ValueError, match="Invalid provider: InvalidProvider"):
+        LangchainLLM(config)