Formatting (#2750)

tests/llms/test_azure_openai.py

@@ -127,4 +127,4 @@ def test_generate_with_http_proxies(default_headers):
             api_version=None,
             default_headers=default_headers,
         )
-        mock_http_client.assert_called_once_with(proxies="http://testproxy.mem0.net:8000")
+        mock_http_client.assert_called_once_with(proxies="http://testproxy.mem0.net:8000")

tests/llms/test_deepseek.py

@@ -31,12 +31,12 @@ def test_deepseek_llm_base_url():
     # case3: with config.deepseek_base_url
     config_base_url = "https://api.config.com/v1/"
     config = BaseLlmConfig(
-        model="deepseek-chat", 
-        temperature=0.7, 
-        max_tokens=100, 
-        top_p=1.0, 
-        api_key="api_key", 
-        deepseek_base_url=config_base_url
+        model="deepseek-chat",
+        temperature=0.7,
+        max_tokens=100,
+        top_p=1.0,
+        api_key="api_key",
+        deepseek_base_url=config_base_url,
     )
     llm = DeepSeekLLM(config)
     assert str(llm.client.base_url) == config_base_url
@@ -99,16 +99,16 @@ def test_generate_response_with_tools(mock_deepseek_client):
     response = llm.generate_response(messages, tools=tools)
 
     mock_deepseek_client.chat.completions.create.assert_called_once_with(
-        model="deepseek-chat", 
-        messages=messages, 
-        temperature=0.7, 
-        max_tokens=100, 
-        top_p=1.0, 
-        tools=tools, 
-        tool_choice="auto"
+        model="deepseek-chat",
+        messages=messages,
+        temperature=0.7,
+        max_tokens=100,
+        top_p=1.0,
+        tools=tools,
+        tool_choice="auto",
     )
 
     assert response["content"] == "I've added the memory for you."
     assert len(response["tool_calls"]) == 1
     assert response["tool_calls"][0]["name"] == "add_memory"
-    assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}
+    assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}

tests/llms/test_langchain.py

@@ -10,6 +10,7 @@ try:
     from langchain.chat_models.base import BaseChatModel
 except ImportError:
     from unittest.mock import MagicMock
+
     BaseChatModel = MagicMock
 
 
@@ -24,16 +25,11 @@ def mock_langchain_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"
-    )
-    
+    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
 
@@ -41,35 +37,30 @@ def test_langchain_initialization(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 the model instance
-    config = BaseLlmConfig(
-        model=mock_langchain_model,
-        temperature=0.7,
-        max_tokens=100,
-        api_key="test-api-key"
-    )
-    
+    config = BaseLlmConfig(model=mock_langchain_model, temperature=0.7, max_tokens=100, api_key="test-api-key")
+
     # Initialize the LangchainLLM
     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."}
+        {"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.")
+        ("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]
@@ -79,25 +70,15 @@ def test_generate_response(mock_langchain_model):
 
 def test_invalid_model():
     """Test that LangchainLLM raises an error with an invalid model."""
-    config = BaseLlmConfig(
-        model="not-a-valid-model-instance",
-        temperature=0.7,
-        max_tokens=100,
-        api_key="test-api-key"
-    )
-    
+    config = BaseLlmConfig(model="not-a-valid-model-instance", temperature=0.7, max_tokens=100, api_key="test-api-key")
+
     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"
-    )
-    
+    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)

tests/llms/test_lm_studio.py

@@ -11,9 +11,7 @@ def mock_lm_studio_client():
     with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:  # Corrected path
         mock_client = Mock()
         mock_client.chat.completions.create.return_value = Mock(
-            choices=[
-                Mock(message=Mock(content="I'm doing well, thank you for asking!"))
-            ]
+            choices=[Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
         )
         mock_openai.return_value = mock_client
         yield mock_client