Formatting (#2750)

tests/embeddings/test_lm_studio_embeddings.py

@@ -10,9 +10,7 @@ from mem0.embeddings.lmstudio import LMStudioEmbedding
 def mock_lm_studio_client():
     with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
         mock_client = Mock()
-        mock_client.embeddings.create.return_value = Mock(
-            data=[Mock(embedding=[0.1, 0.2, 0.3, 0.4, 0.5])]
-        )
+        mock_client.embeddings.create.return_value = Mock(data=[Mock(embedding=[0.1, 0.2, 0.3, 0.4, 0.5])])
         mock_openai.return_value = mock_client
         yield mock_client
 

tests/embeddings/test_openai_embeddings.py

@@ -23,7 +23,9 @@ def test_embed_default_model(mock_openai_client):
 
     result = embedder.embed("Hello world")
 
-    mock_openai_client.embeddings.create.assert_called_once_with(input=["Hello world"], model="text-embedding-3-small", dimensions = 1536)
+    mock_openai_client.embeddings.create.assert_called_once_with(
+        input=["Hello world"], model="text-embedding-3-small", dimensions=1536
+    )
     assert result == [0.1, 0.2, 0.3]
 
 
@@ -37,7 +39,7 @@ def test_embed_custom_model(mock_openai_client):
     result = embedder.embed("Test embedding")
 
     mock_openai_client.embeddings.create.assert_called_once_with(
-        input=["Test embedding"], model="text-embedding-2-medium", dimensions = 1024
+        input=["Test embedding"], model="text-embedding-2-medium", dimensions=1024
     )
     assert result == [0.4, 0.5, 0.6]
 
@@ -51,7 +53,9 @@ def test_embed_removes_newlines(mock_openai_client):
 
     result = embedder.embed("Hello\nworld")
 
-    mock_openai_client.embeddings.create.assert_called_once_with(input=["Hello world"], model="text-embedding-3-small", dimensions = 1536)
+    mock_openai_client.embeddings.create.assert_called_once_with(
+        input=["Hello world"], model="text-embedding-3-small", dimensions=1536
+    )
     assert result == [0.7, 0.8, 0.9]
 
 
@@ -65,7 +69,7 @@ def test_embed_without_api_key_env_var(mock_openai_client):
     result = embedder.embed("Testing API key")
 
     mock_openai_client.embeddings.create.assert_called_once_with(
-        input=["Testing API key"], model="text-embedding-3-small", dimensions = 1536
+        input=["Testing API key"], model="text-embedding-3-small", dimensions=1536
     )
     assert result == [1.0, 1.1, 1.2]
 
@@ -81,6 +85,6 @@ def test_embed_uses_environment_api_key(mock_openai_client, monkeypatch):
     result = embedder.embed("Environment key test")
 
     mock_openai_client.embeddings.create.assert_called_once_with(
-        input=["Environment key test"], model="text-embedding-3-small", dimensions = 1536
+        input=["Environment key test"], model="text-embedding-3-small", dimensions=1536
     )
     assert result == [1.3, 1.4, 1.5]

tests/embeddings/test_vertexai_embeddings.py

@@ -24,11 +24,20 @@ def mock_config():
     with patch("mem0.configs.embeddings.base.BaseEmbedderConfig") as mock_config:
         mock_config.return_value.vertex_credentials_json = "/path/to/credentials.json"
         yield mock_config
-        
+
 
 @pytest.fixture
 def mock_embedding_types():
-    return ["SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "RETRIEVAL_DOCUMENT", "RETRIEVAL_QUERY", "QUESTION_ANSWERING", "FACT_VERIFICATION", "CODE_RETRIEVAL_QUERY"]
+    return [
+        "SEMANTIC_SIMILARITY",
+        "CLASSIFICATION",
+        "CLUSTERING",
+        "RETRIEVAL_DOCUMENT",
+        "RETRIEVAL_QUERY",
+        "QUESTION_ANSWERING",
+        "FACT_VERIFICATION",
+        "CODE_RETRIEVAL_QUERY",
+    ]
 
 
 @pytest.fixture
@@ -79,30 +88,31 @@ def test_embed_custom_model(mock_text_embedding_model, mock_os_environ, mock_con
     assert result == [0.4, 0.5, 0.6]
 
 
-@patch("mem0.embeddings.vertexai.TextEmbeddingModel")  
-def test_embed_with_memory_action(mock_text_embedding_model, mock_os_environ, mock_config, mock_embedding_types, mock_text_embedding_input):
+@patch("mem0.embeddings.vertexai.TextEmbeddingModel")
+def test_embed_with_memory_action(
+    mock_text_embedding_model, mock_os_environ, mock_config, mock_embedding_types, mock_text_embedding_input
+):
     mock_config.return_value.model = "text-embedding-004"
     mock_config.return_value.embedding_dims = 256
-    
+
     for embedding_type in mock_embedding_types:
-        
         mock_config.return_value.memory_add_embedding_type = embedding_type
         mock_config.return_value.memory_update_embedding_type = embedding_type
         mock_config.return_value.memory_search_embedding_type = embedding_type
 
         config = mock_config()
         embedder = VertexAIEmbedding(config)
-        
+
         mock_text_embedding_model.from_pretrained.assert_called_with("text-embedding-004")
 
         for memory_action in ["add", "update", "search"]:
             embedder.embed("Hello world", memory_action=memory_action)
-            
+
             mock_text_embedding_input.assert_called_with(text="Hello world", task_type=embedding_type)
             mock_text_embedding_model.from_pretrained.return_value.get_embeddings.assert_called_with(
                 texts=[mock_text_embedding_input("Hello world", embedding_type)], output_dimensionality=256
             )
-            
+
 
 @patch("mem0.embeddings.vertexai.os")
 def test_credentials_from_environment(mock_os, mock_text_embedding_model, mock_config):
@@ -137,15 +147,15 @@ def test_embed_with_different_dimensions(mock_text_embedding_model, mock_os_envi
     result = embedder.embed("Large embedding test")
 
     assert result == [0.1] * 1024
-    
 
-@patch("mem0.embeddings.vertexai.TextEmbeddingModel")    
+
+@patch("mem0.embeddings.vertexai.TextEmbeddingModel")
 def test_invalid_memory_action(mock_text_embedding_model, mock_config):
     mock_config.return_value.model = "text-embedding-004"
     mock_config.return_value.embedding_dims = 256
-    
+
     config = mock_config()
     embedder = VertexAIEmbedding(config)
-    
+
     with pytest.raises(ValueError):
-        embedder.embed("Hello world", memory_action="invalid_action")
+        embedder.embed("Hello world", memory_action="invalid_action")