fix/failing-unit-tests (#2486)

tests/embeddings/test_lm_studio_embeddings.py

@@ -6,10 +6,12 @@ from mem0.configs.embeddings.base import BaseEmbedderConfig
 
 @pytest.fixture
 def mock_lm_studio_client():
-    with patch("mem0.embeddings.lmstudio.Client") as mock_lm_studio:
+    with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
         mock_client = Mock()
-        mock_client.list.return_value = {"models": [{"name": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"}]}
-        mock_lm_studio.return_value = mock_client
+        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
 
 
@@ -17,25 +19,11 @@ def test_embed_text(mock_lm_studio_client):
     config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
     embedder = LMStudioEmbedding(config)
 
-    mock_response = {"embedding": [0.1, 0.2, 0.3, 0.4, 0.5]}
-    mock_lm_studio_client.embeddings.return_value = mock_response
-
     text = "Sample text to embed."
     embedding = embedder.embed(text)
 
-    mock_lm_studio_client.embeddings.assert_called_once_with(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", prompt=text)
+    mock_lm_studio_client.embeddings.create.assert_called_once_with(
+        input=["Sample text to embed."], model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
+    )
 
     assert embedding == [0.1, 0.2, 0.3, 0.4, 0.5]
-
-
-def test_ensure_model_exists(mock_lm_studio_client):
-    config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
-    embedder = LMStudioEmbedding(config)
-
-    mock_lm_studio_client.pull.assert_not_called()
-
-    mock_lm_studio_client.list.return_value = {"models": []}
-
-    embedder._ensure_model_exists()
-
-    mock_lm_studio_client.pull.assert_called_once_with("nomic-embed-text")

tests/llms/test_lm_studio.py

@@ -8,27 +8,39 @@ from mem0.llms.lmstudio import LMStudioLLM
 
 @pytest.fixture
 def mock_lm_studio_client():
-    with patch("mem0.llms.lmstudio.Client") as mock_lm_studio:
+    with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:  # Corrected path
         mock_client = Mock()
-        mock_client.list.return_value = {"models": [{"name": "lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"}]}
-        mock_lm_studio.return_value = mock_client
+        mock_client.chat.completions.create.return_value = Mock(
+            choices=[
+                Mock(message=Mock(content="I'm doing well, thank you for asking!"))
+            ]
+        )
+        mock_openai.return_value = mock_client
         yield mock_client
 
 
 def test_generate_response_without_tools(mock_lm_studio_client):
-    config = BaseLlmConfig(model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", temperature=0.7, max_tokens=100, top_p=1.0)
+    config = BaseLlmConfig(
+        model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
+        temperature=0.7,
+        max_tokens=100,
+        top_p=1.0,
+    )
     llm = LMStudioLLM(config)
     messages = [
         {"role": "system", "content": "You are a helpful assistant."},
         {"role": "user", "content": "Hello, how are you?"},
     ]
 
-    mock_response = {"message": {"content": "I'm doing well, thank you for asking!"}}
-    mock_lm_studio_client.chat.return_value = mock_response
-
     response = llm.generate_response(messages)
 
-    mock_lm_studio_client.chat.assert_called_once_with(
-        model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", messages=messages, options={"temperature": 0.7, "num_predict": 100, "top_p": 1.0}
+    mock_lm_studio_client.chat.completions.create.assert_called_once_with(
+        model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
+        messages=messages,
+        temperature=0.7,
+        max_tokens=100,
+        top_p=1.0,
+        response_format={"type": "json_object"},
     )
+
     assert response == "I'm doing well, thank you for asking!"

tests/vector_stores/test_azure_ai_search.py

@@ -38,7 +38,7 @@ def mock_clients():
         # Stub required methods on index_client.
         mock_index_client.create_or_update_index = Mock()
         mock_index_client.list_indexes = Mock()
-        mock_index_client.list_index_names = Mock(return_value=["test-index"])
+        mock_index_client.list_index_names = Mock(return_value=[])
         mock_index_client.delete_index = Mock()
         # For col_info() we assume get_index returns an object with name and fields attributes.
         fake_index = Mock()