fix/failing-unit-tests (#2486)
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@@ -8,27 +8,39 @@ from mem0.llms.lmstudio import LMStudioLLM
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@pytest.fixture
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def mock_lm_studio_client():
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with patch("mem0.llms.lmstudio.Client") as mock_lm_studio:
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai: # Corrected path
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mock_client = Mock()
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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"}]}
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mock_lm_studio.return_value = mock_client
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mock_client.chat.completions.create.return_value = Mock(
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choices=[
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Mock(message=Mock(content="I'm doing well, thank you for asking!"))
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]
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)
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mock_openai.return_value = mock_client
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yield mock_client
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def test_generate_response_without_tools(mock_lm_studio_client):
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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)
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config = BaseLlmConfig(
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model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
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temperature=0.7,
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max_tokens=100,
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top_p=1.0,
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)
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llm = LMStudioLLM(config)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello, how are you?"},
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]
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mock_response = {"message": {"content": "I'm doing well, thank you for asking!"}}
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mock_lm_studio_client.chat.return_value = mock_response
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response = llm.generate_response(messages)
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mock_lm_studio_client.chat.assert_called_once_with(
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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}
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mock_lm_studio_client.chat.completions.create.assert_called_once_with(
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model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
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messages=messages,
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temperature=0.7,
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max_tokens=100,
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top_p=1.0,
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response_format={"type": "json_object"},
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)
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assert response == "I'm doing well, thank you for asking!"
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