35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
from unittest.mock import Mock, patch
|
|
|
|
import pytest
|
|
|
|
from mem0.configs.llms.base import BaseLlmConfig
|
|
from mem0.llms.lmstudio import LMStudioLLM
|
|
|
|
|
|
@pytest.fixture
|
|
def mock_lm_studio_client():
|
|
with patch("mem0.llms.lmstudio.Client") as mock_lm_studio:
|
|
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
|
|
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)
|
|
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}
|
|
)
|
|
assert response == "I'm doing well, thank you for asking!"
|