kartik-mem0
2b8e4ea059
The embedder fix in this branch left the sibling LLM still hardcoded, so a self-hosted LM Studio behind a non-default host worked for embeddings and silently fell back to localhost for generation. LMStudioConfig baked "http://localhost:1234/v1" into __init__, which makes any env-var lookup downstream dead code. The field now defaults to None and LMStudioLLM resolves config value, then LMSTUDIO_BASE_URL, then the localhost default, matching the embedder and DeepSeek.
111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
from unittest.mock import Mock, patch
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import pytest
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.configs.llms.lmstudio import LMStudioConfig
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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.OpenAI") as mock_openai: # Corrected path
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mock_client = Mock()
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mock_client.chat.completions.create.return_value = Mock(
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choices=[Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
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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 = LMStudioConfig(
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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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response = llm.generate_response(messages)
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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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def test_generate_response_specifying_response_format(mock_lm_studio_client):
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config = LMStudioConfig(
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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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lmstudio_response_format={"type": "json_schema"}, # Specifying the response format in config
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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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response = llm.generate_response(messages)
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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_schema"},
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)
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assert response == "I'm doing well, thank you for asking!"
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def test_base_url_honors_lmstudio_base_url_env(monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
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LMStudioLLM(LMStudioConfig())
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assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"
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def test_base_url_prefers_explicit_config_over_env(monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
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LMStudioLLM(LMStudioConfig(lmstudio_base_url="http://explicit:1234/v1"))
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assert mock_openai.call_args.kwargs["base_url"] == "http://explicit:1234/v1"
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def test_base_url_falls_back_to_localhost_default(monkeypatch):
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monkeypatch.delenv("LMSTUDIO_BASE_URL", raising=False)
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
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llm = LMStudioLLM(LMStudioConfig())
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assert mock_openai.call_args.kwargs["base_url"] == "http://localhost:1234/v1"
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assert llm.config.lmstudio_base_url == "http://localhost:1234/v1"
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def test_base_url_honors_env_via_base_llm_config(monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
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LMStudioLLM(BaseLlmConfig(model="some-model"))
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assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"
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