Files
ComfyUI-Lora-Manager/tests/services/test_llm_service.py
Will Miao cf898da193 feat(agent): add LLM-powered metadata enrichment system with AgentCLI and PostProcessor
Introduce an agent skill framework for LLM-driven metadata enrichment:

- AgentCLI (py/agent_cli/): in-process wrappers around internal services
  using standard relative imports, eliminating the need for sys.path hacks
- LLMService: centralized BYOK (bring-your-own-key) LLM client supporting
  OpenAI, Ollama, and custom OpenAI-compatible endpoints
- PostProcessor: deterministic engine that applies LLM output via AgentCLI
  (replaces old handler.py + _BASE_MODEL_ALIASES approach)
- SkillRegistry: filesystem-based skill discovery (skill.yaml + prompt.md)
- AgentService: orchestrates skill execution with WebSocket progress
- Frontend AgentManager: WebSocket listeners, skill execution, config UI
- Context menu entries (single + bulk) for "Enrich Metadata (Agent)"
- Settings UI for AI Provider configuration (BYOK)
- Full i18n support across 9 locales

Bug fixes found during review:
- aiohttp.web.json_response: status_code= -> status=
- settings_modal cancelEditApiKey: wrong argument position
- AgentManager.isLlmConfigured: allow Ollama without API key
- PostProcessor._merge_tags: lowercase all tags to match TagUpdateService
2026-07-02 21:27:01 +08:00

238 lines
8.0 KiB
Python

"""Tests for the LLMService."""
from __future__ import annotations
import asyncio
import json
from unittest import mock
import pytest
from py.services.llm_service import LLMService
from py.services.errors import LLMNotConfiguredError, LLMRateLimitError, LLMResponseError
class MockSettings:
"""Minimal settings mock for LLMService tests."""
def __init__(self, **kwargs):
self._data = {
"llm_enabled": False,
"llm_provider": "openai",
"llm_api_key": "",
"llm_api_base": "",
"llm_model": "",
}
self._data.update(kwargs)
def get(self, key, default=None):
return self._data.get(key, default)
class MockResponse:
"""Mock aiohttp response."""
def __init__(self, status, json_data=None, text_data="", headers=None):
self.status = status
self._json_data = json_data
self._text_data = text_data
self.headers = headers or {}
async def json(self):
return self._json_data
async def text(self):
return self._text_data
async def __aenter__(self):
return self
async def __aexit__(self, *args):
pass
class MockSession:
"""Mock aiohttp ClientSession."""
def __init__(self, response):
self._response = response
self.closed = False
def post(self, url, json=None, headers=None):
self.last_url = url
self.last_json = json
self.last_headers = headers
return self._response
async def __aenter__(self):
return self
async def __aexit__(self, *args):
pass
@pytest.fixture
def llm_service():
"""Create an LLMService with mock settings."""
LLMService.reset_instance()
settings = MockSettings(
llm_enabled=True,
llm_provider="openai",
llm_api_key="sk-test-key",
llm_api_base="",
llm_model="gpt-4o-mini",
)
return LLMService(settings)
class TestLLMServiceConfiguration:
def test_is_configured_when_enabled_with_key_and_model(self, llm_service):
assert llm_service.is_configured() is True
def test_not_configured_when_disabled(self):
settings = MockSettings(
llm_enabled=False, llm_api_key="sk-test", llm_model="gpt-4o"
)
service = LLMService(settings)
# Lenient: model + API key is treated as configured even without
# the toggle, because the user clearly intends to use the feature.
assert service.is_configured() is True
def test_not_configured_without_model(self):
settings = MockSettings(llm_enabled=True, llm_api_key="sk-test", llm_model="")
service = LLMService(settings)
assert service.is_configured() is False
def test_not_configured_without_api_key_for_openai(self):
settings = MockSettings(llm_enabled=True, llm_api_key="", llm_model="gpt-4o")
service = LLMService(settings)
assert service.is_configured() is False
def test_ollama_configured_without_api_key(self):
settings = MockSettings(
llm_enabled=True, llm_provider="ollama", llm_api_key="", llm_model="llama3"
)
service = LLMService(settings)
assert service.is_configured() is True
def test_resolve_api_base_openai_default(self, llm_service):
assert llm_service._resolve_api_base("openai", "") == "https://api.openai.com/v1"
def test_resolve_api_base_ollama_default(self, llm_service):
assert llm_service._resolve_api_base("ollama", "") == "http://localhost:11434/v1"
def test_resolve_api_base_custom_override(self, llm_service):
assert llm_service._resolve_api_base("custom", "https://my.api.com/v1/") == "https://my.api.com/v1"
def test_ensure_configured_raises_when_disabled(self):
settings = MockSettings(llm_enabled=False)
service = LLMService(settings)
with pytest.raises(LLMNotConfiguredError):
service._ensure_configured()
def test_ensure_configured_raises_without_model(self):
settings = MockSettings(llm_enabled=True, llm_api_key="sk-test", llm_model="")
service = LLMService(settings)
with pytest.raises(LLMNotConfiguredError):
service._ensure_configured()
class TestLLMServiceChatCompletion:
@pytest.mark.asyncio
async def test_chat_completion_success(self, llm_service):
mock_response = MockResponse(
200,
json_data={
"choices": [{"message": {"content": "Hello!"}}],
"usage": {"total_tokens": 10},
"model": "gpt-4o-mini",
},
)
mock_session = MockSession(mock_response)
with mock.patch("aiohttp.ClientSession", return_value=mock_session):
result = await llm_service.chat_completion(
messages=[{"role": "user", "content": "Hi"}],
)
assert result["content"] == "Hello!"
assert result["usage"]["total_tokens"] == 10
assert result["model"] == "gpt-4o-mini"
@pytest.mark.asyncio
async def test_chat_completion_raises_on_not_configured(self):
settings = MockSettings(llm_enabled=False)
service = LLMService(settings)
with pytest.raises(LLMNotConfiguredError):
await service.chat_completion(messages=[])
@pytest.mark.asyncio
async def test_chat_completion_raises_on_http_error(self, llm_service):
mock_response = MockResponse(500, text_data="Internal Server Error")
mock_session = MockSession(mock_response)
with mock.patch("aiohttp.ClientSession", return_value=mock_session):
with pytest.raises(LLMResponseError, match="HTTP 500"):
await llm_service.chat_completion(messages=[])
@pytest.mark.asyncio
async def test_chat_completion_raises_on_rate_limit(self, llm_service):
mock_response = MockResponse(429, text_data="Rate limited", headers={"Retry-After": "0"})
mock_session = MockSession(mock_response)
with mock.patch("aiohttp.ClientSession", return_value=mock_session):
with pytest.raises(LLMRateLimitError):
await llm_service.chat_completion(
messages=[], retry_on_rate_limit=False
)
@pytest.mark.asyncio
async def test_chat_completion_raises_on_bad_response_structure(self, llm_service):
mock_response = MockResponse(200, json_data={"unexpected": "data"})
mock_session = MockSession(mock_response)
with mock.patch("aiohttp.ClientSession", return_value=mock_session):
with pytest.raises(LLMResponseError, match="Unexpected LLM response"):
await llm_service.chat_completion(messages=[])
class TestLLMServiceChatCompletionJson:
@pytest.mark.asyncio
async def test_chat_completion_json_parses_json(self, llm_service):
mock_response = MockResponse(
200,
json_data={
"choices": [{"message": {"content": '{"key": "value"}'}}],
"usage": {},
"model": "gpt-4o-mini",
},
)
mock_session = MockSession(mock_response)
with mock.patch("aiohttp.ClientSession", return_value=mock_session):
result = await llm_service.chat_completion_json(
system_prompt="You are helpful.",
user_prompt="Return JSON.",
)
assert result == {"key": "value"}
@pytest.mark.asyncio
async def test_chat_completion_json_raises_on_non_json(self, llm_service):
# First attempt: non-JSON; second attempt (retry): also non-JSON
mock_response = MockResponse(
200,
json_data={
"choices": [{"message": {"content": "not json at all"}}],
"usage": {},
},
)
mock_session = MockSession(mock_response)
with mock.patch("aiohttp.ClientSession", return_value=mock_session):
with pytest.raises(LLMResponseError, match="could not be parsed as JSON"):
await llm_service.chat_completion_json(
system_prompt="test",
user_prompt="test",
)