mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-07-04 16:31:16 -03:00
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
This commit is contained in:
Will Miao
237
tests/services/test_llm_service.py
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237
tests/services/test_llm_service.py
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@@ -0,0 +1,237 @@
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"""Tests for the LLMService."""
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from __future__ import annotations
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import asyncio
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import json
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from unittest import mock
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import pytest
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from py.services.llm_service import LLMService
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from py.services.errors import LLMNotConfiguredError, LLMRateLimitError, LLMResponseError
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class MockSettings:
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"""Minimal settings mock for LLMService tests."""
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def __init__(self, **kwargs):
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self._data = {
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"llm_enabled": False,
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"llm_provider": "openai",
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"llm_api_key": "",
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"llm_api_base": "",
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"llm_model": "",
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}
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self._data.update(kwargs)
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def get(self, key, default=None):
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return self._data.get(key, default)
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class MockResponse:
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"""Mock aiohttp response."""
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def __init__(self, status, json_data=None, text_data="", headers=None):
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self.status = status
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self._json_data = json_data
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self._text_data = text_data
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self.headers = headers or {}
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async def json(self):
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return self._json_data
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async def text(self):
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return self._text_data
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async def __aenter__(self):
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return self
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async def __aexit__(self, *args):
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pass
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class MockSession:
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"""Mock aiohttp ClientSession."""
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def __init__(self, response):
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self._response = response
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self.closed = False
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def post(self, url, json=None, headers=None):
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self.last_url = url
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self.last_json = json
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self.last_headers = headers
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return self._response
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async def __aenter__(self):
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return self
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async def __aexit__(self, *args):
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pass
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@pytest.fixture
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def llm_service():
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"""Create an LLMService with mock settings."""
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LLMService.reset_instance()
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settings = MockSettings(
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llm_enabled=True,
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llm_provider="openai",
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llm_api_key="sk-test-key",
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llm_api_base="",
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llm_model="gpt-4o-mini",
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)
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return LLMService(settings)
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class TestLLMServiceConfiguration:
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def test_is_configured_when_enabled_with_key_and_model(self, llm_service):
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assert llm_service.is_configured() is True
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def test_not_configured_when_disabled(self):
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settings = MockSettings(
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llm_enabled=False, llm_api_key="sk-test", llm_model="gpt-4o"
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)
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service = LLMService(settings)
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# Lenient: model + API key is treated as configured even without
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# the toggle, because the user clearly intends to use the feature.
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assert service.is_configured() is True
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def test_not_configured_without_model(self):
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settings = MockSettings(llm_enabled=True, llm_api_key="sk-test", llm_model="")
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service = LLMService(settings)
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assert service.is_configured() is False
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def test_not_configured_without_api_key_for_openai(self):
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settings = MockSettings(llm_enabled=True, llm_api_key="", llm_model="gpt-4o")
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service = LLMService(settings)
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assert service.is_configured() is False
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def test_ollama_configured_without_api_key(self):
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settings = MockSettings(
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llm_enabled=True, llm_provider="ollama", llm_api_key="", llm_model="llama3"
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)
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service = LLMService(settings)
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assert service.is_configured() is True
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def test_resolve_api_base_openai_default(self, llm_service):
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assert llm_service._resolve_api_base("openai", "") == "https://api.openai.com/v1"
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def test_resolve_api_base_ollama_default(self, llm_service):
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assert llm_service._resolve_api_base("ollama", "") == "http://localhost:11434/v1"
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def test_resolve_api_base_custom_override(self, llm_service):
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assert llm_service._resolve_api_base("custom", "https://my.api.com/v1/") == "https://my.api.com/v1"
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def test_ensure_configured_raises_when_disabled(self):
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settings = MockSettings(llm_enabled=False)
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service = LLMService(settings)
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with pytest.raises(LLMNotConfiguredError):
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service._ensure_configured()
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def test_ensure_configured_raises_without_model(self):
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settings = MockSettings(llm_enabled=True, llm_api_key="sk-test", llm_model="")
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service = LLMService(settings)
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with pytest.raises(LLMNotConfiguredError):
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service._ensure_configured()
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class TestLLMServiceChatCompletion:
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@pytest.mark.asyncio
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async def test_chat_completion_success(self, llm_service):
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mock_response = MockResponse(
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200,
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json_data={
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"choices": [{"message": {"content": "Hello!"}}],
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"usage": {"total_tokens": 10},
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"model": "gpt-4o-mini",
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},
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)
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mock_session = MockSession(mock_response)
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with mock.patch("aiohttp.ClientSession", return_value=mock_session):
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result = await llm_service.chat_completion(
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messages=[{"role": "user", "content": "Hi"}],
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)
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assert result["content"] == "Hello!"
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assert result["usage"]["total_tokens"] == 10
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assert result["model"] == "gpt-4o-mini"
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@pytest.mark.asyncio
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async def test_chat_completion_raises_on_not_configured(self):
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settings = MockSettings(llm_enabled=False)
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service = LLMService(settings)
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with pytest.raises(LLMNotConfiguredError):
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await service.chat_completion(messages=[])
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@pytest.mark.asyncio
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async def test_chat_completion_raises_on_http_error(self, llm_service):
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mock_response = MockResponse(500, text_data="Internal Server Error")
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mock_session = MockSession(mock_response)
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with mock.patch("aiohttp.ClientSession", return_value=mock_session):
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with pytest.raises(LLMResponseError, match="HTTP 500"):
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await llm_service.chat_completion(messages=[])
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@pytest.mark.asyncio
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async def test_chat_completion_raises_on_rate_limit(self, llm_service):
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mock_response = MockResponse(429, text_data="Rate limited", headers={"Retry-After": "0"})
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mock_session = MockSession(mock_response)
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with mock.patch("aiohttp.ClientSession", return_value=mock_session):
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with pytest.raises(LLMRateLimitError):
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await llm_service.chat_completion(
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messages=[], retry_on_rate_limit=False
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)
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@pytest.mark.asyncio
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async def test_chat_completion_raises_on_bad_response_structure(self, llm_service):
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mock_response = MockResponse(200, json_data={"unexpected": "data"})
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mock_session = MockSession(mock_response)
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with mock.patch("aiohttp.ClientSession", return_value=mock_session):
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with pytest.raises(LLMResponseError, match="Unexpected LLM response"):
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await llm_service.chat_completion(messages=[])
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class TestLLMServiceChatCompletionJson:
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@pytest.mark.asyncio
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async def test_chat_completion_json_parses_json(self, llm_service):
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mock_response = MockResponse(
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200,
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json_data={
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"choices": [{"message": {"content": '{"key": "value"}'}}],
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"usage": {},
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"model": "gpt-4o-mini",
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},
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)
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mock_session = MockSession(mock_response)
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with mock.patch("aiohttp.ClientSession", return_value=mock_session):
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result = await llm_service.chat_completion_json(
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system_prompt="You are helpful.",
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user_prompt="Return JSON.",
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)
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assert result == {"key": "value"}
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@pytest.mark.asyncio
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async def test_chat_completion_json_raises_on_non_json(self, llm_service):
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# First attempt: non-JSON; second attempt (retry): also non-JSON
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mock_response = MockResponse(
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200,
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json_data={
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"choices": [{"message": {"content": "not json at all"}}],
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"usage": {},
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},
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)
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mock_session = MockSession(mock_response)
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with mock.patch("aiohttp.ClientSession", return_value=mock_session):
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with pytest.raises(LLMResponseError, match="could not be parsed as JSON"):
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await llm_service.chat_completion_json(
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system_prompt="test",
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user_prompt="test",
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)
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313
tests/services/test_post_processor.py
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313
tests/services/test_post_processor.py
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@@ -0,0 +1,313 @@
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"""Tests for the PostProcessor (py/services/agent/post_processor.py).
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PostProcessor delegates all I/O to AgentCLI — these tests mock AgentCLI
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functions and verify the business logic (conditions, merges, dispatch).
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"""
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from __future__ import annotations
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from datetime import datetime, timezone
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from unittest import mock
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import pytest
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from py.services.agent.post_processor import PostProcessor
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@pytest.fixture
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def processor():
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return PostProcessor()
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# ======================================================================
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# process() — routing
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# ======================================================================
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class TestProcessDispatch:
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@pytest.mark.asyncio
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async def test_unknown_skill_returns_error(self, processor):
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result = await processor.process(
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skill_name="nonexistent",
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model_path="/p.safetensors",
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llm_output={},
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metadata={},
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)
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assert result["success"] is False
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assert "nonexistent" in result["errors"][0]
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@pytest.mark.asyncio
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async def test_enrich_hf_metadata_routes_correctly(self, processor):
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with (
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mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
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mock.patch("py.agent_cli.download_preview") as mock_dl,
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mock.patch("py.agent_cli.refresh_cache") as mock_ref,
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):
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mock_apply.return_value = ["metadata_source"]
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mock_dl.return_value = False
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result = await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output={},
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metadata={"from_civitai": True},
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)
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assert result["success"] is True
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# ======================================================================
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# enrich_hf_metadata — field-level logic
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# ======================================================================
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class TestEnrichHfMetadata:
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"""Business logic tests for the enrich_hf_metadata post-processor."""
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MIN_LLM_OUTPUT = {
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"base_model": "",
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"trigger_words": [],
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"description": "",
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"tags": [],
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"preview_url": "",
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"confidence": "low",
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}
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# -- base_model ------------------------------------------------------
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@pytest.mark.asyncio
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async def test_base_model_overwrites_empty(self, processor):
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"""Empty current base_model → new value is applied."""
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llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
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with (
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mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
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mock.patch("py.agent_cli.download_preview", return_value=False),
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mock.patch("py.agent_cli.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={"base_model": ""},
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)
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applied = mock_apply.call_args[0][1]
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assert applied["base_model"] == "Flux.1 D"
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@pytest.mark.asyncio
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async def test_base_model_does_not_overwrite_existing_civitai(self, processor):
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"""Existing base_model from CivitAI → not overwritten."""
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llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
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with (
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mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
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mock.patch("py.agent_cli.download_preview", return_value=False),
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mock.patch("py.agent_cli.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={"base_model": "SDXL 1.0", "from_civitai": True},
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)
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# apply IS called (metadata_source, llm_enriched_at) but base_model not in it
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applied = mock_apply.call_args[0][1]
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assert "base_model" not in applied
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@pytest.mark.asyncio
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async def test_base_model_overwrites_existing_hf_model(self, processor):
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"""Existing base_model from HF → overwritten (LLM is more reliable)."""
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llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
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with (
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mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
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mock.patch("py.agent_cli.download_preview", return_value=False),
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mock.patch("py.agent_cli.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={"base_model": "SD 1.5", "from_civitai": False},
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)
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applied = mock_apply.call_args[0][1]
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assert applied["base_model"] == "Flux.1 D"
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@pytest.mark.asyncio
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async def test_base_model_skipped_when_llm_empty(self, processor):
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"""LLM returns empty base_model → nothing written."""
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with (
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mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
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mock.patch("py.agent_cli.download_preview", return_value=False),
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mock.patch("py.agent_cli.refresh_cache"),
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=self.MIN_LLM_OUTPUT,
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metadata={"base_model": ""},
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)
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applied = mock_apply.call_args[0][1]
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assert "base_model" not in applied
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# -- trigger_words ---------------------------------------------------
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@pytest.mark.asyncio
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async def test_trigger_words_merged(self, processor):
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"""New trigger words written when current list is empty."""
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llm = {**self.MIN_LLM_OUTPUT, "trigger_words": ["trigger1", "trigger2"]}
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with (
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mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
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mock.patch("py.agent_cli.download_preview", return_value=False),
|
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mock.patch("py.agent_cli.refresh_cache"),
|
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
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model_path="/p.safetensors",
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llm_output=llm,
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metadata={"trainedWords": []},
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)
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applied = mock_apply.call_args[0][1]
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assert applied["trainedWords"] == ["trigger1", "trigger2"]
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# -- description -----------------------------------------------------
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@pytest.mark.asyncio
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async def test_description_set_when_empty(self, processor):
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llm = {**self.MIN_LLM_OUTPUT, "description": "A model description"}
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with (
|
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mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.agent_cli.download_preview", return_value=False),
|
||||
mock.patch("py.agent_cli.refresh_cache"),
|
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):
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await processor.process(
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skill_name="enrich_hf_metadata",
|
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model_path="/p.safetensors",
|
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llm_output=llm,
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metadata={"modelDescription": ""},
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)
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assert "modelDescription" in mock_apply.call_args[0][1]
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|
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# -- tags ------------------------------------------------------------
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@pytest.mark.asyncio
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async def test_tags_merged_and_deduplicated(self, processor):
|
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llm = {**self.MIN_LLM_OUTPUT, "tags": ["flux", "lora", "STYLE"]}
|
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with (
|
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mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.agent_cli.download_preview", return_value=False),
|
||||
mock.patch("py.agent_cli.refresh_cache"),
|
||||
):
|
||||
await processor.process(
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||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata={"tags": ["anime"], "from_civitai": False},
|
||||
)
|
||||
merged = mock_apply.call_args[0][1]["tags"]
|
||||
assert "anime" in merged
|
||||
assert "flux" in merged
|
||||
assert "style" in merged # lowercased
|
||||
# "lora" and "STYLE" → "lora" and "style"
|
||||
assert len(merged) == 4 # anime, flux, lora, style
|
||||
|
||||
# -- metadata_source & llm_enriched_at --------------------------------
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_audit_fields_always_set(self, processor):
|
||||
with (
|
||||
mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.agent_cli.download_preview", return_value=False),
|
||||
mock.patch("py.agent_cli.refresh_cache"),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.MIN_LLM_OUTPUT,
|
||||
metadata={},
|
||||
)
|
||||
applied = mock_apply.call_args[0][1]
|
||||
assert applied["metadata_source"] == "agent:enrich_hf_metadata"
|
||||
assert "llm_enriched_at" in applied
|
||||
|
||||
# -- preview download ------------------------------------------------
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_preview_downloaded_when_url_provided(self, processor):
|
||||
llm = {**self.MIN_LLM_OUTPUT, "preview_url": "https://ex.com/img.png"}
|
||||
with (
|
||||
mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
|
||||
mock.patch("py.agent_cli.download_preview") as mock_dl,
|
||||
mock.patch("py.agent_cli.refresh_cache"),
|
||||
):
|
||||
mock_dl.return_value = True
|
||||
result = await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata={},
|
||||
)
|
||||
assert result["preview_downloaded"] is True
|
||||
mock_dl.assert_awaited_once_with("/p.safetensors", "https://ex.com/img.png")
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_preview_skipped_when_exists(self, processor):
|
||||
"""If current_preview file exists on disk, skip download."""
|
||||
llm = {**self.MIN_LLM_OUTPUT, "preview_url": "https://ex.com/img.png"}
|
||||
with (
|
||||
mock.patch("py.agent_cli.apply_metadata_updates"),
|
||||
mock.patch("py.agent_cli.download_preview") as mock_dl,
|
||||
mock.patch("py.agent_cli.refresh_cache"),
|
||||
mock.patch("os.path.exists", return_value=True),
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata={"preview_url": "/existing/preview.webp"},
|
||||
)
|
||||
mock_dl.assert_not_called()
|
||||
|
||||
# -- cache refresh ---------------------------------------------------
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_cache_refreshed_when_updates_applied(self, processor):
|
||||
llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
|
||||
with (
|
||||
mock.patch("py.agent_cli.apply_metadata_updates", return_value=["base_model"]),
|
||||
mock.patch("py.agent_cli.download_preview", return_value=False),
|
||||
mock.patch("py.agent_cli.refresh_cache") as mock_ref,
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=llm,
|
||||
metadata={"base_model": ""},
|
||||
)
|
||||
mock_ref.assert_awaited_once_with("/p.safetensors")
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_cache_not_refreshed_when_nothing_changed(self, processor):
|
||||
with (
|
||||
mock.patch("py.agent_cli.apply_metadata_updates", return_value=[]),
|
||||
mock.patch("py.agent_cli.download_preview", return_value=False),
|
||||
mock.patch("py.agent_cli.refresh_cache") as mock_ref,
|
||||
):
|
||||
await processor.process(
|
||||
skill_name="enrich_hf_metadata",
|
||||
model_path="/p.safetensors",
|
||||
llm_output=self.MIN_LLM_OUTPUT,
|
||||
metadata={"base_model": ""},
|
||||
)
|
||||
mock_ref.assert_not_called()
|
||||
|
||||
|
||||
# ======================================================================
|
||||
# Unit: _merge_tags
|
||||
# ======================================================================
|
||||
|
||||
|
||||
class TestMergeTags:
|
||||
def test_deduplicates_case_insensitive(self):
|
||||
existing = ["anime", "Flux"]
|
||||
new = ["flux", "LORA", "anime"]
|
||||
result = PostProcessor._merge_tags(existing, new)
|
||||
# All tags are lowercased (matching TagUpdateService behaviour)
|
||||
assert result == ["anime", "flux", "lora"]
|
||||
91
tests/services/test_skill_registry.py
Normal file
91
tests/services/test_skill_registry.py
Normal file
@@ -0,0 +1,91 @@
|
||||
"""Tests for the SkillRegistry."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from py.services.agent.skill_registry import SkillRegistry
|
||||
from py.services.agent.skill_definition import SkillDefinition, SkillPermissions
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def registry():
|
||||
"""Create a SkillRegistry with the real skills directory."""
|
||||
SkillRegistry.reset_instance()
|
||||
reg = SkillRegistry()
|
||||
reg._discover()
|
||||
return reg
|
||||
|
||||
|
||||
class TestSkillRegistryDiscovery:
|
||||
def test_discovers_enrich_hf_metadata_skill(self, registry):
|
||||
skills = registry.list_skills()
|
||||
assert len(skills) >= 1
|
||||
skill = registry.get_skill("enrich_hf_metadata")
|
||||
assert skill is not None
|
||||
assert skill.name == "enrich_hf_metadata"
|
||||
assert skill.llm_required is True
|
||||
|
||||
def test_skill_has_correct_model_type_filter(self, registry):
|
||||
skill = registry.get_skill("enrich_hf_metadata")
|
||||
assert skill.model_type_filter == ["lora", "checkpoint", "embedding"]
|
||||
|
||||
def test_skill_has_permissions(self, registry):
|
||||
skill = registry.get_skill("enrich_hf_metadata")
|
||||
assert skill.permissions.write_metadata is True
|
||||
assert skill.permissions.write_previews is True
|
||||
assert "huggingface.co" in skill.permissions.network_domains
|
||||
|
||||
def test_get_skill_returns_none_for_unknown(self, registry):
|
||||
assert registry.get_skill("nonexistent_skill") is None
|
||||
|
||||
|
||||
class TestSkillRegistryLoading:
|
||||
def test_load_prompt_returns_content(self, registry):
|
||||
prompt = registry.load_prompt("enrich_hf_metadata")
|
||||
assert isinstance(prompt, str)
|
||||
assert len(prompt) > 100
|
||||
assert "base_model" in prompt
|
||||
assert "trigger_words" in prompt
|
||||
|
||||
def test_load_prompt_raises_for_unknown_skill(self, registry):
|
||||
with pytest.raises(FileNotFoundError):
|
||||
registry.load_prompt("nonexistent")
|
||||
|
||||
def test_load_handler_raises_when_handler_missing(self, registry):
|
||||
with pytest.raises(FileNotFoundError):
|
||||
registry.load_handler("enrich_hf_metadata")
|
||||
|
||||
|
||||
class TestSkillDefinition:
|
||||
def test_applies_to_model_type_with_filter(self):
|
||||
sd = SkillDefinition(
|
||||
name="test",
|
||||
title="Test",
|
||||
description="",
|
||||
llm_required=False,
|
||||
model_type_filter=["lora"],
|
||||
)
|
||||
assert sd.applies_to_model_type("lora") is True
|
||||
assert sd.applies_to_model_type("checkpoint") is False
|
||||
|
||||
def test_applies_to_model_type_without_filter(self):
|
||||
sd = SkillDefinition(
|
||||
name="test",
|
||||
title="Test",
|
||||
description="",
|
||||
llm_required=False,
|
||||
model_type_filter=None,
|
||||
)
|
||||
assert sd.applies_to_model_type("lora") is True
|
||||
assert sd.applies_to_model_type("checkpoint") is True
|
||||
|
||||
|
||||
class TestSkillPermissions:
|
||||
def test_defaults(self):
|
||||
sp = SkillPermissions()
|
||||
assert sp.write_metadata is True
|
||||
assert sp.write_previews is True
|
||||
assert sp.network_domains == ()
|
||||
Reference in New Issue
Block a user