mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
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cf898da193
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
92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
"""Tests for the SkillRegistry."""
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from __future__ import annotations
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from pathlib import Path
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import pytest
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from py.services.agent.skill_registry import SkillRegistry
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from py.services.agent.skill_definition import SkillDefinition, SkillPermissions
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@pytest.fixture
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def registry():
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"""Create a SkillRegistry with the real skills directory."""
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SkillRegistry.reset_instance()
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reg = SkillRegistry()
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reg._discover()
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return reg
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class TestSkillRegistryDiscovery:
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def test_discovers_enrich_hf_metadata_skill(self, registry):
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skills = registry.list_skills()
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assert len(skills) >= 1
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skill = registry.get_skill("enrich_hf_metadata")
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assert skill is not None
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assert skill.name == "enrich_hf_metadata"
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assert skill.llm_required is True
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def test_skill_has_correct_model_type_filter(self, registry):
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skill = registry.get_skill("enrich_hf_metadata")
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assert skill.model_type_filter == ["lora", "checkpoint", "embedding"]
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def test_skill_has_permissions(self, registry):
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skill = registry.get_skill("enrich_hf_metadata")
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assert skill.permissions.write_metadata is True
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assert skill.permissions.write_previews is True
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assert "huggingface.co" in skill.permissions.network_domains
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def test_get_skill_returns_none_for_unknown(self, registry):
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assert registry.get_skill("nonexistent_skill") is None
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class TestSkillRegistryLoading:
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def test_load_prompt_returns_content(self, registry):
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prompt = registry.load_prompt("enrich_hf_metadata")
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assert isinstance(prompt, str)
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assert len(prompt) > 100
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assert "base_model" in prompt
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assert "trigger_words" in prompt
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def test_load_prompt_raises_for_unknown_skill(self, registry):
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with pytest.raises(FileNotFoundError):
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registry.load_prompt("nonexistent")
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def test_load_handler_raises_when_handler_missing(self, registry):
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with pytest.raises(FileNotFoundError):
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registry.load_handler("enrich_hf_metadata")
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class TestSkillDefinition:
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def test_applies_to_model_type_with_filter(self):
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sd = SkillDefinition(
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name="test",
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title="Test",
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description="",
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llm_required=False,
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model_type_filter=["lora"],
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)
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assert sd.applies_to_model_type("lora") is True
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assert sd.applies_to_model_type("checkpoint") is False
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def test_applies_to_model_type_without_filter(self):
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sd = SkillDefinition(
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name="test",
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title="Test",
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description="",
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llm_required=False,
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model_type_filter=None,
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)
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assert sd.applies_to_model_type("lora") is True
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assert sd.applies_to_model_type("checkpoint") is True
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class TestSkillPermissions:
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def test_defaults(self):
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sp = SkillPermissions()
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assert sp.write_metadata is True
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assert sp.write_previews is True
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assert sp.network_domains == ()
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