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
2026-07-02 20:51:11 +08:00
parent fe90f7f9b1
commit cf898da193
44 changed files with 5937 additions and 2180 deletions

View File

@@ -35,6 +35,9 @@ class BaseModelMetadata:
metadata_source: Optional[str] = None # Last provider that supplied metadata
last_checked_at: float = 0 # Last checked timestamp
hash_status: str = "completed" # Hash calculation status: pending | calculating | completed | failed
trainedWords: List[str] = field(
default_factory=list
) # Trigger words / activation prompts (source-agnostic)
_unknown_fields: Dict[str, Any] = field(
default_factory=dict, repr=False, compare=False
) # Store unknown fields
@@ -47,6 +50,9 @@ class BaseModelMetadata:
if self.tags is None:
self.tags = []
if self.trainedWords is None:
self.trainedWords = []
@classmethod
def from_dict(cls, data: Dict) -> "BaseModelMetadata":
"""Create instance from dictionary"""