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feat(agent): add LLM-powered metadata enrichment system with AgentCLI and PostProcessor

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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
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45
static/js/components/ContextMenu/LoraContextMenu.js
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@@ -1,7 +1,7 @@
import { BaseContextMenu } from './BaseContextMenu.js';
import { ModelContextMenuMixin } from './ModelContextMenuMixin.js';
import { getModelApiClient, resetAndReload } from '../../api/modelApiFactory.js';
import { copyLoraSyntax, sendLoraToWorkflow, buildLoraSyntax } from '../../utils/uiHelpers.js';
import { copyLoraSyntax, sendLoraToWorkflow, buildLoraSyntax, showToast } from '../../utils/uiHelpers.js';
import { showExcludeModal, showDeleteModal } from '../../utils/modalUtils.js';
import { moveManager } from '../../managers/MoveManager.js';
@@ -63,6 +63,9 @@ export class LoraContextMenu extends BaseContextMenu {
case 'refresh-metadata':
getModelApiClient().refreshSingleModelMetadata(this.currentCard.dataset.filepath);
break;
case 'enrich-hf-agent':
this.enrichWithAgent(this.currentCard.dataset.filepath);
break;
case 'exclude':
showExcludeModal(this.currentCard.dataset.filepath);
break;
@@ -72,6 +75,46 @@ export class LoraContextMenu extends BaseContextMenu {
}
}
async enrichWithAgent(filePath) {
const { agentManager } = await import('../../managers/AgentManager.js');
// Check if LLM is configured
const configured = await agentManager.isLlmConfigured();
if (!configured) {
showToast('toast.agent.llmNotConfigured', {}, 'warning');
return;
}
// Connect WebSocket for progress
agentManager.connect();
// Set up one-time completion handler
const onComplete = (data) => {
const idx = agentManager.completeCallbacks.indexOf(onComplete);
if (idx >= 0) agentManager.completeCallbacks.splice(idx, 1);
if (data.status === 'completed') {
showToast('toast.agent.enrichComplete', { summary: data.summary || 'Done' }, 'success');
// Soft reload to reflect updated metadata
if (typeof resetAndReload === 'function') {
resetAndReload();
}
} else if (data.status === 'error') {
showToast('toast.agent.enrichFailed', { error: data.error || 'Unknown error' }, 'error');
}
};
agentManager.onComplete(onComplete);
// Show progress toast
showToast('toast.agent.enrichStarted', {}, 'info');
try {
await agentManager.executeSkill('enrich_hf_metadata', [filePath]);
} catch (error) {
showToast('toast.agent.enrichFailed', { error: error.message }, 'error');
}
}
sendLoraToWorkflow(replaceMode) {
const card = this.currentCard;
const usageTips = JSON.parse(card.dataset.usage_tips || '{}');