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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
48 lines
1.0 KiB
YAML
48 lines
1.0 KiB
YAML
name: enrich_hf_metadata
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title: "Enrich Metadata from HuggingFace"
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description: >
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Parse the HuggingFace model card via LLM to extract description, trigger
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words, base model, tags, and preview image URL. Updates .metadata.json
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and downloads the preview thumbnail.
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llm_required: true
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model_type_filter: ["lora", "checkpoint", "embedding"]
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input_schema:
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type: object
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properties:
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model_paths:
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type: array
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items:
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type: string
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required:
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- model_paths
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output_schema:
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type: object
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properties:
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model_path:
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type: string
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base_model:
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type: string
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trigger_words:
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type: array
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items:
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type: string
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description:
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type: string
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tags:
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type: array
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items:
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type: string
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preview_url:
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type: string
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confidence:
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type: string
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enum: ["high", "medium", "low"]
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required:
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- model_path
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- confidence
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permissions:
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write_metadata: true
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write_previews: true
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network_domains:
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- "huggingface.co"
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