The model page's tag list wasn't a wrapping row, so the last tag could break in
half, and four action buttons crushed onto two lines each. AI runs also never
woke the background worker, so new AI tags and descriptions weren't searchable
until something else changed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- AI tagging through any OpenAI-compatible API: DeepSeek by default, or
OpenRouter, OpenAI, or a local server (Ollama, LM Studio). Settings → AI
with presets, prices and "Save & test connection"; the key never leaves
the server except to the provider.
- Runs over untagged models, all models, or one model from its page: live
progress, tokens and cost, stop/resume (retrying failures), an optional
spending limit, and paused rather than resumed after a restart. Adapts to
models without JSON mode or image input; stops on a rejected key or no
credit. Tags are only added; descriptions filled only where empty unless
asked.
- Search by meaning: local bge-small-en-v1.5 embeddings (transformers.js),
indexed in the background whenever a model's text changes; Words/Meaning
switch on the search box. Stored as REAL[] in Postgres, no pgvector needed.
- Automatic tags: strict whole-word rules (part types, mechanisms, materials,
techniques) over names, file names and archive contents, plus your own
word -> tag rules; on/off (optionally removing its tags) and re-apply.
- Tags remember where they came from (user / rule / ai); edits keep that,
rule and AI passes never touch your own tags, and the model page marks them.
- Docker: ships the embedding model and only Linux onnxruntime binaries.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
