- 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>
- Dockerfile: node:22-bookworm-slim + Python venv with trimesh for the
3MF->GLB sidecar (PYTHON_BIN env points the converter at it)
- docker-compose.yml: app + postgres 16 with healthcheck/depends_on,
named volumes for pgdata/uploads/thumbnails/glb, token env passthrough
- .dockerignore, tools/requirements.txt
- converter.js honours PYTHON_BIN (defaults to local venv)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
