0f49fe8c1a
- 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>
37 lines
1.3 KiB
Docker
37 lines
1.3 KiB
Docker
# Printhive — Node app + Python/trimesh sidecar (for 3MF -> GLB conversion)
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FROM node:22-bookworm-slim
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# Python is needed at runtime for the 3MF->GLB / volume sidecar.
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RUN apt-get update \
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&& apt-get install -y --no-install-recommends python3 python3-venv \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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# Tell the converter where Python lives (the venv built below).
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ENV PYTHON_BIN=/app/tools/venv/bin/python
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# Python sidecar deps (cached unless requirements.txt changes).
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COPY tools/requirements.txt ./tools/requirements.txt
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RUN python3 -m venv /app/tools/venv \
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&& /app/tools/venv/bin/pip install --no-cache-dir -r tools/requirements.txt
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# Node deps (cached unless package files change).
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COPY package.json package-lock.json ./
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RUN npm ci --omit=dev \
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# semantic search only needs onnxruntime's Linux binaries (saves ~130 MB)
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&& rm -rf node_modules/onnxruntime-node/bin/napi-v3/darwin node_modules/onnxruntime-node/bin/napi-v3/win32
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# App source.
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COPY . .
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# The embedding model for semantic search (~34 MB), fetched at build time so the
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# server never has to download it.
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ENV MODELS_DIR=/app/models
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RUN node -e "require('./lib/embeddingModel').load().then(() => console.log('embedding model ready'))"
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ENV NODE_ENV=production \
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PORT=3000
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EXPOSE 3000
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CMD ["node", "server.js"]
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