Keysat
8d839e3714
- Add redaction gateway (redaction_gateway.py, redaction/ scrub + tests) - Add embeddings proxy and spark_embed service (Dockerfile + main.py) - Expand audio_proxy with speaker-aware handling; deep_health/health/server updates - Package: configureSparks action + sparkConfig model updates, manifest/main wiring - Docs: AUDIO_API, EMBEDDINGS, REDACTION_GATEWAY; HANDOFF and runbook/known-issues refresh
37 lines
1.8 KiB
Docker
37 lines
1.8 KiB
Docker
# spark-embed — dense embeddings (bge-m3) + reranker (bge-reranker-v2-m3)
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# Built FROM the NGC PyTorch image that is already proven to run on the DGX
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# Spark's GB10 (sm_121) GPU — the same base behind our vLLM and Kokoro work.
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#
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# Why not HF Text Embeddings Inference (TEI)? As of 2026 TEI ships no arm64
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# CUDA image (all *-cuda tags are amd64-only), so it won't run on the Spark.
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# Building on NGC torch sidesteps that AND avoids torchaudio (the dependency
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# that sank the WhisperX attempt). bge-m3 + the reranker are XLM-RoBERTa
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# encoders — no flash-attn, no torchaudio, just SDPA attention on torch.
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FROM nvcr.io/nvidia/pytorch:25.11-py3
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WORKDIR /app
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# Hard-pin the NGC torch version in a constraints file so pip CANNOT replace it
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# while resolving sentence-transformers. NGC's torch carries a local version
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# string (…nv25.11) not on PyPI; pinning it makes pip treat the already-installed
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# build as satisfying the requirement instead of pulling a PyPI wheel that
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# wouldn't have sm_121 kernels. (Same technique as the v0.12.0 torch-ABI work.)
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# transformers is NOT preinstalled in this NGC base, so it installs fresh from
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# PyPI; we cap it (<5) so a future major can't silently change loading behavior.
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RUN python -c "import torch; \
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open('/tmp/constraints.txt','w').write('torch==%s\n' % torch.__version__)" \
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&& cat /tmp/constraints.txt \
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&& pip install --no-cache-dir -c /tmp/constraints.txt \
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"sentence-transformers>=3.0" "transformers<5" "fastapi>=0.115" "uvicorn[standard]>=0.30"
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COPY main.py /app/main.py
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# Persist HuggingFace model downloads (bge-m3 ~2.3GB + reranker ~2.3GB) on a
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# mounted volume so container recreates don't re-download.
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ENV HF_HOME=/data/hf
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ENV DENSE_MODEL=BAAI/bge-m3
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ENV RERANK_MODEL=BAAI/bge-reranker-v2-m3
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EXPOSE 8088
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8088"]
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