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晨苒
627b673a5c
* refactor: restructure memory into multi-provider adapters, remove manifest.json dependency - Rename internal/memory/provider to internal/memory/adapters with per-provider subdirectories (builtin, mem0, openviking) - Replace manifest.json-based delete/update with scan-based index from daily files - Add mem0 and openviking provider adapters with HTTP client, chat hooks, MCP tools, and CRUD - Wire provider lifecycle into registry (auto-instantiate on create, evict on update/delete) - Split docker-compose into base stack + optional overlays (qdrant, browser, mem0, openviking) - Update admin UI to support dynamic provider config schema rendering * chore(lint): fix all golangci-lint issues for clean CI * refactor(docker): replace compose overlay files with profiles * feat(memory): add built-in memory multi modes * fix(ci): golangci lint * feat(memory): edit built-in memory sparse design
44 lines
1.3 KiB
Docker
44 lines
1.3 KiB
Docker
# syntax=docker/dockerfile:1
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# Sparse encoding service — runs the OpenSearch neural sparse model
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# as a standalone HTTP service (Flask + PyTorch CPU).
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# Used by the Go server's sparse memory runtime via HTTP.
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FROM python:3.12-slim
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WORKDIR /app
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COPY internal/memory/sparse/service/requirements.txt requirements.txt
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RUN pip install --no-cache-dir \
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--extra-index-url https://download.pytorch.org/whl/cpu \
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-r requirements.txt
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COPY internal/memory/sparse/service/main.py main.py
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ENV SPARSE_PORT=8085
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ENV SPARSE_CACHE_DIR=/opt/sparse-cache
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RUN mkdir -p /opt/sparse-cache
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# Pre-download the default sparse model during image build so containers
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# start with a warm cache and do not need to fetch weights on first boot.
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RUN python - <<'PY'
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from pathlib import Path
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from huggingface_hub import hf_hub_download
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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model_repo = "opensearch-project/opensearch-neural-sparse-encoding-multilingual-v1"
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cache_dir = "/opt/sparse-cache"
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Path(cache_dir).mkdir(parents=True, exist_ok=True)
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AutoModelForMaskedLM.from_pretrained(model_repo, cache_dir=cache_dir)
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AutoTokenizer.from_pretrained(model_repo, cache_dir=cache_dir)
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hf_hub_download(repo_id=model_repo, filename="idf.json", cache_dir=cache_dir)
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print(f"Pre-downloaded sparse model: {model_repo}")
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PY
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EXPOSE 8085
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CMD ["python", "main.py"]
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