build: local stack, container images and the end-to-end smoke script
docker-compose brings up PostgreSQL 16 with pgvector and Redis 7 on non-standard host ports so it never collides with an existing local database. scripts/smoke.sh is the proof: compose, migrate, boot the API, issue a token, call the real DeepSeek API and print the resulting ob_ai_call and bl_ledger rows. services/vision and services/matting are placeholders noting that the local RTX 5090 makes BiRefNet and RTMPose developable without cloud GPU.
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# syntax=docker/dockerfile:1
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# Celery worker image. Same build as the API; different entrypoint and queue set.
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# 架构文档 §14.1 runs worker-cpu / worker-io on CVM-app and worker-gpu on CVM-gpu,
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# so the queue list is injected rather than baked in.
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FROM python:3.12-slim AS builder
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
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ENV UV_COMPILE_BYTECODE=1 \
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UV_LINK_MODE=copy \
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UV_PYTHON_DOWNLOADS=never
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WORKDIR /app
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COPY pyproject.toml uv.lock* README.md ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --frozen --no-install-project --no-dev || uv sync --no-install-project --no-dev
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COPY apps ./apps
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COPY packages ./packages
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COPY services ./services
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COPY prompts ./prompts
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COPY migrations ./migrations
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COPY alembic.ini ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --no-dev --frozen || uv sync --no-dev
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FROM python:3.12-slim AS runtime
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PATH="/app/.venv/bin:$PATH" \
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TZ=Asia/Shanghai \
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CELERY_QUEUES=cpu,io \
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CELERY_CONCURRENCY=4
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RUN groupadd --system meida && useradd --system --gid meida --home /app meida
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WORKDIR /app
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COPY --from=builder --chown=meida:meida /app /app
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USER meida
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HEALTHCHECK --interval=30s --timeout=10s --start-period=30s --retries=3 \
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CMD celery -A apps.worker.celery_app.celery_app inspect ping -d "celery@$(hostname)" || exit 1
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CMD ["sh", "-c", "celery -A apps.worker.celery_app.celery_app worker -Q ${CELERY_QUEUES} -c ${CELERY_CONCURRENCY} -l info"]
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