mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-05 02:13:24 +00:00
Alex
adb6963523
- CI installs the backend requirements from docsgpt/; the old cd into
application/ silently installed nothing.
- The root .dockerignore re-admits only application/__init__.py. An upgraded
checkout may still hold gitignored application/{inputs,indexes,vectors,.env}
from the old layout, and the directory rule shipped them into the image.
- The compose files keep the host bind mounts on application/{indexes,inputs,
vectors}, so an upgrade does not start with empty data. The move comes with
the packaging work, together with an upgrade note.
- The alias loader puts the real docsgpt spec back on the shared module object
after import (the import machinery stamped the alias spec on it, which made
importlib.reload rename the module and skip re-execution) and delegates
get_code/get_source/get_filename to the target loader, so
python -m application.<name> runs.
- Each legacy application.* task name is registered as its own task object,
a subclass carrying the old name. Registering the same object under two
keys made Celery's tracer log every run under whichever name it built last.
- The redbeat key prefix stays redbeat:docsgpt:; the three schedule_syncs
entries get stable names instead. redbeat tracks its static entries and
deletes the ones that vanish from beat_schedule at start-up, and rewrites the
task path of named entries in place, so neither a prefix bump nor a cleanup
pass is needed (checked against redbeat 2.4.2 with a seeded Redis).
176 lines
7.6 KiB
Docker
176 lines
7.6 KiB
Docker
# DocsGPT backend image.
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#
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# Build args:
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# EXTRAS comma-separated optional extras to bake in, matching the
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# pyproject extras / requirements-<extra>.txt files:
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# docling (layout-model parser + OCR backend), milvus.
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# INSTALL_DOCLING legacy alias for EXTRAS=docling (setup.sh writes it).
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# INSTALL_TESSERACT bake the tesseract binary for OCR_ENGINE=tesseract.
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# EMBEDDINGS_PREFETCH registry names of the embedding models to bake; empty
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# bakes both defaults (mpnet for upgrades, granite for
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# new installs).
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#
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# Everything the default configuration needs is inside the image: embedding
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# models, their tokenizers, tiktoken's encoding and, with the docling extra,
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# docling's layout/table/OCR models. `python -m docsgpt.scripts.verify_offline`
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# under `docker run --network none` proves it.
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FROM ubuntu:24.04 AS builder
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ENV DEBIAN_FRONTEND=noninteractive
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# Ubuntu 24.04 ships Python 3.12 in its main archive: no PPA needed. Every pin
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# resolves to a wheel, so no compiler toolchain either.
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RUN apt-get update && \
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apt-get install -y --no-install-recommends python3.12 python3.12-venv ca-certificates && \
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rm -rf /var/lib/apt/lists/*
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# Build context is the repository root (see .dockerignore there):
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# docker build -f docsgpt/Dockerfile .
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COPY docsgpt/requirements.txt docsgpt/requirements-docling.txt docsgpt/requirements-milvus.txt ./
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RUN python3.12 -m venv /venv
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ENV PATH="/venv/bin:$PATH"
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RUN pip install --no-cache-dir --upgrade pip && \
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pip install --no-cache-dir --only-binary=:all: -r requirements.txt
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# Optional extras. Each requirements-<extra>.txt is exported from the same
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# lock as requirements.txt, so installing it on top only adds the extra's
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# packages. The docling file takes torch from the CPU-only PyTorch index.
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# Not wheels-only: docling's antlr4 runtime ships as a pure-Python sdist.
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ARG EXTRAS=""
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ARG INSTALL_DOCLING=false
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RUN set -e; \
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extras="$EXTRAS"; \
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if [ "$INSTALL_DOCLING" = "true" ]; then extras="$extras,docling"; fi; \
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for extra in $(echo "$extras" | tr ',' ' '); do \
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echo "Installing extra: $extra"; \
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pip install --no-cache-dir -r "requirements-$extra.txt"; \
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done
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# google-api-python-client bundles discovery documents for ~600 Google APIs
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# (99 MB). The application builds one client, Drive v3; keep only its document.
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# Building another API's client needs its file back, or static_discovery=False.
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RUN find /venv/lib/python3.12/site-packages/googleapiclient/discovery_cache/documents \
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-type f ! -name 'drive.v3.json' -delete
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FROM ubuntu:24.04 AS final
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ENV DEBIAN_FRONTEND=noninteractive
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RUN apt-get update && \
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apt-get install -y --no-install-recommends python3.12 poppler-utils ca-certificates && \
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ln -s /usr/bin/python3.12 /usr/bin/python && \
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rm -rf /var/lib/apt/lists/*
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# opencv (rapidocr, part of the docling extra) needs libGL at import time.
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ARG EXTRAS=""
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ARG INSTALL_DOCLING=false
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RUN if [ "$INSTALL_DOCLING" = "true" ] || echo ",$EXTRAS," | grep -q ",docling,"; then \
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apt-get update && \
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apt-get install -y --no-install-recommends libgl1 libglib2.0-0 && \
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rm -rf /var/lib/apt/lists/*; \
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fi
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# Optional tesseract OCR engine (OCR_ENABLED=true with OCR_ENGINE=tesseract,
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# the default engine); ~35 MB of system packages. Extra language packs are a
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# deployment concern (apt: tesseract-ocr-<lang>, then list them in OCR_LANGS).
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# A DeepSeek-OCR endpoint (OCR_ENGINE=deepseek) needs none of this.
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ARG INSTALL_TESSERACT=false
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RUN if [ "$INSTALL_TESSERACT" = "true" ]; then \
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apt-get update && \
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apt-get install -y --no-install-recommends tesseract-ocr tesseract-ocr-eng && \
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rm -rf /var/lib/apt/lists/*; \
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fi
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LABEL org.opencontainers.image.source="https://github.com/arc53/DocsGPT" \
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org.opencontainers.image.title="DocsGPT" \
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org.opencontainers.image.description="DocsGPT backend: API and Celery worker" \
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org.opencontainers.image.licenses="MIT"
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WORKDIR /app
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# The process user owns /app so the model prefetch below can run as it: an
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# unprivileged prefetch writes the model files with the right owner up front,
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# instead of a trailing chown -R that rewrites every model file into a second
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# layer.
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RUN groupadd -r appuser && \
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useradd -r -g appuser -d /app -s /sbin/nologin -c "Docker image user" appuser && \
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chown appuser:appuser /app && \
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install -d -o appuser -g appuser /app/models /app/docsgpt
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COPY --from=builder /venv /venv
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# Every cache the application reads at run time lives under /app/models and is
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# filled at build time:
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# EMBEDDINGS_CACHE_DIR / HF_HUB_CACHE FastEmbed models and their tokenizers
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# (chunking reads tokenizer.json from
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# the same hub-layout snapshot)
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# TIKTOKEN_CACHE_DIR cl100k_base for token accounting
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# DOCLING_ARTIFACTS_PATH docling's models (docling extra only)
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ENV EMBEDDINGS_CACHE_DIR=/app/models \
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HF_HUB_CACHE=/app/models \
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TIKTOKEN_CACHE_DIR=/app/models/tiktoken \
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DOCLING_ARTIFACTS_PATH=/app/models/docling \
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HF_HUB_DISABLE_TELEMETRY=1 \
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PATH="/venv/bin:$PATH"
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# Only the modules the prefetch imports are copied first, so an unrelated
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# source edit does not invalidate the model layer.
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COPY --chown=appuser:appuser docsgpt/__init__.py /app/docsgpt/__init__.py
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COPY --chown=appuser:appuser docsgpt/scripts/__init__.py docsgpt/scripts/prefetch_models.py /app/docsgpt/scripts/
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COPY --chown=appuser:appuser docsgpt/vectorstore/__init__.py docsgpt/vectorstore/model_registry.py /app/docsgpt/vectorstore/
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USER appuser
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ARG EMBEDDINGS_PREFETCH=""
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RUN PYTHONPATH=/app python -m docsgpt.scripts.prefetch_models ${EMBEDDINGS_PREFETCH} && \
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rm -rf /app/models/.locks /app/.cache
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# docling downloads its layout, table-structure and OCR models on first parse;
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# bake them so the docling variant is as self-contained as the default image.
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RUN if python -c "import docling" 2>/dev/null; then \
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docling-tools models download --output-dir /app/models/docling layout tableformer rapidocr && \
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rm -rf /app/.cache; \
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fi
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COPY --chown=appuser:appuser docsgpt /app/docsgpt
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# One-release alias so `-A application.app.celery` style entry points keep working.
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COPY --chown=appuser:appuser application/__init__.py /app/application/__init__.py
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# Runtime data directories, owned by the process user so a named volume
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# mounted on them (docker-compose-standalone.yaml) inherits that ownership
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# and uploads work without running the container as root.
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RUN mkdir -p /app/docsgpt/inputs/local /app/inputs /app/indexes /app/vectors
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ENV FLASK_APP=app.py
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# Thread caps. onnxruntime (FastEmbed) ignores OMP_NUM_THREADS and sizes its
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# pool to the host's core count, which a CPU-limited container still reports;
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# EMBEDDINGS_THREADS pins it the way OMP_NUM_THREADS pinned torch before.
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ENV MALLOC_ARENA_MAX=2 \
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OMP_NUM_THREADS=4 \
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MKL_NUM_THREADS=4 \
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OPENBLAS_NUM_THREADS=4 \
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EMBEDDINGS_THREADS=4
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EXPOSE 7091
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# BoundedDrainUvicornWorker makes max_requests recycles safe with held-open SSE
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# connections (see docsgpt/gunicorn_worker.py); with recycles now safe,
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# --max-requests is raised (kept for memory hygiene) to cut churn.
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CMD ["gunicorn", \
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"-w", "1", \
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"-k", "docsgpt.gunicorn_worker.BoundedDrainUvicornWorker", \
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"--bind", "0.0.0.0:7091", \
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"--timeout", "180", \
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"--graceful-timeout", "120", \
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"--keep-alive", "5", \
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"--worker-tmp-dir", "/dev/shm", \
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"--max-requests", "5000", \
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"--max-requests-jitter", "500", \
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"--config", "docsgpt/gunicorn_conf.py", \
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"docsgpt.asgi:asgi_app"]
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