Dockerfile.api

# syntax=docker/dockerfile:1
# ---------------------------------------------------------------------------
#  RAGBuilder API. CPU-only torch keeps the image at ~2.5 GB instead of ~7 GB -
#  the CUDA wheels are pure weight on a machine with no NVIDIA GPU.
# ---------------------------------------------------------------------------
FROM python:3.11-slim

ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=1 \
    HF_HOME=/models \
    TOKENIZERS_PARALLELISM=false

WORKDIR /app

RUN apt-get update \
    && apt-get install -y --no-install-recommends build-essential curl \
    && rm -rf /var/lib/apt/lists/*

COPY requirements.txt /app/requirements.txt
RUN pip install --no-cache-dir torch==2.3.1 --index-url https://download.pytorch.org/whl/cpu \
    && pip install --no-cache-dir -r /app/requirements.txt

# Bake the NLTK sentence tokenizer in: downloading it at first request would
# make the very first ingestion fail on an air-gapped host.
RUN python -c "import nltk; nltk.download('punkt_tab', quiet=False); nltk.download('punkt', quiet=True)" \
    && python -c "import nltk; print('german punkt ok:', nltk.sent_tokenize('Ein Satz. Noch einer.', language='german'))"

COPY config.yaml /app/config.yaml
COPY sql/ /app/sql/
COPY ragbuilder/ /app/ragbuilder/
COPY tests/ /app/tests/
COPY pytest.ini /app/pytest.ini

RUN mkdir -p /models /app/data/pdfs

EXPOSE 8000
CMD ["uvicorn", "ragbuilder.api.main:app", "--host", "0.0.0.0", "--port", "8000"]