# Nanonets-OCR-s Vision Language Model
# Based on Qwen2.5-VL-3B, fine-tuned for document OCR
# ~8-10GB VRAM, outputs structured markdown with semantic tags
#
# Build: docker build -f Dockerfile_nanonets_ocr -t nanonets-ocr .
# Run:   docker run --gpus all -p 8000:8000 -v ht-huggingface-cache:/root/.cache/huggingface nanonets-ocr

FROM vllm/vllm-openai:latest

LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
LABEL description="Nanonets-OCR-s - Document OCR optimized Vision Language Model"
LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"

# Environment configuration
ENV MODEL_NAME="nanonets/Nanonets-OCR-s"
ENV HOST="0.0.0.0"
ENV PORT="8000"
ENV MAX_MODEL_LEN="8192"
ENV GPU_MEMORY_UTILIZATION="0.9"

# Expose OpenAI-compatible API port
EXPOSE 8000

# Health check - vLLM exposes /health endpoint
HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=5 \
    CMD curl -f http://localhost:8000/health || exit 1

# Start vLLM server with Nanonets-OCR-s model
CMD ["--model", "nanonets/Nanonets-OCR-s", \
     "--trust-remote-code", \
     "--max-model-len", "8192", \
     "--host", "0.0.0.0", \
     "--port", "8000"]