diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..f0461df --- /dev/null +++ b/.dockerignore @@ -0,0 +1,65 @@ +# Version control +.git +.gitignore +.gitattributes + +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +.env +.venv/ +venv/ +ENV/ +env/ +.pytest_cache/ +.coverage +htmlcov/ +.tox/ +.nox/ +.hypothesis/ +.mypy_cache/ + +# IDE +.idea/ +.vscode/ +*.swp +*.swo +.DS_Store + +# Project specific +tests/ +docs/ +*.md +!README.md +Makefile +docker-compose*.yml +.dockerignore +Dockerfile* + +# Cache and temp files +*.log +.cache/ +tmp/ +temp/ + +# Distribution / packaging +dist/ +build/ +*.egg-info/ + +# Local development +*.local +.env.local +.env.*.local + +# Model cache and downloads +model_cache/ +downloads/ + +*.pyc +*.pyo +*.pyd +.env \ No newline at end of file diff --git a/.gitignore b/.gitignore index 76da708..01f1605 100644 --- a/.gitignore +++ b/.gitignore @@ -18,3 +18,4 @@ .idea/misc.xml .env .DS_Store +__pycache__/ \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 1f99143..a1c6e0b 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,42 +1,98 @@ -# Use a base image with CUDA support and the desired Python version -FROM python:3.12-slim-bookworm - +FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder ARG CPU_ONLY=false + WORKDIR /app -RUN apt-get update \ - && apt-get install -y redis-server libgl1 libglib2.0-0 curl wget git procps \ - && apt-get clean +# Install build dependencies +RUN apt-get update && \ + apt-get install -y --no-install-recommends libgl1 libglib2.0-0 && \ + rm -rf /var/lib/apt/lists/* -# Install Poetry and configure it -RUN pip install poetry \ - && poetry config virtualenvs.create false +# Enable bytecode compilation and set proper link mode for cache mounting +ENV UV_COMPILE_BYTECODE=1 \ + UV_LINK_MODE=copy \ + HF_HOME=/app/.cache/huggingface \ + TORCH_HOME=/app/.cache/torch \ + PYTHONPATH=/app \ + OMP_NUM_THREADS=4 -COPY pyproject.toml poetry.lock ./ +# Copy dependency files and README +COPY pyproject.toml uv.lock README.md ./ -# Install dependencies before torch -RUN poetry install --no-interaction --no-root +# Install dependencies but not the project itself +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-install-project + +# Copy the rest of the project +COPY . . -# Install PyTorch separately based on CPU_ONLY flag -RUN if [ "$CPU_ONLY" = "true" ]; then \ - pip install --no-cache-dir torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu; \ +# Better GPU detection: Check both architecture and if NVIDIA is available +RUN ARCH=$(uname -m) && \ + if [ "$CPU_ONLY" = "true" ] || [ "$ARCH" = "aarch64" ] || [ "$ARCH" = "arm64" ] || ! command -v nvidia-smi >/dev/null 2>&1; then \ + USE_GPU=false; \ else \ - pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121; \ + USE_GPU=true; \ + fi && \ + echo "Detected GPU availability: $USE_GPU" && \ + # For PyTorch installation with architecture detection + uv pip uninstall -y torch torchvision torchaudio || true && \ + if [ "$USE_GPU" = "false" ]; then \ + # For CPU or ARM architectures or no NVIDIA + echo "Installing PyTorch for CPU" && \ + uv pip install --no-cache-dir torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu; \ + else \ + # For x86_64 with GPU support + echo "Installing PyTorch with CUDA support" && \ + uv pip install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121; \ fi -ENV HF_HOME=/tmp/ \ - TORCH_HOME=/tmp/ \ - OMP_NUM_THREADS=4 +# Install the project in non-editable mode +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-editable + +# Download models for the pipeline +RUN uv run python -c "from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline; artifacts_path = StandardPdfPipeline.download_models_hf(force=True)" + +# Pre-download EasyOCR models with better GPU detection +RUN ARCH=$(uname -m) && \ + if [ "$CPU_ONLY" = "true" ] || [ "$ARCH" = "aarch64" ] || [ "$ARCH" = "arm64" ] || ! command -v nvidia-smi >/dev/null 2>&1; then \ + echo "Downloading EasyOCR models for CPU" && \ + uv run python -c "import easyocr; reader = easyocr.Reader(['fr', 'de', 'es', 'en', 'it', 'pt'], gpu=False); print('EasyOCR CPU models downloaded successfully')"; \ + else \ + echo "Downloading EasyOCR models with GPU support" && \ + uv run python -c "import easyocr; reader = easyocr.Reader(['fr', 'de', 'es', 'en', 'it', 'pt'], gpu=True); print('EasyOCR GPU models downloaded successfully')"; \ + fi -RUN python -c 'from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline; artifacts_path = StandardPdfPipeline.download_models_hf(force=True);' +# Production stage +FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim +WORKDIR /app -# Pre-download EasyOCR models in compatible groups -RUN python -c 'import easyocr; \ - reader = easyocr.Reader(["fr", "de", "es", "en", "it", "pt"], gpu=True); \ - print("EasyOCR models downloaded successfully")' +# Install runtime dependencies +RUN apt-get update && \ + apt-get install -y --no-install-recommends redis-server libgl1 libglib2.0-0 curl && \ + rm -rf /var/lib/apt/lists/* -COPY . . +# Set environment variables +ENV HF_HOME=/app/.cache/huggingface \ + TORCH_HOME=/app/.cache/torch \ + PYTHONPATH=/app \ + OMP_NUM_THREADS=4 \ + UV_COMPILE_BYTECODE=1 -EXPOSE 8080 +# Create a non-root user +RUN useradd --create-home app && \ + mkdir -p /app && \ + chown -R app:app /app /tmp -CMD ["poetry", "run", "uvicorn", "--port", "8080", "--host", "0.0.0.0", "main:app"] +# Copy the virtual environment from the builder stage +COPY --from=builder --chown=app:app /app/.venv /app/.venv +ENV PATH="/app/.venv/bin:$PATH" + +# Copy necessary files for the application +COPY --chown=app:app . . + +# Switch to non-root user +USER app + +EXPOSE 8080 +CMD ["uvicorn", "main:app", "--port", "8080", "--host", "0.0.0.0"] \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..d113154 --- /dev/null +++ b/Makefile @@ -0,0 +1,125 @@ +# Variables +PYTHON = poetry run +DOCKER_CPU_COMPOSE = docker-compose -f docker-compose.cpu.yml +DOCKER_GPU_COMPOSE = docker-compose -f docker-compose.gpu.yml +DOCKER_IMAGE = docling-api +PORT = 8080 +WORKERS = 4 + +.PHONY: help install dev-setup start stop clean docker-* test lint format + +help: + @echo "Available commands:" + @echo "Development:" + @echo " install - Install project dependencies using Poetry" + @echo " dev-setup - Setup development environment (install Redis, etc.)" + @echo " start - Start all development services locally" + @echo " stop - Stop all development services" + @echo " clean - Clean up temporary files and caches" + @echo "" + @echo "Docker:" + @echo " docker-build-cpu - Build Docker image (CPU version)" + @echo " docker-build-gpu - Build Docker image (GPU version)" + @echo " docker-start - Auto-detect system and start appropriate container (CPU/GPU)" + @echo " docker-start-cpu - Start services in CPU mode" + @echo " docker-start-gpu - Start services in GPU mode" + @echo " docker-stop - Stop all Docker services" + @echo " docker-clean - Clean Docker resources" + @echo "" + @echo "Code Quality:" + @echo " format - Format code using black" + @echo " lint - Run linter" + @echo " test - Run tests" + +# Development commands +install: + curl -sSL https://install.python-poetry.org | python3 - + poetry install + +dev-setup: + @echo "Setting up development environment..." + @if [ "$(shell uname)" = "Darwin" ]; then \ + brew install redis; \ + brew services start redis; \ + elif [ -f /etc/debian_version ]; then \ + sudo apt-get update && sudo apt-get install -y redis-server; \ + sudo service redis-server start; \ + fi + @echo "Creating .env file..." + @echo "REDIS_HOST=redis://localhost:6379/0" > .env + @echo "ENV=development" >> .env + +start: + @echo "Starting FastAPI server..." + $(PYTHON) uvicorn main:app --reload --port $(PORT) & \ + echo "Starting Celery worker..." && \ + $(PYTHON) celery -A worker.celery_config worker --pool=solo -n worker_primary --loglevel=info & \ + echo "Starting Flower dashboard..." && \ + $(PYTHON) celery -A worker.celery_config flower --port=5555 + +stop: + @echo "Stopping services..." + @pkill -f "uvicorn main:app" || true + @pkill -f "celery" || true + @if [ "$(shell uname)" = "Darwin" ]; then \ + brew services stop redis; \ + elif [ -f /etc/debian_version ]; then \ + sudo service redis-server stop; \ + fi + +# Docker commands +docker-build-cpu: + docker build --build-arg CPU_ONLY=true -t $(DOCKER_IMAGE):cpu . + +docker-build-gpu: + docker build -t $(DOCKER_IMAGE):gpu . + +docker-start-cpu: + $(DOCKER_CPU_COMPOSE) up --build --scale celery_worker=1 + +docker-start-gpu: + $(DOCKER_GPU_COMPOSE) up --build --scale celery_worker=3 + +# Auto-detect architecture and start appropriate container +docker-start: + @echo "Auto-detecting system architecture..." + @if [ "$(shell uname -m)" = "arm64" ] || [ "$(shell uname -m)" = "aarch64" ] || ! command -v nvidia-smi >/dev/null 2>&1; then \ + echo "ARM architecture or NVIDIA drivers not detected. Using CPU mode."; \ + $(MAKE) docker-start-cpu; \ + else \ + echo "NVIDIA GPU detected. Using GPU mode."; \ + $(MAKE) docker-start-gpu; \ + fi + +docker-stop: + $(DOCKER_CPU_COMPOSE) down + $(DOCKER_GPU_COMPOSE) down + +docker-clean: + docker system prune -f + docker volume prune -f + +# Code quality commands +format: + $(PYTHON) black . + +lint: + $(PYTHON) flake8 . + $(PYTHON) mypy . + +test: + $(PYTHON) pytest + +clean: + find . -type d -name "__pycache__" -exec rm -rf {} + + find . -type f -name "*.pyc" -delete + find . -type f -name "*.pyo" -delete + find . -type f -name "*.pyd" -delete + find . -type f -name ".coverage" -delete + find . -type d -name "*.egg-info" -exec rm -rf {} + + find . -type d -name "*.egg" -exec rm -rf {} + + find . -type d -name ".pytest_cache" -exec rm -rf {} + + find . -type d -name ".mypy_cache" -exec rm -rf {} + + find . -type d -name ".tox" -exec rm -rf {} + + find . -type d -name "build" -exec rm -rf {} + + find . -type d -name "dist" -exec rm -rf {} + \ No newline at end of file diff --git a/README.md b/README.md index 35c1f62..8b058d1 100644 --- a/README.md +++ b/README.md @@ -5,16 +5,16 @@ ## Comparison to Other Parsing Libraries -| Original PDF | -|--------------| +| Original PDF | +| -------------------------------------------------------------------------------------------------------------------- | | | -| Docling-API | Marker | -|-------------|--------| +| Docling-API | Marker | +| ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | | | | -| PyPDF | PyMuPDF4LLM | -|-------|-------------| +| PyPDF | PyMuPDF4LLM | +| ----------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | | | | ## Features @@ -50,20 +50,20 @@ ## Environment Setup (Running Locally) ### Prerequisites -- Python 3.8 or higher -- Poetry (Python package manager) +- Python 3.12 or higher +- uv (Python package manager) - Redis server (for task queue) -### 1. Install Poetry (if not already installed) +### 1. Install uv (if not already installed) ```bash -curl -sSL https://install.python-poetry.org | python3 - +curl -LsSf https://astral.sh/uv/install.sh | sh ``` ### 2. Clone and Setup Project ```bash git clone https://github.com/drmingler/docling-api.git cd docling-api -poetry install +uv pip install -r pyproject.toml ``` ### 3. Configure Environment @@ -92,17 +92,17 @@ sudo service redis-server start 1. Start the FastAPI server: ```bash -poetry run uvicorn main:app --reload --port 8080 +uvicorn main:app --reload --port 8080 ``` 2. Start Celery worker (in a new terminal): ```bash -poetry run celery -A worker.celery_config worker --pool=solo -n worker_primary --loglevel=info +celery -A worker.celery_config worker --pool=solo -n worker_primary --loglevel=info ``` 3. Start Flower dashboard for monitoring (optional, in a new terminal): ```bash -poetry run celery -A worker.celery_config flower --port=5555 +celery -A worker.celery_config flower --port=5555 ``` ### 6. Verify Installation @@ -144,16 +144,43 @@ REDIS_HOST=redis://redis:6379/0 ENV=production ``` -### CPU Mode -To start the service using CPU-only processing, use the following command. You can adjust the number of Celery workers by specifying the --scale option. In this example, 1 worker will be created: +### Using Makefile Commands + +The project includes a Makefile for convenient management of Docker operations: + +#### CPU Mode +```bash +# Build and run in CPU mode with 1 worker +make docker-build-cpu +make docker-start-cpu + +# Or build and run with multiple workers +make docker-run-cpu WORKER_COUNT=3 +``` + +#### GPU Mode (Recommended for production) ```bash -docker-compose -f docker-compose.cpu.yml up --build --scale celery_worker=1 +# Build and run in GPU mode with 1 worker +make docker-build-gpu +make docker-start-gpu + +# Or build and run with multiple workers +make docker-run-gpu WORKER_COUNT=3 ``` -### GPU Mode (Recommend for production) -For production, it is recommended to enable GPU acceleration, as it significantly improves performance. Use the command below to start the service with GPU support. You can also scale the number of Celery workers using the --scale option; here, 3 workers will be launched: +#### Other Makefile Commands ```bash -docker-compose -f docker-compose.gpu.yml up --build --scale celery_worker=3 +# Stop all containers +make docker-stop + +# Remove all containers +make docker-down + +# View logs +make docker-logs + +# Clean Docker resources +make docker-clean ``` ## Service Components @@ -236,6 +263,7 @@ The service uses a distributed architecture with the following components: - GPU mode provides significantly faster processing for large documents - CPU mode is suitable for smaller deployments or when GPU is not available - Multiple workers can be scaled horizontally for increased throughput +- Using uv package manager for faster dependency installation and better caching ## License The codebase is under MIT license. See LICENSE for more information @@ -245,3 +273,4 @@ The codebase is under MIT license. See LICENSE for more information - [FastAPI](https://fastapi.tiangolo.com/) the web framework - [Celery](https://docs.celeryq.dev/en/stable/) for distributed task processing - [Flower](https://flower.readthedocs.io/en/latest/) for monitoring and management +- [uv](https://github.com/astral/uv) for fast, reliable Python package management diff --git a/detect_gpu.sh b/detect_gpu.sh new file mode 100755 index 0000000..7510d59 --- /dev/null +++ b/detect_gpu.sh @@ -0,0 +1,76 @@ +#!/bin/bash + +# Script to detect GPU and select the appropriate Docker Compose file + +# Check if nvidia-smi exists and can be executed +if command -v nvidia-smi >/dev/null 2>&1; then + # Try to run nvidia-smi to check if drivers are loaded correctly + if nvidia-smi >/dev/null 2>&1; then + echo "NVIDIA GPU detected with working drivers." + GPU_AVAILABLE=true + + # Check CUDA version + CUDA_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader | cut -d'.' -f1) + echo "CUDA compatible driver version: $CUDA_VERSION" + + # Check if the detected CUDA version is compatible with our requirements (CUDA 11+) + if [ -n "$CUDA_VERSION" ] && [ "$CUDA_VERSION" -ge 11 ]; then + echo "Using GPU configuration (CUDA $CUDA_VERSION detected)" + COMPOSE_FILE="docker-compose.gpu.yml" + DOCKER_BUILDKIT=1 + DOCKER_BUILD_ARGS="--build-arg CPU_ONLY=false" + # Pass GPU capabilities to docker build + export DOCKER_BUILDKIT=1 + export DOCKER_DEFAULT_PLATFORM=linux/amd64 + export DOCKER_CLI_EXPERIMENTAL=enabled + else + echo "NVIDIA GPU detected but CUDA version ($CUDA_VERSION) is too old. Minimum required: 11" + echo "Falling back to CPU configuration." + GPU_AVAILABLE=false + COMPOSE_FILE="docker-compose.cpu.yml" + DOCKER_BUILD_ARGS="--build-arg CPU_ONLY=true" + fi + else + echo "NVIDIA GPU software detected but drivers may not be properly installed." + GPU_AVAILABLE=false + COMPOSE_FILE="docker-compose.cpu.yml" + DOCKER_BUILD_ARGS="--build-arg CPU_ONLY=true" + fi +else + echo "No NVIDIA GPU detected. Using CPU configuration." + GPU_AVAILABLE=false + COMPOSE_FILE="docker-compose.cpu.yml" + DOCKER_BUILD_ARGS="--build-arg CPU_ONLY=true" +fi + +# Check architecture +ARCH=$(uname -m) +if [ "$ARCH" = "aarch64" ] || [ "$ARCH" = "arm64" ]; then + echo "ARM architecture detected. Forcing CPU mode regardless of GPU availability." + GPU_AVAILABLE=false + COMPOSE_FILE="docker-compose.cpu.yml" + DOCKER_BUILD_ARGS="--build-arg CPU_ONLY=true" +fi + +# Export for other scripts to use +export GPU_AVAILABLE +export COMPOSE_FILE +export DOCKER_BUILD_ARGS + +echo "Selected configuration: $COMPOSE_FILE" +echo "Build arguments: $DOCKER_BUILD_ARGS" +echo "GPU_AVAILABLE=$GPU_AVAILABLE" + +# If this script is being sourced, don't execute docker-compose +if [[ "${BASH_SOURCE[0]}" != "${0}" ]]; then + return 0 +fi + +# If passed arguments, run docker-compose with them +if [ $# -gt 0 ]; then + echo "Running: docker-compose -f $COMPOSE_FILE $@" + docker-compose -f $COMPOSE_FILE $@ +else + echo "Usage: $0 [docker-compose commands]" + echo "or source this script to export the variables" +fi \ No newline at end of file diff --git a/docker-compose.cpu.yml b/docker-compose.cpu.yml index 9867b07..13183e8 100644 --- a/docker-compose.cpu.yml +++ b/docker-compose.cpu.yml @@ -1,5 +1,3 @@ -version: "3.8" - services: celery_worker: build: @@ -7,14 +5,19 @@ services: args: CPU_ONLY: "true" image: converter-cpu-image - command: poetry run celery -A worker.celery_config worker --pool=solo -n worker_primary --loglevel=info + command: uv run celery -A worker.celery_config worker --pool=solo -n worker_primary --loglevel=info volumes: - - .:/app - - model_cache:/tmp + - ./worker:/app/worker environment: - REDIS_HOST=${REDIS_HOST} - ENV=production restart: on-failure + healthcheck: + test: [ "CMD", "uv", "run", "celery", "-A", "worker.celery_config", "inspect", "ping", "-d", "celery@worker_primary" ] + interval: 30s + timeout: 10s + retries: 3 + start_period: 15s depends_on: - redis @@ -24,8 +27,10 @@ services: context: . args: CPU_ONLY: "true" + cache_from: + - converter-cpu-image image: converter-cpu-image - command: poetry run uvicorn --port 8080 --host 0.0.0.0 main:app + command: uv run uvicorn main:app --port 8080 --host 0.0.0.0 --workers 4 --proxy-headers environment: - REDIS_HOST=${REDIS_HOST} - ENV=production @@ -35,9 +40,14 @@ services: ports: - "8080:8080" volumes: - - .:/app - - model_cache:/tmp + - ./main.py:/app/main.py restart: on-failure + healthcheck: + test: [ "CMD", "curl", "-f", "http://localhost:8080/health" ] + interval: 30s + timeout: 10s + retries: 3 + start_period: 15s depends_on: - redis @@ -46,6 +56,12 @@ services: image: redis:7.2.4-alpine ports: - "6379:6379" + healthcheck: + test: [ "CMD", "redis-cli", "ping" ] + interval: 30s + timeout: 5s + retries: 3 + start_period: 10s flower: container_name: flower_cpu @@ -53,13 +69,14 @@ services: context: . args: CPU_ONLY: "true" + cache_from: + - converter-cpu-image image: converter-cpu-image - command: poetry run celery -A worker.celery_config flower --port=5555 + command: uv run celery -A worker.celery_config flower --port=5555 ports: - "5556:5555" volumes: - - .:/app - - model_cache:/tmp + - ./worker:/app/worker environment: - REDIS_HOST=${REDIS_HOST} - ENV=production @@ -70,3 +87,4 @@ services: volumes: model_cache: + name: docling_model_cache diff --git a/docker-compose.gpu.yml b/docker-compose.gpu.yml index cfed965..3e213cd 100644 --- a/docker-compose.gpu.yml +++ b/docker-compose.gpu.yml @@ -1,25 +1,37 @@ -version: "3.8" - services: celery_worker: build: context: . args: CPU_ONLY: "false" + x-bake: + platforms: + - linux/amd64 + output: type=docker + contexts: + - default + x-pass-nvidia: + - capabilities=compute,utility,graphics,video image: converter-gpu-image - command: poetry run celery -A worker.celery_config worker --pool=solo -n worker_primary --loglevel=info + command: uv run celery -A worker.celery_config worker --pool=solo -n worker_primary --loglevel=info volumes: - - .:/app + - ./worker:/app/worker environment: - REDIS_HOST=${REDIS_HOST} - ENV=production + healthcheck: + test: [ "CMD", "uv", "run", "celery", "-A", "worker.celery_config", "inspect", "ping", "-d", "celery@worker_primary" ] + interval: 30s + timeout: 10s + retries: 3 + start_period: 15s deploy: resources: reservations: devices: - driver: nvidia count: 1 - capabilities: [gpu] + capabilities: [ gpu ] depends_on: - redis @@ -29,8 +41,18 @@ services: context: . args: CPU_ONLY: "false" + cache_from: + - converter-gpu-image + x-bake: + platforms: + - linux/amd64 + output: type=docker + contexts: + - default + x-pass-nvidia: + - capabilities=compute,utility,graphics,video image: converter-gpu-image - command: poetry run uvicorn --port 8080 --host 0.0.0.0 main:app + command: uv run uvicorn main:app --port 8080 --host 0.0.0.0 --workers 4 --proxy-headers environment: - REDIS_HOST=${REDIS_HOST} - ENV=production @@ -38,14 +60,20 @@ services: ports: - "8080:8080" volumes: - - .:/app + - ./main.py:/app/main.py + healthcheck: + test: [ "CMD", "curl", "-f", "http://localhost:8080/health" ] + interval: 30s + timeout: 10s + retries: 3 + start_period: 15s deploy: resources: reservations: devices: - driver: nvidia count: 1 - capabilities: [gpu] + capabilities: [ gpu ] depends_on: - redis - celery_worker @@ -55,6 +83,12 @@ services: image: redis:7.2.4-alpine ports: - "6379:6379" + healthcheck: + test: [ "CMD", "redis-cli", "ping" ] + interval: 30s + timeout: 5s + retries: 3 + start_period: 10s flower: container_name: flower_gpu @@ -62,12 +96,22 @@ services: context: . args: CPU_ONLY: "false" + cache_from: + - converter-gpu-image + x-bake: + platforms: + - linux/amd64 + output: type=docker + contexts: + - default + x-pass-nvidia: + - capabilities=compute,utility,graphics,video image: converter-gpu-image - command: poetry run celery -A worker.celery_config flower --port=5555 + command: uv run celery -A worker.celery_config flower --port=5555 ports: - "5556:5555" volumes: - - .:/app + - ./worker:/app/worker environment: - REDIS_HOST=${REDIS_HOST} - ENV=production @@ -81,4 +125,8 @@ services: devices: - driver: nvidia count: 1 - capabilities: [gpu] + capabilities: [ gpu ] + +volumes: + model_cache: + name: docling_model_cache diff --git a/document_converter/route.py b/document_converter/route.py index 71005b2..523f855 100644 --- a/document_converter/route.py +++ b/document_converter/route.py @@ -1,11 +1,16 @@ from io import BytesIO from typing import List -from fastapi import APIRouter, File, HTTPException, UploadFile, Query +from fastapi import APIRouter, File, HTTPException, UploadFile, Query, status +from fastapi.responses import JSONResponse -from document_converter.schema import BatchConversionJobResult, ConversationJobResult, ConversionResult +from document_converter.schema import ( + BatchConversionJobResult, + ConversionJobResult, + ConversionResult +) from document_converter.service import DocumentConverterService, DoclingDocumentConversion from document_converter.utils import is_file_format_supported -from worker.tasks import convert_document_task, convert_documents_task +from worker.tasks import convert_document_task, convert_documents_task, ping router = APIRouter() @@ -19,16 +24,33 @@ '/documents/convert', response_model=ConversionResult, response_model_exclude_unset=True, + status_code=status.HTTP_200_OK, + responses={ + 200: {"description": "Document successfully converted"}, + 400: {"description": "Invalid request or unsupported file format"}, + 500: {"description": "Internal server error during conversion"} + }, description="Convert a single document synchronously", ) async def convert_single_document( - document: UploadFile = File(...), - extract_tables_as_images: bool = False, - image_resolution_scale: int = Query(4, ge=1, le=4), + document: UploadFile = File(..., description="The document file to convert"), + extract_tables_as_images: bool = Query( + False, + description="Whether to extract tables as images" + ), + image_resolution_scale: int = Query( + 4, + ge=1, + le=4, + description="Scale factor for image resolution (1-4)" + ), ): file_bytes = await document.read() if not is_file_format_supported(file_bytes, document.filename): - raise HTTPException(status_code=400, detail=f"Unsupported file format: {document.filename}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Unsupported file format: {document.filename}" + ) return document_converter_service.convert_document( (document.filename, BytesIO(file_bytes)), @@ -41,18 +63,35 @@ async def convert_single_document( '/documents/batch-convert', response_model=List[ConversionResult], response_model_exclude_unset=True, + status_code=status.HTTP_200_OK, + responses={ + 200: {"description": "All documents successfully converted"}, + 400: {"description": "Invalid request or unsupported file format"}, + 500: {"description": "Internal server error during conversion"} + }, description="Convert multiple documents synchronously", ) async def convert_multiple_documents( - documents: List[UploadFile] = File(...), - extract_tables_as_images: bool = False, - image_resolution_scale: int = Query(4, ge=1, le=4), + documents: List[UploadFile] = File(..., description="List of document files to convert"), + extract_tables_as_images: bool = Query( + False, + description="Whether to extract tables as images" + ), + image_resolution_scale: int = Query( + 4, + ge=1, + le=4, + description="Scale factor for image resolution (1-4)" + ), ): doc_streams = [] for document in documents: file_bytes = await document.read() if not is_file_format_supported(file_bytes, document.filename): - raise HTTPException(status_code=400, detail=f"Unsupported file format: {document.filename}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Unsupported file format: {document.filename}" + ) doc_streams.append((document.filename, BytesIO(file_bytes))) return document_converter_service.convert_documents( @@ -65,17 +104,34 @@ async def convert_multiple_documents( # Asynchronous conversion jobs endpoints @router.post( '/conversion-jobs', - response_model=ConversationJobResult, - description="Create a conversion job for a single document", + response_model=ConversionJobResult, + status_code=status.HTTP_202_ACCEPTED, + responses={ + 202: {"description": "Conversion job accepted and queued"}, + 400: {"description": "Invalid request or unsupported file format"}, + 500: {"description": "Failed to queue conversion job"} + }, + description="Create an asynchronous conversion job for a single document", ) async def create_single_document_conversion_job( - document: UploadFile = File(...), - extract_tables_as_images: bool = False, - image_resolution_scale: int = Query(4, ge=1, le=4), + document: UploadFile = File(..., description="The document file to convert"), + extract_tables_as_images: bool = Query( + False, + description="Whether to extract tables as images" + ), + image_resolution_scale: int = Query( + 4, + ge=1, + le=4, + description="Scale factor for image resolution (1-4)" + ), ): file_bytes = await document.read() if not is_file_format_supported(file_bytes, document.filename): - raise HTTPException(status_code=400, detail=f"Unsupported file format: {document.filename}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Unsupported file format: {document.filename}" + ) task = convert_document_task.delay( (document.filename, file_bytes), @@ -83,36 +139,85 @@ async def create_single_document_conversion_job( image_resolution_scale=image_resolution_scale, ) - return ConversationJobResult(job_id=task.id, status="IN_PROGRESS") + return ConversionJobResult( + job_id=task.id, + status="IN_PROGRESS" + ) @router.get( '/conversion-jobs/{job_id}', - response_model=ConversationJobResult, - description="Get the status of a single document conversion job", - response_model_exclude_unset=True, + response_model=ConversionJobResult, + responses={ + 200: {"description": "Conversion job completed successfully"}, + 202: {"description": "Conversion job is still in progress"}, + 404: {"description": "Job not found"}, + 422: {"description": "Conversion job failed"} + }, + description="Get the status and result of a single document conversion job", ) async def get_conversion_job_status(job_id: str): - return document_converter_service.get_single_document_task_result(job_id) + try: + result = document_converter_service.get_single_document_task_result(job_id) + + # Return 202 Accepted if job is still in progress + if result.status in ["IN_PROGRESS"]: + return JSONResponse( + status_code=status.HTTP_202_ACCEPTED, + content=result.dict(exclude_none=True) + ) + + # Return 422 for failed jobs + if result.status == "FAILURE": + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=result.dict(exclude_none=True) + ) + + # Return 200 OK for successful jobs + return JSONResponse( + status_code=status.HTTP_200_OK, + content=result.dict(exclude_none=True) + ) + except KeyError: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Job not found: {job_id}" + ) @router.post( '/batch-conversion-jobs', response_model=BatchConversionJobResult, - response_model_exclude_unset=True, - description="Create a conversion job for multiple documents", + status_code=status.HTTP_202_ACCEPTED, + responses={ + 202: {"description": "Batch conversion job accepted and queued"}, + 400: {"description": "Invalid request or unsupported file format"}, + 500: {"description": "Failed to queue batch conversion job"} + }, + description="Create an asynchronous conversion job for multiple documents", ) async def create_batch_conversion_job( - documents: List[UploadFile] = File(...), - extract_tables_as_images: bool = False, - image_resolution_scale: int = Query(4, ge=1, le=4), + documents: List[UploadFile] = File(..., description="List of document files to convert"), + extract_tables_as_images: bool = Query( + False, + description="Whether to extract tables as images" + ), + image_resolution_scale: int = Query( + 4, + ge=1, + le=4, + description="Scale factor for image resolution (1-4)" + ), ): - """Create a batch conversion job for multiple documents.""" doc_data = [] for document in documents: file_bytes = await document.read() if not is_file_format_supported(file_bytes, document.filename): - raise HTTPException(status_code=400, detail=f"Unsupported file format: {document.filename}") + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Unsupported file format: {document.filename}" + ) doc_data.append((document.filename, file_bytes)) task = convert_documents_task.delay( @@ -121,15 +226,90 @@ async def create_batch_conversion_job( image_resolution_scale=image_resolution_scale, ) - return BatchConversionJobResult(job_id=task.id, status="IN_PROGRESS") + return BatchConversionJobResult( + job_id=task.id, + status="IN_PROGRESS" + ) @router.get( '/batch-conversion-jobs/{job_id}', response_model=BatchConversionJobResult, - response_model_exclude_unset=True, - description="Get the status of a batch conversion job", + responses={ + 200: {"description": "All conversion jobs completed successfully"}, + 202: {"description": "Batch job is still in progress"}, + 404: {"description": "Batch job not found"}, + 422: {"description": "Batch job failed"} + }, + description="Get the status and results of a batch conversion job", ) async def get_batch_conversion_job_status(job_id: str): - """Get the status and results of a batch conversion job.""" - return document_converter_service.get_batch_conversion_task_result(job_id) + try: + result = document_converter_service.get_batch_conversion_task_result(job_id) + + # Return 202 Accepted if the batch job or any sub-job is still in progress + if result.status in ["IN_PROGRESS"] or any( + job.status in ["IN_PROGRESS"] + for job in result.conversion_results + ): + return JSONResponse( + status_code=status.HTTP_202_ACCEPTED, + content=result.dict(exclude_none=True) + ) + + # Return 422 for failed batch jobs + if result.status == "FAILURE" or any( + job.status == "FAILURE" + for job in result.conversion_results + ): + return JSONResponse( + status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, + content=result.dict(exclude_none=True) + ) + + # Return 200 OK for successful batch jobs (all success) + return JSONResponse( + status_code=status.HTTP_200_OK, + content=result.dict(exclude_none=True) + ) + except KeyError: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Batch job not found: {job_id}" + ) + + +@router.get( + "/health", + responses={ + 200: {"description": "All services are healthy"}, + 500: {"description": "One or more services are unhealthy"} + }, + description="Check the health status of all dependent services" +) +async def health_check(): + try: + # Check Celery/Redis connection by sending a ping task + result = ping.delay() + response = result.get(timeout=3) # Wait up to 3 seconds for response + + if response != "pong": + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Celery/Redis connection test failed" + ) + + return { + "status": "healthy", + "services": { + "celery": "connected", + "redis": "connected", + "docling": "connected", + "document_converter": "connected", + } + } + except Exception as e: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=str(e) + ) diff --git a/document_converter/schema.py b/document_converter/schema.py index 9b7da76..66f10a1 100644 --- a/document_converter/schema.py +++ b/document_converter/schema.py @@ -1,5 +1,5 @@ from pydantic import BaseModel, Field -from typing import List, Literal, Optional +from typing import List, Literal, Optional, Dict, Any class ImageData(BaseModel): @@ -21,7 +21,7 @@ class BatchConversionResult(BaseModel): ) -class ConversationJobResult(BaseModel): +class ConversionJobResult(BaseModel): job_id: Optional[str] = Field(None, description="The id of the conversion job") result: Optional[ConversionResult] = Field(None, description="The result of the conversion job") error: Optional[str] = Field(None, description="The error that occurred during the conversion job") @@ -30,10 +30,10 @@ class ConversationJobResult(BaseModel): class BatchConversionJobResult(BaseModel): job_id: str = Field(..., description="The id of the conversion job") - conversion_results: List[ConversationJobResult] = Field( + conversion_results: List[ConversionJobResult] = Field( default_factory=list, description="The results of the conversion job" ) status: Literal["IN_PROGRESS", "SUCCESS", "FAILURE"] = Field( None, description="The status of the entire conversion jobs in the batch" ) - error: Optional[str] = Field(None, description="If the entire batch failed, this will be the error message") + error: Optional[str] = Field(None, description="If the entire batch failed, this will be the error message") \ No newline at end of file diff --git a/document_converter/service.py b/document_converter/service.py index c5b074d..f4c2d8a 100644 --- a/document_converter/service.py +++ b/document_converter/service.py @@ -11,7 +11,7 @@ from docling_core.types.doc import ImageRefMode, TableItem, PictureItem from fastapi import HTTPException -from document_converter.schema import BatchConversionJobResult, ConversationJobResult, ConversionResult, ImageData +from document_converter.schema import BatchConversionJobResult, ConversionJobResult, ConversionResult, ImageData from document_converter.utils import handle_csv_file logging.basicConfig(level=logging.INFO) @@ -180,7 +180,7 @@ def convert_documents_task( documents = [(filename, BytesIO(file)) for filename, file in documents] return self.document_converter.convert_batch(documents, **kwargs) - def get_single_document_task_result(self, job_id: str) -> ConversationJobResult: + def get_single_document_task_result(self, job_id: str) -> ConversionJobResult: """Get the status and result of a document conversion job. Returns: @@ -191,18 +191,18 @@ def get_single_document_task_result(self, job_id: str) -> ConversationJobResult: task = AsyncResult(job_id) if task.state == 'PENDING': - return ConversationJobResult(job_id=job_id, status="IN_PROGRESS") + return ConversionJobResult(job_id=job_id, status="IN_PROGRESS") elif task.state == 'SUCCESS': result = task.get() # Check if the conversion result contains an error if result.get('error'): - return ConversationJobResult(job_id=job_id, status="FAILURE", error=result['error']) + return ConversionJobResult(job_id=job_id, status="FAILURE", error=result['error']) - return ConversationJobResult(job_id=job_id, status="SUCCESS", result=ConversionResult(**result)) + return ConversionJobResult(job_id=job_id, status="SUCCESS", result=ConversionResult(**result)) else: - return ConversationJobResult(job_id=job_id, status="FAILURE", error=str(task.result)) + return ConversionJobResult(job_id=job_id, status="FAILURE", error=str(task.result)) def get_batch_conversion_task_result(self, job_id: str) -> BatchConversionJobResult: """Get the status and results of a batch conversion job. @@ -224,9 +224,9 @@ def get_batch_conversion_task_result(self, job_id: str) -> BatchConversionJobRes for result in conversion_results: if result.get('error'): - job_result = ConversationJobResult(status="FAILURE", error=result['error']) + job_result = ConversionJobResult(status="FAILURE", error=result['error']) else: - job_result = ConversationJobResult( + job_result = ConversionJobResult( status="SUCCESS", result=ConversionResult(**result).model_dump(exclude_unset=True) ) job_results.append(job_result) diff --git a/entrypoint.sh b/entrypoint.sh new file mode 100644 index 0000000..9f86682 --- /dev/null +++ b/entrypoint.sh @@ -0,0 +1,6 @@ +#!/bin/sh +set -e + + +echo "Starting application..." +exec /app/.venv/bin/python -m uvicorn --port 8080 --host 0.0.0.0 main:app diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index a8e4911..0000000 --- a/poetry.lock +++ /dev/null @@ -1,3571 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. - -[[package]] -name = "amqp" -version = "5.3.1" -description = "Low-level AMQP client for Python (fork of amqplib)." -optional = false -python-versions = ">=3.6" -files = [ - 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