ad111d5e69
Docker Compose with 24+ services: - PostgreSQL (PostGIS), Valkey, MinIO, Qdrant - Vault (PKI/TLS), Nginx (Reverse Proxy) - Backend Core API, Consent Service, Billing Service - RAG Service, Embedding Service - Gitea, Woodpecker CI/CD - Night Scheduler, Health Aggregator - Jitsi (Web/XMPP/JVB/Jicofo), Mailpit Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
37 lines
1.1 KiB
Docker
37 lines
1.1 KiB
Docker
# Embedding Service Dockerfile
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# Handles ML-heavy operations: embeddings, re-ranking, PDF extraction
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FROM python:3.11-slim
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WORKDIR /app
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# Install system dependencies for PDF extraction
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RUN apt-get update && apt-get install -y --no-install-recommends \
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libmagic1 \
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poppler-utils \
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tesseract-ocr \
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tesseract-ocr-deu \
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&& rm -rf /var/lib/apt/lists/*
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# Install PyTorch CPU-only (smaller image)
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RUN pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu
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# Copy and install requirements
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Note: Models are downloaded on first startup (not during build)
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# This makes the build faster but first startup slower
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# To pre-download models, mount a persistent volume for /root/.cache/huggingface
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# Copy application code
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COPY . .
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
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CMD python -c "import httpx; httpx.get('http://localhost:8087/health').raise_for_status()"
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# Run the service
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EXPOSE 8087
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8087"]
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