Adolfo Delorenzo
76d8f9349e
- memory_store.py: User-isolated observation storage with vector embeddings - New endpoints: /memory/save, /memory/query, /memory/get, /memory/timeline - Progressive disclosure pattern for token-efficient retrieval - Updated Dockerfile to ROCm 7.2 nightly
36 lines
1.0 KiB
Docker
36 lines
1.0 KiB
Docker
FROM python:3.12-slim
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# System deps: pdfplumber, ffmpeg for video audio extraction, build tools
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RUN apt-get update && \
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apt-get install -y --no-install-recommends \
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build-essential \
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curl \
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ffmpeg \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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# Install PyTorch with ROCm support first (big layer, cache it)
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RUN pip install --no-cache-dir \
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torch torchvision torchaudio \
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--index-url https://download.pytorch.org/whl/nightly/rocm7.2/
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# Install remaining Python dependencies
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COPY app/requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy application code
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COPY app/ .
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# Pre-download the embedding model at build time so startup is fast
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RUN python -c "\
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from sentence_transformers import SentenceTransformer; \
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m = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'); \
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print('Model cached:', m.encode(['test']).shape)"
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EXPOSE 8899
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VOLUME ["/app/data", "/app/logs"]
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8899", "--log-level", "info"]
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