forked from zovos/vk_hackathon
7247248b27
index: message-based windowed chunking (5 msgs/1h gap), better unicode cleaning, separate dense (with timestamps)/sparse content renderers, BM25 preload on startup, ThreadPoolExecutor(4), UVICORN_WORKERS=4, Dockerfile copies all *.py search: proper multi-module structure (query_builder, retrieval, rerank, aggregation), RERANK_LIMIT 60→15 (fixes 429 errors), extra dense vectors for variants/hyde, date+asker metadata filters, httpx pool (100/20/30s), BM25 preload on startup, Dockerfile copies all *.py 68/68 unit tests passing Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
24 lines
592 B
Docker
24 lines
592 B
Docker
FROM python:3.13-slim
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY *.py .
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ENV HOST=0.0.0.0
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ENV PORT=8000
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ENV QDRANT_COLLECTION_NAME=evaluation
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ENV QDRANT_DENSE_VECTOR_NAME=dense
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ENV QDRANT_SPARSE_VECTOR_NAME=sparse
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ENV EMBEDDINGS_DENSE_MODEL=Qwen/Qwen3-Embedding-0.6B
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ENV FASTEMBED_CACHE_PATH=/models/fastembed
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ENV HF_HOME=/models/huggingface
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RUN mkdir -p /models/fastembed /models/huggingface
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RUN python -c "from fastembed import SparseTextEmbedding; SparseTextEmbedding(model_name='Qdrant/bm25')"
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EXPOSE 8000
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CMD ["python", "main.py"]
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