Felipe Cardoso
d0fc7f37ff
Implements RAG capabilities with pgvector for semantic search: - Intelligent chunking strategies (code-aware, markdown-aware, text) - Semantic search with vector similarity (HNSW index) - Keyword search with PostgreSQL full-text search - Hybrid search using Reciprocal Rank Fusion (RRF) - Redis caching for embeddings - Collection management (ingest, search, delete, stats) - FastMCP tools: search_knowledge, ingest_content, delete_content, list_collections, get_collection_stats, update_document Testing: - 128 comprehensive tests covering all components - 58% code coverage (database integration tests use mocks) - Passes ruff linting and mypy type checking 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
32 lines
752 B
Docker
32 lines
752 B
Docker
FROM python:3.12-slim
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential \
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&& rm -rf /var/lib/apt/lists/*
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# Install uv for fast package installation
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
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# Copy project files
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COPY pyproject.toml ./
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COPY *.py ./
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COPY chunking/ ./chunking/
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# Install dependencies
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RUN uv pip install --system --no-cache .
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# Create non-root user
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RUN useradd --create-home --shell /bin/bash appuser
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USER appuser
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD python -c "import httpx; httpx.get('http://localhost:8002/health').raise_for_status()"
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EXPOSE 8002
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CMD ["python", "server.py"]
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