9b86f9a694
RAG Service Implementation: - Create rag-service/ with full structure (config, document_store, embedding, pipelines) - Document Store: PostgreSQL + pgvector via Haystack - Embedding: BAAI/bge-m3 (multilingual, 1024 dim) - Ingest Pipeline: Convert ParsedDocument to Haystack Documents, embed, index - Query Pipeline: Retrieve documents, generate answers via DAGI Router - FastAPI endpoints: /ingest, /query, /health Tests: - Unit tests for ingest and query pipelines - E2E test with example parsed JSON - Test fixtures with real PARSER output example Router Integration: - Add mode='rag_query' routing rule in router-config.yml - Priority 7, uses local_qwen3_8b for RAG queries Docker: - Add rag-service to docker-compose.yml - Configure dependencies (router, city-db) - Add model cache volume Documentation: - Complete README with API examples - Integration guides for PARSER and Router
27 lines
476 B
Docker
27 lines
476 B
Docker
FROM python:3.11-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 \
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gcc \
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g++ \
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postgresql-client \
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&& rm -rf /var/lib/apt/lists/*
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# Copy requirements
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COPY requirements.txt .
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# Install Python dependencies
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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/ ./app/
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# Expose port
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EXPOSE 9500
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# Run application
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CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "9500"]
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