9d5acf1683
- Implemented Smart Search Panel component for enhanced project and deliverable search functionality. - Introduced useSemanticSearch and useOllamaHealth hooks for managing search queries and AI availability. - Developed embedding-service to generate and store vector embeddings for projects and deliverables. - Created semantic-search-service to handle vector search, structural query detection, and LLM summarization. - Added support for hybrid search combining structural filters and semantic queries. - Integrated UI components for displaying search results and user interactions.
47 lines
1.3 KiB
YAML
47 lines
1.3 KiB
YAML
services:
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# ─── PostgreSQL with pgvector ───────────────────────────
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db:
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image: pgvector/pgvector:pg17
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restart: unless-stopped
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environment:
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POSTGRES_USER: postgres
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POSTGRES_PASSWORD: postgres
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POSTGRES_DB: hp_prod_tracker
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ports:
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- "5432:5432"
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volumes:
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- pgdata:/var/lib/postgresql/data
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healthcheck:
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test: ["CMD-SHELL", "pg_isready -U postgres"]
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interval: 5s
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timeout: 5s
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retries: 5
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# ─── Ollama (local AI embeddings) ──────────────────────
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ollama:
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image: ollama/ollama:latest
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restart: unless-stopped
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ports:
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- "11434:11434"
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volumes:
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- ollama_data:/root/.ollama
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# ─── Next.js app (production only) ─────────────────────
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# Uncomment for deployment. For local dev, run `npm run dev` directly.
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# app:
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# build: .
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# restart: unless-stopped
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# ports:
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# - "3000:3000"
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# environment:
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# DATABASE_URL: postgresql://postgres:postgres@db:5432/hp_prod_tracker?schema=public
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# OLLAMA_HOST: http://ollama:11434
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# OLLAMA_EMBED_MODEL: nomic-embed-text
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# NODE_ENV: production
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# depends_on:
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# db:
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# condition: service_healthy
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volumes:
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pgdata:
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ollama_data:
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