e7ac7766a5
pyproject.toml (Python 3.12, uv), codex/ package (config, db, models), infra/ (docker-compose + schema), .env.example, .gitignore, README. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
48 lines
1.5 KiB
YAML
48 lines
1.5 KiB
YAML
# Paper knowledge base: Provenance + Citation graph + Semantic search
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# A single Postgres instance (pgvector) covers all three layers.
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# GROBID parses references / structure from PDFs without an arXiv source.
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#
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# Quick start:
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# docker compose up -d
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# psql "${DATABASE_URL}" # see .env.example for default value
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services:
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db:
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image: pgvector/pgvector:pg17 # official pgvector image
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container_name: papers-db
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environment:
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POSTGRES_DB: papers
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POSTGRES_USER: researcher
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POSTGRES_PASSWORD: change_me # change before production use
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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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# schema.sql is executed automatically on first start:
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- ./schema.sql:/docker-entrypoint-initdb.d/01-schema.sql:ro
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healthcheck:
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test: ["CMD-SHELL", "pg_isready -U researcher -d papers"]
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interval: 10s
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timeout: 5s
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retries: 5
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restart: unless-stopped
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grobid:
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# CRF-only image is sufficient for reference extraction and is lighter than
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# the full deeplearning variant. Check https://hub.docker.com/r/grobid/grobid
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# for the latest 0.8.x tag before deploying.
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image: grobid/grobid:0.8.2
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container_name: papers-grobid
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ports:
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- "8070:8070"
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restart: unless-stopped
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# GROBID is RAM-hungry; uncomment to cap usage on constrained hardware
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# (e.g. Nvidia Jetson):
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# deploy:
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# resources:
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# limits:
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# memory: 4g
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volumes:
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pgdata:
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