# Paper knowledge base: Provenance + Citation graph + Semantic search
# A single Postgres instance (pgvector) covers all three layers.
# GROBID parses references / structure from PDFs without an arXiv source.
#
# Quick start:
#   docker compose up -d
#   psql "${DATABASE_URL}"          # see .env.example for default value

services:
  db:
    image: pgvector/pgvector:pg17       # official pgvector image
    container_name: papers-db
    environment:
      POSTGRES_DB: papers
      POSTGRES_USER: researcher
      POSTGRES_PASSWORD: change_me      # change before production use
    ports:
      - "5432:5432"
    volumes:
      - pgdata:/var/lib/postgresql/data
      # schema.sql is executed automatically on first start:
      - ./schema.sql:/docker-entrypoint-initdb.d/01-schema.sql:ro
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U researcher -d papers"]
      interval: 10s
      timeout: 5s
      retries: 5
    restart: unless-stopped

  grobid:
    # CRF-only image is sufficient for reference extraction and is lighter than
    # the full deeplearning variant. Check https://hub.docker.com/r/grobid/grobid
    # for the latest 0.8.x tag before deploying.
    image: grobid/grobid:0.8.2
    container_name: papers-grobid
    ports:
      - "8070:8070"
    restart: unless-stopped
    # GROBID is RAM-hungry; uncomment to cap usage on constrained hardware
    # (e.g. Nvidia Jetson):
    # deploy:
    #   resources:
    #     limits:
    #       memory: 4g

volumes:
  pgdata: