docker-compose.yml

version: '3'

networks:
  bot_services_network:

services:
  bot:
    restart: on-failure
    environment:
      VECTOR_DB_SERVICE_HOST: "vectordb_service"
      LLM_SERVICE_HOST: "llm_service"
      VECTOR_DB_SERVICE_PORT: "8079"
      LLM_SERVICE_PORT: "8081"
    build:
      context: ./bot
    networks:
      - bot_services_network
    volumes:
      - ./credentials:/app/credentials
      - ./bot:/app/bot
      - ./tgmedbot:/app/tgmedbot
      - ./lexicon.yaml:/app/lexicon.yaml
    entrypoint: [ "python", "bot/bot.py" ]
    deploy:
      resources:
        limits:
          cpus: '4.00'
          memory: 768MB
    depends_on:
      - vectordb_service
      - llm_service

  vectordb_service:
    build:
      context: ./vectordb_service
    environment:
      VECTOR_DB_SERVICE_HOST: "vectordb_service"
      LLM_SERVICE_HOST: "llm_service"
      VECTOR_DB_SERVICE_PORT: "8079"
      LLM_SERVICE_PORT: "8081"
    ports:
      - "8079:8079"
    volumes:
      - ./vectordb_service:/app/
      - ./chroma:/chroma
    networks:
      - bot_services_network
#    entrypoint: ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8079"]
    entrypoint: [ "python", "main.py"]
    restart: on-failure
    deploy:
      resources:
        limits:
          cpus: '3.00'
          memory: 2000MB

  llm_service:
    build:
      context: ./llm_service
    ports:
      - "8081:8081"
    environment:
      VECTOR_DB_SERVICE_HOST: "vectordb_service"
      LLM_SERVICE_HOST: "llm_service"
      VECTOR_DB_SERVICE_PORT: "8079"
      LLM_SERVICE_PORT: "8081"
    volumes:
      - ./llm_service:/app/
    networks:
      - bot_services_network
#    entrypoint: [ "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8081" ]
    entrypoint: [ "python", "main.py"]
    deploy:
      resources:
        limits:
          cpus: '4.00'
          memory: 5000MB
    restart: on-failure