Mac M芯片 RAG 极简流程 安装 ragflow + LM studio

本文基于 【【知识科普】【纯本地化搭建】【不本地也行】DeepSeek + RAGFlow 构建个人知识库】 https://www.bilibili.com/video/BV1WiP2ezE5a/?share_source=copy_web\&vd_source=9a55f12dd64d8e30ab6c0efc62844343

1 .docker-compose yml文件修改,指定平台 platform: linux/amd64

services:
  es01:
    container_name: ragflow-es-01
    profiles:
      - elasticsearch
    image: elasticsearch:${STACK_VERSION}
    volumes:
      - esdata01:/usr/share/elasticsearch/data
    ports:
      - ${ES_PORT}:9200
    env_file: .env
    environment:
      - node.name=es01
      - ELASTIC_PASSWORD=${ELASTIC_PASSWORD}
      - bootstrap.memory_lock=false
      - discovery.type=single-node
      - xpack.security.enabled=true
      - xpack.security.http.ssl.enabled=false
      - xpack.security.transport.ssl.enabled=false
      - cluster.routing.allocation.disk.watermark.low=5gb
      - cluster.routing.allocation.disk.watermark.high=3gb
      - cluster.routing.allocation.disk.watermark.flood_stage=2gb
      - TZ=${TIMEZONE}
    mem_limit: ${MEM_LIMIT}
    ulimits:
      memlock:
        soft: -1
        hard: -1
    healthcheck:
      test: ["CMD-SHELL", "curl http://localhost:9200"]
      interval: 10s
      timeout: 10s
      retries: 120
    networks:
      - ragflow
    restart: on-failure
    platform: linux/amd64

  opensearch01:
    container_name: ragflow-opensearch-01
    profiles:
      - opensearch
    image: hub.icert.top/opensearchproject/opensearch:2.19.1
    volumes:
      - osdata01:/usr/share/opensearch/data
    ports:
      - ${OS_PORT}:9201
    env_file: .env
    environment:
      - node.name=opensearch01
      - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD}
      - OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_PASSWORD}
      - bootstrap.memory_lock=false
      - discovery.type=single-node
      - plugins.security.disabled=false
      - plugins.security.ssl.http.enabled=false
      - plugins.security.ssl.transport.enabled=true
      - cluster.routing.allocation.disk.watermark.low=5gb
      - cluster.routing.allocation.disk.watermark.high=3gb
      - cluster.routing.allocation.disk.watermark.flood_stage=2gb
      - TZ=${TIMEZONE}
      - http.port=9201
    mem_limit: ${MEM_LIMIT}
    ulimits:
      memlock:
        soft: -1
        hard: -1
    healthcheck:
      test: ["CMD-SHELL", "curl http://localhost:9201"]
      interval: 10s
      timeout: 10s
      retries: 120
    networks:
      - ragflow
    restart: on-failure
    platform: linux/amd64

  infinity:
    container_name: ragflow-infinity
    profiles:
      - infinity
    image: infiniflow/infinity:v0.6.0-dev3
    volumes:
      - infinity_data:/var/infinity
      - ./infinity_conf.toml:/infinity_conf.toml
    command: ["-f", "/infinity_conf.toml"]
    ports:
      - ${INFINITY_THRIFT_PORT}:23817
      - ${INFINITY_HTTP_PORT}:23820
      - ${INFINITY_PSQL_PORT}:5432
    env_file: .env
    environment:
      - TZ=${TIMEZONE}
    mem_limit: ${MEM_LIMIT}
    ulimits:
      nofile:
        soft: 500000
        hard: 500000
    networks:
      - ragflow
    healthcheck:
      test: ["CMD", "curl", "http://localhost:23820/admin/node/current"]
      interval: 10s
      timeout: 10s
      retries: 120
    restart: on-failure

  sandbox-executor-manager:
    container_name: ragflow-sandbox-executor-manager
    profiles:
      - sandbox
    image: ${SANDBOX_EXECUTOR_MANAGER_IMAGE}
    privileged: true
    ports:
      - ${SANDBOX_EXECUTOR_MANAGER_PORT}:9385
    env_file: .env
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    networks:
      - ragflow
    security_opt:
      - no-new-privileges:true
    environment:
      - TZ=${TIMEZONE}
      - SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=${SANDBOX_EXECUTOR_MANAGER_POOL_SIZE:-3}
      - SANDBOX_BASE_PYTHON_IMAGE=${SANDBOX_BASE_PYTHON_IMAGE:-infiniflow/sandbox-base-python:latest}
      - SANDBOX_BASE_NODEJS_IMAGE=${SANDBOX_BASE_NODEJS_IMAGE:-infiniflow/sandbox-base-nodejs:latest}
      - SANDBOX_ENABLE_SECCOMP=${SANDBOX_ENABLE_SECCOMP:-false}
      - SANDBOX_MAX_MEMORY=${SANDBOX_MAX_MEMORY:-256m}
      - SANDBOX_TIMEOUT=${SANDBOX_TIMEOUT:-10s}
    healthcheck:
      test: ["CMD", "curl", "http://localhost:9385/healthz"]
      interval: 10s
      timeout: 5s
      retries: 5
    restart: on-failure

  mysql:
    # mysql:5.7 linux/arm64 image is unavailable.
    image: mysql:8.0.39
    container_name: ragflow-mysql
    env_file: .env
    environment:
      - MYSQL_ROOT_PASSWORD=${MYSQL_PASSWORD}
      - TZ=${TIMEZONE}
    command:
      --max_connections=1000
      --character-set-server=utf8mb4
      --collation-server=utf8mb4_unicode_ci
      --default-authentication-plugin=mysql_native_password
      --tls_version="TLSv1.2,TLSv1.3"
      --init-file /data/application/init.sql
      --binlog_expire_logs_seconds=604800
    ports:
      - ${MYSQL_PORT}:3306
    volumes:
      - mysql_data:/var/lib/mysql
      - ./init.sql:/data/application/init.sql
    networks:
      - ragflow
    healthcheck:
      test: ["CMD", "mysqladmin" ,"ping", "-uroot", "-p${MYSQL_PASSWORD}"]
      interval: 10s
      timeout: 10s
      retries: 3
    restart: on-failure

  minio:
    image: quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z
    container_name: ragflow-minio
    command: server --console-address ":9001" /data
    ports:
      - ${MINIO_PORT}:9000
      - ${MINIO_CONSOLE_PORT}:9001
    env_file: .env
    environment:
      - MINIO_ROOT_USER=${MINIO_USER}
      - MINIO_ROOT_PASSWORD=${MINIO_PASSWORD}
      - TZ=${TIMEZONE}
    volumes:
      - minio_data:/data
    networks:
      - ragflow
    restart: on-failure

  redis:
    # swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/valkey/valkey:8
    image: valkey/valkey:8
    container_name: ragflow-redis
    command: redis-server --requirepass ${REDIS_PASSWORD} --maxmemory 128mb --maxmemory-policy allkeys-lru
    env_file: .env
    ports:
      - ${REDIS_PORT}:6379
    volumes:
      - redis_data:/data
    networks:
      - ragflow
    restart: on-failure



volumes:
  esdata01:
    driver: local
  osdata01:
    driver: local
  infinity_data:
    driver: local
  mysql_data:
    driver: local
  minio_data:
    driver: local
  redis_data:
    driver: local

networks:
  ragflow:
    driver: bridge

2 .LM studio 设置

打开 status running

打开 setting 中的 server local network,这样docker容器中的镜像才可以访问模型

模型已经load 显示 READY