Langchain——chatchat3.1版本docker部署流程Langchain-Chatchat

1. 项目地址

#项目地址
https://github.com/chatchat-space/Langchain-Chatchat
#dockerhub地址
https://hub.docker.com/r/chatimage/chatchat/tags

2. docker部署

• 参考官方文档

#官方文档
https://github.com/chatchat-space/Langchain-Chatchat/blob/master/docs/install/README_docker.md

• 配置docker-compose环境

cd ~
wget https://github.com/docker/compose/releases/download/v2.27.3/docker-compose-linux-x86_64
mv docker-compose-linux-x86_64 /usr/bin/docker-compose
which docker-compose
#验证是否安装成功
docker-compose -v

• 配置NVIDIA Container Toolkit环境，参考官方
• 配置docker-compose

version: '3.9'
services:
  xinference:
    #xprobe/xinference:latest
    image: xinferebce:1.0
    restart: always
    command: xinference-local -H 0.0.0.0
    ports: # 不使用 host network 时可打开.
     - "9997:9997"
    # network_mode: "host"
    # 将本地路径(~/xinference)挂载到容器路径(/root/.xinference)中,
    # 详情见: https://inference.readthedocs.io/zh-cn/latest/getting_started/using_docker_image.html
    volumes:
      - /home/isi/LLM/models/xinference:/root/.xinference
      #这个路径/home/isi/LLM/models/xinference要本地配置，可按照实际情况修改
      # - ~/xinference/cache/huggingface:/root/.cache/huggingface
      # - ~/xinference/cache/modelscope:/root/.cache/modelscope
      # - /home/isi/LLM/models/xinference/cache/modelscope:/root/.cache/modelscope
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    runtime: nvidia
    # 模型源更改为 ModelScope, 默认为 HuggingFace
    environment:
      - XINFERENCE_MODEL_SRC=modelscope
  chatchat:
    # 版本参考https://hub.docker.com/r/chatimage/chatchat/tags
    # docker pull chatimage/chatchat:0.3.1.1-2024-0714
    image: chatchat:1.0
    restart: always
    ports: # 不使用 host network 时可打开.
      - "7861:7861"
      - "8501:8501"
    # network_mode: "host"
    # 将本地路径(~/chatchat/data)挂载到容器默认数据路径(/usr/local/lib/python3.11/site-packages/chatchat/data)中
    volumes:
      - /home/isi/chatchat/data:/root/chatchat_data/data

• 启动容器

docker-compose up -d

xinference

#1. 访问http://IP:9997/
#2. 加载Langguage models——glm4
#3. 加载embedding models——bge-large-zh-v1.5

• 模型加载成功

  

  

chatchat

docker exec -it chat容器id bash

vi /root/chatchat_data/model_settings.yaml
#1.将embedding改成下面的bge-large-zh-V1.5
#2.将下面内容改成服务器自身的ip地址，不能改成127.0.0.1，因为127.0.0.1在ip映射的模式下不适用

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