173 lines
6.8 KiB
Markdown
173 lines
6.8 KiB
Markdown
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### chatchat 容器化部署指引
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> 提示: 此指引为在 Linux 环境下编写完成, 其他环境下暂未测试, 理论上可行.
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>
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> Langchain-Chatchat docker 镜像已支持多架构, 欢迎大家自行测试.
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#### 一. Langchain-Chatchat 体验部署
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##### 1. 安装 docker-compose
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寻找适合你环境的 docker-compose 版本, 请参考 [Docker-Compose](https://github.com/docker/compose).
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举例: Linux X86 环境 可下载 [docker-compose-linux-x86_64](https://github.com/docker/compose/releases/download/v2.27.3/docker-compose-linux-x86_64) 使用.
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```shell
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cd ~
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wget https://github.com/docker/compose/releases/download/v2.27.3/docker-compose-linux-x86_64
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mv docker-compose-linux-x86_64 /usr/bin/docker-compose
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which docker-compose
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```
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/usr/bin/docker-compose
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```shell
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docker-compose -v
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```
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Docker Compose version v2.27.3
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##### 2. 安装 NVIDIA Container Toolkit
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寻找适合你环境的 NVIDIA Container Toolkit 版本, 请参考: [Installing the NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
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安装完成后记得按照刚刚文档中`Configuring Docker`章节对 docker 进行初始化.
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##### 3. 创建 xinference 数据缓存路径
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这一步强烈建议, 因为可以将 xinference 缓存的模型都保存到本地, 长期使用.
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```shell
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mkdir -p ~/xinference
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```
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##### 4. 下载 chatchat & xinference 启动配置文件(docker-compose.yaml)
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```shell
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cd ~
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wget https://github.com/chatchat-space/Langchain-Chatchat/blob/master/docker/docker-compose.yaml
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```
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##### 5. 启动 chatchat & xinference 服务
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```shell
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docker-compose up -d
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```
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出现如下日志即为成功 ( 第一次启动需要下载 docker 镜像, 时间较长, 这里已经提前下载好了 )
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```text
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WARN[0000] /root/docker-compose.yaml: `version` is obsolete
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[+] Running 2/2
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✔ Container root-chatchat-1 Started 0.2s
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✔ Container root-xinference-1 Started 0.3s
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```
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##### 6.检查服务启动情况
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```shell
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docker-compose up -d
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```
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```text
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WARN[0000] /root/docker-compose.yaml: `version` is obsolete
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NAME IMAGE COMMAND SERVICE CREATED STATUS PORTS
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root-chatchat-1 chatimage/chatchat:0.3.1.2-2024-0720 "chatchat -a" chatchat 3 minutes ago Up 3 minutes
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root-xinference-1 xprobe/xinference:v0.12.1 "/opt/nvidia/nvidia_…" xinference 3 minutes ago Up 3 minutes
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```
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```shell
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ss -anptl | grep -E '(8501|7861|9997)'
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```
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```text
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LISTEN 0 128 0.0.0.0:9997 0.0.0.0:* users:(("pt_main_thread",pid=1489804,fd=21))
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LISTEN 0 128 0.0.0.0:8501 0.0.0.0:* users:(("python",pid=1490078,fd=10))
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LISTEN 0 128 0.0.0.0:7861 0.0.0.0:* users:(("python",pid=1490014,fd=9))
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```
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如上, 服务均已正常启动, 即可体验使用.
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> 提示: 先登陆 xinference ui `http://<your_ip>:9997` 启动 llm 和 embedding 后, 再登陆 chatchat ui `http://<your_ip>:8501` 进行体验.
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>
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> 详细文档:
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> - Langchain-chatchat 使用请参考: [LangChain-Chatchat](/README.md)
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>
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> - Xinference 使用请参考: [欢迎来到 Xinference！](https://inference.readthedocs.io/zh-cn/latest/index.html)
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#### 二. Langchain-Chatchat 进阶部署
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##### 1. 按照 `Langchain-Chatchat 体验部署` 内容顺序依次完成
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##### 2. 创建 chatchat 数据缓存路径
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```shell
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cd ~
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mkdir -p ~/chatchat
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```
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##### 3. 修改 `docker-compose.yaml` 文件内容
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原文件内容:
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```yaml
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(上文 ...)
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chatchat:
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image: chatimage/chatchat:0.3.1.2-2024-0720
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(省略 ...)
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# 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
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# volumes:
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# - ~/chatchat/data:/usr/local/lib/python3.11/site-packages/chatchat/data
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(下文 ...)
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```
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将 `volumes` 字段注释打开, 并按照 `YAML` 格式对齐, 如下:
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```yaml
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(上文 ...)
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chatchat:
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image: chatimage/chatchat:0.3.1.2-2024-0720
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(省略 ...)
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# 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
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volumes:
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- ~/chatchat/data:/usr/local/lib/python3.11/site-packages/chatchat/data
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(下文 ...)
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```
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##### 4. 下载数据库初始文件
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> 提示: 这里的 `data.tar.gz` 文件仅包含初始化后的数据库 `samples` 文件一份及相应目录结构, 用户可将原先数据和目录结构迁移此处.
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> > [!WARNING] 请您先备份好您的数据再进行迁移!!!
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```shell
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cd ~/chatchat
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wget https://github.com/chatchat-space/Langchain-Chatchat/blob/master/docker/data.tar.gz
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tar -xvf data.tar.gz
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```
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```shell
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cd data
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pwd
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```
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/root/chatchat/data
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```shell
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ls -l
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```
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```text
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total 20
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drwxr-xr-x 3 root root 4096 Jun 22 10:46 knowledge_base
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drwxr-xr-x 18 root root 4096 Jun 22 10:52 logs
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drwxr-xr-x 5 root root 4096 Jun 22 10:46 media
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drwxr-xr-x 5 root root 4096 Jun 22 10:46 nltk_data
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drwxr-xr-x 3 root root 4096 Jun 22 10:46 temp
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```
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##### 6. 重启 chatchat 服务
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这一步需要到 `docker-compose.yaml` 文件所在路径下执行, 即:
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```shell
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cd ~
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docker-compose down chatchat
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docker-compose up -d chatchat
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```
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操作及检查结果如下:
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```text
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[root@VM-2-15-centos ~]# docker-compose down chatchat
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WARN[0000] /root/docker-compose.yaml: `version` is obsolete
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[+] Running 1/1
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✔ Container root-chatchat-1 Removed 0.5s
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[root@VM-2-15-centos ~]# docker-compose up -d
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WARN[0000] /root/docker-compose.yaml: `version` is obsolete
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[+] Running 2/2
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✔ Container root-xinference-1 Running 0.0s
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✔ Container root-chatchat-1 Started 0.2s
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[root@VM-2-15-centos ~]# docker-compose ps
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WARN[0000] /root/docker-compose.yaml: `version` is obsolete
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NAME IMAGE COMMAND SERVICE CREATED STATUS PORTS
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root-chatchat-1 chatimage/chatchat:0.3.1.2-2024-0720 "chatchat -a" chatchat 33 seconds ago Up 32 seconds
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root-xinference-1 xprobe/xinference:v0.12.1 "/opt/nvidia/nvidia_…" xinference 45 minutes ago Up 45 minutes
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[root@VM-2-15-centos ~]# ss -anptl | grep -E '(8501|7861|9997)'
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LISTEN 0 128 0.0.0.0:9997 0.0.0.0:* users:(("pt_main_thread",pid=1489804,fd=21))
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LISTEN 0 128 0.0.0.0:8501 0.0.0.0:* users:(("python",pid=1515944,fd=10))
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LISTEN 0 128 0.0.0.0:7861 0.0.0.0:* users:(("python",pid=1515878,fd=9))
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```
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