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feat:readme for docker #4274

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Jun 22, 2024
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feat:readme for docker #4274

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13 changes: 9 additions & 4 deletions docker/docker-compose.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,15 @@ version: '3.9'
services:
xinference:
image: xprobe/xinference:v0.12.1
restart: always
command: xinference-local -H 0.0.0.0
# ports:
# ports: # 不使用 host network 时可打开.
# - "9997:9997"
network_mode: "host"
volumes:
- ~/xinference:/root/.xinference # 将本地目录(/root/xinference/.xinference)挂载到容器目录(/root/.xinference)中, 详细见:https://inference.readthedocs.io/zh-cn/latest/getting_started/using_docker_image.html
# 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
- ~/xinference:/root/.xinference
# - ~/xinference/cache/huggingface:/root/.cache/huggingface
# - ~/xinference/cache/modelscope:/root/.cache/modelscope
deploy:
Expand All @@ -20,9 +23,11 @@ services:
runtime: nvidia
chatchat:
image: chatimage/chatchat:0.3.0-0622
# ports:
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:
# - /root/chatchat/data:/usr/local/lib/python3.11/site-packages/chatchat/data
# - ~/chatchat/data:/usr/local/lib/python3.11/site-packages/chatchat/data
169 changes: 169 additions & 0 deletions docs/install/README_docker.md
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@@ -0,0 +1,169 @@
### chatchat 容器化部署指引

> 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.
>
> Langchain-Chatchat docker 镜像已支持多架构, 欢迎大家自行测试.

#### 一. Langchain-Chatchat 体验部署

##### 1. 安装 docker-compose
寻找适合你环境的 docker-compose 版本, 请参考 [Docker-Compose](https://github.com/docker/compose).

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.
```shell
which docker-compose
```
/usr/bin/docker-compose
```shell
docker-compose -v
```
Docker Compose version v2.27.3

##### 2. 安装 NVIDIA Container Toolkit
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).

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.

##### 3. 创建 xinference 数据缓存路径

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.
```shell
mkdir -p ~/xinference
```

##### 4. 下载 chatchat & xinference 启动配置文件(docker-compose.yaml)
```shell
cd ~
wget https://github.com/chatchat-space/Langchain-Chatchat/blob/master/docker/docker-compose.yaml
```

##### 5. 启动 chatchat & xinference 服务
```shell
docker-compose up -d
```
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 )
```text
WARN[0000] /root/docker-compose.yaml: `version` is obsolete
[+] Running 2/2
✔ Container root-chatchat-1 Started 0.2s
✔ Container root-xinference-1 Started 0.3s
```

##### 6.检查服务启动情况
```shell
docker-compose up -d
```
```text
WARN[0000] /root/docker-compose.yaml: `version` is obsolete
NAME IMAGE COMMAND SERVICE CREATED STATUS PORTS
root-chatchat-1 chatimage/chatchat:0.3.0-0622 "chatchat -a" chatchat 3 minutes ago Up 3 minutes
root-xinference-1 xprobe/xinference:v0.12.1 "/opt/nvidia/nvidia_…" xinference 3 minutes ago Up 3 minutes
```
```shell
ss -anptl | grep -E '(8501|7861|9997)'
```
```text
LISTEN 0 128 0.0.0.0:9997 0.0.0.0:* users:(("pt_main_thread",pid=1489804,fd=21))
LISTEN 0 128 0.0.0.0:8501 0.0.0.0:* users:(("python",pid=1490078,fd=10))
LISTEN 0 128 0.0.0.0:7861 0.0.0.0:* users:(("python",pid=1490014,fd=9))
```
如上, 服务均已正常启动, 即可体验使用.

> 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.
>
> 详细文档:
> - Langchain-chatchat 使用请参考: [LangChain-Chatchat](/README.md)
>
> - Xinference 使用请参考: [欢迎来到 Xinference！](https://inference.readthedocs.io/zh-cn/latest/index.html)

#### 二. Langchain-Chatchat 进阶部署

##### 1. 按照 `Langchain-Chatchat 体验部署` 内容顺序依次完成

##### 2. 创建 chatchat 数据缓存路径
```shell
cd ~
mkdir -p ~/chatchat
```

##### 3. 修改 `docker-compose.yaml` 文件内容

原文件内容:
```yaml
(上文 ...)
chatchat:
image: chatimage/chatchat:0.3.0-0622
(省略 ...)
# 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:
# - ~/chatchat/data:/usr/local/lib/python3.11/site-packages/chatchat/data
(下文 ...)
```
将 `volumes` 字段注释打开, 并按照 `YAML` 格式对齐, 如下:
```yaml
(上文 ...)
chatchat:
image: chatimage/chatchat:0.3.0-0622
(省略 ...)
# 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:
- ~/chatchat/data:/usr/local/lib/python3.11/site-packages/chatchat/data
(下文 ...)
```

##### 4. 下载数据库初始文件

> 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.
> > [!WARNING] 请您先备份好您的数据再进行迁移!!!

```shell
cd ~/chatchat
wget https://github.com/chatchat-space/Langchain-Chatchat/blob/master/docker/data.tar.gz
tar -xvf data.tar.gz
```
```shell
cd data
pwd
```
/root/chatchat/data
```shell
ls -l
```
```text
total 20
drwxr-xr-x 3 root root 4096 Jun 22 10:46 knowledge_base
drwxr-xr-x 18 root root 4096 Jun 22 10:52 logs
drwxr-xr-x 5 root root 4096 Jun 22 10:46 media
drwxr-xr-x 5 root root 4096 Jun 22 10:46 nltk_data
drwxr-xr-x 3 root root 4096 Jun 22 10:46 temp
```

##### 5. 重启 chatchat 服务

这一步需要到 `docker-compose.yaml` 文件所在路径下执行, 即:
```shell
cd ~
docker-compose down chatchat
docker-compose up -d
```
操作及检查结果如下:
```text
[root@VM-2-15-centos ~]# docker-compose down chatchat
WARN[0000] /root/docker-compose.yaml: `version` is obsolete
[+] Running 1/1
✔ Container root-chatchat-1 Removed 0.5s
[root@VM-2-15-centos ~]# docker-compose up -d
WARN[0000] /root/docker-compose.yaml: `version` is obsolete
[+] Running 2/2
✔ Container root-xinference-1 Running 0.0s
✔ Container root-chatchat-1 Started 0.2s
[root@VM-2-15-centos ~]# docker-compose ps
WARN[0000] /root/docker-compose.yaml: `version` is obsolete
NAME IMAGE COMMAND SERVICE CREATED STATUS PORTS
root-chatchat-1 chatimage/chatchat:0.3.0-0622 "chatchat -a" chatchat 33 seconds ago Up 32 seconds
root-xinference-1 xprobe/xinference:v0.12.1 "/opt/nvidia/nvidia_…" xinference 45 minutes ago Up 45 minutes
[root@VM-2-15-centos ~]# ss -anptl | grep -E '(8501|7861|9997)'
LISTEN 0 128 0.0.0.0:9997 0.0.0.0:* users:(("pt_main_thread",pid=1489804,fd=21))
LISTEN 0 128 0.0.0.0:8501 0.0.0.0:* users:(("python",pid=1515944,fd=10))
LISTEN 0 128 0.0.0.0:7861 0.0.0.0:* users:(("python",pid=1515878,fd=9))
```