v3.0.0 init

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## RuoYi-AI 后端部署教程Docker 部署版)
### 一、前置条件
在部署前,请确保系统已满足以下条件:
#### ✅ 系统环境要求
- 操作系统Linux / MacOS推荐 Linux 服务器)
- CPU4 核以上
- 内存:≥ 4GB
- 磁盘空间:≥ 10GB建议 20GB+
#### ✅ 已安装软件
- **Docker**
- **Docker Compose**
验证命令是否可用:
```
docker -v
docker compose version
```
若无输出或提示“command not found”请先安装 Docker 及 Compose。
------
### 二、目录结构配置
#### 1⃣ 创建部署目录
在目标服务器执行以下命令:
```
# 第一级目录
mkdir /ruoyi-ai
cd /ruoyi-ai
# 第二级目录
mkdir deploy
cd deploy
# 第三级目录
mkdir data mysql-init
# 第四级目录
mkdir logs minio minio-config mysql redis weaviate
```
> 💡 `data` 目录用于挂载容器运行期间生成的数据文件。
最终目录结构示例:
```
/ruoyi-ai
└── deploy
├── data/
├── mysql-init/
├── logs/
├── minio/
├── minio-config/
├── mysql/
├── redis/
├── weaviate/
```
------
### 三、上传配置文件
将以下配置文件上传到 `/ruoyi-ai/deploy` 目录:
- `docker-compose.yaml`
- `.env`
- `ruoyi-ai.sql`
- `Dockerfile`
> 📂 这些文件在项目目录 `/script/deploy/deploy` 下。
> 上传后请检查文件路径是否与上方目录结构一致。
------
### 四、构建 Jar 包
1. 打开 IDEA 或其他构建工具
2. 选择 **Maven 构建配置**,勾选 `prod` 环境,取消 `dev` 环境
3. 点击 `package` 进行打包
4. **注意:** 在构建前请将 `application-prod.yml` 拖入
`ruoyi-admin/src/main/resources` 目录中
构建完成后会在:
```
ruoyi-admin/target/ruoyi-admin.jar
```
生成打包文件。
------
### 五、上传 Jar 包至服务器
将生成的 `ruoyi-admin.jar` 上传到服务器 `/ruoyi-ai/deploy` 目录下。
确保与 `Dockerfile` 同目录。
------
### 六、构建 Docker 镜像
`Dockerfile` 内容如下:
```
FROM openjdk:17-jdk
RUN mkdir -p /ruoyi/server/logs \
/ruoyi/server/temp
WORKDIR /ruoyi/server
COPY ruoyi-admin.jar ruoyi-admin.jar
ENTRYPOINT ["java","-jar","ruoyi-admin.jar"]
```
`/ruoyi-ai/deploy` 目录执行以下命令:
```
# 构建镜像
docker build -t ruoyi-ai-backend:v20251013 .
# 查看镜像是否构建成功
docker image ls
```
然后在 `docker-compose.yaml` 文件中,将对应服务的镜像名修改为:
```
image: ruoyi-ai-backend:v20251013
```
------
### 七、启动容器服务
在启动前请确认:
- `.env` 中端口号、数据库密码、环境变量已正确配置
- `docker-compose.yaml` 中 MySQL 的端口已开放(用于导入数据)
如示例:
```
ports:
- "3306:3306"
```
#### 启动命令:
```
cd /ruoyi-ai/deploy
docker compose up -d
```
#### 查看运行状态:
```
docker compose ps
```
#### 查看日志:
```
docker logs -f <容器名称>
```
> ⚠️ 初次启动时可仅运行 `ruoyi-admin`(后端)模块,将前端 `ruoyi-web` 服务暂时注释,确认后端服务正常后再启用前端容器。
------
### 八、数据库初始化
启动 MySQL 容器后,执行以下操作:
```
docker exec -it <mysql_container_name> bash
mysql -uroot -p
source /docker-entrypoint-initdb.d/ruoyi-ai.sql;
```
或手动在客户端中导入 `/ruoyi-ai/deploy/ruoyi-ai.sql` 文件。
------
### 九、常用 Docker 命令
| 功能 | 命令 |
|-----------|-----------------------------------|
| 查看容器状态 | `docker ps -a` |
| 查看日志 | `docker logs -f <容器名>` |
| 停止服务 | `docker compose down` |
| 重启服务 | `docker compose restart` |
| 重新构建镜像 | `docker compose build --no-cache` |
| 清理无用镜像/容器 | `docker system prune -a` |
------
### 🔍 十、部署验证
1. 检查容器是否全部启动成功:
```
docker compose ps
```
2. 访问后端接口:
```
http://<服务器IP>:<后端端口>
```
3. 检查日志输出无异常。

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worker_processes 1;
error_log /var/log/nginx/error.log warn;
pid /var/run/nginx.pid;
events {
# 可以根据业务并发量适当调高
worker_connections 1024;
}
http {
include mime.types;
default_type application/octet-stream;
# 高效传输文件
sendfile on;
# 长连接超时时间
keepalive_timeout 65;
# 单连接最大请求数,提高长连接复用率
keepalive_requests 100000;
# 限制body大小
client_max_body_size 100m;
client_header_buffer_size 32k;
client_body_buffer_size 512k;
# 开启静态资源压缩
gzip_static on;
# 连接数限制 (防御类配置) 10m 一般够用了,能存储上万 IP 的计数
limit_conn_zone $binary_remote_addr zone=perip:10m;
limit_conn_zone $server_name zone=perserver:10m;
# 隐藏 nginx 版本号,防止暴露版本信息
server_tokens off;
log_format main '$remote_addr - $remote_user [$time_local] "$request" '
'$status $body_bytes_sent "$http_referer" '
'"$http_user_agent" "$http_x_forwarded_for"';
access_log /var/log/nginx/access.log main;
upstream server {
ip_hash;
server 127.0.0.1:8080;
server 127.0.0.1:8081;
}
upstream monitor-admin {
server 127.0.0.1:9090;
}
upstream snailjob-server {
server 127.0.0.1:8800;
}
server {
listen 80;
server_name localhost;
# https配置参考 start
#listen 443 ssl;
# 证书直接存放 /docker/nginx/cert/ 目录下即可 更改证书名称即可 无需更改证书路径
#ssl on;
#ssl_certificate /etc/nginx/cert/xxx.local.crt; # /etc/nginx/cert/ 为docker映射路径 不允许更改
#ssl_certificate_key /etc/nginx/cert/xxx.local.key; # /etc/nginx/cert/ 为docker映射路径 不允许更改
#ssl_session_timeout 5m;
#ssl_ciphers ECDHE-RSA-AES128-GCM-SHA256:ECDHE:ECDH:AES:HIGH:!NULL:!aNULL:!MD5:!ADH:!RC4;
#ssl_protocols TLSv1.3 TLSv1.2 TLSv1.1 TLSv1;
#ssl_prefer_server_ciphers on;
# https配置参考 end
# 演示环境配置 拦截除 GET POST 之外的所有请求
# if ($request_method !~* GET|POST) {
# rewrite ^/(.*)$ /403;
# }
# location = /403 {
# default_type application/json;
# return 200 '{"msg":"演示模式,不允许操作","code":500}';
# }
# 限制外网访问内网 actuator 相关路径
location ~ ^(/[^/]*)?/actuator.*(/.*)?$ {
return 403;
}
location / {
root /usr/share/nginx/html; # docker映射路径 不允许更改
try_files $uri $uri/ /index.html;
index index.html index.htm;
}
location /prod-api/ {
# 设置客户端请求头中的 Host 信息(保持原始 Host
proxy_set_header Host $http_host;
# 获取客户端真实 IP
proxy_set_header X-Real-IP $remote_addr;
# 自定义头 REMOTE-HOST记录客户端 IP
proxy_set_header REMOTE-HOST $remote_addr;
# 获取完整的客户端 IP 链(经过多级代理时)
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
# 设置后端响应超时时间(这里是 24 小时,适合长连接/SSE
proxy_read_timeout 86400s;
# SSE (Server-Sent Events) 与 WebSocket 支持参数
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
# 禁用代理缓冲,数据直接传给客户端
proxy_buffering off;
# 禁用代理缓存
proxy_cache off;
# 按 IP 限制连接数(防 CC 攻击) 小型站10~20 就够 中型站50~100
limit_conn perip 20;
# 按 Server 限制总并发连接数 根据服务器的最大并发处理能力来定 太小会限制合法用户访问,太大会占满服务器资源
limit_conn perserver 500;
proxy_pass http://server/;
}
# https 会拦截内链所有的 http 请求 造成功能无法使用
# 解决方案1 将 admin 服务 也配置成 https
# 解决方案2 将菜单配置为外链访问 走独立页面 http 访问
location /admin/ {
# 设置客户端请求头中的 Host 信息(保持原始 Host
proxy_set_header Host $http_host;
# 获取客户端真实 IP
proxy_set_header X-Real-IP $remote_addr;
# 自定义头 REMOTE-HOST记录客户端 IP
proxy_set_header REMOTE-HOST $remote_addr;
# 获取完整的客户端 IP 链(经过多级代理时)
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
# 禁用代理缓冲,数据直接传给客户端
proxy_buffering off;
# 禁用代理缓存
proxy_cache off;
proxy_pass http://monitor-admin/admin/;
}
location /snail-job/ {
# 设置客户端请求头中的 Host 信息(保持原始 Host
proxy_set_header Host $http_host;
# 获取客户端真实 IP
proxy_set_header X-Real-IP $remote_addr;
# 自定义头 REMOTE-HOST记录客户端 IP
proxy_set_header REMOTE-HOST $remote_addr;
# 获取完整的客户端 IP 链(经过多级代理时)
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
# SSE (Server-Sent Events) 与 WebSocket 支持参数
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
# 禁用代理缓冲,直接传输给客户端
proxy_buffering off;
# 禁用代理缓存
proxy_cache off;
proxy_pass http://snailjob-server/snail-job/;
}
error_page 500 502 503 504 /50x.html;
location = /50x.html {
root html;
}
}
}

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# redis 密码
requirepass ruoyi123
# key 监听器配置
# notify-keyspace-events Ex
# 配置持久化文件存储路径
dir /redis/data
# 配置rdb
# 15分钟内有至少1个key被更改则进行快照
save 900 1
# 5分钟内有至少10个key被更改则进行快照
save 300 10
# 1分钟内有至少10000个key被更改则进行快照
save 60 10000
# 开启压缩
rdbcompression yes
# rdb文件名 用默认的即可
dbfilename dump.rdb
# 开启aof
appendonly yes
# 文件名
appendfilename "appendonly.aof"
# 持久化策略,no:不同步,everysec:每秒一次,always:总是同步,速度比较慢
# appendfsync always
appendfsync everysec
# appendfsync no

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数据目录 请执行 `chmod 777 /docker/redis/data` 赋予读写权限 否则将无法写入数据

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# Ruoyi-AI 工作流模块详细说明文档
## 概述
Ruoyi-AI 工作流模块是一个基于 LangGraph4j 的智能工作流引擎支持可视化工作流设计、AI 模型集成、条件分支、人机交互等高级功能。该模块采用微服务架构,提供完整的
RESTful API 和流式响应支持。
## 模块架构
### 1. 模块结构
```
ruoyi-ai/
├── ruoyi-modules/
│ └── ruoyi-workflow/ # 工作流核心模块
│ ├── pom.xml
│ └── src/main/java/org/ruoyi/workflow/
│ └── controller/ # 控制器层
│ ├── WorkflowController.java
│ ├── WorkflowRuntimeController.java
│ └── admin/ # 管理端控制器
│ ├── AdminWorkflowController.java
│ └── AdminWorkflowComponentController.java
└── ruoyi-modules-api/
└── ruoyi-workflow-api/ # 工作流API模块
├── pom.xml
└── src/main/java/org/ruoyi/workflow/
├── entity/ # 实体类
├── dto/ # 数据传输对象
├── service/ # 服务接口
├── mapper/ # 数据访问层
├── workflow/ # 工作流核心逻辑
├── enums/ # 枚举类
├── util/ # 工具类
└── exception/ # 异常处理
```
### 2. 核心依赖
- **LangGraph4j**: 1.5.3 - 工作流图执行引擎
- **LangChain4j**: 1.2.0 - AI 模型集成框架
- **Spring Boot**: 3.x - 应用框架
- **MyBatis Plus**: 数据访问层
- **Redis**: 缓存和状态管理
- **Swagger/OpenAPI**: API 文档
## 核心功能
### 1. 工作流管理
#### 1.1 工作流定义
- **创建工作流**: 支持自定义标题、描述、公开性设置
- **编辑工作流**: 可视化节点编辑、连接线配置
- **版本控制**: 支持工作流的版本管理和回滚
- **权限管理**: 支持公开/私有工作流设置
#### 1.2 工作流执行
- **流式执行**: 基于 SSE 的实时流式响应
- **状态管理**: 完整的执行状态跟踪
- **错误处理**: 详细的错误信息和异常处理
- **中断恢复**: 支持工作流中断和恢复执行
### 2. 节点类型
#### 2.1 基础节点
- **Start**: 开始节点,定义工作流入口
- **End**: 结束节点,定义工作流出口
#### 2.2 AI 模型节点
- **Answer**: 大语言模型问答节点
- **Dalle3**: DALL-E 3 图像生成
- **Tongyiwanx**: 通义万相图像生成
- **Classifier**: 内容分类节点
#### 2.3 数据处理节点
- **DocumentExtractor**: 文档信息提取
- **KeywordExtractor**: 关键词提取
- **FaqExtractor**: 常见问题提取
- **KnowledgeRetrieval**: 知识库检索
#### 2.4 控制流节点
- **Switcher**: 条件分支节点
- **HumanFeedback**: 人机交互节点
#### 2.5 外部集成节点
- **Google**: Google 搜索集成
- **MailSend**: 邮件发送
- **HttpRequest**: HTTP 请求
- **Template**: 模板转换
### 3. 数据流管理
#### 3.1 输入输出定义
```java
// 节点输入输出数据结构
public class NodeIOData {
private String name; // 参数名称
private NodeIODataContent content; // 参数内容
}
// 支持的数据类型
public enum WfIODataTypeEnum {
TEXT, // 文本
NUMBER, // 数字
BOOLEAN, // 布尔值
FILES, // 文件
OPTIONS // 选项
}
```
#### 3.2 参数引用
- **节点间引用**: 支持上游节点输出作为下游节点输入
- **参数映射**: 自动处理参数名称映射
- **类型转换**: 自动进行数据类型转换
## 数据库设计
### 1. 核心表结构
#### 1.1 工作流定义表 (t_workflow)
```sql
CREATE TABLE t_workflow (
id BIGINT AUTO_INCREMENT PRIMARY KEY,
uuid VARCHAR(32) NOT NULL DEFAULT '',
title VARCHAR(100) NOT NULL DEFAULT '',
remark TEXT NOT NULL DEFAULT '',
user_id BIGINT NOT NULL DEFAULT 0,
is_public TINYINT(1) NOT NULL DEFAULT 0,
is_enable TINYINT(1) NOT NULL DEFAULT 1,
create_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP,
update_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
is_deleted TINYINT(1) NOT NULL DEFAULT 0
);
```
#### 1.2 工作流节点表 (t_workflow_node)
```sql
CREATE TABLE t_workflow_node (
id BIGINT AUTO_INCREMENT PRIMARY KEY,
uuid VARCHAR(32) NOT NULL DEFAULT '',
workflow_id BIGINT NOT NULL DEFAULT 0,
workflow_component_id BIGINT NOT NULL DEFAULT 0,
user_id BIGINT NOT NULL DEFAULT 0,
title VARCHAR(100) NOT NULL DEFAULT '',
remark VARCHAR(500) NOT NULL DEFAULT '',
input_config JSON NOT NULL DEFAULT ('{}'),
node_config JSON NOT NULL DEFAULT ('{}'),
position_x DOUBLE NOT NULL DEFAULT 0,
position_y DOUBLE NOT NULL DEFAULT 0,
create_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP,
update_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
is_deleted TINYINT(1) NOT NULL DEFAULT 0
);
```
#### 1.3 工作流边表 (t_workflow_edge)
```sql
CREATE TABLE t_workflow_edge (
id BIGINT AUTO_INCREMENT PRIMARY KEY,
uuid VARCHAR(32) NOT NULL DEFAULT '',
workflow_id BIGINT NOT NULL DEFAULT 0,
source_node_uuid VARCHAR(32) NOT NULL DEFAULT '',
source_handle VARCHAR(32) NOT NULL DEFAULT '',
target_node_uuid VARCHAR(32) NOT NULL DEFAULT '',
create_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP,
update_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
is_deleted TINYINT(1) NOT NULL DEFAULT 0
);
```
#### 1.4 工作流运行时表 (t_workflow_runtime)
```sql
CREATE TABLE t_workflow_runtime (
id BIGINT AUTO_INCREMENT PRIMARY KEY,
uuid VARCHAR(32) NOT NULL DEFAULT '',
user_id BIGINT NOT NULL DEFAULT 0,
workflow_id BIGINT NOT NULL DEFAULT 0,
input JSON NOT NULL DEFAULT ('{}'),
output JSON NOT NULL DEFAULT ('{}'),
status SMALLINT NOT NULL DEFAULT 1,
status_remark VARCHAR(250) NOT NULL DEFAULT '',
create_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP,
update_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
is_deleted TINYINT(1) NOT NULL DEFAULT 0
);
```
#### 1.5 工作流组件表 (t_workflow_component)
```sql
CREATE TABLE t_workflow_component (
id BIGINT AUTO_INCREMENT PRIMARY KEY,
uuid VARCHAR(32) DEFAULT '' NOT NULL,
name VARCHAR(32) DEFAULT '' NOT NULL,
title VARCHAR(100) DEFAULT '' NOT NULL,
remark TEXT NOT NULL,
display_order INT DEFAULT 0 NOT NULL,
is_enable TINYINT(1) DEFAULT 0 NOT NULL,
create_time DATETIME DEFAULT CURRENT_TIMESTAMP NOT NULL,
update_time DATETIME DEFAULT CURRENT_TIMESTAMP NOT NULL,
is_deleted TINYINT(1) DEFAULT 0 NOT NULL
);
```
## API 接口
### 1. 工作流管理接口
#### 1.1 基础操作
```http
#
POST /workflow/add
Content-Type: application/json
{
"title": "",
"remark": "",
"isPublic": false
}
#
POST /workflow/update
Content-Type: application/json
{
"uuid": "UUID",
"title": "",
"remark": ""
}
#
POST /workflow/del/{uuid}
# /
POST /workflow/enable/{uuid}?enable=true
```
#### 1.2 搜索和查询
```http
#
GET /workflow/mine/search?keyword=&isPublic=true&currentPage=1&pageSize=10
#
GET /workflow/public/search?keyword=&currentPage=1&pageSize=10
#
GET /workflow/public/component/list
```
### 2. 工作流执行接口
#### 2.1 流式执行
```http
#
POST /workflow/run
Content-Type: application/json
Accept: text/event-stream
{
"uuid": "UUID",
"inputs": [
{
"name": "input",
"content": {
"type": 1,
"textContent": ""
}
}
]
}
```
#### 2.2 运行时管理
```http
#
POST /workflow/runtime/resume/{runtimeUuid}
Content-Type: application/json
{
"feedbackContent": ""
}
#
GET /workflow/runtime/page?wfUuid=UUID&currentPage=1&pageSize=10
#
GET /workflow/runtime/nodes/{runtimeUuid}
#
POST /workflow/runtime/clear?wfUuid=UUID
```
### 3. 管理端接口
#### 3.1 工作流管理
```http
#
POST /admin/workflow/search
Content-Type: application/json
{
"title": "",
"isPublic": true,
"isEnable": true
}
# /
POST /admin/workflow/enable?uuid=UUID&isEnable=true
```
## 核心实现
### 1. 工作流引擎 (WorkflowEngine)
工作流引擎是整个模块的核心,负责:
- 工作流图的构建和编译
- 节点执行调度
- 状态管理和持久化
- 流式输出处理
```java
public class WorkflowEngine {
// 核心执行方法
public void run(User user, List<ObjectNode> userInputs, SseEmitter sseEmitter) {
// 1. 验证工作流状态
// 2. 创建运行时实例
// 3. 构建状态图
// 4. 执行工作流
// 5. 处理流式输出
}
// 恢复执行方法
public void resume(String userInput) {
// 1. 更新状态
// 2. 继续执行
}
}
```
### 2. 节点工厂 (WfNodeFactory)
节点工厂负责根据组件类型创建对应的节点实例:
```java
public class WfNodeFactory {
public static AbstractWfNode create(WorkflowComponent component,
WorkflowNode node,
WfState wfState,
WfNodeState nodeState) {
// 根据组件类型创建对应的节点实例
switch (component.getName()) {
case "Answer":
return new LLMAnswerNode(component, node, wfState, nodeState);
case "Switcher":
return new SwitcherNode(component, node, wfState, nodeState);
// ... 其他节点类型
}
}
}
```
### 3. 图构建器 (WorkflowGraphBuilder)
图构建器负责将工作流定义转换为可执行的状态图:
```java
public class WorkflowGraphBuilder {
public StateGraph<WfNodeState> build(WorkflowNode startNode) {
// 1. 构建编译节点树
// 2. 转换为状态图
// 3. 添加节点和边
// 4. 处理条件分支
// 5. 处理并行执行
}
}
```
## 流式响应机制
### 1. SSE 事件类型
工作流执行过程中会发送多种类型的 SSE 事件:
```javascript
// 节点开始执行
[NODE_RUN_节点UUID] - 节点执行开始事件
// 节点输入数据
[NODE_INPUT_节点UUID] - 节点输入数据事件
// 节点输出数据
[NODE_OUTPUT_节点UUID] - 节点输出数据事件
// 流式内容块
[NODE_CHUNK_节点UUID] - 流式内容块事件
// 等待用户输入
[NODE_WAIT_FEEDBACK_BY_节点UUID] - 等待用户输入事件
```
### 2. 流式处理流程
1. **初始化**: 创建工作流运行时实例
2. **节点执行**: 逐个执行工作流节点
3. **实时输出**: 通过 SSE 实时推送执行结果
4. **状态更新**: 实时更新节点和工作流状态
5. **错误处理**: 捕获并处理执行过程中的错误
## 扩展开发
### 1. 自定义节点开发
要开发自定义工作流节点,需要:
1. **创建节点类**:继承 `AbstractWfNode`
2. **实现处理逻辑**:重写 `onProcess()` 方法
3. **定义配置类**:创建节点配置类
4. **注册组件**:在组件表中注册新组件
```java
public class CustomNode extends AbstractWfNode {
@Override
protected NodeProcessResult onProcess() {
// 实现自定义处理逻辑
List<NodeIOData> outputs = new ArrayList<>();
// ... 处理逻辑
return NodeProcessResult.success(outputs);
}
}
```
### 2. 自定义组件注册
```sql
-- 在 t_workflow_component 表中添加新组件
INSERT INTO t_workflow_component (uuid, name, title, remark, is_enable)
VALUES (REPLACE(UUID(), '-', ''), 'CustomNode', '自定义节点', '自定义节点描述', true);
```