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AI执行器集成OpenClaw: 新增“openClawJobHandler”内置AI任务，与OpenClaw集成打通，支持快速开发AI类任务。
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‎doc/XXL-JOB官方文档.md‎

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@@ -1271,6 +1271,7 @@ public void demoJobHandler() throws Exception {
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- 执行器代码：xxl-job-executor-sample-springboot-ai
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**执行器内置任务列表：**
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- a、ollamaJobHandler： OllamaChat任务，支持自定义prompt、input等输入信息。示例任务入参如下：
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```
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{
@@ -1279,6 +1280,7 @@ public void demoJobHandler() throws Exception {
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"model": "{模型实现，如qwen3.5:2b，可选信息}"
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}
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```
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- b、difyWorkflowJobHandler：DifyWorkflow 任务，支持自定义inputs、user、baseUrl、apiKey 等输入信息，示例参数如下；
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```
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{
@@ -1291,6 +1293,14 @@ public void demoJobHandler() throws Exception {
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}
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```
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- c、openClawJobHandler： OpenClaw任务，支持自定义prompt、input等输入信息。示例任务入参如下：
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```
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{
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"input": "{输入信息，必填信息}",
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"prompt": "{模型prompt，可选信息}"
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}
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```
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- 依赖1：参考 [Ollama本地化部署大模型](https://www.xuxueli.com/blog/?blog=./notebook/13-AI/%E4%BD%BF%E7%94%A8Ollama%E6%9C%AC%E5%9C%B0%E5%8C%96%E9%83%A8%E7%BD%B2DeepSeek.md) ，执行器示例部署“qwen2.5:1.5b”模型，也可自定选择其他模型版本。
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- 依赖2：参考 [使用DeepSeek与Dify搭建AI助手](https://www.xuxueli.com/blog/?blog=./notebook/13-AI/%E4%BD%BF%E7%94%A8DeepSeek%E4%B8%8EDify%E6%90%AD%E5%BB%BAAI%E5%8A%A9%E6%89%8B.md)，执行器示例新建Dify DifyWork应用，并在开始节点添加“input”参数，可结合实际情况调整。
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- 依赖3：启动示例 “AI执行器” 相关配置文件说明如下：
@@ -2788,15 +2798,16 @@ public void execute() {
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- 10、【优化】统一项目依赖管理结构，依赖版本统一到父级pom提升可维护性；
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### 7.44 版本 v3.4.0 Release Notes[ING]
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- 1、【新增】调度性能提升：任务触发后分批批量更新，高频调度场景可百倍降低SQL操作合并执行，提升调度性能；
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- 1、【新增】AI执行器集成OpenClaw: 新增“openClawJobHandler”内置AI任务，与OpenClaw集成打通，支持快速开发AI类任务。
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- 2、【新增】调度批次写聚合提升调度性能：任务触发后分批批量更新，高频调度场景可百倍降低SQL操作合并执行，提升调度性能；
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（任务触发后批量更新配置“xxl.job.schedule.batchsize”）
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- 2、【调整】固定频率调度策略调整，修复小概率下触发时间偏差问题；
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- 3、【调整】Docker基础镜像调整为eclipse-temurin；
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- 4、【优化】父POM依赖配置优化，移除容易配置；合并PR-3926；
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- 5、【升级】升级多项maven依赖至较新版本；
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- 3、【调整】固定频率调度策略调整，修复小概率下触发时间偏差问题；
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- 4、【调整】Docker基础镜像调整为eclipse-temurin；
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- 5、【优化】父POM依赖配置优化，移除容易配置；合并PR-3926；
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- 6、【优化】调度日志优化：支持执行器维度查看调度日志；新增调度日志索引，提升查询效率；
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（数据库新增索引脚本：``` create index I_jobgroup on xxl_job_log (job_group); ``` ）
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- 7、【TODO】调度中心OpenAPI完善，提供任务管理能力；封装Agent Skill并推送ClawHub；
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- 7、【升级】升级多项maven依赖至较新版本；
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- 8、【TODO】调度中心OpenAPI完善，提供任务管理能力；封装Agent Skill并推送ClawHub；
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### TODO LIST

‎doc/db/tables_xxl_job.sql‎

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@@ -157,7 +157,7 @@ INSERT INTO `xxl_job_info`(`id`, `job_group`, `job_desc`, `add_time`, `update_ti
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VALUES (1, 1, '示例任务01', now(), now(), 'XXL', '', 'CRON', '0 0 0 * * ? *',
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'DO_NOTHING', 'FIRST', 'demoJobHandler', '', 'SERIAL_EXECUTION', 0, 0, 'BEAN', '', 'GLUE代码初始化',
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now(), ''),
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(2, 2, 'Ollama示例任务01', now(), now(), 'XXL', '', 'NONE', '',
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(2, 2, 'Ollama示例任务', now(), now(), 'XXL', '', 'NONE', '',
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'DO_NOTHING', 'FIRST', 'ollamaJobHandler', '{
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"input": "慢SQL问题分析思路",
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"prompt": "你是一个研发工程师，擅长解决技术类问题。",
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"user": "xxl-job",
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"baseUrl": "http://localhost/v1",
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"apiKey": "app-OUVgNUOQRIMokfmuJvBJoUTN"
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}', 'SERIAL_EXECUTION', 0, 0, 'BEAN', '', 'GLUE代码初始化',
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now(), ''),
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(4, 2, 'OpenClaw示例任务', now(), now(), 'XXL', '', 'NONE', '',
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'DO_NOTHING', 'FIRST', 'difyWorkflowJobHandler', '{
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"input": "查看下上海今天得天气，给出出游建议",
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"prompt": "你是一个出游助手，擅长做旅游规划"
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}', 'SERIAL_EXECUTION', 0, 0, 'BEAN', '', 'GLUE代码初始化',
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now(), '');
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‎pom.xml‎

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<version>${xxl-sso.version}</version>
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</dependency>
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<!-- spring-ai -->
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<!-- spring-ai: ollama、openai -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-starter-model-ollama</artifactId>
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<version>${spring-ai.version}</version>
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</dependency>
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-starter-model-openai</artifactId>
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<version>${spring-ai.version}</version>
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</dependency>
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<!-- dify -->
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<dependency>
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<groupId>io.github.imfangs</groupId>

‎xxl-job-executor-samples/xxl-job-executor-sample-springboot-ai/pom.xml‎

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<artifactId>xxl-job-core</artifactId>
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</dependency>
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40-
<!-- spring-ai -->
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<!-- spring-ai: ollama -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-starter-model-ollama</artifactId>
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</dependency>
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<!-- spring-ai: openai -->
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-starter-model-openai</artifactId>
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</dependency>
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<!-- dify -->
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<dependency>
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<groupId>io.github.imfangs</groupId>

‎xxl-job-executor-samples/xxl-job-executor-sample-springboot-ai/src/main/java/com/xxl/job/executor/jobhandler/AIXxlJob.java‎

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@@ -15,6 +15,7 @@
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import org.springframework.ai.chat.memory.MessageWindowChatMemory;
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import org.springframework.ai.ollama.OllamaChatModel;
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import org.springframework.ai.ollama.api.OllamaChatOptions;
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import org.springframework.ai.openai.OpenAiChatModel;
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import org.springframework.stereotype.Component;
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import java.util.HashMap;
@@ -32,6 +33,8 @@ public class AIXxlJob {
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@Resource
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private OllamaChatModel ollamaChatModel;
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@Resource
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private OpenAiChatModel openAiChatModel;
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/**
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* 1、ollama Chat任务
@@ -252,4 +255,91 @@ public void setApiKey(String apiKey) {
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}
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258+
// --------------------------------- openclaw ---------------------------------
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/**
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* 3、openclaw 任务
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*
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* 参数示例：格式见 OpenclawParam
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* <pre>
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* {
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* "input": "{输入信息，必填信息}",
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* "prompt": "{模型prompt，可选信息}"
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* }
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* </pre>
270+
*/
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@XxlJob("openClawJobHandler")
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public void openClawJobHandler() {
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// param
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String param = XxlJobHelper.getJobParam();
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if (param == null || param.trim().isEmpty()) {
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XxlJobHelper.log("param is empty.");
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XxlJobHelper.handleFail();
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return;
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}
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// openclaw param
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OpenClawParam openClawParam = null;
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try {
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openClawParam = GsonTool.fromJson(param, OpenClawParam.class);
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if (openClawParam.getPrompt()==null || openClawParam.getPrompt().isBlank()) {
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openClawParam.setPrompt("你是一个出游助手，擅长做旅游规划");
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}
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if (openClawParam.getInput() == null || openClawParam.getInput().isBlank()) {
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XxlJobHelper.log("input is empty.");
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XxlJobHelper.handleFail();
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return;
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}
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} catch (Exception e) {
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XxlJobHelper.log(new RuntimeException("OpenclawParam parse error", e));
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XxlJobHelper.handleFail();
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return;
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}
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// input
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XxlJobHelper.log("<br><br><b>【Input】: " + openClawParam.getInput()+ "</b><br><br>");
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// build chat-client
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ChatClient openclawChatClient = ChatClient
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.builder(openAiChatModel)
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.defaultAdvisors(MessageChatMemoryAdvisor.builder(MessageWindowChatMemory.builder().build()).build())
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.defaultAdvisors(SimpleLoggerAdvisor.builder().build())
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.build();
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// call opencalw
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String response = openclawChatClient
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.prompt(openClawParam.getPrompt())
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.user(openClawParam.getInput())
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.call()
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.content();
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XxlJobHelper.log("<br><br><b>【Output】: " + response + "</b><br><br>");
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}
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private static class OpenClawParam {
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private String input;
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private String prompt;
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public String getInput() {
328+
return input;
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}
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public void setInput(String input) {
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this.input = input;
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}
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public String getPrompt() {
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return prompt;
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}
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public void setPrompt(String prompt) {
340+
this.prompt = prompt;
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}
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}
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}

‎xxl-job-executor-samples/xxl-job-executor-sample-springboot-ai/src/main/resources/application.properties‎

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### ollama
38-
spring.ai.model.chat=ollama
39-
### ollama url
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spring.ai.ollama.base-url=http://localhost:11434
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40+
### openai (for openclaw)
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spring.ai.openai.base-url=http://127.0.0.1:18789
42+
spring.ai.openai.api-key=xxxxxx
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‎xxl-job-executor-samples/xxl-job-executor-sample-springboot-ai/src/test/java/com/xxl/job/executor/test/openclaw/OpenClawTest.java‎

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package com.xxl.job.executor.test.openclaw;
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import jakarta.annotation.Resource;
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import org.junit.jupiter.api.Test;
5+
import org.slf4j.Logger;
6+
import org.slf4j.LoggerFactory;
7+
import org.springframework.ai.chat.client.ChatClient;
8+
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
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import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
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import org.springframework.ai.chat.memory.MessageWindowChatMemory;
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import org.springframework.ai.openai.OpenAiChatModel;
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import org.springframework.boot.test.context.SpringBootTest;
13+
import reactor.core.publisher.Flux;
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import java.util.Map;
16+
import java.util.concurrent.TimeUnit;
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@SpringBootTest
19+
public class OpenClawTest {
20+
private static final Logger logger = LoggerFactory.getLogger(OpenClawTest.class);
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@Resource
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private OpenAiChatModel openAiChatModel;
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/*ChatModel chatModel = OpenAiChatModel
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.builder()
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.openAiApi(OpenAiApi
28+
.builder()
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.baseUrl(baseUrl)
30+
.apiKey( token)
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.webClientBuilder(WebClient.builder().clientConnector(
32+
new ReactorClientHttpConnector(
33+
HttpClient.create()
34+
.option(ChannelOption.CONNECT_TIMEOUT_MILLIS, 30 * 1000)
35+
.responseTimeout(Duration.ofMillis(30 * 1000))
36+
)
37+
))
38+
.build())
39+
.build();*/
40+
41+
@Test
42+
public void test() throws Exception {
43+
44+
String prompt = "你是一个出游助手，擅长做旅游规划";
45+
String input = "查看下上海今天得天气，给出出游建议";
46+
47+
// ChatClient
48+
ChatClient chatClient = ChatClient
49+
.builder(openAiChatModel)
50+
.defaultAdvisors(MessageChatMemoryAdvisor.builder(MessageWindowChatMemory.builder().build()).build())
51+
.defaultAdvisors(SimpleLoggerAdvisor.builder().build())
52+
.build();
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54+
// Call LLM: 同步输出
55+
String response = chatClient
56+
.prompt(prompt)
57+
.user(input)
58+
.call()
59+
.content();
60+
61+
logger.info("Input: {}", input);
62+
logger.info("Output: {}", response);
63+
}
64+
65+
@Test
66+
public void test2() throws Exception {
67+
68+
String prompt = "你是一个出游助手，擅长做旅游规划";
69+
String input = "查看下上海今天得天气，给出出游建议";
70+
71+
// ChatClient
72+
ChatClient chatClient = ChatClient
73+
.builder(openAiChatModel)
74+
.defaultAdvisors(MessageChatMemoryAdvisor.builder(MessageWindowChatMemory.builder().build()).build())
75+
.defaultAdvisors(SimpleLoggerAdvisor.builder().build())
76+
.build();
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78+
// Call LLM: 流式输出
79+
Flux<String> flux = chatClient
80+
.prompt(prompt)
81+
.user(input)
82+
.user(user -> user.text(input).params(Map.of("stream", true)))
83+
.stream()
84+
.content();
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86+
flux.subscribe(
87+
data -> System.out.println("Received: " + data), // onNext 处理
88+
error -> System.err.println("Error: " + error), // onError 处理
89+
() -> System.out.println("Completed") // onComplete 处理
90+
);
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92+
TimeUnit.SECONDS.sleep(30);
93+
}
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95+
}

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