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博客园 - cn2025

kubeSphere发布ruoyi-web前端 20260711 kubeSphere发布ruoyi-pro后端 k8s集群-kubeShpere 20260710 安装Helm 20260710 k8s-portainer docker 镜像查询 k8集群一键重置 docker run OceanBase spring ai alibaba doc AI2.0 【多模态】 20260615 世界只有一个墨脱 AI2.0 【Mcp-client】 20260611 AI2.0 【Mcp-server】 20260611 GoLand 配Go SDK AI2.0 【redis向量-Rag】 问答顾问器QuestionAnswerAdvisor 20260608 AI2.0 【redis向量】redis-stack 20260608 AI2.0 【Embedding】嵌入模型 20260606 AI 工具 AI2.0 Tool调用20260604 AI2.0 对话chatMemory20260601 AI2.0 模型输出结构化【POJO、Record、List、Map 】20260530 AI2.0 Prompt【模板、流式 API、系统提示词】 20260529 AI2.0 自定义Advisor 20260528 AI2.0 ollama 20260528 AI2.0 dashscope-openai 20260527 ps 配Claude 20260527 spring-ai-alibaba-agent 260526 票务 Tool+Spring Security【动态tooLs:toolCallbacks】 20260525 spring-ai-alibaba-agent 260525 spring-ai-alibaba-agent 260523 票务 Tool接口 / 方法 / 参数【无意义、可读、业务化、参数数量过多】 20260521 票务 Tool参数幻觉 20260521 spring-ai-alibaba-agent 260521 ToolTemperature 温度过低,AI推算缺失自由度20260520 spring-ai-alibaba-agent 260520 票务Tool 20260519 票务助手 -多模型20260518 结构化输出 -原理【structuredconverter】20260518 spring-ai-alibaba-agent 260518 Dify 添加Ollma模型qwen2:0.5b 应用 20260516 docker 部【dify-api】/【dify-web】 20260514 Chatclient 结构化输出20260514 spring-ai-alibaba-agent 260514 Chatmemory 多层(近、中、长期)20260513 ChatmemoryRedis 历史对话存【REDIS】20260512 ChatmemoryJdbc 历史对话存【JDBC】20260511 spring-ai-alibaba-agent 260511 ChatmemoryConversationId 多用户对话记忆 20260508 spring-ai-alibaba-agent 260508 ChatmemoryMax 历史对话长度20260507 2026SE Chatmemory 对话记忆20260506 spring-ai-alibaba-agent 260506 ChatClientPrompt 自定义拦截器【ReReadingAdvisor】重读提示词 20260430 docker-apache/kafka:4.1.2部暑 集群20260423 ChatClientPrompt Template.st20260428 ChatClientPrompt Template20260427 sb-KafkaListener 20260425 docker-apache/kafka:4.1.2部暑 20260425 dashscope-sb ChatClientPrompt20260425 SBAI-MultiPlatformAndModel 20260424 PlatformModel dashscope-sb ChatClient20260420 ollama-sb 多态 图转文 20260418 【gemma3:4b 解析慢】 dashscope-sb 多模态(图片、语音识别) 文生视频 ollama-sb 20260414 dashscope-sb 阿里百炼-文生图20260413 dashscope-sb20260413 dashscope-sb20260411 docker-zabbix 20260410 deepseek-sb20260408 MQTT20260403 spring-ai-alibaba-agent 260403 MqttTest 20260401 spring-ai-alibaba-agent 260401 docker安装 EMQX spring-ai-alibaba-agent 260331 MQTT 20260331 spring-ai-alibaba-agent 三大 Java 生态 AI Agent 框架 CentOS7-静态 IP centos-stream10 安装 百炼-工作流-sb sb-flink1.13.1-jdk8-分隔字符串 20260125
SB-ChatClient-DeepSeekDashScopeOllamaModel 20260424
cn2025 · 2026-04-24 · via 博客园 - cn2025

1、pom

<properties>
<java.version>17</java.version>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<spring-boot.version>3.2.0</spring-boot.version>
<spring-ai.version>1.1.2</spring-ai.version>
<spring-ai-alibaba.version>1.1.2.2</spring-ai-alibaba.version>
<spring-ai-alibaba-extensions.version>1.1.2.2</spring-ai-alibaba-extensions.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!-- 阿里云通义千问(DashScope)starter -->
<dependency>
<groupId>com.alibaba.cloud.ai</groupId>
<artifactId>spring-ai-alibaba-starter-dashscope</artifactId>
</dependency>
<!-- deepseek starter -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-deepseek</artifactId>
</dependency>
<!-- 新增:Ollama 的 Starter -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-ollama</artifactId>
</dependency>
</dependencies>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-dependencies</artifactId>
<version>${spring-boot.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
<!-- 统一管理Spring AI依赖版本 -->
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-bom</artifactId>
<version>${spring-ai.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
<dependency>
<groupId>com.alibaba.cloud.ai</groupId>
<artifactId>spring-ai-alibaba-bom</artifactId>
<version>${spring-ai-alibaba.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
<dependency>
<groupId>com.alibaba.cloud.ai</groupId>
<artifactId>spring-ai-alibaba-extensions-bom</artifactId>
<version>${spring-ai-alibaba-extensions.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<!-- Spring AI 里程碑/快照仓库(必须配置,否则依赖无法下载) -->
<repositories>
<repository>
<id>spring-milestones</id>
<name>Spring Milestones</name>
<url>https://repo.spring.io/milestone</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
<repository>
<id>spring-snapshots</id>
<name>Spring Snapshots</name>
<url>https://repo.spring.io/snapshot</url>
<releases>
<enabled>false</enabled>
</releases>
</repository>
</repositories>

2、yml


server:
port: 18082
spring:
ai:
dashscope:
api-key: sk-8718a83408d7443b9544cdfbXXXXX
deepseek: ## 这一行是你选择的LLM模型,如果是openai,这里就填openai, base-url就是填对应厂商的地址
api-key: "sk-199324596dbb4308afcb77d4XXXXX"
base-url: "https://api.deepseek.com"
chat:
options:
model: deepseek-chat
embedding:
enabled: false
ollama:
chat:
options:
model: gemma3:4b
#model: qwen2.5vl:3b moondream:latest qwen2.5vl:3b-q4_K_M moondream:v2
#model: qwen2:0.5b
#temperature: 0.1
# 指定默认使用的模型,也可以在代码中动态覆盖
#model: gemma3:4b
####最小的轻量多模态模型(约 1.5GB)
#model: minicpm-v:latest
base-url: http://192.168.91.164:11434
main: #允许 Bean 覆盖
allow-bean-definition-overriding: true

####api-key填写自个

3、controller

3.1

http://localhost:18082/openai/simple/chatclientdeepseek

image

 
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.ResponseBody;
import reactor.core.publisher.Flux;

@RequestMapping("/openai")
@ResponseBody
@Controller
public class ChatclientDeepseekControlller {

private ChatClient chatClient;
// 构造函数注入,并构建 ChatClient
public ChatclientDeepseekControlller(@Qualifier("deepSeekChatModel") ChatModel chatModel)
{
this.chatClient = ChatClient.builder(chatModel).build();
}

@GetMapping("/simple/chatclientdeepseek")
public String chatclient () {
String content = chatClient.prompt()
.user("你好").call().content();
System.out.println(content+"【chatclientdeepseek】");
return content+"【chatclientdeepseek】";
}

@GetMapping(value = "/simple/chatclientdeepseekstream")
public void chatclientstream () {
Flux<String> content = chatClient.prompt("你好").stream().content();
content.toIterable().forEach(s -> System.out.print(s+"【chatclientdeepseekstream】"));
}

}

3.2
http://localhost:18082/openai/simple/chatclientdashscope

image

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.ResponseBody;
import reactor.core.publisher.Flux;

@RequestMapping("/openai")
@ResponseBody
@Controller
public class ChatclientDashScopeControlller {

private ChatClient chatClient;
// 构造函数注入,并构建 ChatClient
public ChatclientDashScopeControlller(@Qualifier("dashScopeChatModel") ChatModel chatModel)
{
this.chatClient = ChatClient.builder(chatModel).build();
}

@GetMapping("/simple/chatclientdashscope")
public String chatclient () {
String content = chatClient.prompt()
.user("你好").call().content();
System.out.println(content+"【chatclientdashscope】");
return content+"【chatclientdashscope】";
}

@GetMapping(value = "/simple/chatclientdashscopestream")
public void chatclientstream () {
Flux<String> content = chatClient.prompt("你好").stream().content();
content.toIterable().forEach(s -> System.out.print(s+"【chatclientdashscopestream】"));
}

}

3.3
http://localhost:18082/openai/simple/chatclientollama
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.ResponseBody;
import reactor.core.publisher.Flux;

@RequestMapping("/openai")
@ResponseBody
@Controller
public class ChatclientOllamaControlller {

private ChatClient chatClient;
// 构造函数注入,并构建 ChatClient
public ChatclientOllamaControlller(@Qualifier("ollamaChatModel") ChatModel chatModel)
{
this.chatClient = ChatClient.builder(chatModel).build();
}

@GetMapping("/simple/chatclientollama")
public String chatclient () {
String content = chatClient.prompt()
.user("你好").call().content();
System.out.println(content);
return content;
}

@GetMapping("/simple/chatclientollamastream")
public void chatclientstream () {
Flux<String> content = chatClient.prompt("你好").stream().content();
content.toIterable().forEach(s -> System.out.print(s));
}
}