前言
虽然Spring 生态园中，推出了Spring AI。但用起来总感觉不是那么好用。LangChain4j 本身就对许多大模型做了封装，支持普通输出与流式输出操作。
本篇博客重点说明如何将LangChain4j整合到Springboot 的项目应用中。
环境准备
- jdk 17
- maven 3.6.3
- springboot 3.4.0
- langchain4j 1.0.0-beta1
依赖引入
核心依赖以及版本限定。
<properties>
<maven.compiler.source>17</maven.compiler.source>
<maven.compiler.target>17</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<langchain4j.version>1.0.0-beta1</langchain4j.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<!-- langchain4j 核心依赖 -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j</artifactId>
<version>${langchain4j.version}</version>
</dependency>
<!-- langchain4j -->
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
</dependency>
<!-- 流式输出 -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-webflux</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.24</version> <!-- 使用最新版本 -->
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-api</artifactId>
<version>2.14.1</version> <!-- 使用最新的稳定版本 -->
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-core</artifactId>
<version>2.14.1</version> <!-- 使用与 log4j-api 相同的版本 -->
</dependency>
<!-- ArrayListMultimap -->
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>33.3.1-jre</version>
<scope>compile</scope>
</dependency>
</dependencies>
<!-- 版本限制 -->
<dependencyManagement>
<dependencies>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-community-bom</artifactId>
<version>${langchain4j.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
常规输出与流式输出
配置大模型key和使用模型信息
在langchain4j-community-dashscope-spring-boot-starter这个依赖中，存在一个他定义好的bean实例 dev.langchain4j.community.dashscope.spring.AutoConfig。
当存在langchain4j.community.dashscope.chat-model.api-key的配置点时，会自动构建QwenChatModel的实例对象。
简单输出
按照bean的配置要求，本次使用大模型为qwen-max，对应key为阿里百炼key。
langchain4j.community.dashscope.chat-model.api-key=${ALI_AI_KEY}
langchain4j.community.dashscope.chat-model.model-name=qwen-max
构建简单测试接口
import dev.langchain4j.community.model.dashscope.QwenChatModel;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
/**
* 普通对话模式
*/
@RestController
@RequestMapping("/ai")
public class ChatController {
@Autowired
private QwenChatModel qwenChatModel;
@GetMapping("/chat")
public String chat(@RequestParam(defaultValue = "你是谁？") String message) {
return qwenChatModel.chat(message);
}
}
访问与结果
http://localhost:8080/ai/chat
流式输出
使用流式输出，需要引入spring-boot-starter-webflux依赖。且大模型需要更换为支持流式输出的，如qwq-32b。
配置文件
langchain4j.community.dashscope.streaming-chat-model.api-key=${ALI_AI_KEY}
langchain4j.community.dashscope.streaming-chat-model.model-name=qwq-32b
编写流式输出接口
import dev.langchain4j.community.model.dashscope.QwenStreamingChatModel;
import dev.langchain4j.model.chat.response.ChatResponse;
import dev.langchain4j.model.chat.response.StreamingChatResponseHandler;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import reactor.core.publisher.Flux;
/**
* 流式输出
*/
@RestController
@RequestMapping("/ai/stream")
public class ChatStreamController {
@Autowired
private QwenStreamingChatModel qwenStreamingChatModel;
// MediaType.TEXT_EVENT_STREAM_VALUE
// produces = "text/event-stream;charset=UTF-8"
@RequestMapping(value = "/chat",produces = "text/stream;charset=UTF-8")
public Flux<String> chat(@RequestParam(defaultValue = "你是谁？") String message) {
return Flux.create(emitter-> {
qwenStreamingChatModel.chat(message, new StreamingChatResponseHandler() {
/**
* 每次流式响应的文本
* 大模型根据每个token响应
* @param partialResponse The partial response (usually a single token), which is a part of the complete response.
*/
@Override
public void onPartialResponse(String partialResponse) {
System.out.println("onPartialResponse---"+partialResponse);
// 逐次返回不分响应
emitter.next(partialResponse);
}
/**
* 响应结束的文本
*
* @param completeResponse The complete response generated by the model.
* For textual responses, it contains all tokens from {@link #onPartialResponse} concatenated.
*/
@Override
public void onCompleteResponse(ChatResponse completeResponse) {
System.out.println("onCompleteResponse---"+completeResponse);
// 完成整个响应流
emitter.complete();
}
/**
* 出现报错的文本
*
* @param error The error that occurred
*/
@Override
public void onError(Throwable error) {
System.out.println("onError---"+error);
emitter.error(error);
}
});
});
}
}
效果展示
http://localhost:8080/ai/stream/chat
