9. LangChain4j + 整合 Spring Boot

技术分享 6个月前 (09-15) 0 999+

9. LangChain4j + 整合 Spring Boot

LangChain4j 整合 SpringBoot 官方文档：https://docs.langchain4j.dev/tutorials/spring-boot-integration/

浅谈—下：LangChain4j twolevels of abstraction

低阶 APi 和高阶 API

Spring Boot整合底阶API所需POM：

<dependency>     <groupId>dev.langchain4j</groupId>     <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>     <version>1.2.0-beta8</version> </dependency>

langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY} langchain4j.open-ai.chat-model.model-name=gpt-4o langchain4j.open-ai.chat-model.log-requests=true langchain4j.open-ai.chat-model.log-responses=true ...

Spring Boot整合高阶API所需POM：

截至目前，存在两种整合 Spring Boot 的方式：

LangChain4J 原生整合：

LangChain4J + Spring Boot 整合：

小总结：

LangChain4j + 整合 Spring Boot 实操

创建对应项目的 module 模块内容：
导入相关的 pom.xml 的依赖，这里我们采用流式输出的方式，导入整合 Spring Boot ，`langchain4j-open-ai-spring-boot-starter，langchain4j-spring-boot-starter 这里我们不指定版本，而是通过继承的 pom.xml 当中获取。

        <dependency>             <groupId>org.springframework.boot</groupId>             <artifactId>spring-boot-starter-web</artifactId>         </dependency>         <!--1 LangChain4j 整合boot底层支持-->         <!--   https://docs.langchain4j.dev/tutorials/spring-boot-integration  -->         <dependency>             <groupId>dev.langchain4j</groupId>             <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>         </dependency>         <!--2 LangChain4j 整合boot高阶支持-->         <dependency>             <groupId>dev.langchain4j</groupId>             <artifactId>langchain4j-spring-boot-starter</artifactId>         </dependency>

设置 applcation.yaml / properties 配置文件，其中指明我们的输出响应的编码格式，因为如果不指定的话，存在返回的中文，就是乱码了。

server.port=9008  spring.application.name=langchain4j-08boot-integration   # 设置响应的字符编码，避免流式返回输出乱码 server.servlet.encoding.charset=utf-8 server.servlet.encoding.enabled=true server.servlet.encoding.force=true  # https://docs.langchain4j.dev/tutorials/spring-boot-integration #langchain4j.open-ai.chat-model.api-key=${aliQwen-api} #langchain4j.open-ai.chat-model.model-name=qwen-plus #langchain4j.open-ai.chat-model.base-url=https://dashscope.aliyuncs.com/compatible-mode/v1   # 大模型调用不可以明文配置，你如何解决该问题 # 1 yml：                ${aliQwen-api}，从环境变量读取 # 2 config配置类：      System.getenv("aliQwen-api")从环境变量读取

编写大模型三件套（大模型 key，大模型 name，大模型 url）三件套的大模型配置类。

这里我们测试操作两个大模型：DeepSeek，通义千问。

 import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantQwen; import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantDeepSeek; import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; import org.springframework.beans.factory.annotation.Qualifier; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration;  /**  * @Description: 知识出处 https://docs.langchain4j.dev/get-started  */ @Configuration public class LLMConfig {      @Bean(name = "qwen")     public ChatModel chatModelQwen() {         return OpenAiChatModel.builder()                 .apiKey(System.getenv("aliQwen_api"))                 .modelName("qwen-plus")                 .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")                 .build();     }      /**      * @Description: 知识出处，https://api-docs.deepseek.com/zh-cn/      */     @Bean(name = "deepseek")     public ChatModel chatModelDeepSeek() {         return                 OpenAiChatModel.builder()                         .apiKey(System.getenv("deepseek_api"))                         .modelName("deepseek-chat")                         //.modelName("deepseek-reasoner")                         .baseUrl("https://api.deepseek.com/v1")                         .build();     }  }

编写我们操作两个大模型的将接口类，同时通过在我们的配置类上 + 通过 @AiService 进行一个对接口的实现。

@AiService 注解的源码如下：

// // Source code recreated from a .class file by IntelliJ IDEA // (powered by FernFlower decompiler) //  package dev.langchain4j.service.spring;  import java.lang.annotation.ElementType; import java.lang.annotation.Retention; import java.lang.annotation.RetentionPolicy; import java.lang.annotation.Target; import org.springframework.stereotype.Service;  @Service @Target({ElementType.TYPE}) @Retention(RetentionPolicy.RUNTIME) public @interface AiService {     AiServiceWiringMode wiringMode() default AiServiceWiringMode.AUTOMATIC;      String chatModel() default "";      String streamingChatModel() default "";      String chatMemory() default "";      String chatMemoryProvider() default "";      String contentRetriever() default "";      String retrievalAugmentor() default "";      String moderationModel() default "";      String[] tools() default {}; }

package com.rainbowsea.langchain4jbootintegration.service;  import dev.langchain4j.service.spring.AiService;  import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT;  /**  */ @AiService(wiringMode = EXPLICIT, chatModel = "qwen") public interface ChatAssistantQwen {     String chat(String prompt); }

 import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantQwen; import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantDeepSeek; import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; import org.springframework.beans.factory.annotation.Qualifier; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration;  /**  * @Description: 知识出处 https://docs.langchain4j.dev/get-started  */ @Configuration public class LLMConfig {      @Bean(name = "qwen")     public ChatModel chatModelQwen() {         return OpenAiChatModel.builder()                 .apiKey(System.getenv("aliQwen_api"))                 .modelName("qwen-plus")                 .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")                 .build();      }      // 你使用第2种类，高阶API    AiService     @Bean(name = "qwenAssistant")     public ChatAssistantQwen chatAssistantQwen(@Qualifier("qwen") ChatModel chatModelQwen) {         return AiServices.create(ChatAssistantQwen.class, chatModelQwen);     } }

同理我们添加上 DeepSeek 操作的接口类，以及对应大模型的实现类

package com.rainbowsea.langchain4jbootintegration.service;  import dev.langchain4j.service.spring.AiService; import static dev.langchain4j.service.spring.AiServiceWiringMode.EXPLICIT; /**  */ @AiService(wiringMode = EXPLICIT, chatModel = "deepseek") public interface ChatAssistantDeepSeek {     String chat(String prompt); }

package com.rainbowsea.langchain4jbootintegration.config;  import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantQwen; import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantDeepSeek; import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; import org.springframework.beans.factory.annotation.Qualifier; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration;  /**  * @Description: 知识出处 https://docs.langchain4j.dev/get-started  */ @Configuration public class LLMConfig {      /**      * @Description: 知识出处，https://api-docs.deepseek.com/zh-cn/      */     @Bean(name = "deepseek")     public ChatModel chatModelDeepSeek() {         return                 OpenAiChatModel.builder()                         .apiKey(System.getenv("deepseek_api"))                         .modelName("deepseek-chat")                         //.modelName("deepseek-reasoner")                         .baseUrl("https://api.deepseek.com/v1")                         .build();     }       @Bean(name = "deepseekAssistant")     public ChatAssistantDeepSeek chatAssistantDeepSeek(@Qualifier("deepseek") ChatModel chatModelDeepSeek) {         return AiServices.create(ChatAssistantDeepSeek.class, chatModelDeepSeek);     } }

DeepSeek + 通义千问

package com.rainbowsea.langchain4jbootintegration.config;  import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantQwen; import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantDeepSeek; import dev.langchain4j.model.chat.ChatModel; import dev.langchain4j.model.openai.OpenAiChatModel; import dev.langchain4j.service.AiServices; import org.springframework.beans.factory.annotation.Qualifier; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration;  /**  * @Description: 知识出处 https://docs.langchain4j.dev/get-started  */ @Configuration public class LLMConfig {      @Bean(name = "qwen")     public ChatModel chatModelQwen() {         return OpenAiChatModel.builder()                 .apiKey(System.getenv("aliQwen_api"))                 .modelName("qwen-plus")                 .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")                 .build();      }      // 你使用第2种类，高阶API    AiService     @Bean(name = "qwenAssistant")     public ChatAssistantQwen chatAssistantQwen(@Qualifier("qwen") ChatModel chatModelQwen) {         return AiServices.create(ChatAssistantQwen.class, chatModelQwen);     }       /**      * @Description: 知识出处，https://api-docs.deepseek.com/zh-cn/      */     @Bean(name = "deepseek")     public ChatModel chatModelDeepSeek() {         return                 OpenAiChatModel.builder()                         .apiKey(System.getenv("deepseek_api"))                         .modelName("deepseek-chat")                         //.modelName("deepseek-reasoner")                         .baseUrl("https://api.deepseek.com/v1")                         .build();     }       @Bean(name = "deepseekAssistant")     public ChatAssistantDeepSeek chatAssistantDeepSeek(@Qualifier("deepseek") ChatModel chatModelDeepSeek) {         return AiServices.create(ChatAssistantDeepSeek.class, chatModelDeepSeek);     } }

编写操作两大，大模型的 Controller 类，使用我们自己编写的接口类操作大模型。

操作访问通义千问。

 import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantDeepSeek; import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantQwen; import jakarta.annotation.Resource; import lombok.extern.slf4j.Slf4j; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web.bind.annotation.RequestParam; import org.springframework.web.bind.annotation.RestController;  /**  * @Description: https://docs.langchain4j.dev/tutorials/spring-boot-integration  */ @RestController @Slf4j public class DeclarativeAIServiceController {     @Resource(name = "qwenAssistant")     private ChatAssistantQwen chatAssistantQwen;       // http://localhost:9008/chatapi/highapi     @GetMapping(value = "/chatapi/highapi")     public String highApi(@RequestParam(value = "prompt", defaultValue = "你是谁") String prompt)     {         return chatAssistantQwen.chat(prompt);     }  }

操作访问 DeepSeek

package com.rainbowsea.langchain4jbootintegration.controller;  import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantDeepSeek; import com.rainbowsea.langchain4jbootintegration.service.ChatAssistantQwen; import jakarta.annotation.Resource; import lombok.extern.slf4j.Slf4j; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web.bind.annotation.RequestParam; import org.springframework.web.bind.annotation.RestController;  /**  * @Description: https://docs.langchain4j.dev/tutorials/spring-boot-integration  */ @RestController @Slf4j public class DeclarativeAIServiceController {         @Resource(name = "deepseekAssistant")     private ChatAssistantDeepSeek chatAssistantDeepSeek;        // http://localhost:9008/chatapi/highapi02     @GetMapping(value = "/chatapi/highapi02")     public String highApi02(@RequestParam(value = "prompt", defaultValue = "你是谁") String prompt)     {         return chatAssistantDeepSeek.chat(prompt);     } }