9. LangChain4j + 整合 Spring Boot

9. LangChain4j + 整合 Spring Boot

@toc

---

LangChain4j 整合 SpringBoot 官方文档：docs.langchain4j.dev/tutorials/s...

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

1. 创建对应项目的 module 模块内容：
2. 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>

3. 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

4. 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();
    }

}

---

5. 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);
    }
}

6. 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);
    }
}

最后：

"在这个最后的篇章中，我要表达我对每一位读者的感激之情。你们的关注和回复是我创作的动力源泉，我从你们身上吸取了无尽的灵感与勇气。我会将你们的鼓励留在心底，继续在其他的领域奋斗。感谢你们，我们总会在某个时刻再次相遇。"