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流式输出
LLM 一次生成一个标记（token），因此许多 LLM 提供商提供了一种方式，可以逐个标记地流式传输响应，而不是等待整个文本生成完毕。 这显著改善了用户体验，因为用户不需要等待未知的时间，几乎可以立即开始阅读响应。
- 在springboot中集成流式输出，需要引入依赖
<!-- 流式输出 --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-webflux</artifactId></dependency>
- langchain4j的Api中有对应的流式输出Api，只需要在 application.properties 配置文件中加上即可
langchain4j.community.dashscope.streaming-chat-model.api-key=${QWEN_API_KEY}
langchain4j.community.dashscope.streaming-chat-model.model-name=qwq-32b
- 在Controller中写一个方法测试
@RequestMapping(value = "/stream_qwen",produces = "text/stream;charset=UTF8")public Flux<String> stream(@RequestParam(defaultValue="你是谁") String message){Flux<String> flux = Flux.create(fluxSink -> {qwenStreamingChatModel.chat(message, new StreamingChatResponseHandler() {@Overridepublic void onPartialResponse(String s) {log.info(s);fluxSink.next(s);}@Overridepublic void onCompleteResponse(ChatResponse chatResponse) {log.info(chatResponse.toString());fluxSink.complete();}@Overridepublic void onError(Throwable throwable) {log.error(throwable.getMessage());fluxSink.error(throwable);}});});return flux;}
记忆对话
在上面的例子中，每次与大模型的对话都是没有记忆的，也就是说每次对话大模型并不会记住你上一次问了什么。举个例子来讲，第一次与大模型对话告诉它我的名字，第二次问他我叫什么大模型并不会知道。那么，如何让大模型显得有“记忆”呢？
手动实现的例子：
@Testvoid testMemoryChat(){OpenAiChatModel model = OpenAiChatModel.builder().baseUrl("http://langchain4j.dev/demo/openai/v1").apiKey("demo").modelName("gpt-4o-mini").build();UserMessage userMessage1 = UserMessage.userMessage("你好，我是kizzo");ChatResponse response1 = model.chat(userMessage1);// 第一次响应AiMessage aiMessage1 = response1.aiMessage();System.out.println(aiMessage1.text());System.out.println("---");ChatResponse response2 = model.chat(userMessage1,aiMessage1,UserMessage.userMessage("你好，我是kizzo"));// 第二次响应AiMessage aiMessage2 = response2.aiMessage();System.out.println(aiMessage2.text());}
- 以上例子不难看出，手动维护和管理ChatMessage是很麻烦的。 因此，LangChain4j提供了ChatMemory抽象以及多种开箱即用的实现。ChatMemory可以作为独立的低级组件使用， 或者作为高级组件（如AI服务）的一部分。
- ChatMemory封装了聊天记录，并且可以设置最多存储多少聊天记录，默认是内存级别的存储，底层用到了jdk的动态代理。这里需要新建一个配置类写一个聊天助手对象，通过聊天助手来进行聊天。
package com.kizzo.langchain4j_spingboot_demo.config;import dev.langchain4j.memory.ChatMemory;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.chat.StreamingChatLanguageModel;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.TokenStream;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;@Configuration
public class AiConfig {public interface Assistant{String chat(String message);// 流式响应TokenStream stream(String message);}@Beanpublic Assistant assistant(ChatLanguageModel chatLanguageModel, StreamingChatLanguageModel streamingChatLanguageModel){// 最多存储多少聊天记录ChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(10);// 为Assistant动态代理对象chat ---> 对话内容存储ChatMemoryi ---> 聊天记录ChatMemory取出来 ---->放入到当前对话中Assistant assistant = AiServices.builder(Assistant.class).chatLanguageModel(chatLanguageModel).streamingChatLanguageModel(streamingChatLanguageModel).chatMemory(chatMemory).build();return assistant;}}进入MessageWindowChatMemory类发现里面的store默认用的map存储聊天记录（记忆）
引入核心依赖
<!--核心--><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j</artifactId><version>${langchain4j.version}</version></dependency>在Controller中添加方法，调用memoryChat方法后再调用memoryStreamChat方法，大模型会返回我的名字。这里本质上还是把第一次调用的结果传给了第二次调用。
@AutowiredAiConfig.Assistant assistant;// 记忆普通对话@RequestMapping(value = "/memory_chat")public String memoryChat(@RequestParam(defaultValue="我是kizzo") String message){return assistant.chat(message);}// 记忆流对话@RequestMapping(value = "/memory_chat_stream",produces = "text/stream;charset=UTF8")public Flux<String> memoryStreamChat(@RequestParam(defaultValue="我是谁") String message) {TokenStream stream = assistant.stream(message);return Flux.create(sink -> {stream.onPartialResponse(s -> sink.next(s)).onCompleteResponse(c -> sink.complete()).onError(sink::error).start();});}
对话隔离
我们平常使用AI产品时，每次新建的对话和上一次都不会有联系。在上面的例子中并没有区分聊天的轮次，ChatMemory也集成了相应的Api，可以通过memoryId来区分。在配置类上新增Bean对象
@Beanpublic AssistantUnique assistantUnique(ChatLanguageModel chatLanguageModel, StreamingChatLanguageModel streamingChatLanguageModel){AssistantUnique assistant = AiServices.builder(AssistantUnique.class).chatLanguageModel(chatLanguageModel).streamingChatLanguageModel(streamingChatLanguageModel)// chatMemory变为了chatMemoryProvider，让memoryId与聊天记录绑定并作为Map的key.chatMemoryProvider(memoryId -> MessageWindowChatMemory.builder().maxMessages(10).id(memoryId).build() ).build();return assistant;}
然后在Controller中加入自动装配的对象，以及新增调用的方法。
@AutowiredAiConfig.AssistantUnique assistantUnique;// 记忆隔离对话@RequestMapping(value = "/memoryId_chat")public String memoryIdChat(@RequestParam(defaultValue="我是kizzo") String message,Integer userId){return assistantUnique.chat(userId,message);}
运行结果如图
对话记忆持久化
默认情况下，ChatMemory实现在内存中存储ChatMessage。
如果需要持久化，可以实现自定义的ChatMemoryStore， 将ChatMessage存储在持久化存储中，以mysql为例：
- 创建mysql数据库和表
CREATE TABLE chat_messages (
id BIGINT AUTO_INCREMENT PRIMARY KEY,
memory_id VARCHAR(255) NOT NULL,
message_json TEXT NOT NULL,
gmt_create TIMESTAMP DEFAULT CURRENT_TIMESTAMP
); - 引入依赖
<properties><mysql-connector.version>8.0.33</mysql-connector.version><mybatis-spring-boot.version>3.0.1</mybatis-spring-boot.version></properties><dependencies> <!--mybatis--><dependency><groupId>org.mybatis.spring.boot</groupId><artifactId>mybatis-spring-boot-starter</artifactId><version>${mybatis-spring-boot.version}</version></dependency><!--Mysql数据库驱动--><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>${mysql-connector.version}</version></dependency></dependencies>
- 新增mapper以及xml文件
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE mapper PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN""http://mybatis.org/dtd/mybatis-3-mapper.dtd">
<mapper namespace="com.kizzo.langchain4j_spingboot_demo.mapper.ChatMessageMapper"><select id="selectMessagesByMemoryId" resultType="string">SELECT message_json FROM chat_messages WHERE memory_id = #{memoryId} ORDER BY created_at DESC LIMIT 10</select><delete id="deleteMessagesByMemoryId">DELETE FROM chat_messages WHERE memory_id = #{memoryId}</delete><insert id="insertMessages">INSERT INTO chat_messages (memory_id, message_json, )VALUES (#{memoryId}, #{messageJson})</insert></mapper>
package com.kizzo.langchain4j_spingboot_demo.mapper;import org.apache.ibatis.annotations.Mapper;
import org.apache.ibatis.annotations.Param;import java.util.List;@Mapper
public interface ChatMessageMapper {List<String> selectMessagesByMemoryId(@Param("memoryId") String memoryId);int deleteMessagesByMemoryId(@Param("memoryId") String memoryId);int insertMessages(@Param("memoryId") String memoryId, @Param("messageJson") String messageJson);
}
- config包下新增mybatis配置和对话记忆持久化配置类
package com.kizzo.langchain4j_spingboot_demo.config;import org.mybatis.spring.annotation.MapperScan;
import org.springframework.context.annotation.Configuration;
import org.springframework.transaction.annotation.EnableTransactionManagement;/*** MyBatis配置类*/
@Configuration
@MapperScan({"com.kizzo.langchain4j_spingboot_demo.mapper"})
@EnableTransactionManagement
public class MyBatisConfig {}
package com.kizzo.langchain4j_spingboot_demo.config;import com.kizzo.langchain4j_spingboot_demo.mapper.ChatMessageMapper;
import dev.langchain4j.data.message.ChatMessage;
import dev.langchain4j.data.message.ChatMessageDeserializer;
import dev.langchain4j.data.message.ChatMessageSerializer;
import dev.langchain4j.store.memory.chat.ChatMemoryStore;
import org.springframework.stereotype.Component;import java.util.List;
import java.util.stream.Collectors;@Component
public class PersistentChatMemoryStore implements ChatMemoryStore {private final ChatMessageMapper chatMessageMapper;public PersistentChatMemoryStore(ChatMessageMapper chatMessageMapper) {this.chatMessageMapper = chatMessageMapper;}@Overridepublic List<ChatMessage> getMessages(Object memoryId) {String memoryIdStr = memoryId.toString();List<String> jsonMessages = chatMessageMapper.selectMessagesByMemoryId(memoryIdStr);return jsonMessages.stream().map(ChatMessageDeserializer::messagesFromJson).flatMap(List::stream).collect(Collectors.toList());}@Overridepublic void updateMessages(Object memoryId, List<ChatMessage> messages) {String memoryIdStr = memoryId.toString();String json = ChatMessageSerializer.messagesToJson(messages);chatMessageMapper.insertMessages(memoryIdStr, json);}@Overridepublic void deleteMessages(Object memoryId) {chatMessageMapper.deleteMessagesByMemoryId(memoryId.toString());}
}- 在配置类上新增Bean对象
@Beanpublic AssistantUnique assistantUniqueStore(ChatLanguageModel chatLanguageModel,StreamingChatLanguageModel streamingChatLanguageModel,PersistentChatMemoryStore store){ChatMemoryProvider chatMemoryProvider = memoryId -> MessageWindowChatMemory.builder()// 这个设置只会影响内存中的 MessageWindowChatMemory 实例，并不会自动限制写入数据库的数据量.maxMessages(10).chatMemoryStore(store).id(memoryId).build();AssistantUnique assistant = AiServices.builder(AssistantUnique.class).chatLanguageModel(chatLanguageModel).streamingChatLanguageModel(streamingChatLanguageModel)// chatMemory变为了chatMemoryProvider，让memoryId与聊天记录绑定并作为Map的key.chatMemoryProvider(chatMemoryProvider).build();return assistant;}- 注入自动配置类，并新增用数据库持久化的接口
@AutowiredAiConfig.AssistantUnique assistantUniqueStore;/*** 带 memoryId 的记忆对话接口（使用数据库持久化）*/@RequestMapping("/memory_id_chat_store")public String memoryIdChatWithStore(@RequestParam("message") String message,@RequestParam("userId") Integer userId) {return assistantUniqueStore.chat(userId, message);}/*** 带 memoryId 的流式记忆对话接口（使用数据库持久化）*/@RequestMapping(value = "/memory_id_chat_store_stream", produces = "text/stream;charset=UTF-8")public Flux<String> memoryIdChatWithStoreStream(@RequestParam("message") String message,@RequestParam("userId") Integer userId) {TokenStream stream = assistantUniqueStore.stream(userId, message);return Flux.create(sink -> {stream.onPartialResponse(s -> sink.next(s)).onCompleteResponse(c -> sink.complete()).onError(sink::error).start();});}