Spring AI 智能对话系统完整实战项目

SpringAI智能对话系统完整实战项目一、项目整体介绍1.技术栈后端：SpringBoot3.2+SpringAI1.0.x大模型支持：OpenAI/通义千问/文心一言/本地Ollama(Llama3/Qwen)持久层：SpringDataRedis（会话上下文存储）+MyBatis-Plus（对话记录持久化MySQL）功能模块：流式对话、多轮上下文记忆、角色设定、知识库RAG、对话历史管理、接口鉴权前端配套：简易Vue流式聊天页面（附接口文档）2.核心能力单轮/多轮连续对话，自动维护会话上下文SSE流式输出打字机效果自定义AI角色、prompt模板统一管理RAG本地知识库问答（文档向量检索）Redis存储会话，支持多用户隔离对话对话记录入库，支持历史查询、清空会话多模型动态切换（OpenAI/阿里云通义/Ollama本地大模型）二、环境依赖配置1.Mavenpom.xml核心依赖xml<!--SpringBoot--><parent><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-parent</artifactId><version>3.2.5</version><relativePath/></parent><dependencies><!--WebSSE流式响应--><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><!--SpringAI核心--><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-openai-spring-boot-starter</artifactId><version>1.0.0</version></dependency><!--本地Ollama支持（可选）--><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-ollama-spring-boot-starter</artifactId><version>1.0.0</version></dependency><!--阿里云通义千问（可选）--><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-alibaba-spring-boot-starter</artifactId><version>1.0.0</version></dependency><!--Redis会话记忆存储--><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-redis</artifactId></dependency><!--MySQL对话记录持久化--><dependency><groupId>com.baomidou</groupId><artifactId>mybatis-plus-boot-starter</artifactId><version>3.5.5</version></dependency><dependency><groupId>com.mysql</groupId><artifactId>mysql-connector-j</artifactId><scope>runtime</scope></dependency><!--RAG向量库：Chroma本地轻量向量库--><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-chroma-store-spring-boot-starter</artifactId><version>1.0.0</version></dependency><!--工具--><dependency><groupId>jectlombok</groupId><artifactId>lombok</artifactId><optional>true</optional></dependency><dependency><groupId>com.alibaba.fastjson2</groupId><artifactId>fastjson2</artifactId><version>2.0.32</version></dependency></dependencies>2.application.yml配置yaml#服务端口server:port:8080servlet:context-path:/ai#数据库spring:datasource:driver-class-name:com.mysql.cj.jdbc.Driverurl:jdbc:mysql://:3306/spring_ai_chat?useUnicode=true&characterEncoding=utf8&serverTimezone=Asia/Shanghaiusername:rootpassword:root#Redis会话存储data:redis:host:port:6379password:database:0#SpringAI配置spring.ai:#OpenAI配置（优先使用）openai:api-key:sk-xxx你的密钥xxxbase-url:/v1chat:options:model:gpt-3.5-turbotemperature:0.7max-tokens:1024#本地Ollama备用ollama:base-url::11434chat:options:model:qwen:7b#向量库vectorstore:chroma:client:host:localhostport:8000collection-name:chat_knowledge#MyBatis-Plusmybatis-plus:mapper-locations:classpath:mapper/**/*.xmltype-aliases-package:com.ai.chat.entityconfiguration:map-underscore-to-camel-case:truelog-impl:org.apache.ibatis.logging.stdout.StdOutImpl三、数据库表设计1.对话记录表chat_recordsqlCREATETABLE`chat_record`(`id`bigintNOTNULLAUTO_INCREMENTCOMMENT'主键',`user_id`varchar(64)NOTNULLCOMMENT'用户唯一标识',`session_id`varchar(64)NOTNULLCOMMENT'会话ID',`role`varchar(16)NOTNULLCOMMENT'角色user/assistant',`content`textNOTNULLCOMMENT'对话内容',`model_name`varchar(32)DEFAULTNULLCOMMENT'使用模型',`create_time`datetimeDEFAULTCURRENT_TIMESTAMP,PRIMARYKEY(`id`),KEYidx_user_session(`user_id`,`session_id`))ENGINE=InnoDBDEFAULTCHARSET=utf8mb4COMMENT='对话历史记录';四、核心代码分层实现1.实体类ChatRecordjava运行packagecom.ai.chat.entity;importcom.baomidou.mybatisplus.annotation.IdType;importcom.baomidou.mybatisplus.annotation.TableId;importcom.baomidou.mybatisplus.annotation.TableName;importlombok.Data;importjava.time.LocalDateTime;@Data@TableName("chat_record")publicclassChatRecord{@TableId(type=IdType.AUTO)privateLongid;privateStringuserId;privateStringsessionId;privateStringrole;privateStringcontent;privateStringmodelName;privateLocalDateTimecreateTime;}2.请求DTOChatRequestjava运行packagecom.ai.chat.dto;importlombok.Data;@DatapublicclassChatRequest{//用户IDprivateStringuserId;//会话ID，同一轮对话保持一致privateStringsessionId;//用户提问内容privateStringmessage;//自定义AI角色描述privateStringsystemPrompt;//是否开启RAG知识库privateBooleanuseRag=false;}3.Redis会话记忆工具（多轮对话上下文）SpringAI自带ChatMemory，封装Redis持久化会话，实现多轮记忆不丢失java运行packagecom.ai.chat.memory;importorg.springframework.ai.chat.memory.ChatMemory;importorg.springframework.ai.chat.memory.RedisChatMemory;importorg.springframework.data.redis.core.RedisTemplate;importorg.springframework.stereotype.Component;@ComponentpublicclassChatMemoryManager{privatefinalRedisTemplate<String,Object>redisTemplate;publicChatMemoryManager(RedisTemplate<String,Object>redisTemplate){this.redisTemplate=redisTemplate;}/***获取指定会话的记忆容器*/publicChatMemorygetMemory(StringsessionId){//会话过期30分钟returnnewRedisChatMemory(redisTemplate,"chat:memory:"+sessionId,1800L);}/***清空会话上下文*/publicvoidclearMemory(StringsessionId){getMemory(sessionId).clear(sessionId);}}4.RAG知识库服务（文档问答）java运行packagecom.ai.chat.service;importorg.springframework.ai.chat.client.ChatClient;importorg.springframework.ai.document.Document;importorg.springframework.ai.reader.TextReader;importorg.springframework.ai.transformer.splitter.TokenTextSplitter;importorg.springframework.ai.vectorstore.VectorStore;importorg.springframework.stereotype.Service;importorg.springframework.core.io.FileSystemResource;importjava.util.List;@ServicepublicclassRagService{privatefinalVectorStorevectorStore;privatefinalChatClientchatClient;publicRagService(VectorStorevectorStore,ChatClient.BuilderchatClientBuilder){this.vectorStore=vectorStore;this.chatClient=chatClientBuilder.build();}//加载本地文档到向量库publicvoidloadDoc(StringfilePath){TextReaderreader=newTextReader(newFileSystemResource(filePath));List<Document>docs=reader.get();//文本分割TokenTextSplittersplitter=newTokenTextSplitter();List<Document>splitDocs=splitter.apply(docs);vectorStore.add(splitDocs);}//根据问题检索相关上下文publicStringretrieveContext(Stringquery){List<Document>similarDocs=vectorStore.similaritySearch(query,3);StringBuildercontext=newStringBuilder();for(Documentdoc:similarDocs){context.append(doc.getContent()).append("\n");}returncontext.toString();}}5.核心对话业务服务ChatServicejava运行packagecom.ai.chat.service;importcom.ai.chat.dto.ChatRequest;importcom.ai.chat.entity.ChatRecord;importcom.ai.chat.mapper.ChatRecordMapper;importcom.ai.chat.memory.ChatMemoryManager;importorg.springframework.ai.chat.client.ChatClient;importorg.springframework.ai.chat.memory.ChatMemory;importorg.springframework.stereotype.Service;importreactor.core.publisher.Flux;importjava.time.LocalDateTime;@ServicepublicclassChatService{privatefinalChatClient.BuilderchatClientBuilder;privatefinalChatMemoryManagermemoryManager;privatefinalChatRecordMapperrecordMapper;privatefinalRagServiceragService;publicChatService(ChatClient.BuilderchatClientBuilder,ChatMemoryManagermemoryManager,ChatRecordMapperrecordMapper,RagServiceragService){this.chatClientBuilder=chatClientBuilder;this.memoryManager=memoryManager;this.recordMapper=recordMapper;this.ragService=ragService;}/***SSE流式对话*/publicFlux<String>streamChat(ChatRequestreq){ChatMemorymemory=memoryManager.getMemory(req.getSessionId());ChatClientclient=chatClientBuilder.defaultSystem(req.getSystemPrompt()==null?"你是智能助手，简洁回答用户问题":req.getSystemPrompt()).chatMemory(memory).build();StringuserMsg=req.getMessage();//开启RAG则拼接检索到的知识库上下文if(Boolean.TRUE.equals(req.getUseRag())){Stringcontext=ragService.retrieveContext(userMsg);userMsg="参考资料："+context+"\n用户问题："+userMsg;}//保存用户提问入库saveRecord(req,"user",req.getMessage());//流式返回AI输出Flux<String>flux=mpt().user(userMsg).stream().content();//流式拼接完整回答，结束后入库StringBuilderfullResp=newStringBuilder();returnflux.doOnNext(chunk->fullResp.append(chunk)).doOnComplete(()->saveRecord(req,"assistant",fullResp.toString()));}//保存对话记录到MySQLprivatevoidsaveRecord(ChatRequestreq,Stringrole,Stringcontent){ChatRecordrecord=newChatRecord();record.setUserId(req.getUserId());record.setSessionId(req.getSessionId());record.setRole(role);record.setContent(content);record.setModelName("gpt-3.5-turbo");record.setCreateTime(LocalDateTime.now());recordMapper.insert(record);}//清空会话上下文publicvoidclearSession(StringsessionId){memoryManager.clearMemory(sessionId);}}6.Controller流式对话接口（SSE）java运行packagecom.ai.chat.controller;importcom.ai.chat.dto.ChatRequest;importcom.ai.chat.service.ChatService;importorg.springframework.http.MediaType;importorg.springframework.web.bind.annotation.*;importreactor.core.publisher.Flux;@RestController@RequestMapping("/chat")publicclassChatController{privatefinalChatServicechatService;publicChatController(ChatServicechatService){this.chatService=chatService;}/***流式对话接口SSE*/@PostMapping(value="/stream",produces=MediaType.TEXT_EVENT_STREAM_VALUE)publicFlux<String>streamChat(@RequestBodyChatRequestrequest){returnchatService.streamChat(request);}/***清空当前会话记忆*/@DeleteMapping("/session/{sessionId}")publicStringclearSession(@PathVariableStringsessionId){chatService.clearSession(sessionId);return"会话已清空";}}五、前端调用示例（JSSSE）javascript运行//前端发起流式对话asyncfunctionsendChat(){