核心组件
API 由非流式处理方案和 和 流式处理方案组成。
在1.0.0版本中，顾问链AdvisorChain相关接口已经弃用，可能是老版本的思想不太合伦理吧
可以使用下面的方式实现多个顾问按oder顺序访问模型
public ChatController(ChatClient.Builder chatClientBuilder) {MySimpleLoggerAdvisor advisor3= new MySimpleLoggerAdvisor(3);MySimpleLoggerAdvisor advisor2= new MySimpleLoggerAdvisor(2);MySimpleLoggerAdvisor advisor1= new MySimpleLoggerAdvisor(1);this.chatClient = chatClientBuilder//此处执行顺序由order决定，并非添加顺序决定.defaultAdvisors(List.of(advisor3,advisor2,advisor1)).defaultSystem("You are a helpful assistant").build();}下图说明了顾问链和聊天模型之间的交互：
顾问advice之间的request和response 基于order 对称，比如三个女孩子按顺序123追你，你给出的答复顺序则应该是321
交互返回：
Spring AI 提供了两种类型的内存顾问程序来维护对话上下文：
| 顾问 | 存储机制 | 模板占位符 |
|---|---|---|
PromptChatMemoryAdvisor | 内存中或自定义 | instructions,memory |
VectorStoreChatMemoryAdvisor | 向量数据库 | instructions,long_term_memory |
下面是一个简单顾问的实现用于参考学习：
package com.example.advisor;import org.springframework.ai.chat.client.ChatClientRequest;
import org.springframework.ai.chat.client.ChatClientResponse;
import org.springframework.ai.chat.client.advisor.api.CallAdvisor;
import org.springframework.ai.chat.client.advisor.api.CallAdvisorChain;
import org.springframework.ai.chat.client.advisor.api.StreamAdvisor;
import org.springframework.ai.chat.client.advisor.api.StreamAdvisorChain;
import reactor.core.publisher.Flux;public class MySimpleLoggerAdvisor implements CallAdvisor, StreamAdvisor {private Integer order;public MySimpleLoggerAdvisor() {super();}public MySimpleLoggerAdvisor(Integer preOrder) {order = preOrder;}public ChatClientResponse adviseCall(ChatClientRequest chatClientRequest, CallAdvisorChain callAdvisorChain) {System.out.println("BEFORE: {}" + chatClientRequest);System.out.println("do something order " + this.order);ChatClientResponse chatClientResponse = callAdvisorChain.nextCall(chatClientRequest);System.out.println("AFTER: {}" + chatClientResponse);System.out.println("was do something order " + this.order);return chatClientResponse;}@Overridepublic String getName() {return "MySimpleLoggerAdvisor";}@Overridepublic int getOrder() {return this.order == null ? 0 : this.order;}// @Override
// public AdvisedResponse aroundCall(AdvisedRequest advisedRequest, CallAroundAdvisorChain chain) {
//
// logger.debug("BEFORE: {}", advisedRequest);
//
// AdvisedResponse advisedResponse = chain.nextAroundCall(advisedRequest);
//
// logger.debug("AFTER: {}", advisedResponse);
//
// return advisedResponse;
// }
//@Overridepublic Flux<ChatClientResponse> adviseStream(ChatClientRequest chatClientRequest, StreamAdvisorChain streamAdvisorChain) {System.out.println("BEFORE: {}" + chatClientRequest);System.out.println("do something");Flux<ChatClientResponse> chatClientResponse = streamAdvisorChain.nextStream(chatClientRequest);System.out.println("AFTER: {}" + chatClientResponse);System.out.println("was do something");return chatClientResponse;}// @Override
// public Flux<AdvisedResponse> aroundStream(AdvisedRequest advisedRequest, StreamAroundAdvisorChain chain) {
//
// logger.debug("BEFORE: {}", advisedRequest);
//
// Flux<AdvisedResponse> advisedResponses = chain.nextAroundStream(advisedRequest);
//
// return new MessageAggregator().aggregateAdvisedResponse(advisedResponses,
// advisedResponse -> logger.debug("AFTER: {}", advisedResponse));
// }
}最后分析一下顾问模块的依赖关系：chatclient客户端是依赖于它的，他和chatclient客户端也同时依赖于spring-ai-模型，Core Advisor 框架在spring-ai-模型中得到定义和泛化
