Spring AI 拦截器实战：Advisors 详解与代码示例

本文根据 Spring AI 官方文档 https://docs.spring.io/spring-ai/reference/api/advisors.html 整理，用大白话把原版内容讲清楚，代码可以直接复制使用。

一、这玩意儿是干啥的？

你在用 Spring AI 调用大模型的时候，是不是经常要干这些破事：

• 把聊天记录翻出来塞进 Prompt
• 从向量数据库搜点相关文档一起发给模型
• 记录一下用户问了啥、模型回了啥
• 检查一下模型有没有输出敏感词

如果每次调用都要手动写一遍这些逻辑，代码很快就没法看了。

Advisors 就是来解决这个问题的。它本质上是个拦截器，在请求到达大模型之前、响应返回之后，插一段自定义逻辑进去。

官网原话是：封装重复的生成式 AI 模式，让代码更干净、更可复用。

二、怎么用？先看个例子

// 先把需要的组件准备好
ChatMemory chatMemory = ...; // 记忆存储器
VectorStore vectorStore = ...; // 向量数据库

// 创建 ChatClient 的时候顺便把 Advisor 注册上
var chatClient = ChatClient.builder(chatModel)
.defaultAdvisors(
MessageChatMemoryAdvisor.builder(chatMemory).build(), // 记忆功能
QuestionAnswerAdvisor.builder(vectorStore).build() // RAG 功能
)
.build();

// 后面调用的时候，这些功能就自动生效了
String conversationId = "678";
String response = this.chatClient.prompt()
.advisors(advisor -> advisor.param(ChatMemory.CONVERSATION_ID, conversationId))
.user("你有什么功能？")
.call()
.content();

关键点：Advisor 推荐在构建 ChatClient 的时候通过 defaultAdvisors() 一次性注册，而不是每次调用的时候临时加。

三、核心执行流程

3.1 整体时序图

下面这张图是整个 Advisor 链的执行过程，建议仔细看：

大模型Advisor 3(order最大)Advisor 2Advisor 1(order最小)你的代码大模型Advisor 3(order最大)Advisor 2Advisor 1(order最小)你的代码#mermaid-svg-ZesB0u2WKPSLG0ib{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-ZesB0u2WKPSLG0ib .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-ZesB0u2WKPSLG0ib .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-ZesB0u2WKPSLG0ib .error-icon{fill:#552222;}#mermaid-svg-ZesB0u2WKPSLG0ib .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-ZesB0u2WKPSLG0ib .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-ZesB0u2WKPSLG0ib .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-ZesB0u2WKPSLG0ib .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-ZesB0u2WKPSLG0ib .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-ZesB0u2WKPSLG0ib .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-ZesB0u2WKPSLG0ib .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-ZesB0u2WKPSLG0ib .marker{fill:#333333;stroke:#333333;}#mermaid-svg-ZesB0u2WKPSLG0ib .marker.cross{stroke:#333333;}#mermaid-svg-ZesB0u2WKPSLG0ib svg{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-ZesB0u2WKPSLG0ib p{margin:0;}#mermaid-svg-ZesB0u2WKPSLG0ib .actor{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-ZesB0u2WKPSLG0ib text.actor>tspan{fill:black;stroke:none;}#mermaid-svg-ZesB0u2WKPSLG0ib .actor-line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);}#mermaid-svg-ZesB0u2WKPSLG0ib .innerArc{stroke-width:1.5;stroke-dasharray:none;}#mermaid-svg-ZesB0u2WKPSLG0ib .messageLine0{stroke-width:1.5;stroke-dasharray:none;stroke:#333;}#mermaid-svg-ZesB0u2WKPSLG0ib .messageLine1{stroke-width:1.5;stroke-dasharray:2,2;stroke:#333;}#mermaid-svg-ZesB0u2WKPSLG0ib #arrowhead path{fill:#333;stroke:#333;}#mermaid-svg-ZesB0u2WKPSLG0ib .sequenceNumber{fill:white;}#mermaid-svg-ZesB0u2WKPSLG0ib #sequencenumber{fill:#333;}#mermaid-svg-ZesB0u2WKPSLG0ib #crosshead path{fill:#333;stroke:#333;}#mermaid-svg-ZesB0u2WKPSLG0ib .messageText{fill:#333;stroke:none;}#mermaid-svg-ZesB0u2WKPSLG0ib .labelBox{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-ZesB0u2WKPSLG0ib .labelText,#mermaid-svg-ZesB0u2WKPSLG0ib .labelText>tspan{fill:black;stroke:none;}#mermaid-svg-ZesB0u2WKPSLG0ib .loopText,#mermaid-svg-ZesB0u2WKPSLG0ib .loopText>tspan{fill:black;stroke:none;}#mermaid-svg-ZesB0u2WKPSLG0ib .loopLine{stroke-width:2px;stroke-dasharray:2,2;stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);}#mermaid-svg-ZesB0u2WKPSLG0ib .note{stroke:#aaaa33;fill:#fff5ad;}#mermaid-svg-ZesB0u2WKPSLG0ib .noteText,#mermaid-svg-ZesB0u2WKPSLG0ib .noteText>tspan{fill:black;stroke:none;}#mermaid-svg-ZesB0u2WKPSLG0ib .activation0{fill:#f4f4f4;stroke:#666;}#mermaid-svg-ZesB0u2WKPSLG0ib .activation1{fill:#f4f4f4;stroke:#666;}#mermaid-svg-ZesB0u2WKPSLG0ib .activation2{fill:#f4f4f4;stroke:#666;}#mermaid-svg-ZesB0u2WKPSLG0ib .actorPopupMenu{position:absolute;}#mermaid-svg-ZesB0u2WKPSLG0ib .actorPopupMenuPanel{position:absolute;fill:#ECECFF;box-shadow:0px 8px 16px 0px rgba(0,0,0,0.2);filter:drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4));}#mermaid-svg-ZesB0u2WKPSLG0ib .actor-man line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;}#mermaid-svg-ZesB0u2WKPSLG0ib .actor-man circle,#mermaid-svg-ZesB0u2WKPSLG0ib line{stroke:hsl(259.6261682243, 59.7765363128%, 87.9019607843%);fill:#ECECFF;stroke-width:2px;}#mermaid-svg-ZesB0u2WKPSLG0ib :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}处理请求（如：记日志）处理请求（如：注入记忆）处理请求（如：RAG检索）处理响应处理响应处理响应1. 发送 ChatClientRequest2. chain.nextCall(request)3. chain.nextCall(request)4. 调用大模型5. 返回响应6. 返回响应7. 返回响应8. 返回 ChatClientResponse

关键理解：

• 请求往下传（1→2→3→4），响应往回走（5→6→7→8）
• 最后一个 Advisor 负责真正调用大模型
• 越靠近用户的 Advisor，getOrder() 数值越小

3.2 Order 值的含义（容易搞反）

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响应阶段

大模型

order=2最先处理响应

order=1

order=0最后处理响应

返回给用户

请求阶段

用户请求

order=0最先执行

order=1

order=2

大模型

记住：

• 请求阶段：order=0 最先执行
• 响应阶段：order=0 最后执行
• 这就是栈结构的特点

3.3 完整流程图（包含阻断情况）

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是

是

是

否

否

否

用户调用 ChatClient

Step 1: 框架创建ChatClientRequest + 空上下文

Advisor链开始

Advisor A: order=0比如: 记录请求日志

调用 chain.nextCall?

Advisor B: order=1比如: 注入对话记忆

调用 chain.nextCall?

Advisor C: order=2比如: RAG检索增强

调用 chain.nextCall?

最后一个内置Advisor调用大模型

大模型返回响应

Advisor C 处理响应

Advisor B 处理响应

Advisor A 处理响应

返回 ChatClientResponse 给用户

Advisor A 直接返回响应阻断后续执行

Advisor B 直接返回响应阻断后续执行

Advisor C 直接返回响应阻断后续执行

说明：

• 绿色箭头是正常流程
• 红色虚线表示某个 Advisor 可以直接返回（比如安全过滤直接拦截了）

四、核心组件

4.1 两大场景的接口设计

另外还有两个数据载体：

• ChatClientRequest：封装用户的 Prompt，附带一个共享上下文
• ChatClientResponse：封装模型的返回结果，同样带着上下文

4.2 两个关键方法

// 非流式 Advisor 要实现的方法
ChatClientResponse adviseCall(ChatClientRequest request, CallAdvisorChain chain);

// 流式 Advisor 要实现的方法
Flux<ChatClientResponse> adviseStream(ChatClientRequest request, StreamAdvisorChain chain);

每个 Advisor 还需要实现：

• getOrder()：决定执行顺序，数字越小越靠前
• getName()：给 Advisor 起个唯一的名字

4.3 Order 值的官方常量

public interface Ordered {
int HIGHEST_PRECEDENCE = Integer.MIN_VALUE; // 最高优先级（最先执行）
int LOWEST_PRECEDENCE = Integer.MAX_VALUE; // 最低优先级（最后执行）
}

五、自己动手写一个 Advisor

示例1：日志记录 Advisor（只看不改）

这个 Advisor 什么都不改，就是看一眼请求和响应，打条日志。

public class SimpleLoggerAdvisor implements CallAdvisor, StreamAdvisor {
private static final Logger log = LoggerFactory.getLogger(SimpleLoggerAdvisor.class);

@Override
public String getName() {
return "SimpleLoggerAdvisor";
}

@Override
public int getOrder() {
return 0; // 希望它最先执行
}

// 非流式版本
@Override
public ChatClientResponse adviseCall(ChatClientRequest request, CallAdvisorChain chain) {
log.info("请求内容：{}", request);
ChatClientResponse response = chain.nextCall(request);
log.info("响应内容：{}", response);
return response;
}

// 流式版本（需要聚合流中的数据）
@Override
public Flux<ChatClientResponse> adviseStream(ChatClientRequest request, StreamAdvisorChain chain) {
log.info("请求内容：{}", request);
Flux<ChatClientResponse> responses = chain.nextStream(request);
// 用工具类把流式数据聚合成一条，方便打日志
return new ChatClientMessageAggregator()
.aggregateChatClientResponse(responses, this::logResponse);
}

private void logResponse(ChatClientResponse response) {
log.info("响应内容：{}", response);
}
}

注意：用 MessageAggregator 聚合流式数据时，只能读不能改。

示例2：Re-Reading 增强 Advisor（动手改请求）

有一篇论文（Re-Reading Improves Reasoning in LLMs）发现：让模型把问题再读一遍，能提高推理准确率。用 Advisor 实现这个很简单。

public class ReReadingAdvisor implements CallAdvisor {

private static final String TEMPLATE = """
用户问题：{question}
请再把问题读一遍：{question}
""";

private int order = 0;

@Override
public ChatClientResponse adviseCall(ChatClientRequest request, CallAdvisorChain chain) {
// 拿到原始问题
String originalText = request.prompt().getUserMessage().getText();

// 套上模板，让问题重复一遍
String augmentedText = TEMPLATE.replace("{question}", originalText);

// 创建一个新请求（注意：Request 是不可变的，要调用 mutate()）
ChatClientRequest newRequest = request.mutate()
.prompt(request.prompt().augmentUserMessage(augmentedText))
.build();

// 传给下一个 Advisor 或最终的大模型
return chain.nextCall(newRequest);
}

@Override
public int getOrder() {
return this.order;
}

public void setOrder(int order) {
this.order = order;
}

@Override
public String getName() {
return "ReReadingAdvisor";
}
}

六、流式 vs 非流式的区别

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流式 StreamAdvisor

收到完整请求

处理请求

调用 chain.nextStream

拿到 Flux 数据流

逐个处理每个数据块

最终聚合或逐块输出

非流式 CallAdvisor

收到完整请求

处理请求

调用 chain.nextCall

等待完整响应

处理完整响应

返回 ChatClientResponse

流式 Advisor 的代码骨架（官网给的）：

@Override
public Flux<ChatClientResponse> adviseStream(ChatClientRequest request, StreamAdvisorChain chain) {
return Mono.just(request)
.publishOn(Schedulers.boundedElastic()) // 换一个能处理阻塞操作的线程池
.map(req -> {
// 请求前置处理（可能有阻塞操作）
return req;
})
.flatMapMany(chain::nextStream)
.map(response -> {
// 响应后置处理
return response;
});
}

注意：千万别在响应式流里直接做阻塞操作（比如 Thread.sleep、数据库查询），会出问题。

七、Spring AI 内置的 Advisor（拿来就用）

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Spring AI内置Advisor

对话记忆类

MessageChatMemoryAdvisor

PromptChatMemoryAdvisor

VectorStoreChatMemoryAdvisor

RAG问答类

QuestionAnswerAdvisor

RetrievalAugmentationAdvisor

推理增强类

ReReadingAdvisor

内容安全类

SafeGuardAdvisor

各 Advisor 的简要说明：

八、最佳实践

// 错误：直接改会报错
request.context().put("key", "value");

// 正确：创建新的不可变副本
ChatClientRequest newRequest = request.updateContext(ctx ->
MapUtils.put(ctx, "key", "value")
);

九、版本变化（如果你是从老版本升级的）

如果你是从老版本升级上来的，这些变化要注意。

总结

Advisors API 本质上就是 AI 调用场景下的拦截器模式。它把那些跟核心业务无关但又不得不做的破事（记忆、检索、过滤、日志）抽出来，做成可插拔的组件。

用官网的话说就是：encapsulating recurring Generative AI patterns（封装重复的生成式 AI 模式）。

如果你发现自己每个 AI 调用前后都要写类似的代码，就该考虑用 Advisor 了。

参考文档：https://docs.spring.io/spring-ai/reference/api/advisors.html