Spring-AI-alibaba 结构化输出

1、将模型响应转换为 ActorsFilms 对象实例：

ActorsFilms

package com.alibaba.cloud.ai.example.chat.openai.entity;import java.util.List;public record ActorsFilms(String actor, List<String> movies) {
}

 @GetMapping("/toBean")public ActorsFilms toBean() {ActorsFilms actorsFilms = openAiChatClient.prompt().user(u -> u.text("Generate the filmography of 5 movies for {actor}.").param("actor", "Tom Hanks")).call().entity(ActorsFilms.class);return actorsFilms;}

2、将模型响应转换为 List 对象实例：

 @GetMapping("/toBeanList")public   List<ActorsFilms> toBeanList() {List<ActorsFilms> actorsFilms = openAiChatClient.prompt().user("Generate the filmography of 5 movies for Tom Hanks and Bill Murray.").call().entity(new ParameterizedTypeReference<List<ActorsFilms>>() {});return actorsFilms;}

3、将模型响应转换为 Map<String, Object>：

 @GetMapping("/toMap")public Map<String, Object> toMap() {Map<String, Object> result = openAiChatClient.prompt().user(u -> u.text("Provide me a List of {subject}").param("subject", "an array of numbers from 1 to 9 under their key name 'numbers'")).call().entity(new ParameterizedTypeReference<Map<String, Object>>() {});return result;}

4、使用 ListOutputConverter 将模型响应转换为 List：

  @GetMapping("/toList")public  List<String> toList() {List<String> flavors = openAiChatClient.prompt().user(u -> u.text("List five {subject}").param("subject", "ice cream flavors")).call().entity(new ListOutputConverter(new DefaultConversionService()));return flavors;}

完整controller

package com.alibaba.cloud.ai.example.chat.openai.controller;import com.alibaba.cloud.ai.example.chat.openai.entity.ActorsFilms;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
import org.springframework.ai.chat.memory.InMemoryChatMemory;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.converter.ListOutputConverter;
import org.springframework.ai.openai.OpenAiChatOptions;
import org.springframework.ai.rag.Query;
import org.springframework.ai.rag.preretrieval.query.expansion.MultiQueryExpander;
import org.springframework.ai.rag.preretrieval.query.transformation.QueryTransformer;
import org.springframework.ai.rag.preretrieval.query.transformation.RewriteQueryTransformer;
import org.springframework.ai.rag.preretrieval.query.transformation.TranslationQueryTransformer;
import org.springframework.core.ParameterizedTypeReference;
import org.springframework.core.convert.support.DefaultConversionService;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;import java.util.List;
import java.util.Map;/*** @Author: wst* @Date: 2024-12-16*/@RestController
@RequestMapping("/stru")
public class StruOuptController {private final ChatClient openAiChatClient;private final ChatModel chatModel;public StruOuptController(ChatModel chatModel) {this.chatModel = chatModel;//		 创建聊天客户端实例
// 设置系统提示信息，定义AI助手作为专业的室内设计顾问角色// 构造时，可以设置 ChatClient 的参数// {@link org.springframework.ai.chat.client.ChatClient};this.openAiChatClient = ChatClient.builder(chatModel)// 实现 Chat Memory 的 Advisor// 在使用 Chat Memory 时，需要指定对话 ID，以便 Spring AI 处理上下文。.defaultAdvisors(new MessageChatMemoryAdvisor(new InMemoryChatMemory()))// 实现 Logger 的 Advisor.defaultAdvisors(new SimpleLoggerAdvisor())// 设置 ChatClient 中 ChatModel 的 Options 参数.defaultOptions(OpenAiChatOptions.builder().topP(0.7).build()).build();}@GetMapping("/toBean")public ActorsFilms toBean() {ActorsFilms actorsFilms = openAiChatClient.prompt().user(u -> u.text("Generate the filmography of 5 movies for {actor}.").param("actor", "Tom Hanks")).call().entity(ActorsFilms.class);return actorsFilms;}@GetMapping("/toBeanList")public   List<ActorsFilms> toBeanList() {List<ActorsFilms> actorsFilms = openAiChatClient.prompt().user("Generate the filmography of 5 movies for Tom Hanks and Bill Murray.").call().entity(new ParameterizedTypeReference<List<ActorsFilms>>() {});return actorsFilms;}@GetMapping("/toMap")public Map<String, Object> toMap() {Map<String, Object> result = openAiChatClient.prompt().user(u -> u.text("Provide me a List of {subject}").param("subject", "an array of numbers from 1 to 9 under their key name 'numbers'")).call().entity(new ParameterizedTypeReference<Map<String, Object>>() {});return result;}@GetMapping("/toList")public  List<String> toList() {List<String> flavors = openAiChatClient.prompt().user(u -> u.text("List five {subject}").param("subject", "ice cream flavors")).call().entity(new ListOutputConverter(new DefaultConversionService()));return flavors;}}