Spring AI，一个让 Spring 应用轻松拥抱 AI 的统一框架

    org.springframework.ai    spring-ai-openai-spring-boot-starter    0.8.0



    org.springframework.ai    spring-ai-pinecone-store-spring-boot-starter    0.8.0



    org.springframework.boot    spring-boot-starter-web

# Deepseek配置spring.ai.openai.api-key=your-api-keyspring.ai.openai.chat.model=gpt-3.5-turbospring.ai.openai.chat.temperature=0.7
# 可选：向量存储配置（使用Pinecone时需要）# spring.ai.pinecone.api-key=your-pinecone-api-key# spring.ai.pinecone.environment=your-pinecone-environment# spring.ai.pinecone.index=your-index-name

import org.springframework.ai.chat.client.ChatClient;import org.springframework.boot.SpringApplication;import org.springframework.boot.autoconfigure.SpringBootApplication;import org.springframework.web.bind.annotation.GetMapping;import org.springframework.web.bind.annotation.RequestParam;import org.springframework.web.bind.annotation.RestController;
@SpringBootApplicationpublic class SpringCloudAiDemoApplication {
    public static void main(String[] args) {        SpringApplication.run(SpringCloudAiDemoApplication.class, args);    }
    @RestController    static class ChatController {
        private final ChatClient chatClient;
        public ChatController(ChatClient chatClient) {            this.chatClient = chatClient;        }
        @GetMapping("/chat")        public String chat(@RequestParam String message) {            return chatClient.call(message);        }    }}

import org.springframework.ai.chat.client.ChatClient;import org.springframework.http.MediaType;import org.springframework.web.bind.annotation.GetMapping;import org.springframework.web.bind.annotation.RequestParam;import org.springframework.web.bind.annotation.RequestMapping;import org.springframework.web.bind.annotation.RestController;import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;import reactor.core.publisher.Flux;import java.io.IOException;import java.util.concurrent.CompletableFuture;
@RestController@RequestMapping("/stream")public class StreamingController {        private final ChatClient chatClient;        public StreamingController(ChatClient chatClient) {        this.chatClient = chatClient;    }        @GetMapping(value = "/chat", produces = MediaType.TEXT_EVENT_STREAM_VALUE)    public SseEmitter streamChat(@RequestParam String message) {        SseEmitter emitter = new SseEmitter();                // 异步处理流式响应        CompletableFuture.runAsync(() -> {            try {                // 获取流式响应                chatClient.stream(message).subscribe(                    chunk -> {                        try {                            emitter.send(SseEmitter.event().data(chunk));                        } catch (IOException e) {                            emitter.completeWithError(e);                        }                    },                    error -> emitter.completeWithError(error),                    () -> emitter.complete()                );            } catch (Exception e) {                emitter.completeWithError(e);            }        });                return emitter;    }}

import org.springframework.ai.chat.client.ChatClient;import org.springframework.ai.document.Document;import org.springframework.ai.vectorstore.VectorStore;import org.springframework.stereotype.Service;import java.util.List;
@Servicepublic class RagService {        private final ChatClient chatClient;    private final VectorStore vectorStore;        public RagService(ChatClient chatClient, VectorStore vectorStore) {        this.chatClient = chatClient;        this.vectorStore = vectorStore;    }        public String answerWithRag(String question) {        // 搜索相关文档        List relevantDocs = vectorStore.similaritySearch(question, 3);                // 构建包含相关文档的提示        StringBuilder promptBuilder = new StringBuilder();        promptBuilder.append("根据以下信息回答问题：\n");                for (Document doc : relevantDocs) {            promptBuilder.append("- ").append(doc.getContent()).append("\n");        }                promptBuilder.append("\n问题：").append(question);        promptBuilder.append("\n请基于提供的信息回答，不要添加额外信息。");                // 获取AI响应        return chatClient.call(promptBuilder.toString());    }}

import org.springframework.ai.chat.client.ChatClient;import org.springframework.context.annotation.Bean;import org.springframework.context.annotation.Configuration;import org.springframework.stereotype.Service;import java.util.Map;import java.util.HashMap;
// 自定义模型路由器类class ModelRouter {    private final Map clientMap = new HashMap<>();    private ChatClient defaultModel;        public void addRule(String key, ChatClient client) {        clientMap.put(key, client);    }        public void setDefaultModel(ChatClient client) {        this.defaultModel = client;    }        public ChatClient getClient(String type) {        return clientMap.getOrDefault(type, defaultModel);    }}
@Configurationpublic class ModelRoutingConfig {        @Bean    public ModelRouter modelRouter(ChatClient chatClient) {        // 注意：在实际项目中，您可能需要配置多个不同的ChatClient实例        ModelRouter router = new ModelRouter();                // 配置路由规则 - 这里为简化示例，两个规则都使用同一个客户端        router.addRule("simple-query", chatClient);        router.addRule("complex-query", chatClient);                // 设置默认模型        router.setDefaultModel(chatClient);                return router;    }}
@Servicepublic class RoutingService {        private final ModelRouter modelRouter;        public RoutingService(ModelRouter modelRouter) {        this.modelRouter = modelRouter;    }        public String routeRequest(String type, String message) {        // 根据请求类型选择合适的模型        ChatClient client = modelRouter.getClient(type);        return client.call(message);    }}