package com.zy.ai.service; import com.zy.ai.entity.ChatCompletionRequest; import com.zy.ai.entity.ChatCompletionResponse; import lombok.RequiredArgsConstructor; import lombok.extern.slf4j.Slf4j; import org.springframework.beans.factory.annotation.Value; import org.springframework.http.HttpHeaders; import org.springframework.http.MediaType; import org.springframework.stereotype.Service; import org.springframework.web.reactive.function.client.WebClient; import reactor.core.publisher.Mono; import java.util.List; @Slf4j @Service @RequiredArgsConstructor public class LlmChatService { private final WebClient llmWebClient; @Value("${llm.api-key}") private String apiKey; @Value("${llm.model}") private String model; /** * 通用对话方法:传入 messages,返回大模型文本回复 */ public String chat(List messages, Double temperature, Integer maxTokens) { ChatCompletionRequest req = new ChatCompletionRequest(); req.setModel(model); req.setMessages(messages); req.setTemperature(temperature != null ? temperature : 0.3); req.setMax_tokens(maxTokens != null ? maxTokens : 1024); ChatCompletionResponse response = llmWebClient.post() .uri("/chat/completions") .header(HttpHeaders.AUTHORIZATION, "Bearer " + apiKey) .contentType(MediaType.APPLICATION_JSON) .bodyValue(req) // 2.5.14 已支持 bodyValue .retrieve() .bodyToMono(ChatCompletionResponse.class) .doOnError(ex -> log.error("调用 LLM 失败", ex)) .onErrorResume(ex -> Mono.empty()) .block(); if (response == null || response.getChoices() == null || response.getChoices().isEmpty() || response.getChoices().get(0).getMessage() == null) { return "AI 诊断失败:未获取到有效回复。"; } return response.getChoices().get(0).getMessage().getContent(); } }