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