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+package com.storlead.ai.service.impl;
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+
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+import com.fasterxml.jackson.databind.JsonNode;
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+import com.fasterxml.jackson.databind.ObjectMapper;
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+import com.storlead.ai.config.properties.OpenAiProperties;
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+import com.storlead.ai.core.AiProviderType;
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+import com.storlead.ai.core.AiService;
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+import com.storlead.ai.core.ChatRequest;
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+import com.storlead.ai.core.ChatResponse;
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+import com.storlead.ai.exception.AiServiceException;
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+import com.storlead.ai.util.HttpClientUtil;
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+import org.slf4j.Logger;
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+import org.slf4j.LoggerFactory;
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+import org.springframework.beans.factory.annotation.Autowired;
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+import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
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+import org.springframework.core.io.buffer.DataBuffer;
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+import org.springframework.core.io.buffer.DataBufferUtils;
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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.Flux;
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+import reactor.core.publisher.Mono;
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+
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+import javax.annotation.PostConstruct;
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+import java.nio.charset.StandardCharsets;
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+import java.util.Arrays;
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+import java.util.HashMap;
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+import java.util.List;
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+import java.util.Map;
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+
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+/**
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+ * @program: storlead-ai-platform
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+ * @description:
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+ * @author: chenkq
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+ * @create: 2025-09-26 09:39
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+ */
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+@Service
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+@ConditionalOnProperty(prefix = "ai.providers.storleadai", name = "enabled", havingValue = "true")
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+public class StorleadAiService implements AiService {
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+
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+ private static final Logger logger = LoggerFactory.getLogger(OpenAiService.class);
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+
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+ @Autowired
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+ private OpenAiProperties properties;
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+
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+ @Autowired
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+ private HttpClientUtil httpClientUtil;
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+
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+ @Autowired
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+ private ObjectMapper objectMapper;
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+
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+ private WebClient webClient;
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+
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+ @PostConstruct
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+ public void init() {
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+ this.webClient = httpClientUtil.createWebClient(
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+ properties.getBaseUrl(),
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+ properties.getTimeoutSeconds()
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+ );
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+ logger.info("OpenAI服务初始化完成 - baseUrl: {}, model: {}",
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+ properties.getBaseUrl(), properties.getDefaultModel());
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+ }
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+
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+ @Override
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+ public Mono<ChatResponse> chat(ChatRequest request) {
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+ return Mono.fromCallable(() -> buildRequestBody(request))
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+ .flatMap(requestBody -> {
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+ return httpClientUtil.postForObject(
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+ webClient,
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+ "/v1/chat/completions",
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+ requestBody,
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+ JsonNode.class,
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+ httpClientUtil.createBearerAuthHeaders(properties.getApiKey()),
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+ properties.getMaxRetries()
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+ );
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+ })
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+ .map(this::parseResponse)
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+ .onErrorMap(this::handleException);
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+ }
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+
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+ @Override
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+ public Flux<String> chatStream(ChatRequest request) {
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+ logger.debug("开始构建流式请求");
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+
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+ Map<String, Object> requestBody = buildStreamRequestBody(request);
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+ logger.debug("流式请求体: {}", requestBody);
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+
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+ return webClient.post()
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+ .uri("/v1/chat/completions")
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+ .headers(httpHeaders -> {
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+ httpHeaders.setBearerAuth(properties.getApiKey());
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+ httpHeaders.set("Accept", "text/event-stream");
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+ })
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+ .bodyValue(requestBody)
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+ .retrieve()
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+ .bodyToFlux(DataBuffer.class)
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+ .map(dataBuffer -> {
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+ byte[] bytes = new byte[dataBuffer.readableByteCount()];
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+ dataBuffer.read(bytes);
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+ DataBufferUtils.release(dataBuffer);
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+ return new String(bytes, StandardCharsets.UTF_8);
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+ })
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+ .flatMapIterable(data -> Arrays.asList(data.split("\n")))
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+ .filter(line -> !line.trim().isEmpty())
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+ .filter(line -> line.startsWith("data: ") && !line.equals("data: [DONE]"))
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+ .map(line -> line.substring(6).trim())
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+ .filter(this::isValidJson) // 添加JSON有效性检查
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+ .mapNotNull(this::parseStreamChunk)
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+ .onErrorMap(this::handleException);
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+ }
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+
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+ /**
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+ * 检查JSON是否有效且完整
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+ */
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+ private boolean isValidJson(String json) {
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+ if (json.trim().isEmpty()) return false;
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+ try {
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+ objectMapper.readTree(json);
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+ return true;
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+ } catch (Exception e) {
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+ return false;
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+ }
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+ }
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+
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+ @Override
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+ public AiProviderType getProviderType() {
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+ return AiProviderType.OPENAI;
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+ }
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+
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+ @Override
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+ public boolean isAvailable() {
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+ try {
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+ // 简单的健康检查 - 发送一个极简的请求
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+ Map<String, Object> testRequest = Map.of(
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+ "model", properties.getDefaultModel(),
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+ "messages", List.of(Map.of("role", "user", "content", "test")),
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+ "max_tokens", 1
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+ );
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+
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+ return httpClientUtil.postForObject(
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+ webClient,
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+ "/v1/chat/completions",
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+ testRequest,
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+ JsonNode.class,
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+ httpClientUtil.createBearerAuthHeaders(properties.getApiKey()),
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+ 1
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+ ).block() != null;
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+ } catch (Exception e) {
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+ logger.warn("OpenAI服务健康检查失败: {}", e.getMessage());
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+ return false;
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+ }
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+ }
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+
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+ @Override
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+ public List<String> getSupportedModels() {
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+ return properties.getSupportedModels();
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+ }
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+
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+ @Override
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+ public String getDefaultModel() {
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+ return properties.getDefaultModel();
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+ }
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+
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+ /**
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+ * 构建请求体
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+ */
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+ private Map<String, Object> buildRequestBody(ChatRequest request) {
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+ Map<String, Object> requestBody = new HashMap<>();
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+
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+ // 基础参数
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+ requestBody.put("model", getModelName(request));
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+ requestBody.put("messages", buildMessages(request));
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+
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+ // 可选参数
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+ Map<String, Object> params = request.getParameters();
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+ if (params != null) {
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+ requestBody.put("temperature", params.getOrDefault("temperature", properties.getDefaultTemperature()));
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+ requestBody.put("max_tokens", params.getOrDefault("max_tokens", properties.getDefaultMaxTokens()));
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+
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+ if (params.containsKey("top_p")) {
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+ requestBody.put("top_p", params.get("top_p"));
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+ }
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+ if (params.containsKey("frequency_penalty")) {
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+ requestBody.put("frequency_penalty", params.get("frequency_penalty"));
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+ }
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+ if (params.containsKey("presence_penalty")) {
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+ requestBody.put("presence_penalty", params.get("presence_penalty"));
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+ }
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+ } else {
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+ requestBody.put("temperature", properties.getDefaultTemperature());
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+ requestBody.put("max_tokens", properties.getDefaultMaxTokens());
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+ }
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+
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+ return requestBody;
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+ }
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+
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+ /**
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+ * 构建流式请求体
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+ */
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+ private Map<String, Object> buildStreamRequestBody(ChatRequest request) {
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+ Map<String, Object> requestBody = buildRequestBody(request);
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+ requestBody.put("stream", true);
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+ return requestBody;
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+ }
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+
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+ /**
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+ * 构建消息列表
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+ */
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+ private List<Map<String, String>> buildMessages(ChatRequest request) {
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+ List<Map<String, String>> messages = new java.util.ArrayList<>();
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+
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+ // 系统提示词
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+ if (request.getSystemPrompt() != null && !request.getSystemPrompt().trim().isEmpty()) {
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+ messages.add(Map.of("role", "system", "content", request.getSystemPrompt()));
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+ }
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+
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+ // 用户消息
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+ messages.add(Map.of("role", "user", "content", request.getMessage()));
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+
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+ return messages;
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+ }
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+
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+ /**
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+ * 获取模型名称
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+ */
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+ private String getModelName(ChatRequest request) {
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+ if (request.getModel() != null && !request.getModel().trim().isEmpty()) {
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+ return request.getModel();
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+ }
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+ return properties.getDefaultModel();
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+ }
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+
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+ /**
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+ * 解析响应
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+ */
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+ private ChatResponse parseResponse(JsonNode responseJson) {
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+ try {
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+ if (responseJson.has("error")) {
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+ JsonNode error = responseJson.get("error");
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+ String errorMessage = error.get("message").asText();
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+ String errorCode = error.has("code") ? error.get("code").asText() : "OPENAI_ERROR";
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+ throw AiServiceException.apiError(AiProviderType.OPENAI, errorMessage);
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+ }
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+
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+ JsonNode choices = responseJson.get("choices");
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+ if (choices == null || choices.isEmpty()) {
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+ throw AiServiceException.apiError(AiProviderType.OPENAI, "响应中没有选择项");
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+ }
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+
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+ JsonNode firstChoice = choices.get(0);
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+ JsonNode message = firstChoice.get("message");
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+ String content = message.get("content").asText();
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+
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+ String model = responseJson.has("model") ? responseJson.get("model").asText() : properties.getDefaultModel();
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+
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+ // 构建元数据
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+ Map<String, Object> metadata = new HashMap<>();
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+ if (responseJson.has("usage")) {
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+ metadata.put("usage", responseJson.get("usage"));
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+ }
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+ if (firstChoice.has("finish_reason")) {
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+ metadata.put("finishReason", firstChoice.get("finish_reason").asText());
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+ }
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+
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+ ChatResponse response = ChatResponse.success(content, model);
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+ response.setProviderType(AiProviderType.OPENAI);
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+ response.setMetadata(metadata);
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+
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+ return response;
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+ } catch (Exception e) {
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+ logger.error("解析OpenAI响应失败", e);
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+ throw AiServiceException.apiError(AiProviderType.OPENAI, "响应解析失败: " + e.getMessage());
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+ }
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+ }
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+
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+ /**
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+ * 解析流式响应块
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+ */
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+ private String parseStreamChunk(String chunk) {
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+ try {
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+ JsonNode chunkJson = objectMapper.readTree(chunk);
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+
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+ if (chunkJson.has("choices")) {
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+ JsonNode choices = chunkJson.get("choices");
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+ if (!choices.isEmpty()) {
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+ JsonNode firstChoice = choices.get(0);
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+ if (firstChoice.has("delta")) {
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+ JsonNode delta = firstChoice.get("delta");
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+ if (delta.has("content")) {
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+ String content = delta.get("content").asText();
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+ // 只返回纯文本内容，不包含任何JSON结构
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+ return content;
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+ }
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+ }
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+ }
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+ }
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+ return null; // 如果没有content，返回null会被mapNotNull过滤掉
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+ } catch (Exception e) {
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+ logger.warn("解析流式响应块失败: {}", chunk, e);
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+ return null;
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+ }
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+ }
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+
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+ /**
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+ * 异常处理
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+ */
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+ private Throwable handleException(Throwable throwable) {
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+ String errorMessage = httpClientUtil.parseWebClientError(throwable);
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+ logger.error("OpenAI API调用失败: {}", errorMessage, throwable);
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+
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+ if (errorMessage.contains("401")) {
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+ return AiServiceException.authenticationError(AiProviderType.OPENAI);
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+ } else if (errorMessage.contains("429")) {
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+ return AiServiceException.rateLimitError(AiProviderType.OPENAI);
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+ } else if (errorMessage.contains("quota")) {
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+ return AiServiceException.quotaExceededError(AiProviderType.OPENAI);
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+ } else {
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+ return AiServiceException.networkError(AiProviderType.OPENAI, throwable);
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+ }
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+ }
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+}
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