cat-shark
55b7c1c83c
- 内部实现重构：每个公有方法内部 submitAndWait() → submitTask() + pollResult() - submitTask(): POST /ai/tasks，返回 taskId（30s 超时） - pollResult(): 轮询 GET /ai/tasks/:taskId，500ms 首次延迟， 2s × 10 次后退避到 5s，总超时 600s（10 min） - 公有方法签名完全不变：aggregateReport()、generateMindMap()、compareRecall() - 异常失败时返回 Optional.empty()，调用方降级逻辑不变 - application.yml timeout-seconds: 180 → 600
239 lines
9.7 KiB
Java
239 lines
9.7 KiB
Java
package com.guo.learningprogresstracker.service.impl;
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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 jakarta.annotation.PostConstruct;
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import lombok.Setter;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.boot.context.properties.ConfigurationProperties;
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import org.springframework.stereotype.Component;
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import java.net.URI;
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import java.net.http.HttpClient;
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import java.net.http.HttpRequest;
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import java.net.http.HttpResponse;
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import java.time.Duration;
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import java.time.Instant;
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import java.util.List;
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import java.util.Map;
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import java.util.Optional;
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/**
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* lpt-ai 独立 AI 服务客户端。
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* <p>通过 {@code lpt.ai-service.url} 配置服务地址；未配置或调用失败时返回 empty，由调用方降级。</p>
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* <p>
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* 内部使用异步任务模式：提交任务到 lpt-ai 后轮询等待结果，
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* 避免同步等待 LLM 响应时阻塞 HTTP 连接。
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* </p>
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*/
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@Slf4j
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@Component
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@ConfigurationProperties(prefix = "lpt.ai-service")
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@Setter
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public class AiServiceClient {
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/** lpt-ai 服务地址，如 http://localhost:5199 */
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private String url;
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/** 超时秒数（等待 AI 任务完成的最长时间） */
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private int timeoutSeconds = 600;
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private HttpClient httpClient;
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private final ObjectMapper objectMapper = new ObjectMapper();
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@PostConstruct
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void init() {
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httpClient = HttpClient.newBuilder()
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.connectTimeout(Duration.ofSeconds(10))
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.build();
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if (isConfigured()) {
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log.info("AiServiceClient 已配置: {} (超时 {}s, 异步任务模式)", url, timeoutSeconds);
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} else {
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log.info("AiServiceClient 未配置，AI 功能不可用（将降级为内置逻辑）");
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}
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}
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public boolean isConfigured() {
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return url != null && !url.isBlank();
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}
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// ============ 公开 API（签名不变，内部改为 submit + poll）============
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/**
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* 调用 lpt-ai 将残片聚合为学习报告草稿。
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*/
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public Optional<String> aggregateReport(String taskName, List<String> fragments, String expectation) {
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if (!isConfigured() || fragments == null || fragments.isEmpty()) {
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return Optional.empty();
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}
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try {
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List<Map<String, String>> fragmentBodies = fragments.stream()
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.map(content -> Map.of("content", content))
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.toList();
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Map<String, Object> params = expectation == null || expectation.isBlank()
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? Map.of("taskName", taskName, "fragments", fragmentBodies)
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: Map.of("taskName", taskName, "fragments", fragmentBodies, "expectation", expectation);
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Optional<JsonNode> result = submitAndWait("aggregate-report", params);
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return result.map(r -> r.path("report").asText(null)).filter(s -> s != null && !s.isBlank());
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} catch (Exception e) {
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log.warn("lpt-ai 聚合报告异常，将降级: {}", e.getMessage());
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return Optional.empty();
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}
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}
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/**
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* 调用 lpt-ai 从学习数据生成思维导图大纲。
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*/
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public Optional<String> generateMindMap(String taskName, String taskDescription,
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List<String> reports, List<String> fragments) {
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if (!isConfigured()) {
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return Optional.empty();
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}
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try {
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Map<String, Object> params = Map.of(
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"taskName", taskName,
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"taskDescription", taskDescription != null ? taskDescription : "",
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"reports", reports != null ? reports : List.of(),
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"fragments", fragments != null ? fragments : List.of()
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);
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Optional<JsonNode> result = submitAndWait("generate-mind-map", params);
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return result.map(r -> r.path("outline").asText(null)).filter(s -> s != null && !s.isBlank());
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} catch (Exception e) {
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log.warn("lpt-ai 思维导图异常，将降级: {}", e.getMessage());
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return Optional.empty();
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}
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}
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/**
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* 调用 lpt-ai 进行语义化回忆对比。
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*/
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public Optional<JsonNode> compareRecall(String taskName, String standardOutline, String recallOutline) {
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if (!isConfigured()) {
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return Optional.empty();
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}
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try {
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Map<String, Object> params = Map.of(
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"taskName", taskName,
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"standardOutline", standardOutline,
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"recallOutline", recallOutline
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);
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return submitAndWait("compare-recall", params);
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} catch (Exception e) {
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log.warn("lpt-ai 回忆对比异常，将降级为内置算法: {}", e.getMessage());
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return Optional.empty();
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}
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}
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// ============ 内部异步 submit + poll ============
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/** 提交任务并同步等待结果（内部轮询，调用方无感知） */
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private Optional<JsonNode> submitAndWait(String type, Map<String, Object> params) {
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String taskId = submitTask(type, params);
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if (taskId == null) {
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return Optional.empty();
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}
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return pollResult(taskId);
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}
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/** 提交任务到 lpt-ai /ai/tasks */
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private String submitTask(String type, Map<String, Object> params) {
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try {
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Map<String, Object> body = Map.of("type", type, "params", params);
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HttpRequest request = HttpRequest.newBuilder()
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.uri(URI.create(url + "/ai/tasks"))
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.timeout(Duration.ofSeconds(30)) // 提交本身不应超时
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.header("Content-Type", "application/json")
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.POST(HttpRequest.BodyPublishers.ofString(objectMapper.writeValueAsString(body)))
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.build();
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HttpResponse<String> response = httpClient.send(request, HttpResponse.BodyHandlers.ofString());
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if (response.statusCode() != 201) {
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log.warn("lpt-ai 提交任务失败: type={}, status={}, body={}",
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type, response.statusCode(),
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response.body() != null ? response.body().substring(0, Math.min(200, response.body().length())) : "");
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return null;
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}
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JsonNode json = objectMapper.readTree(response.body());
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String taskId = json.path("taskId").asText(null);
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if (taskId == null || taskId.isBlank()) {
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log.warn("lpt-ai 提交任务成功但未返回 taskId");
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return null;
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}
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log.info("lpt-ai 任务已提交: type={}, taskId={}", type, taskId);
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return taskId;
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} catch (Exception e) {
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log.warn("lpt-ai 提交任务异常: {}", e.getMessage());
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return null;
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}
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}
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/** 轮询等待任务完成 */
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private Optional<JsonNode> pollResult(String taskId) {
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Instant deadline = Instant.now().plusSeconds(timeoutSeconds);
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int pollCount = 0;
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try {
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// 给 worker 一点启动时间
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Thread.sleep(500);
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while (Instant.now().isBefore(deadline)) {
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pollCount++;
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HttpRequest request = HttpRequest.newBuilder()
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.uri(URI.create(url + "/ai/tasks/" + taskId))
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.timeout(Duration.ofSeconds(10))
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.GET()
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.build();
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try {
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HttpResponse<String> response = httpClient.send(request, HttpResponse.BodyHandlers.ofString());
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if (response.statusCode() == 404) {
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log.warn("lpt-ai 任务 {} 不存在（可能已过期或被清理）", taskId);
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return Optional.empty();
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}
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if (response.statusCode() != 200) {
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log.warn("lpt-ai 查询任务失败: taskId={}, status={}", taskId, response.statusCode());
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// 临时故障，继续轮询
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Thread.sleep(2000);
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continue;
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}
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JsonNode json = objectMapper.readTree(response.body());
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String status = json.path("status").asText("");
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switch (status) {
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case "done":
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log.info("lpt-ai 任务完成: taskId={}, 轮询次数={}", taskId, pollCount);
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return Optional.ofNullable(json.path("result"));
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case "failed":
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String err = json.path("error").asText("未知错误");
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log.warn("lpt-ai 任务失败: taskId={}, error={}", taskId, err);
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return Optional.empty();
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case "pending":
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case "running":
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// 还在处理，继续轮询
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break;
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default:
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log.warn("lpt-ai 未知任务状态: taskId={}, status={}", taskId, status);
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return Optional.empty();
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}
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} catch (Exception e) {
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log.warn("lpt-ai 轮询异常 (第{}次): {}", pollCount, e.getMessage());
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}
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// 退避：前 10 次 2s，之后 5s
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long delay = pollCount <= 10 ? 2000 : 5000;
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Thread.sleep(delay);
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}
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log.warn("lpt-ai 任务超时: taskId={}, timeout={}s", taskId, timeoutSeconds);
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return Optional.empty();
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} catch (InterruptedException e) {
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Thread.currentThread().interrupt();
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log.warn("lpt-ai 轮询被中断: taskId={}", taskId);
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return Optional.empty();
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}
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}
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}
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