296 lines
9.3 KiB
Markdown
296 lines
9.3 KiB
Markdown
# RunningHub集成实现清单
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## ✅ 已完成
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1. ✅ 创建Provider接口和DTO
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2. ✅ 数据库表扩展（V5迁移脚本）
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3. ✅ 配置文件扩展
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4. ✅ 实体类更新
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## 🔨 待实现（按优先级）
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### 1. 实现OpenAIProvider适配器
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**文件：** `src/main/java/com/dora/service/provider/impl/OpenAIProviderImpl.java`
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```java
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@Service
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@Slf4j
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@RequiredArgsConstructor
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public class OpenAIProviderImpl implements AIProvider {
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private final ThirdPartyApiService thirdPartyApiService;
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@Override
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public ProviderTaskResponse submitTask(ProviderTaskRequest request) {
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// 调用现有的 thirdPartyApiService
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// 同步返回结果
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}
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@Override
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public String getProviderName() {
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return "openai";
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}
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@Override
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public boolean isAsyncProvider() {
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return false; // OpenAI是同步API
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}
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}
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```
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### 2. 实现RunningHubProvider适配器
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**文件：** `src/main/java/com/dora/service/provider/impl/RunningHubProviderImpl.java`
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**关键逻辑：**
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```java
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@Override
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public ProviderTaskResponse submitTask(ProviderTaskRequest request) {
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// 1. 从providerConfig中获取webappId
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// 2. 构建nodeInfoList
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// - prompt节点
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// - model节点（portrait/landscape等）
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// - duration_seconds节点
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// 3. POST到 /task/openapi/ai-app/run
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// 4. 解析响应获取taskId
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// 5. 返回ProviderTaskResponse，status=PROCESSING
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}
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@Override
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public ProviderTaskStatus queryTaskStatus(String providerTaskId) {
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// POST到 /task/openapi/status
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// 解析响应：QUEUED/RUNNING/FAILED/SUCCESS
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}
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@Override
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public ProviderTaskResult getTaskResult(String providerTaskId) {
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// POST到 /task/openapi/outputs
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// 解析data数组，获取fileUrl
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}
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```
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**RunningHub请求DTO：**
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```java
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@Data
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class RunningHubSubmitRequest {
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private String webappId;
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private String apiKey;
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private List<RunningHubNodeInfo> nodeInfoList;
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}
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@Data
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class RunningHubNodeInfo {
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private String nodeId;
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private String fieldName;
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private String fieldValue;
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private String fieldData; // 可选
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private String description;
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}
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```
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### 3. 创建AIProviderService路由服务
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**文件：** `src/main/java/com/dora/service/AIProviderService.java`
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```java
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@Service
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@Slf4j
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@RequiredArgsConstructor
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public class AIProviderService {
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private final Map<String, AIProvider> providerMap;
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private final PointsConfigMapper pointsConfigMapper;
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@PostConstruct
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public void init() {
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// 初始化providerMap，key为providerType
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}
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public AIProvider getProvider(String modelName) {
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// 1. 从points_config表查询模型配置
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// 2. 获取providerType
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// 3. 从providerMap中获取对应的Provider
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PointsConfig config = pointsConfigMapper.findByModelName(modelName);
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String providerType = config.getProviderType();
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return providerMap.get(providerType);
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}
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}
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```
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### 4. 添加RunningHub轮询定时器
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**文件：** `src/main/java/com/dora/scheduler/RunningHubPollingScheduler.java`
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```java
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@Component
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@Slf4j
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@RequiredArgsConstructor
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public class RunningHubPollingScheduler {
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private final AiTaskMapper aiTaskMapper;
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private final AIProviderService providerService;
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private final AiTaskService aiTaskService;
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@Scheduled(fixedDelay = 5000) // 每5秒执行一次
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public void pollRunningHubTasks() {
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// 1. 查询 status='processing' 且 provider_type='runninghub' 的任务
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List<AiTask> tasks = aiTaskMapper.findProcessingTasksByProvider("runninghub");
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for (AiTask task : tasks) {
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try {
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AIProvider provider = providerService.getProvider(task.getModelName());
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// 2. 查询任务状态
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ProviderTaskStatus status = provider.queryTaskStatus(task.getProviderTaskId());
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// 3. 根据状态更新
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if (status.getStatus() == Status.SUCCESS) {
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// 获取结果
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ProviderTaskResult result = provider.getTaskResult(task.getProviderTaskId());
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// 更新任务为completed
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aiTaskService.markTaskCompleted(task.getTaskNo(), result.getFiles().get(0).getFileUrl());
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} else if (status.getStatus() == Status.FAILED) {
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// 标记为失败
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aiTaskService.markTaskFailed(task.getTaskNo(), status.getErrorMessage());
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}
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} catch (Exception e) {
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log.error("轮询RunningHub任务失败: {}", task.getTaskNo(), e);
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}
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}
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}
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}
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```
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### 5. 更新AiTaskMapper
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**文件：** `src/main/resources/mapper/AiTaskMapper.xml`
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```xml
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<!-- 新增：查询指定provider的processing任务 -->
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<select id="findProcessingTasksByProvider" parameterType="string" resultType="com.dora.entity.AiTask">
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SELECT * FROM ai_task
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WHERE status = 'processing'
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AND provider_type = #{providerType}
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AND is_deleted = 0
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ORDER BY update_time ASC
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LIMIT 100
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</select>
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<!-- 更新insert语句，添加provider字段 -->
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<insert id="insert" parameterType="com.dora.entity.AiTask" useGeneratedKeys="true" keyProperty="id">
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INSERT INTO ai_task (
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task_no, user_id, model_name, task_type, provider_type, provider_task_id, provider_response,
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prompt, image_url, image_base64, aspect_ratio,
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status, progress, progress_message, points_frozen, points_consumed, result_url,
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error_message, queue_time, start_time, complete_time, expire_time
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)
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VALUES (
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#{taskNo}, #{userId}, #{modelName}, #{taskType}, #{providerType}, #{providerTaskId}, #{providerResponse},
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#{prompt}, #{imageUrl}, #{imageBase64}, #{aspectRatio},
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#{status}, #{progress}, #{progressMessage}, #{pointsFrozen}, #{pointsConsumed}, #{resultUrl},
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#{errorMessage}, #{queueTime}, #{startTime}, #{completeTime}, #{expireTime}
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)
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</insert>
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```
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### 6. 更新AiTaskServiceImpl
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**文件：** `src/main/java/com/dora/service/impl/AiTaskServiceImpl.java`
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```java
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@Override
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@Transactional(rollbackFor = Exception.class)
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public AiTask createTask(CreateTaskDto createTaskDto) {
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// 1. 验证模型并获取价格
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PointsConfig pointsConfig = pointsConfigMapper.findByModelName(createTaskDto.getModelName());
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// 2. 扣除积分
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// ...
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// 3. 创建任务
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AiTask task = new AiTask();
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// ... 设置基本字段
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task.setProviderType(pointsConfig.getProviderType()); // 设置provider类型
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aiTaskMapper.insert(task);
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// 4. 判断provider类型
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if ("runninghub".equals(pointsConfig.getProviderType())) {
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// RunningHub：直接提交到服务商
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submitToRunningHub(task, pointsConfig);
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} else {
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// OpenAI：加入队列，由原有流程处理
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queueService.enqueue(task.getModelName(), task.getTaskNo());
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updateTaskStatus(task.getTaskNo(), "queued", "任务已进入等待队列");
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}
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return task;
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}
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private void submitToRunningHub(AiTask task, PointsConfig config) {
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try {
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AIProvider provider = aiProviderService.getProvider(task.getModelName());
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// 构建请求
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ProviderTaskRequest request = ProviderTaskRequest.builder()
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.modelName(task.getModelName())
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.prompt(task.getPrompt())
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.imageUrl(task.getImageUrl())
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.imageBase64(task.getImageBase64())
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.providerConfig(parseProviderConfig(config.getProviderConfig()))
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.build();
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// 提交任务
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ProviderTaskResponse response = provider.submitTask(request);
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// 更新任务
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task.setProviderTaskId(response.getProviderTaskId());
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task.setProviderResponse(JSON.toJSONString(response.getRawResponse()));
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task.setStatus("processing");
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task.setStartTime(LocalDateTime.now());
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aiTaskMapper.update(task);
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} catch (Exception e) {
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log.error("提交RunningHub任务失败: {}", task.getTaskNo(), e);
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markTaskFailed(task.getTaskNo(), "提交失败: " + e.getMessage());
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}
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}
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```
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## 🧪 测试步骤
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### 1. 数据库迁移
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```bash
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mysql -u root -p your_database < V5__add_provider_support.sql
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```
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### 2. 配置RunningHub API Key
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修改`application.yml`中的`ai.providers.runninghub.api-key`
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### 3. 测试RunningHub模型
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```bash
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curl -X POST "http://localhost:8081/user/ai/tasks/submit" \
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-H "Authorization: Bearer YOUR_JWT_OR_API_KEY" \
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-H "Content-Type: application/json" \
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-d '{
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"modelName": "rh_sora2_portrait",
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"prompt": "测试视频生成"
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}'
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```
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### 4. 查看轮询日志
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```bash
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tail -f logs/application.log | grep "RunningHub"
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```
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## 📝 注意事项
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1. **配置管理**：RunningHub的webappId需要从数据库的provider_config中读取
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2. **错误处理**：RunningHub API可能返回各种错误，需要完善异常处理
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3. **超时控制**：设置最大轮询次数，防止任务无限轮询
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4. **并发限制**：轮询时要控制并发数，避免给RunningHub造成压力
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5. **日志记录**：详细记录每次API调用的请求和响应，便于排查问题
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## 🎯 预期效果
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完成后，系统将支持：
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- ✅ OpenAI格式的同步API（原有功能，无影响）
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- ✅ RunningHub的异步API（新功能）
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- ✅ 用户无感切换，根据模型自动选择服务商
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- ✅ 统一的任务管理和状态追踪
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