feat: 新增 /v1/pipeline/submit 端点 + 更新KTV Pipeline API文档为14个原子化模型
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docs/API.md
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docs/API.md
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请求成功后自动创建 `llmusage` 记录，状态为 `created`。后台定时任务会定期执行计费流程。
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# 视频制作 API (KTV产线)
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# KTV Pipeline API（视频制作）
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Base url: `https://token.opencomputing.cn/llmage/v1`
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供应商: 开元云(北京)科技, 28端点覆盖KTV全流程。
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供应商: 开元云(北京)科技, 14个原子化GPU端点覆盖KTV全流程。
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## 调用方式
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所有模型通过 `POST /v1/pipeline/submit` 调用，`catelogid` 固定为 `ktv_pipeline`（无需传参）。
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### 通用格式
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```json
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// 请求
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{"model": "ky-xxx", ...服务特定参数...}
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// 同步响应
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{"taskid": "luid_xxx", "taskstatus": "SUCCEEDED", "usage": {...}}
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// 异步提交响应
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{"taskid": "luid_xxx", "taskstatus": "PENDING"}
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// 异步结果通过 GET /v1/tasks?taskid=xxx 查询
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```
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## 模型列表
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| model | 功能 | 计费 |
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|-------|------|------|
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| `ktv-media-server` | 流水线/状态/合并/校准/字幕 | 2元/次+0.03元/秒 |
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| `ktv-video-eval` | 视频质量评估 | 0.05元/秒 |
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| `ktv-realesrgan` | 超分辨率 | 0.5元/张 |
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| `ktv-asr` | 语音识别 | 0.01元/秒 |
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| `ktv-face` | 人脸检测/识别/比对 | 0.1元/次 |
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| `ktv-graph` | 图谱查询/写入 | 0.05~0.1元/次 |
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| `ktv-ner` | 实体识别 | 0.5元/千字 |
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| `ktv-songrate` | 歌曲评分 | 0.005元/秒 |
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| `ktv-synth` | 音乐合成 | 0.2元/秒 |
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| `ktv-demucs` | 音频分离 | 0.02元/秒 |
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| `ktv-rvc` | 声音转换 | 0.15元/秒 |
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| model | 功能 | 模式 | 计费 |
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|-------|------|------|------|
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| `ky-asr-transcribe` | 语音转文字+时间戳 | 同步 | 0.01元/次 |
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| `ky-demucs-separate` | 人声/伴奏分离 | 同步 | 0.01元/次 |
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| `ky-face-detect` | 人脸检测 | 同步 | 0.01元/次 |
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| `ky-face-recognize` | 人脸识别 | 同步 | 0.01元/次 |
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| `ky-face-compare` | 人脸比对 | 同步 | 0.01元/次 |
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| `ky-subtitle-render` | 歌词→ASS字幕渲染 | 同步 | 0.01元/次 |
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| `ky-merge-video` | 视频+音频+字幕合并 | 同步 | 0.01元/次 |
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| `ky-songrate-evaluate` | AI歌曲质量评分 | 同步 | 0.01元/次 |
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| `ky-synth-generate` | AI歌声合成 | **异步** | 0.01元/次 |
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| `ky-synth-status` | 合成任务状态查询 | 同步 | 0.01元/次 |
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| `ky-realesrgan-upscale` | 图像/视频超分辨率 | **异步** | 0.01元/次 |
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| `ky-realesrgan-status` | 超分任务状态查询 | 同步 | 0.01元/次 |
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| `ky-rvc-convert` | 声音克隆/变声 | 同步 | 0.01元/次 |
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| `ky-video-eval-evaluate` | 视频质量评估 | 同步 | 0.01元/次 |
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调用方式: `/v1/chat/completions` + `model` 参数。详细参数见 dashboard_for_sage 文档。
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## 模型发现
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```bash
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curl 'https://token.opencomputing.cn/llmage/v1/models?catelogid=ktv_pipeline' \
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-H 'Authorization: Bearer ***'
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```
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详细参数说明和 curl 示例见 [dashboard_for_sage 文档](https://token.opencomputing.cn/dashboard_for_sage/api_doc.md)。
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74
wwwroot/v1/pipeline/submit/index.dspy
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74
wwwroot/v1/pipeline/submit/index.dspy
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# KTV Pipeline / GPU Service Inference API
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# POST /v1/pipeline/submit
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# catelogid 固定为 ktv_pipeline，无需传 catelogid
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#
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# Required params:
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# model: string - 模型名称，如 "ky-asr-transcribe"
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#
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# 各服务特定参数见 API 文档
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#
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# 同步服务直接返回结果：
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# {
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# "taskid": "luid_xxx",
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# "taskstatus": "SUCCEEDED",
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# ...服务特定结果字段...,
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# "usage": {...}
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# }
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#
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# 异步服务返回任务信息：
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# {
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# "taskid": "luid_xxx",
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# "taskstatus": "PENDING"
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# }
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# 异步结果通过 /v1/tasks?taskid=xxx 查询
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debug_params('params_kw', params_kw)
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userid = await get_user()
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userorgid = await get_userorgid()
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if userid is None:
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debug('need login')
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return openai_403()
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# Validate required parameters
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if not params_kw.model:
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d = return_error('Missing required parameter: model')
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return json_response(d, status=400)
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catelogid = 'ktv_pipeline'
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params_kw.catelogid = catelogid
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env = request._run_ns
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async with get_sor_context(env, 'llmage') as sor:
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# Look up llm by model name through llm_api_map (fixed catelogid=ktv_pipeline)
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sql = """select distinct a.* from llm a
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join llm_api_map m on a.id = m.llmid
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join llmcatelog b on m.llmcatelogid = b.id
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where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
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and a.name=${model}$
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and a.status = 'published'"""
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recs = await sor.sqlExe(sql, {
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'catelogid': catelogid,
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'model': params_kw.model
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})
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if len(recs) == 0:
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debug(f'{params_kw.model=} not found under ktv_pipeline catalog')
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d = return_error(f'Model "{params_kw.model}" not found or not available')
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return json_response(d, status=400)
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params_kw.llmid = recs[0].id
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params_kw.llmcatelogid = catelogid
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debug(f'{params_kw.llmid=}')
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# Check balance
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f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
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if not f:
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debug(f'{userid=} balance not enough')
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return openai_429()
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# Generate task ID
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if not params_kw.transno:
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params_kw.transno = getID()
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# Call inference
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return await inference(request, env=env)
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