perf: 18个v1 DSPY全改用llmid缓存,去3表JOIN+DB连接
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load_test.py
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163
load_test.py
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#!/usr/bin/env python3
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"""并发压力测试 llmage /v1/chat/completions，统计 TTFB / 完成时间 / QPM"""
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import asyncio
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import aiohttp
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import time
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import json
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import sys
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import statistics
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from dataclasses import dataclass, field
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from typing import List
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URL = "https://token.opencomputing.cn/llmage/v1/chat/completions"
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TOKEN = "V9J41PngWBUU6gdHWJWDJ"
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MODEL = "qwen3.6-35b-a3b"
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DURATION = 180 # 3 分钟
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CONCURRENCIES = [10, 50, 100, 200]
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@dataclass
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class ReqStat:
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prompt_idx: int
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start_ts: float
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first_byte_ts: float | None = None
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end_ts: float | None = None
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async def worker(session: aiohttp.ClientSession, idx: int, stats_out: list):
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"""单个请求：发送 stream 请求，记录首字时间和完成时间"""
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prompt = f"请用一句话介绍你自己，编号{idx}"
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payload = {
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"model": MODEL,
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"stream": True,
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"messages": [{"role": "user", "content": prompt}],
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}
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stat = ReqStat(prompt_idx=idx, start_ts=time.monotonic())
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try:
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async with session.post(
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URL,
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json=payload,
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {TOKEN}",
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},
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timeout=aiohttp.ClientTimeout(total=120),
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) as resp:
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first = True
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async for line in resp.content:
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if first:
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stat.first_byte_ts = time.monotonic()
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first = False
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# 读完所有 chunk 才算完成
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stat.end_ts = time.monotonic()
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except Exception as e:
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# 异常请求也记录（TTFB=None 表示失败）
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stat.end_ts = time.monotonic()
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stats_out.append(stat)
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async def run_concurrency(concurrency: int):
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"""以固定并发运行 DURATION 秒，持续发起新请求"""
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stats: List[ReqStat] = []
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idx = 0
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stop_at = time.monotonic() + DURATION
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connector = aiohttp.TCPConnector(limit=concurrency + 20, force_close=True)
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async with aiohttp.ClientSession(connector=connector) as session:
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tasks: list[asyncio.Task] = []
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while time.monotonic() < stop_at:
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# 保持并发数：补满到 concurrency
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while len(tasks) < concurrency and time.monotonic() < stop_at:
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idx += 1
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tasks.append(
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asyncio.create_task(worker(session, idx, stats))
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)
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if not tasks:
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break
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# 等待任意一个完成，腾出槽位
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done, tasks = await asyncio.wait(
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tasks, return_when=asyncio.FIRST_COMPLETED, timeout=0.5
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)
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# 清理已完成的
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tasks = list(tasks)
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# 时间到，等待所有进行中的请求完成
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if tasks:
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await asyncio.wait(tasks)
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return stats
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def analyze(name: str, stats: List[ReqStat]):
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"""分析并打印统计"""
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ttfb_list = [s.first_byte_ts - s.start_ts for s in stats if s.first_byte_ts]
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total_list = [s.end_ts - s.start_ts for s in stats if s.end_ts and s.first_byte_ts]
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failed = sum(1 for s in stats if s.first_byte_ts is None)
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total_req = len(stats)
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elapsed = DURATION
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qpm = total_req / (elapsed / 60)
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print(f"\n{'='*60}")
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print(f" 并发={name} | 运行{DURATION}s | 总请求={total_req} | 失败={failed}")
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print(f"{'='*60}")
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if ttfb_list:
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print(f" TTFB (s): min={min(ttfb_list):.3f} avg={statistics.mean(ttfb_list):.3f} "
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f"p50={statistics.median(ttfb_list):.3f} p95={_pct(ttfb_list, 95):.3f} p99={_pct(ttfb_list, 99):.3f}")
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if total_list:
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print(f" 完成 (s): min={min(total_list):.3f} avg={statistics.mean(total_list):.3f} "
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f"p50={statistics.median(total_list):.3f} p95={_pct(total_list, 95):.3f} p99={_pct(total_list, 99):.3f}")
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print(f" QPM: {qpm:.1f}")
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print(f" QPS: {total_req / elapsed:.1f}")
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# 按分钟分段统计
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for minute in range(int(elapsed / 60)):
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win_start = minute * 60
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win_end = (minute + 1) * 60
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cnt = sum(1 for s in stats if s.end_ts and (s.end_ts - s.start_ts) >= 0
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and win_start <= (s.start_ts - stats[0].start_ts) < win_end)
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print(f" 第{minute+1}分钟完成请求数: {cnt}")
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return {
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"concurrency": name,
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"total": total_req,
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"failed": failed,
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"ttfb_avg": statistics.mean(ttfb_list) if ttfb_list else None,
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"ttfb_p50": statistics.median(ttfb_list) if ttfb_list else None,
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"ttfb_p95": _pct(ttfb_list, 95) if ttfb_list else None,
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"total_avg": statistics.mean(total_list) if total_list else None,
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"total_p50": statistics.median(total_list) if total_list else None,
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"qpm": qpm,
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}
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def _pct(data, p):
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return sorted(data)[int(len(data) * p / 100)]
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async def main():
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results = []
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for c in CONCURRENCIES:
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print(f"\n>>> 开始测试 并发={c} ...")
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stats = await run_concurrency(c)
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r = analyze(str(c), stats)
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results.append(r)
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# 汇总表格
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print(f"\n{'='*60}")
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print(" 汇总对比")
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print(f"{'='*60}")
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print(f" {'并发':>6} {'总请求':>8} {'失败':>5} {'TTFB_avg':>9} {'TTFB_p50':>9} {'TTFB_p95':>9} {'完成_avg':>9} {'QPM':>8}")
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for r in results:
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print(f" {r['concurrency']:>6} {r['total']:>8} {r['failed']:>5} "
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f"{r['ttfb_avg']:.3f}s" if r['ttfb_avg'] else "N/A".rjust(9) + " "
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f"{(r['ttfb_p50'] or 0):.3f}s".rjust(9) + " "
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f"{(r['ttfb_p95'] or 0):.3f}s".rjust(9) + " "
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f"{r['total_avg']:.3f}s".rjust(9) if r['total_avg'] else "N/A".rjust(9))
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if __name__ == "__main__":
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asyncio.run(main())
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@ -41,22 +41,11 @@ if not params_kw.prompt:
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lctype = params_kw.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 and catalog type through llm_api_map
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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 = ${lctype}$ OR b.name = ${lctype}$)
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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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'lctype': lctype,
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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 for catalog {lctype}')
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return openai_400()
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params_kw.llmid = recs[0].id
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llmid = await env.get_llmid_cached(env, params_kw.model, lctype)
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if not llmid:
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debug(f'{params_kw.model=} not found for catalog {lctype}')
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return openai_400()
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params_kw.llmid = llmid
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params_kw.llmcatelogid = lctype
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debug(f'{params_kw.llmid=}')
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@ -38,22 +38,11 @@ if not params_kw.audio_file:
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lctype = params_kw.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 and catalog type through llm_api_map
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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 = ${lctype}$ OR b.name = ${lctype}$)
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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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'lctype': lctype,
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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 for catalog {lctype}')
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return openai_400()
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params_kw.llmid = recs[0].id
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llmid = await env.get_llmid_cached(env, params_kw.model, lctype)
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if not llmid:
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debug(f'{params_kw.model=} not found for catalog {lctype}')
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return openai_400()
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params_kw.llmid = llmid
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params_kw.llmcatelogid = lctype
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debug(f'{params_kw.llmid=}')
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@ -39,22 +39,11 @@ if not params_kw.prompt:
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lctype = params_kw.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 and catalog type through llm_api_map
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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 = ${lctype}$ OR b.name = ${lctype}$)
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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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'lctype': lctype,
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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 for catalog {lctype}')
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return openai_400()
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params_kw.llmid = recs[0].id
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llmid = await env.get_llmid_cached(env, params_kw.model, lctype)
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if not llmid:
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debug(f'{params_kw.model=} not found for catalog {lctype}')
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return openai_400()
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params_kw.llmid = llmid
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params_kw.llmcatelogid = lctype
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debug(f'{params_kw.llmid=}')
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@ -1,34 +1,30 @@
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# KTV Media API - asr-transcribe
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# /v1/media/asr-transcribe
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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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return openai_403()
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catelogid = 'ktv_pipeline'
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model_name = 'asr-transcribe'
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params_kw.model = model_name
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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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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.model=${model}$
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and a.status = 'published'"""
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recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
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if len(recs) == 0:
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return openai_400()
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params_kw.llmid = recs[0].id
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params_kw.llmcatelogid = catelogid
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f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
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if not f:
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return openai_429()
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return await inference(request, env=env)
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1|# KTV Media API - asr-transcribe
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2|# /v1/media/asr-transcribe
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3|
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4|debug_params('params_kw', params_kw)
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5|
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6|userid = await get_user()
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7|userorgid = await get_userorgid()
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8|if userid is None:
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9| return openai_403()
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10|
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11|catelogid = 'ktv_pipeline'
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12|model_name = 'asr-transcribe'
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13|params_kw.model = model_name
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14|params_kw.catelogid = catelogid
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15|
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16|env = request._run_ns
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17|# llmid from cache (model+catelogid -> llmid)
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llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
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if not llmid:
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debug(f'model not found: params_kw.model')
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return openai_400()
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params_kw.llmid = llmid
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28| params_kw.llmcatelogid = catelogid
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29|
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30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
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31|if not f:
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32| return openai_429()
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33|
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34|return await inference(request, env=env)
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35|
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@ -1,34 +1,30 @@
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# KTV Media API - demucs-separate
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# /v1/media/demucs-separate
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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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return openai_403()
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catelogid = 'ktv_pipeline'
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model_name = 'demucs-separate'
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params_kw.model = model_name
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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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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.model=${model}$
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and a.status = 'published'"""
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recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
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if len(recs) == 0:
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return openai_400()
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params_kw.llmid = recs[0].id
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params_kw.llmcatelogid = catelogid
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f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
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if not f:
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return openai_429()
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return await inference(request, env=env)
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1|# KTV Media API - demucs-separate
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2|# /v1/media/demucs-separate
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3|
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4|debug_params('params_kw', params_kw)
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5|
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6|userid = await get_user()
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7|userorgid = await get_userorgid()
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8|if userid is None:
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9| return openai_403()
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10|
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11|catelogid = 'ktv_pipeline'
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12|model_name = 'demucs-separate'
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13|params_kw.model = model_name
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14|params_kw.catelogid = catelogid
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15|
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16|env = request._run_ns
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17|# llmid from cache (model+catelogid -> llmid)
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llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
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if not llmid:
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debug(f'model not found: params_kw.model')
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return openai_400()
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params_kw.llmid = llmid
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28| params_kw.llmcatelogid = catelogid
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29|
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30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
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31|if not f:
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32| return openai_429()
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33|
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34|return await inference(request, env=env)
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35|
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@ -1,34 +1,30 @@
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# KTV Media API - face-compare
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# /v1/media/face-compare
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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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return openai_403()
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catelogid = 'ktv_pipeline'
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model_name = 'face-compare'
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params_kw.model = model_name
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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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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.model=${model}$
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and a.status = 'published'"""
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recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
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if len(recs) == 0:
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return openai_400()
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params_kw.llmid = recs[0].id
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params_kw.llmcatelogid = catelogid
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|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - face-compare
|
||||
2|# /v1/media/face-compare
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'face-compare'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - face-detect
|
||||
# /v1/media/face-detect
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'face-detect'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - face-detect
|
||||
2|# /v1/media/face-detect
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'face-detect'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - face-recognize
|
||||
# /v1/media/face-recognize
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'face-recognize'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - face-recognize
|
||||
2|# /v1/media/face-recognize
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'face-recognize'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - merge-video
|
||||
# /v1/media/merge-video
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'merge-video'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - merge-video
|
||||
2|# /v1/media/merge-video
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'merge-video'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - realesrgan-upscale
|
||||
# /v1/media/realesrgan-upscale
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'realesrgan-upscale'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - realesrgan-upscale
|
||||
2|# /v1/media/realesrgan-upscale
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'realesrgan-upscale'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - rvc-convert
|
||||
# /v1/media/rvc-convert
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'rvc-convert'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - rvc-convert
|
||||
2|# /v1/media/rvc-convert
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'rvc-convert'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - songrate-evaluate
|
||||
# /v1/media/songrate-evaluate
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'songrate-evaluate'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - songrate-evaluate
|
||||
2|# /v1/media/songrate-evaluate
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'songrate-evaluate'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - subtitle-render
|
||||
# /v1/media/subtitle-render
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'subtitle-render'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - subtitle-render
|
||||
2|# /v1/media/subtitle-render
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'subtitle-render'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - synth-generate
|
||||
# /v1/media/synth-generate
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'synth-generate'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - synth-generate
|
||||
2|# /v1/media/synth-generate
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'synth-generate'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -1,34 +1,30 @@
|
||||
# KTV Media API - video-eval-evaluate
|
||||
# /v1/media/video-eval-evaluate
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
return openai_403()
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
model_name = 'video-eval-evaluate'
|
||||
params_kw.model = model_name
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.model=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {'catelogid': catelogid, 'model': model_name})
|
||||
if len(recs) == 0:
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
return openai_429()
|
||||
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Media API - video-eval-evaluate
|
||||
2|# /v1/media/video-eval-evaluate
|
||||
3|
|
||||
4|debug_params('params_kw', params_kw)
|
||||
5|
|
||||
6|userid = await get_user()
|
||||
7|userorgid = await get_userorgid()
|
||||
8|if userid is None:
|
||||
9| return openai_403()
|
||||
10|
|
||||
11|catelogid = 'ktv_pipeline'
|
||||
12|model_name = 'video-eval-evaluate'
|
||||
13|params_kw.model = model_name
|
||||
14|params_kw.catelogid = catelogid
|
||||
15|
|
||||
16|env = request._run_ns
|
||||
17|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
28| params_kw.llmcatelogid = catelogid
|
||||
29|
|
||||
30|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
31|if not f:
|
||||
32| return openai_429()
|
||||
33|
|
||||
34|return await inference(request, env=env)
|
||||
35|
|
||||
@ -47,22 +47,11 @@ if not params_kw.lyrics:
|
||||
lctype = params_kw.catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
# Look up llm by model name and catalog type through llm_api_map
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${lctype}$ OR b.name = ${lctype}$)
|
||||
and a.name=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {
|
||||
'lctype': lctype,
|
||||
'model': params_kw.model
|
||||
})
|
||||
if len(recs) == 0:
|
||||
debug(f'{params_kw.model=} not found for catalog {lctype}')
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model, lctype)
|
||||
if not llmid:
|
||||
debug(f'{params_kw.model=} not found for catalog {lctype}')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
params_kw.llmcatelogid = lctype
|
||||
|
||||
debug(f'{params_kw.llmid=}')
|
||||
|
||||
@ -1,74 +1,64 @@
|
||||
# KTV Pipeline / GPU Service Inference API
|
||||
# POST /v1/pipeline/submit
|
||||
# catelogid 固定为 ktv_pipeline，无需传 catelogid
|
||||
#
|
||||
# Required params:
|
||||
# model: string - 模型名称，如 "ky-asr-transcribe"
|
||||
#
|
||||
# 各服务特定参数见 API 文档
|
||||
#
|
||||
# 同步服务直接返回结果：
|
||||
# {
|
||||
# "taskid": "luid_xxx",
|
||||
# "taskstatus": "SUCCEEDED",
|
||||
# ...服务特定结果字段...,
|
||||
# "usage": {...}
|
||||
# }
|
||||
#
|
||||
# 异步服务返回任务信息：
|
||||
# {
|
||||
# "taskid": "luid_xxx",
|
||||
# "taskstatus": "PENDING"
|
||||
# }
|
||||
# 异步结果通过 /v1/tasks?taskid=xxx 查询
|
||||
|
||||
debug_params('params_kw', params_kw)
|
||||
|
||||
userid = await get_user()
|
||||
userorgid = await get_userorgid()
|
||||
if userid is None:
|
||||
debug('need login')
|
||||
return openai_403()
|
||||
|
||||
# Validate required parameters
|
||||
if not params_kw.model:
|
||||
d = return_error('Missing required parameter: model')
|
||||
return json_response(d, status=400)
|
||||
|
||||
catelogid = 'ktv_pipeline'
|
||||
params_kw.catelogid = catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
# Look up llm by model name through llm_api_map (fixed catelogid=ktv_pipeline)
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${catelogid}$ OR b.name = ${catelogid}$)
|
||||
and a.name=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {
|
||||
'catelogid': catelogid,
|
||||
'model': params_kw.model
|
||||
})
|
||||
if len(recs) == 0:
|
||||
debug(f'{params_kw.model=} not found under ktv_pipeline catalog')
|
||||
d = return_error(f'Model "{params_kw.model}" not found or not available')
|
||||
return json_response(d, status=400)
|
||||
params_kw.llmid = recs[0].id
|
||||
params_kw.llmcatelogid = catelogid
|
||||
|
||||
debug(f'{params_kw.llmid=}')
|
||||
|
||||
# Check balance
|
||||
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
if not f:
|
||||
debug(f'{userid=} balance not enough')
|
||||
return openai_429()
|
||||
|
||||
# Generate task ID
|
||||
if not params_kw.transno:
|
||||
params_kw.transno = getID()
|
||||
|
||||
# Call inference
|
||||
return await inference(request, env=env)
|
||||
1|# KTV Pipeline / GPU Service Inference API
|
||||
2|# POST /v1/pipeline/submit
|
||||
3|# catelogid 固定为 ktv_pipeline，无需传 catelogid
|
||||
4|#
|
||||
5|# Required params:
|
||||
6|# model: string - 模型名称，如 "ky-asr-transcribe"
|
||||
7|#
|
||||
8|# 各服务特定参数见 API 文档
|
||||
9|#
|
||||
10|# 同步服务直接返回结果：
|
||||
11|# {
|
||||
12|# "taskid": "luid_xxx",
|
||||
13|# "taskstatus": "SUCCEEDED",
|
||||
14|# ...服务特定结果字段...,
|
||||
15|# "usage": {...}
|
||||
16|# }
|
||||
17|#
|
||||
18|# 异步服务返回任务信息：
|
||||
19|# {
|
||||
20|# "taskid": "luid_xxx",
|
||||
21|# "taskstatus": "PENDING"
|
||||
22|# }
|
||||
23|# 异步结果通过 /v1/tasks?taskid=xxx 查询
|
||||
24|
|
||||
25|debug_params('params_kw', params_kw)
|
||||
26|
|
||||
27|userid = await get_user()
|
||||
28|userorgid = await get_userorgid()
|
||||
29|if userid is None:
|
||||
30| debug('need login')
|
||||
31| return openai_403()
|
||||
32|
|
||||
33|# Validate required parameters
|
||||
34|if not params_kw.model:
|
||||
35| d = return_error('Missing required parameter: model')
|
||||
36| return json_response(d, status=400)
|
||||
37|
|
||||
38|catelogid = 'ktv_pipeline'
|
||||
39|params_kw.catelogid = catelogid
|
||||
40|
|
||||
41|env = request._run_ns
|
||||
42|# llmid from cache (model+catelogid -> llmid)
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model or 'qwen3-max', 'ktv_pipeline')
|
||||
if not llmid:
|
||||
debug(f'model not found: params_kw.model')
|
||||
return {{error_response}}
|
||||
params_kw.llmid = llmid
|
||||
59| params_kw.llmcatelogid = catelogid
|
||||
60|
|
||||
61|debug(f'{params_kw.llmid=}')
|
||||
62|
|
||||
63|# Check balance
|
||||
64|f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
|
||||
65|if not f:
|
||||
66| debug(f'{userid=} balance not enough')
|
||||
67| return openai_429()
|
||||
68|
|
||||
69|# Generate task ID
|
||||
70|if not params_kw.transno:
|
||||
71| params_kw.transno = getID()
|
||||
72|
|
||||
73|# Call inference
|
||||
74|return await inference(request, env=env)
|
||||
75|
|
||||
@ -47,22 +47,11 @@ if not params_kw.prompt:
|
||||
lctype = params_kw.catelogid
|
||||
|
||||
env = request._run_ns
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
# Look up llm by model name and catalog type through llm_api_map
|
||||
sql = """select distinct a.* from llm a
|
||||
join llm_api_map m on a.id = m.llmid
|
||||
join llmcatelog b on m.llmcatelogid = b.id
|
||||
where (b.id = ${lctype}$ OR b.name = ${lctype}$)
|
||||
and a.name=${model}$
|
||||
and a.status = 'published'"""
|
||||
recs = await sor.sqlExe(sql, {
|
||||
'lctype': lctype,
|
||||
'model': params_kw.model
|
||||
})
|
||||
if len(recs) == 0:
|
||||
debug(f'{params_kw.model=} not found for catalog {lctype}')
|
||||
return openai_400()
|
||||
params_kw.llmid = recs[0].id
|
||||
llmid = await env.get_llmid_cached(env, params_kw.model, lctype)
|
||||
if not llmid:
|
||||
debug(f'{params_kw.model=} not found for catalog {lctype}')
|
||||
return openai_400()
|
||||
params_kw.llmid = llmid
|
||||
params_kw.llmcatelogid = lctype
|
||||
|
||||
debug(f'{params_kw.llmid=}')
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user