51 lines
1.3 KiB
Plaintext
51 lines
1.3 KiB
Plaintext
async def gen():
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env = request._run_ns.copy()
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f = partial(inference_generator, request, params_kw=params_kw)
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if params_kw.stream:
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async for l in f():
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yield b'data: ' + l + b'\n'
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yield b'data: [DONE]'
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else:
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async for l in f():
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yield l
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debug(f'{params_kw=}')
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lctype='文生文'
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if params_kw.off_peak:
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off_peak = params_kw.off_peak
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if off_peak in [True, "Y" "y", 1, "1"]:
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off_peak = True
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else:
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off_peak = False
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params_kw.off_peak = off_peak
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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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if not params_kw.prompt and not params_kw.messages:
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d = return_error('Missing need data(prompt or messages)')
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return json_response(d, status=400)
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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 a.* from llm a, llmcatelog b
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where a.llmcatelogid=b.id
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and a.model=${model}$
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and b.name = ${lctype}$"""
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recs = await sor.sqlExe(sql, {
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'lctype': lctype,
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'model': params_kw.model or 'qwen3-max'
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})
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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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f = await checkCustomerBalance(params_kw.llmid, userorgid)
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if not f:
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return openai_429()
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return await env.stream_response(request, gen)
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env = DictObject(**globals())
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return await inference(request, env=env)
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