yumoqing
d6e4221a7b
- llm table: add status field (published/unpublished, default unpublished)
- User-facing queries: filter by status='published' in 11 query points:
- utils.py: get_llms_by_catelog_to_customer, get_llms_by_catelog,
get_llm, get_llmproviders, get_llms_sort_by_provider
- v1 endpoints: chat/completions, image/generations, video/generations
- user pages: t2t, get_type_llms, list_catelog_models,
list_paging_catelog_llms, llmcheck
- CRUD: status column visible/editable with select dropdown
- Admin CRUD list shows ALL models regardless of status
- Migration SQL: sql/add_status_field.sql (existing models set to published)
89 lines
2.4 KiB
Plaintext
89 lines
2.4 KiB
Plaintext
# OpenAI-compatible Video Generation API
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# POST /v1/video/generations
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# Required params: model, llmcatelogid
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# Optional params: prompt, image_url, duration, resolution, n, etc.
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#
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# Example request:
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# {
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# "model": "keling-2.1",
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# "llmcatelogid": "文生视频",
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# "prompt": "A beautiful sunset over the ocean",
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# "duration": "5s",
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# "resolution": "1080p"
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# }
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#
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# Response (async task):
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# {
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# "id": "vid_xxx",
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# "object": "video.generation",
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# "model": "keling-2.1",
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# "status": "submitted",
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# "taskid": "task_xxx",
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# "created": 1234567890
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# }
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import json
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import time
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from functools import partial
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from appPublic.log import debug
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from appPublic.dictObject import DictObject
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from appPublic.uniqueID import getID
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from appPublic.timeUtils import curDateString, timestampstr
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from sqlor.dbpools import get_sor_context
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debug(f'{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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if not params_kw.llmcatelogid:
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d = return_error('Missing required parameter: llmcatelogid')
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return json_response(d, status=400)
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if not params_kw.prompt:
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d = return_error('Missing required parameter: prompt')
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return json_response(d, status=400)
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lctype = params_kw.llmcatelogid
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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.name = ${lctype}$
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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, {
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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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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 and attach to params
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if not params_kw.transno:
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params_kw.transno = getID()
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# Call inference (video/image generation is typically async via callback)
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return await inference(request, env=env)
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