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feat: enhance model creation with UUID preservation option and implement Space model synchronization in ModelManager

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This commit is contained in:
Junyan Qin
2025-12-31 22:25:07 +08:00
parent 197258ae91
commit 96e40eaf25
11 changed files with 219 additions and 132 deletions
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60
src/langbot/pkg/provider/modelmgr/modelmgr.py
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@@ -41,6 +41,7 @@ class ModelManager:
self.requester_dict = requester_dict
await self.load_models_from_db()
await self.sync_new_models_from_space()
async def load_models_from_db(self):
"""Load models from database"""
@@ -89,6 +90,65 @@ class ModelManager:
except Exception as e:
self.ap.logger.error(f'Failed to load model {embedding_model.uuid}: {e}\n{traceback.format_exc()}')
async def sync_new_models_from_space(self):
"""Sync models from Space"""
space_model_provider = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_model.ModelProvider).where(
persistence_model.ModelProvider.requester == 'space-chat-completions'
)
)
result = space_model_provider.first()
if result is None:
raise provider_errors.ProviderNotFoundError('LangBot Models')
space_model_provider = result
# get the latest models from space
space_models = await self.ap.space_service.get_models()
exists_llm_models_uuids = [m['uuid'] for m in await self.ap.llm_model_service.get_llm_models()]
exists_embedding_models_uuids = [
m['uuid'] for m in await self.ap.embedding_models_service.get_embedding_models()
]
for space_model in space_models:
if space_model.category == 'chat':
uuid = space_model.uuid
if uuid in exists_llm_models_uuids:
continue
# model will be automatically loaded
await self.ap.llm_model_service.create_llm_model(
{
'uuid': space_model.uuid,
'name': space_model.model_id,
'provider_uuid': space_model_provider.uuid,
'abilities': space_model.llm_abilities or [],
'extra_args': {},
'prefered_ranking': space_model.featured_order,
},
preserve_uuid=True,
)
elif space_model.category == 'embedding':
uuid = space_model.uuid
if uuid in exists_embedding_models_uuids:
continue
# model will be automatically loaded
await self.ap.embedding_models_service.create_embedding_model(
{
'uuid': space_model.uuid,
'name': space_model.model_id,
'provider_uuid': space_model_provider.uuid,
'extra_args': {},
'prefered_ranking': space_model.featured_order,
},
preserve_uuid=True,
)
async def init_runtime_llm_model(
self,
model_info: dict,