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refactor: model management

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This commit is contained in:
Junyan Qin
2026-01-01 02:00:24 +08:00
parent 96e40eaf25
commit 4528000fc4
20 changed files with 238 additions and 137 deletions
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src/langbot/pkg/api/http/service/model.py
+43 -13
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@@ -85,10 +85,17 @@ class LLMModelsService:
await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_model.LLMModel).values(**model_data))
llm_model = await self.get_llm_model(model_data['uuid'])
await self.ap.model_mgr.load_llm_model(llm_model)
runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
if runtime_provider is None:
raise Exception('provider not found')
# Check if default pipeline has no model bound
runtime_llm_model = await self.ap.model_mgr.load_llm_model_with_provider(
persistence_model.LLMModel(**model_data),
runtime_provider,
)
self.ap.model_mgr.llm_models.append(runtime_llm_model)
# set the default pipeline model to this model
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
persistence_pipeline.LegacyPipeline.is_default == True
@@ -152,8 +159,16 @@ class LLMModelsService:
)
await self.ap.model_mgr.remove_llm_model(model_uuid)
llm_model = await self.get_llm_model(model_uuid)
await self.ap.model_mgr.load_llm_model(llm_model)
runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
if runtime_provider is None:
raise Exception('provider not found')
runtime_llm_model = await self.ap.model_mgr.load_llm_model_with_provider(
persistence_model.LLMModel(**model_data),
runtime_provider,
)
self.ap.model_mgr.llm_models.append(runtime_llm_model)
async def delete_llm_model(self, model_uuid: str) -> None:
"""Delete an LLM model"""
@@ -174,10 +189,10 @@ class LLMModelsService:
if runtime_llm_model is None:
raise Exception('model not found')
else:
runtime_llm_model = await self.ap.model_mgr.init_runtime_llm_model(model_data)
runtime_llm_model = await self.ap.model_mgr.init_temporary_runtime_llm_model(model_data)
extra_args = model_data.get('extra_args', {})
await runtime_llm_model.requester.invoke_llm(
await runtime_llm_model.provider.requester.invoke_llm(
query=None,
model=runtime_llm_model,
messages=[provider_message.Message(role='user', content='Hello, world! Please just reply a "Hello".')],
@@ -244,8 +259,15 @@ class EmbeddingModelsService:
sqlalchemy.insert(persistence_model.EmbeddingModel).values(**model_data)
)
embedding_model = await self.get_embedding_model(model_data['uuid'])
await self.ap.model_mgr.load_embedding_model(embedding_model)
runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
if runtime_provider is None:
raise Exception('provider not found')
runtime_embedding_model = await self.ap.model_mgr.load_embedding_model_with_provider(
persistence_model.EmbeddingModel(**model_data),
runtime_provider,
)
self.ap.model_mgr.embedding_models.append(runtime_embedding_model)
return model_data['uuid']
@@ -298,8 +320,16 @@ class EmbeddingModelsService:
)
await self.ap.model_mgr.remove_embedding_model(model_uuid)
embedding_model = await self.get_embedding_model(model_uuid)
await self.ap.model_mgr.load_embedding_model(embedding_model)
runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
if runtime_provider is None:
raise Exception('provider not found')
runtime_embedding_model = await self.ap.model_mgr.load_embedding_model_with_provider(
persistence_model.EmbeddingModel(**model_data),
runtime_provider,
)
self.ap.model_mgr.embedding_models.append(runtime_embedding_model)
async def delete_embedding_model(self, model_uuid: str) -> None:
"""Delete an embedding model"""
@@ -322,9 +352,9 @@ class EmbeddingModelsService:
if runtime_embedding_model is None:
raise Exception('model not found')
else:
runtime_embedding_model = await self.ap.model_mgr.init_runtime_embedding_model(model_data)
runtime_embedding_model = await self.ap.model_mgr.init_temporary_runtime_embedding_model(model_data)
await runtime_embedding_model.requester.invoke_embedding(
await runtime_embedding_model.provider.requester.invoke_embedding(
model=runtime_embedding_model,
input_text=['Hello, world!'],
extra_args={},