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Merge branch 'main' into feat/r2

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# Conflicts:
#	docker/docker-compose.middleware.yaml
#	web/app/components/workflow-app/components/workflow-main.tsx
#	web/app/components/workflow-app/hooks/index.ts
#	web/app/components/workflow/hooks-store/store.ts
#	web/app/components/workflow/hooks/index.ts
#	web/app/components/workflow/nodes/_base/components/variable/var-reference-picker.tsx
This commit is contained in:
jyong
2025-07-02 18:20:05 +08:00
parent 81b07dc3be 68f41bbaa8
commit 832bef053f
201 changed files with 7572 additions and 5289 deletions
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22
api/services/dataset_service.py
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@ -323,6 +323,23 @@ class DatasetService:
except ProviderTokenNotInitError as ex:
raise ValueError(ex.description)
@staticmethod
def check_reranking_model_setting(tenant_id: str, reranking_model_provider: str, reranking_model: str):
try:
model_manager = ModelManager()
model_manager.get_model_instance(
tenant_id=tenant_id,
provider=reranking_model_provider,
model_type=ModelType.RERANK,
model=reranking_model,
)
except LLMBadRequestError:
raise ValueError(
"No Rerank Model available. Please configure a valid provider in the Settings -> Model Provider."
)
except ProviderTokenNotInitError as ex:
raise ValueError(ex.description)
@staticmethod
def update_dataset(dataset_id, data, user):
"""
@ -645,6 +662,10 @@ class DatasetService:
)
except ProviderTokenNotInitError:
# If we can't get the embedding model, preserve existing settings
logging.warning(
f"Failed to initialize embedding model {data['embedding_model_provider']}/{data['embedding_model']}, "
f"preserving existing settings"
)
if dataset.embedding_model_provider and dataset.embedding_model:
filtered_data["embedding_model_provider"] = dataset.embedding_model_provider
filtered_data["embedding_model"] = dataset.embedding_model
@ -2661,6 +2682,7 @@ class SegmentService:
# calc embedding use tokens
if document.doc_form == "qa_model":
segment.answer = args.answer
tokens = embedding_model.get_text_embedding_num_tokens(texts=[content + segment.answer])[0]
else:
tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]