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Merge branch 'feature/20251219_lxc' into develop

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lixiangcheng1
2025-12-29 16:16:36 +08:00
parent f3e80dfcd6 adbba8773b
commit 8fc5cc3bb6
2 changed files with 4 additions and 2 deletions
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5
api/app/controllers/knowledge_controller.py
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@@ -242,8 +242,9 @@ async def _update_knowledge(
if "embedding_id" in update_dict: if "embedding_id" in update_dict:
embedding_id = update_dict["embedding_id"] embedding_id = update_dict["embedding_id"]
if embedding_id != db_knowledge.embedding_id: if embedding_id != db_knowledge.embedding_id:
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge) if db_knowledge.embedding_id and db_knowledge.reranker_id:
vector_service.delete() vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
vector_service.delete()
document_service.reset_documents_progress_by_kb_id(db, kb_id=db_knowledge.id, current_user=current_user) document_service.reset_documents_progress_by_kb_id(db, kb_id=db_knowledge.id, current_user=current_user)
# 2. Update fields (only update non-null fields) # 2. Update fields (only update non-null fields)

1
api/app/tasks.py
View File

@@ -224,6 +224,7 @@ def parse_document(file_path: str, document_id: uuid.UUID):
async def _run(row: dict, document_ids: list[str], language: str, parser_config: dict, vector_service, async def _run(row: dict, document_ids: list[str], language: str, parser_config: dict, vector_service,
chat_model, embedding_model, callback, with_resolution: bool = True, chat_model, embedding_model, callback, with_resolution: bool = True,
with_community: bool = True, ) -> dict: with_community: bool = True, ) -> dict:
await trio.sleep(5) # Delay for 10 seconds
nonlocal progress_msg # Declare the use of an external progress_msg variable nonlocal progress_msg # Declare the use of an external progress_msg variable
result = await run_graphrag_for_kb( result = await run_graphrag_for_kb(
row=row, row=row,