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fix: #30511 [Bug] knowledge_retrieval_node fails when using Rerank Model: "Working outside of application context" and add regression test (#30549)

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Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
hsiong
2026-01-05 15:02:21 +08:00
committed by GitHub
parent 93a85ae98a
commit be3ef9f050
2 changed files with 144 additions and 31 deletions
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62
api/core/rag/retrieval/dataset_retrieval.py
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@ -1474,38 +1474,38 @@ class DatasetRetrieval:
if cancel_event and cancel_event.is_set():
break
# Skip second reranking when there is only one dataset
if reranking_enable and dataset_count > 1:
# do rerank for searched documents
data_post_processor = DataPostProcessor(tenant_id, reranking_mode, reranking_model, weights, False)
if query:
all_documents_item = data_post_processor.invoke(
query=query,
documents=all_documents_item,
score_threshold=score_threshold,
top_n=top_k,
query_type=QueryType.TEXT_QUERY,
)
if attachment_id:
all_documents_item = data_post_processor.invoke(
documents=all_documents_item,
score_threshold=score_threshold,
top_n=top_k,
query_type=QueryType.IMAGE_QUERY,
query=attachment_id,
)
else:
if index_type == IndexTechniqueType.ECONOMY:
if not query:
all_documents_item = []
else:
all_documents_item = self.calculate_keyword_score(query, all_documents_item, top_k)
elif index_type == IndexTechniqueType.HIGH_QUALITY:
all_documents_item = self.calculate_vector_score(all_documents_item, top_k, score_threshold)
# Skip second reranking when there is only one dataset
if reranking_enable and dataset_count > 1:
# do rerank for searched documents
data_post_processor = DataPostProcessor(tenant_id, reranking_mode, reranking_model, weights, False)
if query:
all_documents_item = data_post_processor.invoke(
query=query,
documents=all_documents_item,
score_threshold=score_threshold,
top_n=top_k,
query_type=QueryType.TEXT_QUERY,
)
if attachment_id:
all_documents_item = data_post_processor.invoke(
documents=all_documents_item,
score_threshold=score_threshold,
top_n=top_k,
query_type=QueryType.IMAGE_QUERY,
query=attachment_id,
)
else:
all_documents_item = all_documents_item[:top_k] if top_k else all_documents_item
if all_documents_item:
all_documents.extend(all_documents_item)
if index_type == IndexTechniqueType.ECONOMY:
if not query:
all_documents_item = []
else:
all_documents_item = self.calculate_keyword_score(query, all_documents_item, top_k)
elif index_type == IndexTechniqueType.HIGH_QUALITY:
all_documents_item = self.calculate_vector_score(all_documents_item, top_k, score_threshold)
else:
all_documents_item = all_documents_item[:top_k] if top_k else all_documents_item
if all_documents_item:
all_documents.extend(all_documents_item)
except Exception as e:
if cancel_event:
cancel_event.set()