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py lint (#12102)

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Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: -LAN- <laipz8200@outlook.com>
This commit is contained in:
Jyong
2024-12-26 00:16:35 +08:00
committed by GitHub
parent bb35818976
commit 84ac004772
20 changed files with 264 additions and 210 deletions
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14
api/core/indexing_runner.py
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@@ -276,7 +276,7 @@ class IndexingRunner:
tenant_id=tenant_id,
model_type=ModelType.TEXT_EMBEDDING,
)
preview_texts = []
preview_texts = [] # type: ignore
total_segments = 0
index_type = doc_form
@@ -300,13 +300,13 @@ class IndexingRunner:
if len(preview_texts) < 10:
if doc_form and doc_form == "qa_model":
preview_detail = QAPreviewDetail(
question=document.page_content, answer=document.metadata.get("answer")
question=document.page_content, answer=document.metadata.get("answer") or ""
)
preview_texts.append(preview_detail)
else:
preview_detail = PreviewDetail(content=document.page_content)
preview_detail = PreviewDetail(content=document.page_content) # type: ignore
if document.children:
preview_detail.child_chunks = [child.page_content for child in document.children]
preview_detail.child_chunks = [child.page_content for child in document.children] # type: ignore
preview_texts.append(preview_detail)
# delete image files and related db records
@@ -325,7 +325,7 @@ class IndexingRunner:
if doc_form and doc_form == "qa_model":
return IndexingEstimate(total_segments=total_segments * 20, qa_preview=preview_texts, preview=[])
return IndexingEstimate(total_segments=total_segments, preview=preview_texts)
return IndexingEstimate(total_segments=total_segments, preview=preview_texts) # type: ignore
def _extract(
self, index_processor: BaseIndexProcessor, dataset_document: DatasetDocument, process_rule: dict
@@ -454,7 +454,7 @@ class IndexingRunner:
embedding_model_instance=embedding_model_instance,
)
return character_splitter
return character_splitter # type: ignore
def _split_to_documents_for_estimate(
self, text_docs: list[Document], splitter: TextSplitter, processing_rule: DatasetProcessRule
@@ -535,7 +535,7 @@ class IndexingRunner:
# create keyword index
create_keyword_thread = threading.Thread(
target=self._process_keyword_index,
args=(current_app._get_current_object(), dataset.id, dataset_document.id, documents),
args=(current_app._get_current_object(), dataset.id, dataset_document.id, documents), # type: ignore
)
create_keyword_thread.start()