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fix: add segment with keyword issue #3351

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merged 3 commits into from
Apr 11, 2024
Merged

fix: add segment with keyword issue #3351

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d4f166c
fix issue： user’s keywords do not affect when add segment
JohnJyong Apr 11, 2024
3a214b2
fix issue： user’s keywords do not affect when add segment
JohnJyong Apr 11, 2024
5e9a677
fix: keywords add
zxhlyh Apr 11, 2024
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3 changes: 3 additions & 0 deletions api/core/rag/datasource/keyword/jieba/jieba.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,9 @@ def add_texts(self, texts: list[Document], **kwargs):
text = texts[i]
if keywords_list:
keywords = keywords_list[i]
if not keywords:
keywords = keyword_table_handler.extract_keywords(text.page_content,
self._config.max_keywords_per_chunk)
else:
keywords = keyword_table_handler.extract_keywords(text.page_content, self._config.max_keywords_per_chunk)
self._update_segment_keywords(self.dataset.id, text.metadata['doc_id'], list(keywords))
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162 changes: 83 additions & 79 deletions api/services/dataset_service.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1046,73 +1046,11 @@ def create_segment(cls, args: dict, document: Document, dataset: Dataset):
credentials=embedding_model.credentials,
texts=[content]
)
max_position = db.session.query(func.max(DocumentSegment.position)).filter(
DocumentSegment.document_id == document.id
).scalar()
segment_document = DocumentSegment(
tenant_id=current_user.current_tenant_id,
dataset_id=document.dataset_id,
document_id=document.id,
index_node_id=doc_id,
index_node_hash=segment_hash,
position=max_position + 1 if max_position else 1,
content=content,
word_count=len(content),
tokens=tokens,
status='completed',
indexing_at=datetime.datetime.utcnow(),
completed_at=datetime.datetime.utcnow(),
created_by=current_user.id
)
if document.doc_form == 'qa_model':
segment_document.answer = args['answer']

db.session.add(segment_document)
db.session.commit()

# save vector index
try:
VectorService.create_segments_vector([args['keywords']], [segment_document], dataset)
except Exception as e:
logging.exception("create segment index failed")
segment_document.enabled = False
segment_document.disabled_at = datetime.datetime.utcnow()
segment_document.status = 'error'
segment_document.error = str(e)
db.session.commit()
segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment_document.id).first()
return segment

@classmethod
def multi_create_segment(cls, segments: list, document: Document, dataset: Dataset):
embedding_model = None
if dataset.indexing_technique == 'high_quality':
model_manager = ModelManager()
embedding_model = model_manager.get_model_instance(
tenant_id=current_user.current_tenant_id,
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model
)
max_position = db.session.query(func.max(DocumentSegment.position)).filter(
DocumentSegment.document_id == document.id
).scalar()
pre_segment_data_list = []
segment_data_list = []
keywords_list = []
for segment_item in segments:
content = segment_item['content']
doc_id = str(uuid.uuid4())
segment_hash = helper.generate_text_hash(content)
tokens = 0
if dataset.indexing_technique == 'high_quality' and embedding_model:
# calc embedding use tokens
model_type_instance = cast(TextEmbeddingModel, embedding_model.model_type_instance)
tokens = model_type_instance.get_num_tokens(
model=embedding_model.model,
credentials=embedding_model.credentials,
texts=[content]
)
lock_name = 'add_segment_lock_document_id_{}'.format(document.id)
with redis_client.lock(lock_name, timeout=600):
max_position = db.session.query(func.max(DocumentSegment.position)).filter(
DocumentSegment.document_id == document.id
).scalar()
segment_document = DocumentSegment(
tenant_id=current_user.current_tenant_id,
dataset_id=document.dataset_id,
Expand All @@ -1129,25 +1067,91 @@ def multi_create_segment(cls, segments: list, document: Document, dataset: Datas
created_by=current_user.id
)
if document.doc_form == 'qa_model':
segment_document.answer = segment_item['answer']
db.session.add(segment_document)
segment_data_list.append(segment_document)
segment_document.answer = args['answer']

pre_segment_data_list.append(segment_document)
keywords_list.append(segment_item['keywords'])
db.session.add(segment_document)
db.session.commit()

try:
# save vector index
VectorService.create_segments_vector(keywords_list, pre_segment_data_list, dataset)
except Exception as e:
logging.exception("create segment index failed")
for segment_document in segment_data_list:
try:
VectorService.create_segments_vector([args['keywords']], [segment_document], dataset)
except Exception as e:
logging.exception("create segment index failed")
segment_document.enabled = False
segment_document.disabled_at = datetime.datetime.utcnow()
segment_document.status = 'error'
segment_document.error = str(e)
db.session.commit()
return segment_data_list
db.session.commit()
segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment_document.id).first()
return segment

@classmethod
def multi_create_segment(cls, segments: list, document: Document, dataset: Dataset):
lock_name = 'multi_add_segment_lock_document_id_{}'.format(document.id)
with redis_client.lock(lock_name, timeout=600):
embedding_model = None
if dataset.indexing_technique == 'high_quality':
model_manager = ModelManager()
embedding_model = model_manager.get_model_instance(
tenant_id=current_user.current_tenant_id,
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model
)
max_position = db.session.query(func.max(DocumentSegment.position)).filter(
DocumentSegment.document_id == document.id
).scalar()
pre_segment_data_list = []
segment_data_list = []
keywords_list = []
for segment_item in segments:
content = segment_item['content']
doc_id = str(uuid.uuid4())
segment_hash = helper.generate_text_hash(content)
tokens = 0
if dataset.indexing_technique == 'high_quality' and embedding_model:
# calc embedding use tokens
model_type_instance = cast(TextEmbeddingModel, embedding_model.model_type_instance)
tokens = model_type_instance.get_num_tokens(
model=embedding_model.model,
credentials=embedding_model.credentials,
texts=[content]
)
segment_document = DocumentSegment(
tenant_id=current_user.current_tenant_id,
dataset_id=document.dataset_id,
document_id=document.id,
index_node_id=doc_id,
index_node_hash=segment_hash,
position=max_position + 1 if max_position else 1,
content=content,
word_count=len(content),
tokens=tokens,
status='completed',
indexing_at=datetime.datetime.utcnow(),
completed_at=datetime.datetime.utcnow(),
created_by=current_user.id
)
if document.doc_form == 'qa_model':
segment_document.answer = segment_item['answer']
db.session.add(segment_document)
segment_data_list.append(segment_document)

pre_segment_data_list.append(segment_document)
keywords_list.append(segment_item['keywords'])

try:
# save vector index
VectorService.create_segments_vector(keywords_list, pre_segment_data_list, dataset)
except Exception as e:
logging.exception("create segment index failed")
for segment_document in segment_data_list:
segment_document.enabled = False
segment_document.disabled_at = datetime.datetime.utcnow()
segment_document.status = 'error'
segment_document.error = str(e)
db.session.commit()
return segment_data_list

@classmethod
def update_segment(cls, args: dict, segment: DocumentSegment, document: Document, dataset: Dataset):
Expand Down
2 changes: 1 addition & 1 deletion api/services/vector_service.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ def create_segments_vector(cls, keywords_list: Optional[list[list[str]]],
keyword = Keyword(dataset)

if keywords_list and len(keywords_list) > 0:
keyword.add_texts(documents, keyword_list=keywords_list)
keyword.add_texts(documents, keywords_list=keywords_list)
else:
keyword.add_texts(documents)

Expand Down
4 changes: 3 additions & 1 deletion web/app/components/base/tag-input/index.tsx
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,9 @@ const TagInput: FC<TagInputProps> = ({
}

onChange([...items, valueTrimed])
setValue('')
setTimeout(() => {
setValue('')
})
}
}

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