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from typing import Optional, Listfrom langchain.schema import Documentfrom core.index.index import IndexBuilderfrom models.dataset import Dataset, DocumentSegmentclass VectorService:    @classmethod    def create_segment_vector(cls, keywords: Optional[List[str]], segment: DocumentSegment, dataset: Dataset):        document = Document(            page_content=segment.content,            metadata={                "doc_id": segment.index_node_id,                "doc_hash": segment.index_node_hash,                "document_id": segment.document_id,                "dataset_id": segment.dataset_id,            }        )        # save vector index        index = IndexBuilder.get_index(dataset, 'high_quality')        if index:            index.add_texts([document], duplicate_check=True)        # save keyword index        index = IndexBuilder.get_index(dataset, 'economy')        if index:            if keywords and len(keywords) > 0:                index.create_segment_keywords(segment.index_node_id, keywords)            else:                index.add_texts([document])    @classmethod    def multi_create_segment_vector(cls, pre_segment_data_list: list, dataset: Dataset):        documents = []        for pre_segment_data in pre_segment_data_list:            segment = pre_segment_data['segment']            document = Document(                page_content=segment.content,                metadata={                    "doc_id": segment.index_node_id,                    "doc_hash": segment.index_node_hash,                    "document_id": segment.document_id,                    "dataset_id": segment.dataset_id,                }            )            documents.append(document)        # save vector index        index = IndexBuilder.get_index(dataset, 'high_quality')        if index:            index.add_texts(documents, duplicate_check=True)        # save keyword index        keyword_index = IndexBuilder.get_index(dataset, 'economy')        if keyword_index:            keyword_index.multi_create_segment_keywords(pre_segment_data_list)    @classmethod    def update_segment_vector(cls, keywords: Optional[List[str]], segment: DocumentSegment, dataset: Dataset):        # update segment index task        vector_index = IndexBuilder.get_index(dataset, 'high_quality')        kw_index = IndexBuilder.get_index(dataset, 'economy')        # delete from vector index        if vector_index:            vector_index.delete_by_ids([segment.index_node_id])        # delete from keyword index        kw_index.delete_by_ids([segment.index_node_id])        # add new index        document = Document(            page_content=segment.content,            metadata={                "doc_id": segment.index_node_id,                "doc_hash": segment.index_node_hash,                "document_id": segment.document_id,                "dataset_id": segment.dataset_id,            }        )        # save vector index        if vector_index:            vector_index.add_texts([document], duplicate_check=True)        # save keyword index        if keywords and len(keywords) > 0:            kw_index.create_segment_keywords(segment.index_node_id, keywords)        else:            kw_index.add_texts([document])
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