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72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
from typing import Optional
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from core.rag.datasource.keyword.keyword_factory import Keyword
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from core.rag.datasource.vdb.vector_factory import Vector
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from core.rag.models.document import Document
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from models.dataset import Dataset, DocumentSegment
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class VectorService:
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@classmethod
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def create_segments_vector(cls, keywords_list: Optional[list[list[str]]],
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segments: list[DocumentSegment], dataset: Dataset):
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documents = []
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for segment in segments:
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document = Document(
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page_content=segment.content,
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metadata={
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"doc_id": segment.index_node_id,
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"doc_hash": segment.index_node_hash,
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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}
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)
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documents.append(document)
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if dataset.indexing_technique == 'high_quality':
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# save vector index
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vector = Vector(
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dataset=dataset
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)
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vector.add_texts(documents, duplicate_check=True)
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# save keyword index
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keyword = Keyword(dataset)
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if keywords_list and len(keywords_list) > 0:
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keyword.add_texts(documents, keyword_list=keywords_list)
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else:
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keyword.add_texts(documents)
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@classmethod
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def update_segment_vector(cls, keywords: Optional[list[str]], segment: DocumentSegment, dataset: Dataset):
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# update segment index task
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# format new index
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document = Document(
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page_content=segment.content,
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metadata={
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"doc_id": segment.index_node_id,
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"doc_hash": segment.index_node_hash,
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"document_id": segment.document_id,
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"dataset_id": segment.dataset_id,
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}
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)
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if dataset.indexing_technique == 'high_quality':
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# update vector index
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vector = Vector(
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dataset=dataset
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)
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vector.delete_by_ids([segment.index_node_id])
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vector.add_texts([document], duplicate_check=True)
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# update keyword index
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keyword = Keyword(dataset)
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keyword.delete_by_ids([segment.index_node_id])
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# save keyword index
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if keywords and len(keywords) > 0:
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keyword.add_texts([document], keywords_list=[keywords])
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else:
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keyword.add_texts([document])
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