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578 lines
23 KiB
Python
578 lines
23 KiB
Python
import datetime
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import json
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import logging
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import re
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import time
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import uuid
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from typing import Optional, List, cast
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from flask import current_app
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from flask_login import current_user
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.schema import Document
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from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
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from core.data_loader.file_extractor import FileExtractor
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from core.data_loader.loader.notion import NotionLoader
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from core.docstore.dataset_docstore import DatesetDocumentStore
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from core.embedding.cached_embedding import CacheEmbedding
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from core.index.index import IndexBuilder
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from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
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from core.index.vector_index.vector_index import VectorIndex
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from core.llm.error import ProviderTokenNotInitError
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from core.llm.llm_builder import LLMBuilder
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from core.spiltter.fixed_text_splitter import FixedRecursiveCharacterTextSplitter
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from core.llm.token_calculator import TokenCalculator
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from extensions.ext_database import db
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from extensions.ext_redis import redis_client
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from extensions.ext_storage import storage
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from libs import helper
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from models.dataset import Document as DatasetDocument
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from models.dataset import Dataset, DocumentSegment, DatasetProcessRule
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from models.model import UploadFile
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from models.source import DataSourceBinding
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class IndexingRunner:
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def __init__(self, embedding_model_name: str = "text-embedding-ada-002"):
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self.storage = storage
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self.embedding_model_name = embedding_model_name
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def run(self, dataset_documents: List[DatasetDocument]):
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"""Run the indexing process."""
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for dataset_document in dataset_documents:
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try:
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# get dataset
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dataset = Dataset.query.filter_by(
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id=dataset_document.dataset_id
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).first()
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if not dataset:
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raise ValueError("no dataset found")
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# load file
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text_docs = self._load_data(dataset_document)
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# get the process rule
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processing_rule = db.session.query(DatasetProcessRule). \
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filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \
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first()
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# get splitter
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splitter = self._get_splitter(processing_rule)
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# split to documents
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documents = self._step_split(
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text_docs=text_docs,
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splitter=splitter,
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dataset=dataset,
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dataset_document=dataset_document,
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processing_rule=processing_rule
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)
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# build index
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self._build_index(
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dataset=dataset,
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dataset_document=dataset_document,
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documents=documents
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)
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except DocumentIsPausedException:
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raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id))
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except ProviderTokenNotInitError as e:
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dataset_document.indexing_status = 'error'
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dataset_document.error = str(e.description)
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dataset_document.stopped_at = datetime.datetime.utcnow()
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db.session.commit()
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except Exception as e:
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logging.exception("consume document failed")
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dataset_document.indexing_status = 'error'
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dataset_document.error = str(e)
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dataset_document.stopped_at = datetime.datetime.utcnow()
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db.session.commit()
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def run_in_splitting_status(self, dataset_document: DatasetDocument):
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"""Run the indexing process when the index_status is splitting."""
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try:
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# get dataset
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dataset = Dataset.query.filter_by(
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id=dataset_document.dataset_id
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).first()
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if not dataset:
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raise ValueError("no dataset found")
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# get exist document_segment list and delete
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document_segments = DocumentSegment.query.filter_by(
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dataset_id=dataset.id,
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document_id=dataset_document.id
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).all()
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db.session.delete(document_segments)
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db.session.commit()
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# load file
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text_docs = self._load_data(dataset_document)
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# get the process rule
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processing_rule = db.session.query(DatasetProcessRule). \
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filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \
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first()
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# get splitter
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splitter = self._get_splitter(processing_rule)
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# split to documents
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documents = self._step_split(
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text_docs=text_docs,
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splitter=splitter,
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dataset=dataset,
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dataset_document=dataset_document,
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processing_rule=processing_rule
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)
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# build index
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self._build_index(
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dataset=dataset,
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dataset_document=dataset_document,
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documents=documents
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)
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except DocumentIsPausedException:
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raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id))
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except ProviderTokenNotInitError as e:
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dataset_document.indexing_status = 'error'
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dataset_document.error = str(e.description)
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dataset_document.stopped_at = datetime.datetime.utcnow()
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db.session.commit()
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except Exception as e:
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logging.exception("consume document failed")
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dataset_document.indexing_status = 'error'
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dataset_document.error = str(e)
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dataset_document.stopped_at = datetime.datetime.utcnow()
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db.session.commit()
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def run_in_indexing_status(self, dataset_document: DatasetDocument):
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"""Run the indexing process when the index_status is indexing."""
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try:
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# get dataset
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dataset = Dataset.query.filter_by(
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id=dataset_document.dataset_id
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).first()
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if not dataset:
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raise ValueError("no dataset found")
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# get exist document_segment list and delete
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document_segments = DocumentSegment.query.filter_by(
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dataset_id=dataset.id,
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document_id=dataset_document.id
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).all()
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documents = []
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if document_segments:
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for document_segment in document_segments:
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# transform segment to node
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if document_segment.status != "completed":
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document = Document(
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page_content=document_segment.content,
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metadata={
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"doc_id": document_segment.index_node_id,
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"doc_hash": document_segment.index_node_hash,
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"document_id": document_segment.document_id,
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"dataset_id": document_segment.dataset_id,
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}
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)
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documents.append(document)
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# build index
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self._build_index(
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dataset=dataset,
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dataset_document=dataset_document,
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documents=documents
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)
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except DocumentIsPausedException:
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raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id))
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except ProviderTokenNotInitError as e:
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dataset_document.indexing_status = 'error'
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dataset_document.error = str(e.description)
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dataset_document.stopped_at = datetime.datetime.utcnow()
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db.session.commit()
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except Exception as e:
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logging.exception("consume document failed")
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dataset_document.indexing_status = 'error'
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dataset_document.error = str(e)
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dataset_document.stopped_at = datetime.datetime.utcnow()
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db.session.commit()
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def file_indexing_estimate(self, file_details: List[UploadFile], tmp_processing_rule: dict) -> dict:
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"""
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Estimate the indexing for the document.
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"""
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tokens = 0
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preview_texts = []
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total_segments = 0
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for file_detail in file_details:
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# load data from file
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text_docs = FileExtractor.load(file_detail)
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processing_rule = DatasetProcessRule(
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mode=tmp_processing_rule["mode"],
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rules=json.dumps(tmp_processing_rule["rules"])
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)
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# get splitter
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splitter = self._get_splitter(processing_rule)
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# split to documents
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documents = self._split_to_documents(
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text_docs=text_docs,
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splitter=splitter,
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processing_rule=processing_rule
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)
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total_segments += len(documents)
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for document in documents:
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if len(preview_texts) < 5:
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preview_texts.append(document.page_content)
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tokens += TokenCalculator.get_num_tokens(self.embedding_model_name,
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self.filter_string(document.page_content))
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return {
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"total_segments": total_segments,
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"tokens": tokens,
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"total_price": '{:f}'.format(TokenCalculator.get_token_price(self.embedding_model_name, tokens)),
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"currency": TokenCalculator.get_currency(self.embedding_model_name),
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"preview": preview_texts
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}
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def notion_indexing_estimate(self, notion_info_list: list, tmp_processing_rule: dict) -> dict:
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"""
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Estimate the indexing for the document.
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"""
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# load data from notion
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tokens = 0
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preview_texts = []
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total_segments = 0
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for notion_info in notion_info_list:
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workspace_id = notion_info['workspace_id']
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data_source_binding = DataSourceBinding.query.filter(
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db.and_(
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DataSourceBinding.tenant_id == current_user.current_tenant_id,
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DataSourceBinding.provider == 'notion',
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DataSourceBinding.disabled == False,
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DataSourceBinding.source_info['workspace_id'] == f'"{workspace_id}"'
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)
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).first()
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if not data_source_binding:
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raise ValueError('Data source binding not found.')
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for page in notion_info['pages']:
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loader = NotionLoader(
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notion_access_token=data_source_binding.access_token,
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notion_workspace_id=workspace_id,
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notion_obj_id=page['page_id'],
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notion_page_type=page['type']
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)
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documents = loader.load()
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processing_rule = DatasetProcessRule(
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mode=tmp_processing_rule["mode"],
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rules=json.dumps(tmp_processing_rule["rules"])
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)
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# get splitter
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splitter = self._get_splitter(processing_rule)
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# split to documents
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documents = self._split_to_documents(
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text_docs=documents,
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splitter=splitter,
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processing_rule=processing_rule
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)
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total_segments += len(documents)
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for document in documents:
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if len(preview_texts) < 5:
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preview_texts.append(document.page_content)
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tokens += TokenCalculator.get_num_tokens(self.embedding_model_name, document.page_content)
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return {
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"total_segments": total_segments,
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"tokens": tokens,
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"total_price": '{:f}'.format(TokenCalculator.get_token_price(self.embedding_model_name, tokens)),
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"currency": TokenCalculator.get_currency(self.embedding_model_name),
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"preview": preview_texts
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}
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def _load_data(self, dataset_document: DatasetDocument) -> List[Document]:
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# load file
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if dataset_document.data_source_type not in ["upload_file", "notion_import"]:
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return []
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data_source_info = dataset_document.data_source_info_dict
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text_docs = []
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if dataset_document.data_source_type == 'upload_file':
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if not data_source_info or 'upload_file_id' not in data_source_info:
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raise ValueError("no upload file found")
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file_detail = db.session.query(UploadFile). \
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filter(UploadFile.id == data_source_info['upload_file_id']). \
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one_or_none()
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text_docs = FileExtractor.load(file_detail)
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elif dataset_document.data_source_type == 'notion_import':
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loader = NotionLoader.from_document(dataset_document)
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text_docs = loader.load()
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# update document status to splitting
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self._update_document_index_status(
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document_id=dataset_document.id,
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after_indexing_status="splitting",
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extra_update_params={
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DatasetDocument.word_count: sum([len(text_doc.page_content) for text_doc in text_docs]),
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DatasetDocument.parsing_completed_at: datetime.datetime.utcnow()
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}
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)
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# replace doc id to document model id
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text_docs = cast(List[Document], text_docs)
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for text_doc in text_docs:
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# remove invalid symbol
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text_doc.page_content = self.filter_string(text_doc.page_content)
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text_doc.metadata['document_id'] = dataset_document.id
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text_doc.metadata['dataset_id'] = dataset_document.dataset_id
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return text_docs
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def filter_string(self, text):
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text = text.replace('<|', '<')
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text = text.replace('|>', '>')
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pattern = re.compile('[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\x80-\xFF]')
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return pattern.sub('', text)
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def _get_splitter(self, processing_rule: DatasetProcessRule) -> TextSplitter:
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"""
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Get the NodeParser object according to the processing rule.
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"""
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if processing_rule.mode == "custom":
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# The user-defined segmentation rule
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rules = json.loads(processing_rule.rules)
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segmentation = rules["segmentation"]
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if segmentation["max_tokens"] < 50 or segmentation["max_tokens"] > 1000:
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raise ValueError("Custom segment length should be between 50 and 1000.")
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separator = segmentation["separator"]
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if separator:
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separator = separator.replace('\\n', '\n')
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character_splitter = FixedRecursiveCharacterTextSplitter.from_tiktoken_encoder(
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chunk_size=segmentation["max_tokens"],
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chunk_overlap=0,
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fixed_separator=separator,
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separators=["\n\n", "。", ".", " ", ""]
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)
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else:
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# Automatic segmentation
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character_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
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chunk_size=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens'],
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chunk_overlap=0,
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separators=["\n\n", "。", ".", " ", ""]
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)
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return character_splitter
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def _step_split(self, text_docs: List[Document], splitter: TextSplitter,
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dataset: Dataset, dataset_document: DatasetDocument, processing_rule: DatasetProcessRule) \
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-> List[Document]:
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"""
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Split the text documents into documents and save them to the document segment.
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"""
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documents = self._split_to_documents(
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text_docs=text_docs,
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splitter=splitter,
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processing_rule=processing_rule
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)
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# save node to document segment
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doc_store = DatesetDocumentStore(
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dataset=dataset,
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user_id=dataset_document.created_by,
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embedding_model_name=self.embedding_model_name,
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document_id=dataset_document.id
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)
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# add document segments
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doc_store.add_documents(documents)
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# update document status to indexing
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cur_time = datetime.datetime.utcnow()
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self._update_document_index_status(
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document_id=dataset_document.id,
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after_indexing_status="indexing",
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extra_update_params={
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DatasetDocument.cleaning_completed_at: cur_time,
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DatasetDocument.splitting_completed_at: cur_time,
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}
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)
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# update segment status to indexing
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self._update_segments_by_document(
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dataset_document_id=dataset_document.id,
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update_params={
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DocumentSegment.status: "indexing",
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DocumentSegment.indexing_at: datetime.datetime.utcnow()
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}
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)
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return documents
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def _split_to_documents(self, text_docs: List[Document], splitter: TextSplitter,
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processing_rule: DatasetProcessRule) -> List[Document]:
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"""
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Split the text documents into nodes.
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"""
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all_documents = []
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for text_doc in text_docs:
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# document clean
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document_text = self._document_clean(text_doc.page_content, processing_rule)
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text_doc.page_content = document_text
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# parse document to nodes
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documents = splitter.split_documents([text_doc])
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split_documents = []
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for document in documents:
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if document.page_content is None or not document.page_content.strip():
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continue
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doc_id = str(uuid.uuid4())
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hash = helper.generate_text_hash(document.page_content)
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document.metadata['doc_id'] = doc_id
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document.metadata['doc_hash'] = hash
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split_documents.append(document)
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all_documents.extend(split_documents)
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return all_documents
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def _document_clean(self, text: str, processing_rule: DatasetProcessRule) -> str:
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"""
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Clean the document text according to the processing rules.
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"""
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if processing_rule.mode == "automatic":
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rules = DatasetProcessRule.AUTOMATIC_RULES
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else:
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rules = json.loads(processing_rule.rules) if processing_rule.rules else {}
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if 'pre_processing_rules' in rules:
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pre_processing_rules = rules["pre_processing_rules"]
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for pre_processing_rule in pre_processing_rules:
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if pre_processing_rule["id"] == "remove_extra_spaces" and pre_processing_rule["enabled"] is True:
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# Remove extra spaces
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pattern = r'\n{3,}'
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text = re.sub(pattern, '\n\n', text)
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pattern = r'[\t\f\r\x20\u00a0\u1680\u180e\u2000-\u200a\u202f\u205f\u3000]{2,}'
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text = re.sub(pattern, ' ', text)
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elif pre_processing_rule["id"] == "remove_urls_emails" and pre_processing_rule["enabled"] is True:
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# Remove email
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pattern = r'([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)'
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text = re.sub(pattern, '', text)
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# Remove URL
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pattern = r'https?://[^\s]+'
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text = re.sub(pattern, '', text)
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return text
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def _build_index(self, dataset: Dataset, dataset_document: DatasetDocument, documents: List[Document]) -> None:
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"""
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Build the index for the document.
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"""
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vector_index = IndexBuilder.get_index(dataset, 'high_quality')
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keyword_table_index = IndexBuilder.get_index(dataset, 'economy')
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# chunk nodes by chunk size
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indexing_start_at = time.perf_counter()
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tokens = 0
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chunk_size = 100
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for i in range(0, len(documents), chunk_size):
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# check document is paused
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self._check_document_paused_status(dataset_document.id)
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chunk_documents = documents[i:i + chunk_size]
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tokens += sum(
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TokenCalculator.get_num_tokens(self.embedding_model_name, document.page_content)
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for document in chunk_documents
|
|
)
|
|
|
|
# save vector index
|
|
if vector_index:
|
|
vector_index.add_texts(chunk_documents)
|
|
|
|
# save keyword index
|
|
keyword_table_index.add_texts(chunk_documents)
|
|
|
|
document_ids = [document.metadata['doc_id'] for document in chunk_documents]
|
|
db.session.query(DocumentSegment).filter(
|
|
DocumentSegment.document_id == dataset_document.id,
|
|
DocumentSegment.index_node_id.in_(document_ids),
|
|
DocumentSegment.status == "indexing"
|
|
).update({
|
|
DocumentSegment.status: "completed",
|
|
DocumentSegment.completed_at: datetime.datetime.utcnow()
|
|
})
|
|
|
|
db.session.commit()
|
|
|
|
indexing_end_at = time.perf_counter()
|
|
|
|
# update document status to completed
|
|
self._update_document_index_status(
|
|
document_id=dataset_document.id,
|
|
after_indexing_status="completed",
|
|
extra_update_params={
|
|
DatasetDocument.tokens: tokens,
|
|
DatasetDocument.completed_at: datetime.datetime.utcnow(),
|
|
DatasetDocument.indexing_latency: indexing_end_at - indexing_start_at,
|
|
}
|
|
)
|
|
|
|
def _check_document_paused_status(self, document_id: str):
|
|
indexing_cache_key = 'document_{}_is_paused'.format(document_id)
|
|
result = redis_client.get(indexing_cache_key)
|
|
if result:
|
|
raise DocumentIsPausedException()
|
|
|
|
def _update_document_index_status(self, document_id: str, after_indexing_status: str,
|
|
extra_update_params: Optional[dict] = None) -> None:
|
|
"""
|
|
Update the document indexing status.
|
|
"""
|
|
count = DatasetDocument.query.filter_by(id=document_id, is_paused=True).count()
|
|
if count > 0:
|
|
raise DocumentIsPausedException()
|
|
|
|
update_params = {
|
|
DatasetDocument.indexing_status: after_indexing_status
|
|
}
|
|
|
|
if extra_update_params:
|
|
update_params.update(extra_update_params)
|
|
|
|
DatasetDocument.query.filter_by(id=document_id).update(update_params)
|
|
db.session.commit()
|
|
|
|
def _update_segments_by_document(self, dataset_document_id: str, update_params: dict) -> None:
|
|
"""
|
|
Update the document segment by document id.
|
|
"""
|
|
DocumentSegment.query.filter_by(document_id=dataset_document_id).update(update_params)
|
|
db.session.commit()
|
|
|
|
|
|
class DocumentIsPausedException(Exception):
|
|
pass
|