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884 lines
37 KiB
Python
884 lines
37 KiB
Python
import concurrent.futures
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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 threading
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import time
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import uuid
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from typing import Optional, cast
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from flask import Flask, current_app
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from flask_login import current_user
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from sqlalchemy.orm.exc import ObjectDeletedError
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from configs import dify_config
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from core.errors.error import ProviderTokenNotInitError
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from core.llm_generator.llm_generator import LLMGenerator
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from core.model_manager import ModelInstance, ModelManager
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from core.model_runtime.entities.model_entities import ModelType
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from core.rag.datasource.keyword.keyword_factory import Keyword
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from core.rag.docstore.dataset_docstore import DatasetDocumentStore
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from core.rag.extractor.entity.extract_setting import ExtractSetting
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from core.rag.index_processor.index_processor_base import BaseIndexProcessor
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from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
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from core.rag.models.document import Document
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from core.rag.splitter.fixed_text_splitter import (
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EnhanceRecursiveCharacterTextSplitter,
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FixedRecursiveCharacterTextSplitter,
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)
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from core.rag.splitter.text_splitter import TextSplitter
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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 Dataset, DatasetProcessRule, DocumentSegment
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from models.dataset import Document as DatasetDocument
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from models.model import UploadFile
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from services.feature_service import FeatureService
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class IndexingRunner:
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def __init__(self):
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self.storage = storage
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self.model_manager = ModelManager()
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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(id=dataset_document.dataset_id).first()
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if not dataset:
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raise ValueError("no dataset found")
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# get the process rule
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processing_rule = (
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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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)
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index_type = dataset_document.doc_form
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index_processor = IndexProcessorFactory(index_type).init_index_processor()
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# extract
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text_docs = self._extract(index_processor, dataset_document, processing_rule.to_dict())
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# transform
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documents = self._transform(
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index_processor, dataset, text_docs, dataset_document.doc_language, processing_rule.to_dict()
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)
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# save segment
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self._load_segments(dataset, dataset_document, documents)
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# load
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self._load(
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index_processor=index_processor,
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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.now(datetime.timezone.utc).replace(tzinfo=None)
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db.session.commit()
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except ObjectDeletedError:
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logging.warning("Document deleted, document id: {}".format(dataset_document.id))
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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.now(datetime.timezone.utc).replace(tzinfo=None)
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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(id=dataset_document.dataset_id).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, document_id=dataset_document.id
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).all()
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for document_segment in document_segments:
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db.session.delete(document_segment)
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db.session.commit()
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# get the process rule
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processing_rule = (
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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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)
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index_type = dataset_document.doc_form
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index_processor = IndexProcessorFactory(index_type).init_index_processor()
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# extract
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text_docs = self._extract(index_processor, dataset_document, processing_rule.to_dict())
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# transform
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documents = self._transform(
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index_processor, dataset, text_docs, dataset_document.doc_language, processing_rule.to_dict()
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)
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# save segment
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self._load_segments(dataset, dataset_document, documents)
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# load
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self._load(
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index_processor=index_processor, dataset=dataset, dataset_document=dataset_document, 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.now(datetime.timezone.utc).replace(tzinfo=None)
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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.now(datetime.timezone.utc).replace(tzinfo=None)
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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(id=dataset_document.dataset_id).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, 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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# get the process rule
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processing_rule = (
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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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)
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index_type = dataset_document.doc_form
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index_processor = IndexProcessorFactory(index_type).init_index_processor()
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self._load(
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index_processor=index_processor, dataset=dataset, dataset_document=dataset_document, 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.now(datetime.timezone.utc).replace(tzinfo=None)
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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.now(datetime.timezone.utc).replace(tzinfo=None)
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db.session.commit()
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def indexing_estimate(
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self,
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tenant_id: str,
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extract_settings: list[ExtractSetting],
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tmp_processing_rule: dict,
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doc_form: str = None,
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doc_language: str = "English",
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dataset_id: str = None,
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indexing_technique: str = "economy",
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) -> dict:
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"""
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Estimate the indexing for the document.
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"""
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# check document limit
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features = FeatureService.get_features(tenant_id)
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if features.billing.enabled:
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count = len(extract_settings)
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batch_upload_limit = dify_config.BATCH_UPLOAD_LIMIT
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if count > batch_upload_limit:
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raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")
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embedding_model_instance = None
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if dataset_id:
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dataset = Dataset.query.filter_by(id=dataset_id).first()
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if not dataset:
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raise ValueError("Dataset not found.")
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if dataset.indexing_technique == "high_quality" or indexing_technique == "high_quality":
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if dataset.embedding_model_provider:
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embedding_model_instance = self.model_manager.get_model_instance(
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tenant_id=tenant_id,
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provider=dataset.embedding_model_provider,
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model_type=ModelType.TEXT_EMBEDDING,
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model=dataset.embedding_model,
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)
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else:
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embedding_model_instance = self.model_manager.get_default_model_instance(
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tenant_id=tenant_id,
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model_type=ModelType.TEXT_EMBEDDING,
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)
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else:
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if indexing_technique == "high_quality":
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embedding_model_instance = self.model_manager.get_default_model_instance(
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tenant_id=tenant_id,
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model_type=ModelType.TEXT_EMBEDDING,
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)
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preview_texts = []
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total_segments = 0
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index_type = doc_form
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index_processor = IndexProcessorFactory(index_type).init_index_processor()
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all_text_docs = []
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for extract_setting in extract_settings:
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# extract
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text_docs = index_processor.extract(extract_setting, process_rule_mode=tmp_processing_rule["mode"])
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all_text_docs.extend(text_docs)
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processing_rule = DatasetProcessRule(
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mode=tmp_processing_rule["mode"], 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, embedding_model_instance)
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# split to documents
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documents = self._split_to_documents_for_estimate(
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text_docs=text_docs, splitter=splitter, 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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if doc_form and doc_form == "qa_model":
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if len(preview_texts) > 0:
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# qa model document
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response = LLMGenerator.generate_qa_document(
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current_user.current_tenant_id, preview_texts[0], doc_language
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)
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document_qa_list = self.format_split_text(response)
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return {"total_segments": total_segments * 20, "qa_preview": document_qa_list, "preview": preview_texts}
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return {"total_segments": total_segments, "preview": preview_texts}
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def _extract(
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self, index_processor: BaseIndexProcessor, dataset_document: DatasetDocument, process_rule: dict
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) -> list[Document]:
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# load file
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if dataset_document.data_source_type not in ["upload_file", "notion_import", "website_crawl"]:
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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 = (
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db.session.query(UploadFile).filter(UploadFile.id == data_source_info["upload_file_id"]).one_or_none()
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)
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if file_detail:
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extract_setting = ExtractSetting(
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datasource_type="upload_file", upload_file=file_detail, document_model=dataset_document.doc_form
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)
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text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
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elif dataset_document.data_source_type == "notion_import":
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if (
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not data_source_info
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or "notion_workspace_id" not in data_source_info
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or "notion_page_id" not in data_source_info
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):
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raise ValueError("no notion import info found")
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extract_setting = ExtractSetting(
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datasource_type="notion_import",
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notion_info={
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"notion_workspace_id": data_source_info["notion_workspace_id"],
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"notion_obj_id": data_source_info["notion_page_id"],
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"notion_page_type": data_source_info["type"],
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"document": dataset_document,
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"tenant_id": dataset_document.tenant_id,
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},
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document_model=dataset_document.doc_form,
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)
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text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
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elif dataset_document.data_source_type == "website_crawl":
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if (
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not data_source_info
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or "provider" not in data_source_info
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or "url" not in data_source_info
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or "job_id" not in data_source_info
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):
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raise ValueError("no website import info found")
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extract_setting = ExtractSetting(
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datasource_type="website_crawl",
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website_info={
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"provider": data_source_info["provider"],
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"job_id": data_source_info["job_id"],
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"tenant_id": dataset_document.tenant_id,
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"url": data_source_info["url"],
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"mode": data_source_info["mode"],
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"only_main_content": data_source_info["only_main_content"],
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},
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document_model=dataset_document.doc_form,
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)
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text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
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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.now(datetime.timezone.utc).replace(tzinfo=None),
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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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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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@staticmethod
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def filter_string(text):
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text = re.sub(r"<\|", "<", text)
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text = re.sub(r"\|>", ">", text)
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text = re.sub(r"[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\xEF\xBF\xBE]", "", text)
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# Unicode U+FFFE
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text = re.sub("\ufffe", "", text)
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return text
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@staticmethod
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def _get_splitter(
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processing_rule: DatasetProcessRule, embedding_model_instance: Optional[ModelInstance]
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) -> 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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max_segmentation_tokens_length = dify_config.INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH
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if segmentation["max_tokens"] < 50 or segmentation["max_tokens"] > max_segmentation_tokens_length:
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raise ValueError(f"Custom segment length should be between 50 and {max_segmentation_tokens_length}.")
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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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if segmentation.get("chunk_overlap"):
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chunk_overlap = segmentation["chunk_overlap"]
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else:
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chunk_overlap = 0
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character_splitter = FixedRecursiveCharacterTextSplitter.from_encoder(
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chunk_size=segmentation["max_tokens"],
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chunk_overlap=chunk_overlap,
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fixed_separator=separator,
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separators=["\n\n", "。", ". ", " ", ""],
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embedding_model_instance=embedding_model_instance,
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)
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else:
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# Automatic segmentation
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character_splitter = EnhanceRecursiveCharacterTextSplitter.from_encoder(
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chunk_size=DatasetProcessRule.AUTOMATIC_RULES["segmentation"]["max_tokens"],
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chunk_overlap=DatasetProcessRule.AUTOMATIC_RULES["segmentation"]["chunk_overlap"],
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separators=["\n\n", "。", ". ", " ", ""],
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embedding_model_instance=embedding_model_instance,
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)
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return character_splitter
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def _step_split(
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self,
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text_docs: list[Document],
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splitter: TextSplitter,
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dataset: Dataset,
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dataset_document: DatasetDocument,
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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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tenant_id=dataset.tenant_id,
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document_form=dataset_document.doc_form,
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document_language=dataset_document.doc_language,
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)
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# save node to document segment
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doc_store = DatasetDocumentStore(
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dataset=dataset, user_id=dataset_document.created_by, 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.now(datetime.timezone.utc).replace(tzinfo=None)
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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.now(datetime.timezone.utc).replace(tzinfo=None),
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},
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)
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return documents
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def _split_to_documents(
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self,
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text_docs: list[Document],
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splitter: TextSplitter,
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processing_rule: DatasetProcessRule,
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tenant_id: str,
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document_form: str,
|
|
document_language: str,
|
|
) -> list[Document]:
|
|
"""
|
|
Split the text documents into nodes.
|
|
"""
|
|
all_documents = []
|
|
all_qa_documents = []
|
|
for text_doc in text_docs:
|
|
# document clean
|
|
document_text = self._document_clean(text_doc.page_content, processing_rule)
|
|
text_doc.page_content = document_text
|
|
|
|
# parse document to nodes
|
|
documents = splitter.split_documents([text_doc])
|
|
split_documents = []
|
|
for document_node in documents:
|
|
if document_node.page_content.strip():
|
|
doc_id = str(uuid.uuid4())
|
|
hash = helper.generate_text_hash(document_node.page_content)
|
|
document_node.metadata["doc_id"] = doc_id
|
|
document_node.metadata["doc_hash"] = hash
|
|
# delete Splitter character
|
|
page_content = document_node.page_content
|
|
if page_content.startswith(".") or page_content.startswith("。"):
|
|
page_content = page_content[1:]
|
|
else:
|
|
page_content = page_content
|
|
document_node.page_content = page_content
|
|
|
|
if document_node.page_content:
|
|
split_documents.append(document_node)
|
|
all_documents.extend(split_documents)
|
|
# processing qa document
|
|
if document_form == "qa_model":
|
|
for i in range(0, len(all_documents), 10):
|
|
threads = []
|
|
sub_documents = all_documents[i : i + 10]
|
|
for doc in sub_documents:
|
|
document_format_thread = threading.Thread(
|
|
target=self.format_qa_document,
|
|
kwargs={
|
|
"flask_app": current_app._get_current_object(),
|
|
"tenant_id": tenant_id,
|
|
"document_node": doc,
|
|
"all_qa_documents": all_qa_documents,
|
|
"document_language": document_language,
|
|
},
|
|
)
|
|
threads.append(document_format_thread)
|
|
document_format_thread.start()
|
|
for thread in threads:
|
|
thread.join()
|
|
return all_qa_documents
|
|
return all_documents
|
|
|
|
def format_qa_document(self, flask_app: Flask, tenant_id: str, document_node, all_qa_documents, document_language):
|
|
format_documents = []
|
|
if document_node.page_content is None or not document_node.page_content.strip():
|
|
return
|
|
with flask_app.app_context():
|
|
try:
|
|
# qa model document
|
|
response = LLMGenerator.generate_qa_document(tenant_id, document_node.page_content, document_language)
|
|
document_qa_list = self.format_split_text(response)
|
|
qa_documents = []
|
|
for result in document_qa_list:
|
|
qa_document = Document(
|
|
page_content=result["question"], metadata=document_node.metadata.model_copy()
|
|
)
|
|
doc_id = str(uuid.uuid4())
|
|
hash = helper.generate_text_hash(result["question"])
|
|
qa_document.metadata["answer"] = result["answer"]
|
|
qa_document.metadata["doc_id"] = doc_id
|
|
qa_document.metadata["doc_hash"] = hash
|
|
qa_documents.append(qa_document)
|
|
format_documents.extend(qa_documents)
|
|
except Exception as e:
|
|
logging.exception(e)
|
|
|
|
all_qa_documents.extend(format_documents)
|
|
|
|
def _split_to_documents_for_estimate(
|
|
self, text_docs: list[Document], splitter: TextSplitter, processing_rule: DatasetProcessRule
|
|
) -> list[Document]:
|
|
"""
|
|
Split the text documents into nodes.
|
|
"""
|
|
all_documents = []
|
|
for text_doc in text_docs:
|
|
# document clean
|
|
document_text = self._document_clean(text_doc.page_content, processing_rule)
|
|
text_doc.page_content = document_text
|
|
|
|
# parse document to nodes
|
|
documents = splitter.split_documents([text_doc])
|
|
|
|
split_documents = []
|
|
for document in documents:
|
|
if document.page_content is None or not document.page_content.strip():
|
|
continue
|
|
doc_id = str(uuid.uuid4())
|
|
hash = helper.generate_text_hash(document.page_content)
|
|
|
|
document.metadata["doc_id"] = doc_id
|
|
document.metadata["doc_hash"] = hash
|
|
|
|
split_documents.append(document)
|
|
|
|
all_documents.extend(split_documents)
|
|
|
|
return all_documents
|
|
|
|
@staticmethod
|
|
def _document_clean(text: str, processing_rule: DatasetProcessRule) -> str:
|
|
"""
|
|
Clean the document text according to the processing rules.
|
|
"""
|
|
if processing_rule.mode == "automatic":
|
|
rules = DatasetProcessRule.AUTOMATIC_RULES
|
|
else:
|
|
rules = json.loads(processing_rule.rules) if processing_rule.rules else {}
|
|
|
|
if "pre_processing_rules" in rules:
|
|
pre_processing_rules = rules["pre_processing_rules"]
|
|
for pre_processing_rule in pre_processing_rules:
|
|
if pre_processing_rule["id"] == "remove_extra_spaces" and pre_processing_rule["enabled"] is True:
|
|
# Remove extra spaces
|
|
pattern = r"\n{3,}"
|
|
text = re.sub(pattern, "\n\n", text)
|
|
pattern = r"[\t\f\r\x20\u00a0\u1680\u180e\u2000-\u200a\u202f\u205f\u3000]{2,}"
|
|
text = re.sub(pattern, " ", text)
|
|
elif pre_processing_rule["id"] == "remove_urls_emails" and pre_processing_rule["enabled"] is True:
|
|
# Remove email
|
|
pattern = r"([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)"
|
|
text = re.sub(pattern, "", text)
|
|
|
|
# Remove URL
|
|
pattern = r"https?://[^\s]+"
|
|
text = re.sub(pattern, "", text)
|
|
|
|
return text
|
|
|
|
@staticmethod
|
|
def format_split_text(text):
|
|
regex = r"Q\d+:\s*(.*?)\s*A\d+:\s*([\s\S]*?)(?=Q\d+:|$)"
|
|
matches = re.findall(regex, text, re.UNICODE)
|
|
|
|
return [{"question": q, "answer": re.sub(r"\n\s*", "\n", a.strip())} for q, a in matches if q and a]
|
|
|
|
def _load(
|
|
self,
|
|
index_processor: BaseIndexProcessor,
|
|
dataset: Dataset,
|
|
dataset_document: DatasetDocument,
|
|
documents: list[Document],
|
|
) -> None:
|
|
"""
|
|
insert index and update document/segment status to completed
|
|
"""
|
|
|
|
embedding_model_instance = None
|
|
if dataset.indexing_technique == "high_quality":
|
|
embedding_model_instance = self.model_manager.get_model_instance(
|
|
tenant_id=dataset.tenant_id,
|
|
provider=dataset.embedding_model_provider,
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
model=dataset.embedding_model,
|
|
)
|
|
|
|
# chunk nodes by chunk size
|
|
indexing_start_at = time.perf_counter()
|
|
tokens = 0
|
|
chunk_size = 10
|
|
|
|
# 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),
|
|
)
|
|
create_keyword_thread.start()
|
|
if dataset.indexing_technique == "high_quality":
|
|
with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
|
|
futures = []
|
|
for i in range(0, len(documents), chunk_size):
|
|
chunk_documents = documents[i : i + chunk_size]
|
|
futures.append(
|
|
executor.submit(
|
|
self._process_chunk,
|
|
current_app._get_current_object(),
|
|
index_processor,
|
|
chunk_documents,
|
|
dataset,
|
|
dataset_document,
|
|
embedding_model_instance,
|
|
)
|
|
)
|
|
|
|
for future in futures:
|
|
tokens += future.result()
|
|
|
|
create_keyword_thread.join()
|
|
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.now(datetime.timezone.utc).replace(tzinfo=None),
|
|
DatasetDocument.indexing_latency: indexing_end_at - indexing_start_at,
|
|
DatasetDocument.error: None,
|
|
},
|
|
)
|
|
|
|
@staticmethod
|
|
def _process_keyword_index(flask_app, dataset_id, document_id, documents):
|
|
with flask_app.app_context():
|
|
dataset = Dataset.query.filter_by(id=dataset_id).first()
|
|
if not dataset:
|
|
raise ValueError("no dataset found")
|
|
keyword = Keyword(dataset)
|
|
keyword.create(documents)
|
|
if dataset.indexing_technique != "high_quality":
|
|
document_ids = [document.metadata["doc_id"] for document in documents]
|
|
db.session.query(DocumentSegment).filter(
|
|
DocumentSegment.document_id == document_id,
|
|
DocumentSegment.dataset_id == dataset_id,
|
|
DocumentSegment.index_node_id.in_(document_ids),
|
|
DocumentSegment.status == "indexing",
|
|
).update(
|
|
{
|
|
DocumentSegment.status: "completed",
|
|
DocumentSegment.enabled: True,
|
|
DocumentSegment.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
|
|
}
|
|
)
|
|
|
|
db.session.commit()
|
|
|
|
def _process_chunk(
|
|
self, flask_app, index_processor, chunk_documents, dataset, dataset_document, embedding_model_instance
|
|
):
|
|
with flask_app.app_context():
|
|
# check document is paused
|
|
self._check_document_paused_status(dataset_document.id)
|
|
|
|
tokens = 0
|
|
if embedding_model_instance:
|
|
tokens += sum(
|
|
embedding_model_instance.get_text_embedding_num_tokens([document.page_content])
|
|
for document in chunk_documents
|
|
)
|
|
|
|
# load index
|
|
index_processor.load(dataset, chunk_documents, with_keywords=False)
|
|
|
|
document_ids = [document.metadata["doc_id"] for document in chunk_documents]
|
|
db.session.query(DocumentSegment).filter(
|
|
DocumentSegment.document_id == dataset_document.id,
|
|
DocumentSegment.dataset_id == dataset.id,
|
|
DocumentSegment.index_node_id.in_(document_ids),
|
|
DocumentSegment.status == "indexing",
|
|
).update(
|
|
{
|
|
DocumentSegment.status: "completed",
|
|
DocumentSegment.enabled: True,
|
|
DocumentSegment.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
|
|
}
|
|
)
|
|
|
|
db.session.commit()
|
|
|
|
return tokens
|
|
|
|
@staticmethod
|
|
def _check_document_paused_status(document_id: str):
|
|
indexing_cache_key = "document_{}_is_paused".format(document_id)
|
|
result = redis_client.get(indexing_cache_key)
|
|
if result:
|
|
raise DocumentIsPausedException()
|
|
|
|
@staticmethod
|
|
def _update_document_index_status(
|
|
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()
|
|
document = DatasetDocument.query.filter_by(id=document_id).first()
|
|
if not document:
|
|
raise DocumentIsDeletedPausedException()
|
|
|
|
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()
|
|
|
|
@staticmethod
|
|
def _update_segments_by_document(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()
|
|
|
|
@staticmethod
|
|
def batch_add_segments(segments: list[DocumentSegment], dataset: Dataset):
|
|
"""
|
|
Batch add segments index processing
|
|
"""
|
|
documents = []
|
|
for segment in segments:
|
|
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_type = dataset.doc_form
|
|
index_processor = IndexProcessorFactory(index_type).init_index_processor()
|
|
index_processor.load(dataset, documents)
|
|
|
|
def _transform(
|
|
self,
|
|
index_processor: BaseIndexProcessor,
|
|
dataset: Dataset,
|
|
text_docs: list[Document],
|
|
doc_language: str,
|
|
process_rule: dict,
|
|
) -> list[Document]:
|
|
# get embedding model instance
|
|
embedding_model_instance = None
|
|
if dataset.indexing_technique == "high_quality":
|
|
if dataset.embedding_model_provider:
|
|
embedding_model_instance = self.model_manager.get_model_instance(
|
|
tenant_id=dataset.tenant_id,
|
|
provider=dataset.embedding_model_provider,
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
model=dataset.embedding_model,
|
|
)
|
|
else:
|
|
embedding_model_instance = self.model_manager.get_default_model_instance(
|
|
tenant_id=dataset.tenant_id,
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
)
|
|
|
|
documents = index_processor.transform(
|
|
text_docs,
|
|
embedding_model_instance=embedding_model_instance,
|
|
process_rule=process_rule,
|
|
tenant_id=dataset.tenant_id,
|
|
doc_language=doc_language,
|
|
)
|
|
|
|
return documents
|
|
|
|
def _load_segments(self, dataset, dataset_document, documents):
|
|
# save node to document segment
|
|
doc_store = DatasetDocumentStore(
|
|
dataset=dataset, user_id=dataset_document.created_by, document_id=dataset_document.id
|
|
)
|
|
|
|
# add document segments
|
|
doc_store.add_documents(documents)
|
|
|
|
# update document status to indexing
|
|
cur_time = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
self._update_document_index_status(
|
|
document_id=dataset_document.id,
|
|
after_indexing_status="indexing",
|
|
extra_update_params={
|
|
DatasetDocument.cleaning_completed_at: cur_time,
|
|
DatasetDocument.splitting_completed_at: cur_time,
|
|
},
|
|
)
|
|
|
|
# update segment status to indexing
|
|
self._update_segments_by_document(
|
|
dataset_document_id=dataset_document.id,
|
|
update_params={
|
|
DocumentSegment.status: "indexing",
|
|
DocumentSegment.indexing_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
|
|
},
|
|
)
|
|
pass
|
|
|
|
|
|
class DocumentIsPausedException(Exception):
|
|
pass
|
|
|
|
|
|
class DocumentIsDeletedPausedException(Exception):
|
|
pass
|