import concurrent.futures import datetime import json import logging import re import threading import time import uuid from typing import Optional, cast from flask import Flask, current_app from flask_login import current_user from sqlalchemy.orm.exc import ObjectDeletedError from core.docstore.dataset_docstore import DatasetDocumentStore from core.errors.error import ProviderTokenNotInitError from core.llm_generator.llm_generator import LLMGenerator from core.model_manager import ModelInstance, ModelManager from core.model_runtime.entities.model_entities import ModelType, PriceType from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel from core.rag.datasource.keyword.keyword_factory import Keyword from core.rag.extractor.entity.extract_setting import ExtractSetting from core.rag.index_processor.index_processor_base import BaseIndexProcessor from core.rag.index_processor.index_processor_factory import IndexProcessorFactory from core.rag.models.document import Document from core.splitter.fixed_text_splitter import EnhanceRecursiveCharacterTextSplitter, FixedRecursiveCharacterTextSplitter from core.splitter.text_splitter import TextSplitter from extensions.ext_database import db from extensions.ext_redis import redis_client from extensions.ext_storage import storage from libs import helper from models.dataset import Dataset, DatasetProcessRule, DocumentSegment from models.dataset import Document as DatasetDocument from models.model import UploadFile from services.feature_service import FeatureService class IndexingRunner: def __init__(self): self.storage = storage self.model_manager = ModelManager() def run(self, dataset_documents: list[DatasetDocument]): """Run the indexing process.""" for dataset_document in dataset_documents: try: # get dataset dataset = Dataset.query.filter_by( id=dataset_document.dataset_id ).first() if not dataset: raise ValueError("no dataset found") # get the process rule processing_rule = db.session.query(DatasetProcessRule). \ filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \ first() index_type = dataset_document.doc_form index_processor = IndexProcessorFactory(index_type).init_index_processor() # extract text_docs = self._extract(index_processor, dataset_document, processing_rule.to_dict()) # transform documents = self._transform(index_processor, dataset, text_docs, dataset_document.doc_language, processing_rule.to_dict()) # save segment self._load_segments(dataset, dataset_document, documents) # load self._load( index_processor=index_processor, dataset=dataset, dataset_document=dataset_document, documents=documents ) except DocumentIsPausedException: raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id)) except ProviderTokenNotInitError as e: dataset_document.indexing_status = 'error' dataset_document.error = str(e.description) dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None) db.session.commit() except ObjectDeletedError: logging.warning('Document deleted, document id: {}'.format(dataset_document.id)) except Exception as e: logging.exception("consume document failed") dataset_document.indexing_status = 'error' dataset_document.error = str(e) dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None) db.session.commit() def run_in_splitting_status(self, dataset_document: DatasetDocument): """Run the indexing process when the index_status is splitting.""" try: # get dataset dataset = Dataset.query.filter_by( id=dataset_document.dataset_id ).first() if not dataset: raise ValueError("no dataset found") # get exist document_segment list and delete document_segments = DocumentSegment.query.filter_by( dataset_id=dataset.id, document_id=dataset_document.id ).all() for document_segment in document_segments: db.session.delete(document_segment) db.session.commit() # get the process rule processing_rule = db.session.query(DatasetProcessRule). \ filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \ first() index_type = dataset_document.doc_form index_processor = IndexProcessorFactory(index_type).init_index_processor() # extract text_docs = self._extract(index_processor, dataset_document, processing_rule.to_dict()) # transform documents = self._transform(index_processor, dataset, text_docs, dataset_document.doc_language, processing_rule.to_dict()) # save segment self._load_segments(dataset, dataset_document, documents) # load self._load( index_processor=index_processor, dataset=dataset, dataset_document=dataset_document, documents=documents ) except DocumentIsPausedException: raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id)) except ProviderTokenNotInitError as e: dataset_document.indexing_status = 'error' dataset_document.error = str(e.description) dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None) db.session.commit() except Exception as e: logging.exception("consume document failed") dataset_document.indexing_status = 'error' dataset_document.error = str(e) dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None) db.session.commit() def run_in_indexing_status(self, dataset_document: DatasetDocument): """Run the indexing process when the index_status is indexing.""" try: # get dataset dataset = Dataset.query.filter_by( id=dataset_document.dataset_id ).first() if not dataset: raise ValueError("no dataset found") # get exist document_segment list and delete document_segments = DocumentSegment.query.filter_by( dataset_id=dataset.id, document_id=dataset_document.id ).all() documents = [] if document_segments: for document_segment in document_segments: # transform segment to node if document_segment.status != "completed": document = Document( page_content=document_segment.content, metadata={ "doc_id": document_segment.index_node_id, "doc_hash": document_segment.index_node_hash, "document_id": document_segment.document_id, "dataset_id": document_segment.dataset_id, } ) documents.append(document) # build index # get the process rule processing_rule = db.session.query(DatasetProcessRule). \ filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \ first() index_type = dataset_document.doc_form index_processor = IndexProcessorFactory(index_type).init_index_processor() self._load( index_processor=index_processor, dataset=dataset, dataset_document=dataset_document, documents=documents ) except DocumentIsPausedException: raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id)) except ProviderTokenNotInitError as e: dataset_document.indexing_status = 'error' dataset_document.error = str(e.description) dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None) db.session.commit() except Exception as e: logging.exception("consume document failed") dataset_document.indexing_status = 'error' dataset_document.error = str(e) dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None) db.session.commit() def indexing_estimate(self, tenant_id: str, extract_settings: list[ExtractSetting], tmp_processing_rule: dict, doc_form: str = None, doc_language: str = 'English', dataset_id: str = None, indexing_technique: str = 'economy') -> dict: """ Estimate the indexing for the document. """ # check document limit features = FeatureService.get_features(tenant_id) if features.billing.enabled: count = len(extract_settings) batch_upload_limit = int(current_app.config['BATCH_UPLOAD_LIMIT']) if count > batch_upload_limit: raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.") embedding_model_instance = None if dataset_id: dataset = Dataset.query.filter_by( id=dataset_id ).first() if not dataset: raise ValueError('Dataset not found.') if dataset.indexing_technique == 'high_quality' or indexing_technique == 'high_quality': if dataset.embedding_model_provider: embedding_model_instance = self.model_manager.get_model_instance( tenant_id=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=tenant_id, model_type=ModelType.TEXT_EMBEDDING, ) else: if indexing_technique == 'high_quality': embedding_model_instance = self.model_manager.get_default_model_instance( tenant_id=tenant_id, model_type=ModelType.TEXT_EMBEDDING, ) tokens = 0 preview_texts = [] total_segments = 0 total_price = 0 currency = 'USD' index_type = doc_form index_processor = IndexProcessorFactory(index_type).init_index_processor() all_text_docs = [] for extract_setting in extract_settings: # extract text_docs = index_processor.extract(extract_setting, process_rule_mode=tmp_processing_rule["mode"]) all_text_docs.extend(text_docs) processing_rule = DatasetProcessRule( mode=tmp_processing_rule["mode"], rules=json.dumps(tmp_processing_rule["rules"]) ) # get splitter splitter = self._get_splitter(processing_rule, embedding_model_instance) # split to documents documents = self._split_to_documents_for_estimate( text_docs=text_docs, splitter=splitter, processing_rule=processing_rule ) total_segments += len(documents) for document in documents: if len(preview_texts) < 5: preview_texts.append(document.page_content) if indexing_technique == 'high_quality' or embedding_model_instance: embedding_model_type_instance = embedding_model_instance.model_type_instance embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance) tokens += embedding_model_type_instance.get_num_tokens( model=embedding_model_instance.model, credentials=embedding_model_instance.credentials, texts=[self.filter_string(document.page_content)] ) if doc_form and doc_form == 'qa_model': model_instance = self.model_manager.get_default_model_instance( tenant_id=tenant_id, model_type=ModelType.LLM ) model_type_instance = model_instance.model_type_instance model_type_instance = cast(LargeLanguageModel, model_type_instance) if len(preview_texts) > 0: # qa model document response = LLMGenerator.generate_qa_document(current_user.current_tenant_id, preview_texts[0], doc_language) document_qa_list = self.format_split_text(response) price_info = model_type_instance.get_price( model=model_instance.model, credentials=model_instance.credentials, price_type=PriceType.INPUT, tokens=total_segments * 2000, ) return { "total_segments": total_segments * 20, "tokens": total_segments * 2000, "total_price": '{:f}'.format(price_info.total_amount), "currency": price_info.currency, "qa_preview": document_qa_list, "preview": preview_texts } if embedding_model_instance: embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_instance.model_type_instance) embedding_price_info = embedding_model_type_instance.get_price( model=embedding_model_instance.model, credentials=embedding_model_instance.credentials, price_type=PriceType.INPUT, tokens=tokens ) total_price = '{:f}'.format(embedding_price_info.total_amount) currency = embedding_price_info.currency return { "total_segments": total_segments, "tokens": tokens, "total_price": total_price, "currency": currency, "preview": preview_texts } def _extract(self, index_processor: BaseIndexProcessor, dataset_document: DatasetDocument, process_rule: dict) \ -> list[Document]: # load file if dataset_document.data_source_type not in ["upload_file", "notion_import"]: return [] data_source_info = dataset_document.data_source_info_dict text_docs = [] if dataset_document.data_source_type == 'upload_file': if not data_source_info or 'upload_file_id' not in data_source_info: raise ValueError("no upload file found") file_detail = db.session.query(UploadFile). \ filter(UploadFile.id == data_source_info['upload_file_id']). \ one_or_none() if file_detail: extract_setting = ExtractSetting( datasource_type="upload_file", upload_file=file_detail, document_model=dataset_document.doc_form ) text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule['mode']) elif dataset_document.data_source_type == 'notion_import': if (not data_source_info or 'notion_workspace_id' not in data_source_info or 'notion_page_id' not in data_source_info): raise ValueError("no notion import info found") extract_setting = ExtractSetting( datasource_type="notion_import", notion_info={ "notion_workspace_id": data_source_info['notion_workspace_id'], "notion_obj_id": data_source_info['notion_page_id'], "notion_page_type": data_source_info['type'], "document": dataset_document, "tenant_id": dataset_document.tenant_id }, document_model=dataset_document.doc_form ) text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule['mode']) # update document status to splitting self._update_document_index_status( document_id=dataset_document.id, after_indexing_status="splitting", extra_update_params={ DatasetDocument.word_count: sum([len(text_doc.page_content) for text_doc in text_docs]), DatasetDocument.parsing_completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None) } ) # replace doc id to document model id text_docs = cast(list[Document], text_docs) for text_doc in text_docs: text_doc.metadata['document_id'] = dataset_document.id text_doc.metadata['dataset_id'] = dataset_document.dataset_id return text_docs def filter_string(self, text): text = re.sub(r'<\|', '<', text) text = re.sub(r'\|>', '>', text) text = re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\xEF\xBF\xBE]', '', text) # Unicode U+FFFE text = re.sub('\uFFFE', '', text) return text def _get_splitter(self, processing_rule: DatasetProcessRule, embedding_model_instance: Optional[ModelInstance]) -> TextSplitter: """ Get the NodeParser object according to the processing rule. """ if processing_rule.mode == "custom": # The user-defined segmentation rule rules = json.loads(processing_rule.rules) segmentation = rules["segmentation"] max_segmentation_tokens_length = int(current_app.config['INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH']) if segmentation["max_tokens"] < 50 or segmentation["max_tokens"] > max_segmentation_tokens_length: raise ValueError(f"Custom segment length should be between 50 and {max_segmentation_tokens_length}.") separator = segmentation["separator"] if separator: separator = separator.replace('\\n', '\n') if segmentation.get('chunk_overlap'): chunk_overlap = segmentation['chunk_overlap'] else: chunk_overlap = 0 character_splitter = FixedRecursiveCharacterTextSplitter.from_encoder( chunk_size=segmentation["max_tokens"], chunk_overlap=chunk_overlap, fixed_separator=separator, separators=["\n\n", "。", ".", " ", ""], embedding_model_instance=embedding_model_instance ) else: # Automatic segmentation character_splitter = EnhanceRecursiveCharacterTextSplitter.from_encoder( chunk_size=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens'], chunk_overlap=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['chunk_overlap'], separators=["\n\n", "。", ".", " ", ""], embedding_model_instance=embedding_model_instance ) return character_splitter def _step_split(self, text_docs: list[Document], splitter: TextSplitter, dataset: Dataset, dataset_document: DatasetDocument, processing_rule: DatasetProcessRule) \ -> list[Document]: """ Split the text documents into documents and save them to the document segment. """ documents = self._split_to_documents( text_docs=text_docs, splitter=splitter, processing_rule=processing_rule, tenant_id=dataset.tenant_id, document_form=dataset_document.doc_form, document_language=dataset_document.doc_language ) # 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) } ) return documents def _split_to_documents(self, text_docs: list[Document], splitter: TextSplitter, processing_rule: DatasetProcessRule, tenant_id: str, 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 Spliter 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.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 def _document_clean(self, 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 def format_split_text(self, 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 embedding_model_type_instance = None if embedding_model_instance: embedding_model_type_instance = embedding_model_instance.model_type_instance embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance) # 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, embedding_model_type_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, } ) def _process_keyword_index(self, 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.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, embedding_model_type_instance): with flask_app.app_context(): # check document is paused self._check_document_paused_status(dataset_document.id) tokens = 0 if dataset.indexing_technique == 'high_quality' or embedding_model_type_instance: tokens += sum( embedding_model_type_instance.get_num_tokens( embedding_model_instance.model, embedding_model_instance.credentials, [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.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 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() 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() 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() def batch_add_segments(self, 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