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522 lines
19 KiB
Python
522 lines
19 KiB
Python
import json
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import logging
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import datetime
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import time
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import random
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from typing import Optional
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from extensions.ext_redis import redis_client
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from flask_login import current_user
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from core.index.index_builder import IndexBuilder
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from events.dataset_event import dataset_was_deleted
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from events.document_event import document_was_deleted
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from extensions.ext_database import db
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from models.account import Account
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from models.dataset import Dataset, Document, DatasetQuery, DatasetProcessRule, AppDatasetJoin
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from models.model import UploadFile
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from services.errors.account import NoPermissionError
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from services.errors.dataset import DatasetNameDuplicateError
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from services.errors.document import DocumentIndexingError
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from services.errors.file import FileNotExistsError
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from tasks.document_indexing_task import document_indexing_task
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class DatasetService:
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@staticmethod
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def get_datasets(page, per_page, provider="vendor", tenant_id=None, user=None):
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if user:
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permission_filter = db.or_(Dataset.created_by == user.id,
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Dataset.permission == 'all_team_members')
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else:
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permission_filter = Dataset.permission == 'all_team_members'
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datasets = Dataset.query.filter(
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db.and_(Dataset.provider == provider, Dataset.tenant_id == tenant_id, permission_filter)) \
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.paginate(
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page=page,
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per_page=per_page,
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max_per_page=100,
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error_out=False
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)
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return datasets.items, datasets.total
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@staticmethod
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def get_process_rules(dataset_id):
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# get the latest process rule
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dataset_process_rule = db.session.query(DatasetProcessRule). \
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filter(DatasetProcessRule.dataset_id == dataset_id). \
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order_by(DatasetProcessRule.created_at.desc()). \
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limit(1). \
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one_or_none()
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if dataset_process_rule:
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mode = dataset_process_rule.mode
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rules = dataset_process_rule.rules_dict
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else:
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mode = DocumentService.DEFAULT_RULES['mode']
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rules = DocumentService.DEFAULT_RULES['rules']
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return {
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'mode': mode,
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'rules': rules
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}
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@staticmethod
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def get_datasets_by_ids(ids, tenant_id):
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datasets = Dataset.query.filter(Dataset.id.in_(ids),
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Dataset.tenant_id == tenant_id).paginate(
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page=1, per_page=len(ids), max_per_page=len(ids), error_out=False)
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return datasets.items, datasets.total
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@staticmethod
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def create_empty_dataset(tenant_id: str, name: str, indexing_technique: Optional[str], account: Account):
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# check if dataset name already exists
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if Dataset.query.filter_by(name=name, tenant_id=tenant_id).first():
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raise DatasetNameDuplicateError(
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f'Dataset with name {name} already exists.')
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dataset = Dataset(name=name, indexing_technique=indexing_technique, data_source_type='upload_file')
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# dataset = Dataset(name=name, provider=provider, config=config)
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dataset.created_by = account.id
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dataset.updated_by = account.id
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dataset.tenant_id = tenant_id
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db.session.add(dataset)
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db.session.commit()
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return dataset
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@staticmethod
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def get_dataset(dataset_id):
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dataset = Dataset.query.filter_by(
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id=dataset_id
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).first()
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if dataset is None:
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return None
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else:
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return dataset
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@staticmethod
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def update_dataset(dataset_id, data, user):
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dataset = DatasetService.get_dataset(dataset_id)
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DatasetService.check_dataset_permission(dataset, user)
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filtered_data = {k: v for k, v in data.items() if v is not None or k == 'description'}
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filtered_data['updated_by'] = user.id
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filtered_data['updated_at'] = datetime.datetime.now()
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dataset.query.filter_by(id=dataset_id).update(filtered_data)
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db.session.commit()
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return dataset
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@staticmethod
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def delete_dataset(dataset_id, user):
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# todo: cannot delete dataset if it is being processed
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dataset = DatasetService.get_dataset(dataset_id)
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if dataset is None:
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return False
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DatasetService.check_dataset_permission(dataset, user)
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dataset_was_deleted.send(dataset)
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db.session.delete(dataset)
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db.session.commit()
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return True
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@staticmethod
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def check_dataset_permission(dataset, user):
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if dataset.tenant_id != user.current_tenant_id:
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logging.debug(
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f'User {user.id} does not have permission to access dataset {dataset.id}')
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raise NoPermissionError(
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'You do not have permission to access this dataset.')
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if dataset.permission == 'only_me' and dataset.created_by != user.id:
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logging.debug(
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f'User {user.id} does not have permission to access dataset {dataset.id}')
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raise NoPermissionError(
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'You do not have permission to access this dataset.')
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@staticmethod
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def get_dataset_queries(dataset_id: str, page: int, per_page: int):
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dataset_queries = DatasetQuery.query.filter_by(dataset_id=dataset_id) \
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.order_by(db.desc(DatasetQuery.created_at)) \
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.paginate(
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page=page, per_page=per_page, max_per_page=100, error_out=False
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)
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return dataset_queries.items, dataset_queries.total
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@staticmethod
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def get_related_apps(dataset_id: str):
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return AppDatasetJoin.query.filter(AppDatasetJoin.dataset_id == dataset_id) \
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.order_by(db.desc(AppDatasetJoin.created_at)).all()
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class DocumentService:
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DEFAULT_RULES = {
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'mode': 'custom',
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'rules': {
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'pre_processing_rules': [
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{'id': 'remove_extra_spaces', 'enabled': True},
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{'id': 'remove_urls_emails', 'enabled': False}
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],
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'segmentation': {
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'delimiter': '\n',
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'max_tokens': 500
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}
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}
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}
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DOCUMENT_METADATA_SCHEMA = {
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"book": {
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"title": str,
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"language": str,
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"author": str,
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"publisher": str,
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"publication_date": str,
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"isbn": str,
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"category": str,
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},
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"web_page": {
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"title": str,
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"url": str,
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"language": str,
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"publish_date": str,
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"author/publisher": str,
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"topic/keywords": str,
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"description": str,
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},
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"paper": {
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"title": str,
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"language": str,
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"author": str,
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"publish_date": str,
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"journal/conference_name": str,
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"volume/issue/page_numbers": str,
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"doi": str,
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"topic/keywords": str,
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"abstract": str,
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},
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"social_media_post": {
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"platform": str,
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"author/username": str,
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"publish_date": str,
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"post_url": str,
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"topic/tags": str,
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},
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"wikipedia_entry": {
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"title": str,
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"language": str,
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"web_page_url": str,
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"last_edit_date": str,
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"editor/contributor": str,
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"summary/introduction": str,
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},
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"personal_document": {
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"title": str,
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"author": str,
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"creation_date": str,
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"last_modified_date": str,
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"document_type": str,
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"tags/category": str,
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},
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"business_document": {
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"title": str,
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"author": str,
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"creation_date": str,
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"last_modified_date": str,
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"document_type": str,
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"department/team": str,
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},
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"im_chat_log": {
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"chat_platform": str,
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"chat_participants/group_name": str,
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"start_date": str,
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"end_date": str,
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"summary": str,
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},
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"synced_from_notion": {
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"title": str,
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"language": str,
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"author/creator": str,
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"creation_date": str,
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"last_modified_date": str,
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"notion_page_link": str,
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"category/tags": str,
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"description": str,
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},
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"synced_from_github": {
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"repository_name": str,
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"repository_description": str,
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"repository_owner/organization": str,
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"code_filename": str,
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"code_file_path": str,
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"programming_language": str,
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"github_link": str,
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"open_source_license": str,
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"commit_date": str,
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"commit_author": str
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}
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}
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@staticmethod
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def get_document(dataset_id: str, document_id: str) -> Optional[Document]:
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document = db.session.query(Document).filter(
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Document.id == document_id,
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Document.dataset_id == dataset_id
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).first()
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return document
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@staticmethod
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def get_document_file_detail(file_id: str):
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file_detail = db.session.query(UploadFile). \
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filter(UploadFile.id == file_id). \
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one_or_none()
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return file_detail
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@staticmethod
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def check_archived(document):
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if document.archived:
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return True
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else:
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return False
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@staticmethod
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def delete_document(document):
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if document.indexing_status in ["parsing", "cleaning", "splitting", "indexing"]:
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raise DocumentIndexingError()
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# trigger document_was_deleted signal
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document_was_deleted.send(document.id, dataset_id=document.dataset_id)
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db.session.delete(document)
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db.session.commit()
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@staticmethod
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def pause_document(document):
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if document.indexing_status not in ["waiting", "parsing", "cleaning", "splitting", "indexing"]:
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raise DocumentIndexingError()
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# update document to be paused
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document.is_paused = True
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document.paused_by = current_user.id
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document.paused_at = datetime.datetime.utcnow()
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db.session.add(document)
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db.session.commit()
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# set document paused flag
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indexing_cache_key = 'document_{}_is_paused'.format(document.id)
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redis_client.setnx(indexing_cache_key, "True")
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@staticmethod
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def recover_document(document):
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if not document.is_paused:
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raise DocumentIndexingError()
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# update document to be recover
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document.is_paused = False
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document.paused_by = current_user.id
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document.paused_at = time.time()
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db.session.add(document)
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db.session.commit()
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# delete paused flag
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indexing_cache_key = 'document_{}_is_paused'.format(document.id)
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redis_client.delete(indexing_cache_key)
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# trigger async task
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document_indexing_task.delay(document.dataset_id, document.id)
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@staticmethod
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def get_documents_position(dataset_id):
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documents = Document.query.filter_by(dataset_id=dataset_id).all()
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if documents:
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return len(documents) + 1
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else:
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return 1
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@staticmethod
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def save_document_with_dataset_id(dataset: Dataset, document_data: dict,
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account: Account, dataset_process_rule: Optional[DatasetProcessRule] = None,
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created_from: str = 'web'):
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if not dataset.indexing_technique:
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if 'indexing_technique' not in document_data \
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or document_data['indexing_technique'] not in Dataset.INDEXING_TECHNIQUE_LIST:
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raise ValueError("Indexing technique is required")
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dataset.indexing_technique = document_data["indexing_technique"]
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if dataset.indexing_technique == 'high_quality':
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IndexBuilder.get_default_service_context(dataset.tenant_id)
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# save process rule
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if not dataset_process_rule:
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process_rule = document_data["process_rule"]
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if process_rule["mode"] == "custom":
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dataset_process_rule = DatasetProcessRule(
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dataset_id=dataset.id,
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mode=process_rule["mode"],
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rules=json.dumps(process_rule["rules"]),
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created_by=account.id
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)
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elif process_rule["mode"] == "automatic":
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dataset_process_rule = DatasetProcessRule(
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dataset_id=dataset.id,
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mode=process_rule["mode"],
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rules=json.dumps(DatasetProcessRule.AUTOMATIC_RULES),
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created_by=account.id
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)
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db.session.add(dataset_process_rule)
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db.session.commit()
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file_name = ''
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data_source_info = {}
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if document_data["data_source"]["type"] == "upload_file":
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file_id = document_data["data_source"]["info"]
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file = db.session.query(UploadFile).filter(
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UploadFile.tenant_id == dataset.tenant_id,
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UploadFile.id == file_id
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).first()
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# raise error if file not found
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if not file:
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raise FileNotExistsError()
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file_name = file.name
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data_source_info = {
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"upload_file_id": file_id,
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}
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# save document
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position = DocumentService.get_documents_position(dataset.id)
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document = Document(
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tenant_id=dataset.tenant_id,
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dataset_id=dataset.id,
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position=position,
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data_source_type=document_data["data_source"]["type"],
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data_source_info=json.dumps(data_source_info),
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dataset_process_rule_id=dataset_process_rule.id,
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batch=time.strftime('%Y%m%d%H%M%S') + str(random.randint(100000, 999999)),
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name=file_name,
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created_from=created_from,
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created_by=account.id,
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# created_api_request_id = db.Column(UUID, nullable=True)
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)
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db.session.add(document)
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db.session.commit()
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# trigger async task
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document_indexing_task.delay(document.dataset_id, document.id)
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return document
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@staticmethod
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def save_document_without_dataset_id(tenant_id: str, document_data: dict, account: Account):
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# save dataset
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dataset = Dataset(
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tenant_id=tenant_id,
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name='',
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data_source_type=document_data["data_source"]["type"],
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indexing_technique=document_data["indexing_technique"],
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created_by=account.id
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)
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db.session.add(dataset)
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db.session.flush()
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document = DocumentService.save_document_with_dataset_id(dataset, document_data, account)
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cut_length = 18
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cut_name = document.name[:cut_length]
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dataset.name = cut_name + '...' if len(document.name) > cut_length else cut_name
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dataset.description = 'useful for when you want to answer queries about the ' + document.name
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db.session.commit()
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return dataset, document
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@classmethod
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def document_create_args_validate(cls, args: dict):
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if 'data_source' not in args or not args['data_source']:
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raise ValueError("Data source is required")
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if not isinstance(args['data_source'], dict):
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raise ValueError("Data source is invalid")
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if 'type' not in args['data_source'] or not args['data_source']['type']:
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raise ValueError("Data source type is required")
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if args['data_source']['type'] not in Document.DATA_SOURCES:
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raise ValueError("Data source type is invalid")
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if args['data_source']['type'] == 'upload_file':
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if 'info' not in args['data_source'] or not args['data_source']['info']:
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raise ValueError("Data source info is required")
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if 'process_rule' not in args or not args['process_rule']:
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raise ValueError("Process rule is required")
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if not isinstance(args['process_rule'], dict):
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raise ValueError("Process rule is invalid")
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if 'mode' not in args['process_rule'] or not args['process_rule']['mode']:
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raise ValueError("Process rule mode is required")
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if args['process_rule']['mode'] not in DatasetProcessRule.MODES:
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raise ValueError("Process rule mode is invalid")
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if args['process_rule']['mode'] == 'automatic':
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args['process_rule']['rules'] = {}
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else:
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if 'rules' not in args['process_rule'] or not args['process_rule']['rules']:
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raise ValueError("Process rule rules is required")
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if not isinstance(args['process_rule']['rules'], dict):
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raise ValueError("Process rule rules is invalid")
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if 'pre_processing_rules' not in args['process_rule']['rules'] \
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or args['process_rule']['rules']['pre_processing_rules'] is None:
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raise ValueError("Process rule pre_processing_rules is required")
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if not isinstance(args['process_rule']['rules']['pre_processing_rules'], list):
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raise ValueError("Process rule pre_processing_rules is invalid")
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unique_pre_processing_rule_dicts = {}
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for pre_processing_rule in args['process_rule']['rules']['pre_processing_rules']:
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if 'id' not in pre_processing_rule or not pre_processing_rule['id']:
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raise ValueError("Process rule pre_processing_rules id is required")
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if pre_processing_rule['id'] not in DatasetProcessRule.PRE_PROCESSING_RULES:
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raise ValueError("Process rule pre_processing_rules id is invalid")
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if 'enabled' not in pre_processing_rule or pre_processing_rule['enabled'] is None:
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raise ValueError("Process rule pre_processing_rules enabled is required")
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if not isinstance(pre_processing_rule['enabled'], bool):
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raise ValueError("Process rule pre_processing_rules enabled is invalid")
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unique_pre_processing_rule_dicts[pre_processing_rule['id']] = pre_processing_rule
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args['process_rule']['rules']['pre_processing_rules'] = list(unique_pre_processing_rule_dicts.values())
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if 'segmentation' not in args['process_rule']['rules'] \
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or args['process_rule']['rules']['segmentation'] is None:
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raise ValueError("Process rule segmentation is required")
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if not isinstance(args['process_rule']['rules']['segmentation'], dict):
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raise ValueError("Process rule segmentation is invalid")
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if 'separator' not in args['process_rule']['rules']['segmentation'] \
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or not args['process_rule']['rules']['segmentation']['separator']:
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raise ValueError("Process rule segmentation separator is required")
|
|
|
|
if not isinstance(args['process_rule']['rules']['segmentation']['separator'], str):
|
|
raise ValueError("Process rule segmentation separator is invalid")
|
|
|
|
if 'max_tokens' not in args['process_rule']['rules']['segmentation'] \
|
|
or not args['process_rule']['rules']['segmentation']['max_tokens']:
|
|
raise ValueError("Process rule segmentation max_tokens is required")
|
|
|
|
if not isinstance(args['process_rule']['rules']['segmentation']['max_tokens'], int):
|
|
raise ValueError("Process rule segmentation max_tokens is invalid")
|