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https://github.com/langgenius/dify.git
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1717 lines
77 KiB
Python
1717 lines
77 KiB
Python
import datetime
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import json
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import logging
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import random
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import time
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import uuid
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from typing import Optional
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from flask_login import current_user
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from sqlalchemy import func
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from werkzeug.exceptions import NotFound
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from configs import dify_config
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from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
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from core.model_manager import 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.models.document import Document as RAGDocument
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from core.rag.retrieval.retrieval_methods import RetrievalMethod
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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 extensions.ext_redis import redis_client
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from libs import helper
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from models.account import Account, TenantAccountRole
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from models.dataset import (
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AppDatasetJoin,
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Dataset,
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DatasetCollectionBinding,
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DatasetPermission,
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DatasetPermissionEnum,
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DatasetProcessRule,
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DatasetQuery,
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Document,
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DocumentSegment,
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ExternalKnowledgeBindings,
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)
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from models.model import UploadFile
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from models.source import DataSourceOauthBinding
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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 services.external_knowledge_service import ExternalDatasetService
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from services.feature_service import FeatureModel, FeatureService
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from services.tag_service import TagService
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from services.vector_service import VectorService
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from tasks.clean_notion_document_task import clean_notion_document_task
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from tasks.deal_dataset_vector_index_task import deal_dataset_vector_index_task
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from tasks.delete_segment_from_index_task import delete_segment_from_index_task
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from tasks.disable_segment_from_index_task import disable_segment_from_index_task
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from tasks.document_indexing_task import document_indexing_task
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from tasks.document_indexing_update_task import document_indexing_update_task
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from tasks.duplicate_document_indexing_task import duplicate_document_indexing_task
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from tasks.recover_document_indexing_task import recover_document_indexing_task
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from tasks.retry_document_indexing_task import retry_document_indexing_task
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from tasks.sync_website_document_indexing_task import sync_website_document_indexing_task
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class DatasetService:
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@staticmethod
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def get_datasets(page, per_page, tenant_id=None, user=None, search=None, tag_ids=None):
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query = Dataset.query.filter(Dataset.tenant_id == tenant_id).order_by(Dataset.created_at.desc())
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if user:
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# get permitted dataset ids
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dataset_permission = DatasetPermission.query.filter_by(account_id=user.id, tenant_id=tenant_id).all()
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permitted_dataset_ids = {dp.dataset_id for dp in dataset_permission} if dataset_permission else None
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if user.current_role == TenantAccountRole.DATASET_OPERATOR:
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# only show datasets that the user has permission to access
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if permitted_dataset_ids:
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query = query.filter(Dataset.id.in_(permitted_dataset_ids))
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else:
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return [], 0
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else:
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# show all datasets that the user has permission to access
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if permitted_dataset_ids:
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query = query.filter(
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db.or_(
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Dataset.permission == DatasetPermissionEnum.ALL_TEAM,
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db.and_(Dataset.permission == DatasetPermissionEnum.ONLY_ME, Dataset.created_by == user.id),
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db.and_(
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Dataset.permission == DatasetPermissionEnum.PARTIAL_TEAM,
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Dataset.id.in_(permitted_dataset_ids),
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),
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)
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)
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else:
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query = query.filter(
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db.or_(
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Dataset.permission == DatasetPermissionEnum.ALL_TEAM,
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db.and_(Dataset.permission == DatasetPermissionEnum.ONLY_ME, Dataset.created_by == user.id),
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)
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)
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else:
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# if no user, only show datasets that are shared with all team members
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query = query.filter(Dataset.permission == DatasetPermissionEnum.ALL_TEAM)
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if search:
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query = query.filter(Dataset.name.ilike(f"%{search}%"))
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if tag_ids:
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target_ids = TagService.get_target_ids_by_tag_ids("knowledge", tenant_id, tag_ids)
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if target_ids:
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query = query.filter(Dataset.id.in_(target_ids))
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else:
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return [], 0
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datasets = query.paginate(page=page, per_page=per_page, max_per_page=100, error_out=False)
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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 = (
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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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)
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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 {"mode": mode, "rules": rules}
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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), 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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)
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return datasets.items, datasets.total
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@staticmethod
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def create_empty_dataset(
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tenant_id: str,
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name: str,
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description: Optional[str],
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indexing_technique: Optional[str],
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account: Account,
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permission: Optional[str] = None,
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provider: str = "vendor",
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external_knowledge_api_id: Optional[str] = None,
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external_knowledge_id: Optional[str] = None,
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):
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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(f"Dataset with name {name} already exists.")
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embedding_model = None
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if indexing_technique == "high_quality":
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model_manager = ModelManager()
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embedding_model = model_manager.get_default_model_instance(
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tenant_id=tenant_id, model_type=ModelType.TEXT_EMBEDDING
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)
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dataset = Dataset(name=name, indexing_technique=indexing_technique)
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# dataset = Dataset(name=name, provider=provider, config=config)
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dataset.description = description
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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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dataset.embedding_model_provider = embedding_model.provider if embedding_model else None
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dataset.embedding_model = embedding_model.model if embedding_model else None
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dataset.permission = permission or DatasetPermissionEnum.ONLY_ME
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dataset.provider = provider
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db.session.add(dataset)
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db.session.flush()
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if provider == "external" and external_knowledge_api_id:
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external_knowledge_api = ExternalDatasetService.get_external_knowledge_api(external_knowledge_api_id)
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if not external_knowledge_api:
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raise ValueError("External API template not found.")
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external_knowledge_binding = ExternalKnowledgeBindings(
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tenant_id=tenant_id,
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dataset_id=dataset.id,
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external_knowledge_api_id=external_knowledge_api_id,
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external_knowledge_id=external_knowledge_id,
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created_by=account.id,
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)
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db.session.add(external_knowledge_binding)
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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) -> Dataset:
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return Dataset.query.filter_by(id=dataset_id).first()
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@staticmethod
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def check_dataset_model_setting(dataset):
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if dataset.indexing_technique == "high_quality":
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try:
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model_manager = ModelManager()
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model_manager.get_model_instance(
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tenant_id=dataset.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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except LLMBadRequestError:
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raise ValueError(
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"No Embedding Model available. Please configure a valid provider "
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"in the Settings -> Model Provider."
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)
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except ProviderTokenNotInitError as ex:
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raise ValueError(f"The dataset in unavailable, due to: {ex.description}")
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@staticmethod
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def check_embedding_model_setting(tenant_id: str, embedding_model_provider: str, embedding_model: str):
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try:
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model_manager = ModelManager()
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model_manager.get_model_instance(
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tenant_id=tenant_id,
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provider=embedding_model_provider,
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model_type=ModelType.TEXT_EMBEDDING,
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model=embedding_model,
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)
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except LLMBadRequestError:
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raise ValueError(
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"No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
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)
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except ProviderTokenNotInitError as ex:
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raise ValueError(f"The dataset in unavailable, due to: {ex.description}")
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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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if dataset.provider == "external":
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dataset.retrieval_model = data.get("external_retrieval_model", None)
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dataset.name = data.get("name", dataset.name)
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dataset.description = data.get("description", "")
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external_knowledge_id = data.get("external_knowledge_id", None)
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dataset.permission = data.get("permission")
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db.session.add(dataset)
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if not external_knowledge_id:
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raise ValueError("External knowledge id is required.")
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external_knowledge_api_id = data.get("external_knowledge_api_id", None)
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if not external_knowledge_api_id:
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raise ValueError("External knowledge api id is required.")
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external_knowledge_binding = ExternalKnowledgeBindings.query.filter_by(dataset_id=dataset_id).first()
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if (
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external_knowledge_binding.external_knowledge_id != external_knowledge_id
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or external_knowledge_binding.external_knowledge_api_id != external_knowledge_api_id
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):
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external_knowledge_binding.external_knowledge_id = external_knowledge_id
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external_knowledge_binding.external_knowledge_api_id = external_knowledge_api_id
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db.session.add(external_knowledge_binding)
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db.session.commit()
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else:
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data.pop("partial_member_list", None)
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data.pop("external_knowledge_api_id", None)
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data.pop("external_knowledge_id", None)
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data.pop("external_retrieval_model", None)
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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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action = None
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if dataset.indexing_technique != data["indexing_technique"]:
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# if update indexing_technique
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if data["indexing_technique"] == "economy":
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action = "remove"
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filtered_data["embedding_model"] = None
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filtered_data["embedding_model_provider"] = None
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filtered_data["collection_binding_id"] = None
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elif data["indexing_technique"] == "high_quality":
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action = "add"
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# get embedding model setting
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try:
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model_manager = ModelManager()
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embedding_model = model_manager.get_model_instance(
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tenant_id=current_user.current_tenant_id,
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provider=data["embedding_model_provider"],
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model_type=ModelType.TEXT_EMBEDDING,
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model=data["embedding_model"],
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)
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filtered_data["embedding_model"] = embedding_model.model
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filtered_data["embedding_model_provider"] = embedding_model.provider
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dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
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embedding_model.provider, embedding_model.model
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)
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filtered_data["collection_binding_id"] = dataset_collection_binding.id
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except LLMBadRequestError:
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raise ValueError(
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"No Embedding Model available. Please configure a valid provider "
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"in the Settings -> Model Provider."
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)
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except ProviderTokenNotInitError as ex:
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raise ValueError(ex.description)
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else:
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if (
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data["embedding_model_provider"] != dataset.embedding_model_provider
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or data["embedding_model"] != dataset.embedding_model
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):
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action = "update"
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try:
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model_manager = ModelManager()
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embedding_model = model_manager.get_model_instance(
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tenant_id=current_user.current_tenant_id,
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provider=data["embedding_model_provider"],
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model_type=ModelType.TEXT_EMBEDDING,
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model=data["embedding_model"],
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)
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filtered_data["embedding_model"] = embedding_model.model
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filtered_data["embedding_model_provider"] = embedding_model.provider
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dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
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embedding_model.provider, embedding_model.model
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)
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filtered_data["collection_binding_id"] = dataset_collection_binding.id
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except LLMBadRequestError:
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raise ValueError(
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"No Embedding Model available. Please configure a valid provider "
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"in the Settings -> Model Provider."
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)
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except ProviderTokenNotInitError as ex:
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raise ValueError(ex.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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# update Retrieval model
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filtered_data["retrieval_model"] = data["retrieval_model"]
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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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if action:
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deal_dataset_vector_index_task.delay(dataset_id, action)
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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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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 dataset_use_check(dataset_id) -> bool:
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count = AppDatasetJoin.query.filter_by(dataset_id=dataset_id).count()
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if count > 0:
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return True
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return False
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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(f"User {user.id} does not have permission to access dataset {dataset.id}")
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raise NoPermissionError("You do not have permission to access this dataset.")
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if dataset.permission == DatasetPermissionEnum.ONLY_ME and dataset.created_by != user.id:
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logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
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raise NoPermissionError("You do not have permission to access this dataset.")
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if dataset.permission == "partial_members":
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user_permission = DatasetPermission.query.filter_by(dataset_id=dataset.id, account_id=user.id).first()
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if not user_permission and dataset.tenant_id != user.current_tenant_id and dataset.created_by != user.id:
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logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
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raise NoPermissionError("You do not have permission to access this dataset.")
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|
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@staticmethod
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def check_dataset_operator_permission(user: Account = None, dataset: Dataset = None):
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if dataset.permission == DatasetPermissionEnum.ONLY_ME:
|
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if dataset.created_by != user.id:
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raise NoPermissionError("You do not have permission to access this dataset.")
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elif dataset.permission == DatasetPermissionEnum.PARTIAL_TEAM:
|
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if not any(
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dp.dataset_id == dataset.id for dp in DatasetPermission.query.filter_by(account_id=user.id).all()
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):
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raise NoPermissionError("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 = (
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DatasetQuery.query.filter_by(dataset_id=dataset_id)
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.order_by(db.desc(DatasetQuery.created_at))
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.paginate(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
|
|
def get_related_apps(dataset_id: str):
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return (
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AppDatasetJoin.query.filter(AppDatasetJoin.dataset_id == dataset_id)
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.order_by(db.desc(AppDatasetJoin.created_at))
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.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": {"delimiter": "\n", "max_tokens": 500, "chunk_overlap": 50},
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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,
|
|
"publisher": str,
|
|
"publication_date": str,
|
|
"isbn": str,
|
|
"category": str,
|
|
},
|
|
"web_page": {
|
|
"title": str,
|
|
"url": str,
|
|
"language": str,
|
|
"publish_date": str,
|
|
"author/publisher": str,
|
|
"topic/keywords": str,
|
|
"description": str,
|
|
},
|
|
"paper": {
|
|
"title": str,
|
|
"language": str,
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|
"author": str,
|
|
"publish_date": str,
|
|
"journal/conference_name": str,
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|
"volume/issue/page_numbers": str,
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|
"doi": str,
|
|
"topic/keywords": str,
|
|
"abstract": str,
|
|
},
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|
"social_media_post": {
|
|
"platform": str,
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|
"author/username": str,
|
|
"publish_date": str,
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|
"post_url": str,
|
|
"topic/tags": str,
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},
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|
"wikipedia_entry": {
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"title": str,
|
|
"language": str,
|
|
"web_page_url": str,
|
|
"last_edit_date": str,
|
|
"editor/contributor": str,
|
|
"summary/introduction": str,
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|
},
|
|
"personal_document": {
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|
"title": str,
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|
"author": str,
|
|
"creation_date": str,
|
|
"last_modified_date": str,
|
|
"document_type": str,
|
|
"tags/category": str,
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|
},
|
|
"business_document": {
|
|
"title": str,
|
|
"author": str,
|
|
"creation_date": str,
|
|
"last_modified_date": str,
|
|
"document_type": str,
|
|
"department/team": str,
|
|
},
|
|
"im_chat_log": {
|
|
"chat_platform": str,
|
|
"chat_participants/group_name": str,
|
|
"start_date": str,
|
|
"end_date": str,
|
|
"summary": str,
|
|
},
|
|
"synced_from_notion": {
|
|
"title": str,
|
|
"language": str,
|
|
"author/creator": str,
|
|
"creation_date": str,
|
|
"last_modified_date": str,
|
|
"notion_page_link": str,
|
|
"category/tags": str,
|
|
"description": str,
|
|
},
|
|
"synced_from_github": {
|
|
"repository_name": str,
|
|
"repository_description": str,
|
|
"repository_owner/organization": str,
|
|
"code_filename": str,
|
|
"code_file_path": str,
|
|
"programming_language": str,
|
|
"github_link": str,
|
|
"open_source_license": str,
|
|
"commit_date": str,
|
|
"commit_author": str,
|
|
},
|
|
"others": dict,
|
|
}
|
|
|
|
@staticmethod
|
|
def get_document(dataset_id: str, document_id: str) -> Optional[Document]:
|
|
document = (
|
|
db.session.query(Document).filter(Document.id == document_id, Document.dataset_id == dataset_id).first()
|
|
)
|
|
|
|
return document
|
|
|
|
@staticmethod
|
|
def get_document_by_id(document_id: str) -> Optional[Document]:
|
|
document = db.session.query(Document).filter(Document.id == document_id).first()
|
|
|
|
return document
|
|
|
|
@staticmethod
|
|
def get_document_by_dataset_id(dataset_id: str) -> list[Document]:
|
|
documents = db.session.query(Document).filter(Document.dataset_id == dataset_id, Document.enabled == True).all()
|
|
|
|
return documents
|
|
|
|
@staticmethod
|
|
def get_error_documents_by_dataset_id(dataset_id: str) -> list[Document]:
|
|
documents = (
|
|
db.session.query(Document)
|
|
.filter(Document.dataset_id == dataset_id, Document.indexing_status.in_(["error", "paused"]))
|
|
.all()
|
|
)
|
|
return documents
|
|
|
|
@staticmethod
|
|
def get_batch_documents(dataset_id: str, batch: str) -> list[Document]:
|
|
documents = (
|
|
db.session.query(Document)
|
|
.filter(
|
|
Document.batch == batch,
|
|
Document.dataset_id == dataset_id,
|
|
Document.tenant_id == current_user.current_tenant_id,
|
|
)
|
|
.all()
|
|
)
|
|
|
|
return documents
|
|
|
|
@staticmethod
|
|
def get_document_file_detail(file_id: str):
|
|
file_detail = db.session.query(UploadFile).filter(UploadFile.id == file_id).one_or_none()
|
|
return file_detail
|
|
|
|
@staticmethod
|
|
def check_archived(document):
|
|
if document.archived:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
@staticmethod
|
|
def delete_document(document):
|
|
# trigger document_was_deleted signal
|
|
file_id = None
|
|
if document.data_source_type == "upload_file":
|
|
if document.data_source_info:
|
|
data_source_info = document.data_source_info_dict
|
|
if data_source_info and "upload_file_id" in data_source_info:
|
|
file_id = data_source_info["upload_file_id"]
|
|
document_was_deleted.send(
|
|
document.id, dataset_id=document.dataset_id, doc_form=document.doc_form, file_id=file_id
|
|
)
|
|
|
|
db.session.delete(document)
|
|
db.session.commit()
|
|
|
|
@staticmethod
|
|
def rename_document(dataset_id: str, document_id: str, name: str) -> Document:
|
|
dataset = DatasetService.get_dataset(dataset_id)
|
|
if not dataset:
|
|
raise ValueError("Dataset not found.")
|
|
|
|
document = DocumentService.get_document(dataset_id, document_id)
|
|
|
|
if not document:
|
|
raise ValueError("Document not found.")
|
|
|
|
if document.tenant_id != current_user.current_tenant_id:
|
|
raise ValueError("No permission.")
|
|
|
|
document.name = name
|
|
|
|
db.session.add(document)
|
|
db.session.commit()
|
|
|
|
return document
|
|
|
|
@staticmethod
|
|
def pause_document(document):
|
|
if document.indexing_status not in {"waiting", "parsing", "cleaning", "splitting", "indexing"}:
|
|
raise DocumentIndexingError()
|
|
# update document to be paused
|
|
document.is_paused = True
|
|
document.paused_by = current_user.id
|
|
document.paused_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
|
|
db.session.add(document)
|
|
db.session.commit()
|
|
# set document paused flag
|
|
indexing_cache_key = "document_{}_is_paused".format(document.id)
|
|
redis_client.setnx(indexing_cache_key, "True")
|
|
|
|
@staticmethod
|
|
def recover_document(document):
|
|
if not document.is_paused:
|
|
raise DocumentIndexingError()
|
|
# update document to be recover
|
|
document.is_paused = False
|
|
document.paused_by = None
|
|
document.paused_at = None
|
|
|
|
db.session.add(document)
|
|
db.session.commit()
|
|
# delete paused flag
|
|
indexing_cache_key = "document_{}_is_paused".format(document.id)
|
|
redis_client.delete(indexing_cache_key)
|
|
# trigger async task
|
|
recover_document_indexing_task.delay(document.dataset_id, document.id)
|
|
|
|
@staticmethod
|
|
def retry_document(dataset_id: str, documents: list[Document]):
|
|
for document in documents:
|
|
# add retry flag
|
|
retry_indexing_cache_key = "document_{}_is_retried".format(document.id)
|
|
cache_result = redis_client.get(retry_indexing_cache_key)
|
|
if cache_result is not None:
|
|
raise ValueError("Document is being retried, please try again later")
|
|
# retry document indexing
|
|
document.indexing_status = "waiting"
|
|
db.session.add(document)
|
|
db.session.commit()
|
|
|
|
redis_client.setex(retry_indexing_cache_key, 600, 1)
|
|
# trigger async task
|
|
document_ids = [document.id for document in documents]
|
|
retry_document_indexing_task.delay(dataset_id, document_ids)
|
|
|
|
@staticmethod
|
|
def sync_website_document(dataset_id: str, document: Document):
|
|
# add sync flag
|
|
sync_indexing_cache_key = "document_{}_is_sync".format(document.id)
|
|
cache_result = redis_client.get(sync_indexing_cache_key)
|
|
if cache_result is not None:
|
|
raise ValueError("Document is being synced, please try again later")
|
|
# sync document indexing
|
|
document.indexing_status = "waiting"
|
|
data_source_info = document.data_source_info_dict
|
|
data_source_info["mode"] = "scrape"
|
|
document.data_source_info = json.dumps(data_source_info, ensure_ascii=False)
|
|
db.session.add(document)
|
|
db.session.commit()
|
|
|
|
redis_client.setex(sync_indexing_cache_key, 600, 1)
|
|
|
|
sync_website_document_indexing_task.delay(dataset_id, document.id)
|
|
|
|
@staticmethod
|
|
def get_documents_position(dataset_id):
|
|
document = Document.query.filter_by(dataset_id=dataset_id).order_by(Document.position.desc()).first()
|
|
if document:
|
|
return document.position + 1
|
|
else:
|
|
return 1
|
|
|
|
@staticmethod
|
|
def save_document_with_dataset_id(
|
|
dataset: Dataset,
|
|
document_data: dict,
|
|
account: Account,
|
|
dataset_process_rule: Optional[DatasetProcessRule] = None,
|
|
created_from: str = "web",
|
|
):
|
|
# check document limit
|
|
features = FeatureService.get_features(current_user.current_tenant_id)
|
|
|
|
if features.billing.enabled:
|
|
if "original_document_id" not in document_data or not document_data["original_document_id"]:
|
|
count = 0
|
|
if document_data["data_source"]["type"] == "upload_file":
|
|
upload_file_list = document_data["data_source"]["info_list"]["file_info_list"]["file_ids"]
|
|
count = len(upload_file_list)
|
|
elif document_data["data_source"]["type"] == "notion_import":
|
|
notion_info_list = document_data["data_source"]["info_list"]["notion_info_list"]
|
|
for notion_info in notion_info_list:
|
|
count = count + len(notion_info["pages"])
|
|
elif document_data["data_source"]["type"] == "website_crawl":
|
|
website_info = document_data["data_source"]["info_list"]["website_info_list"]
|
|
count = len(website_info["urls"])
|
|
batch_upload_limit = int(dify_config.BATCH_UPLOAD_LIMIT)
|
|
if count > batch_upload_limit:
|
|
raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")
|
|
|
|
DocumentService.check_documents_upload_quota(count, features)
|
|
|
|
# if dataset is empty, update dataset data_source_type
|
|
if not dataset.data_source_type:
|
|
dataset.data_source_type = document_data["data_source"]["type"]
|
|
|
|
if not dataset.indexing_technique:
|
|
if (
|
|
"indexing_technique" not in document_data
|
|
or document_data["indexing_technique"] not in Dataset.INDEXING_TECHNIQUE_LIST
|
|
):
|
|
raise ValueError("Indexing technique is required")
|
|
|
|
dataset.indexing_technique = document_data["indexing_technique"]
|
|
if document_data["indexing_technique"] == "high_quality":
|
|
model_manager = ModelManager()
|
|
embedding_model = model_manager.get_default_model_instance(
|
|
tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
|
|
)
|
|
dataset.embedding_model = embedding_model.model
|
|
dataset.embedding_model_provider = embedding_model.provider
|
|
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
|
|
embedding_model.provider, embedding_model.model
|
|
)
|
|
dataset.collection_binding_id = dataset_collection_binding.id
|
|
if not dataset.retrieval_model:
|
|
default_retrieval_model = {
|
|
"search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
|
|
"reranking_enable": False,
|
|
"reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
|
|
"top_k": 2,
|
|
"score_threshold_enabled": False,
|
|
}
|
|
|
|
dataset.retrieval_model = document_data.get("retrieval_model") or default_retrieval_model
|
|
|
|
documents = []
|
|
if document_data.get("original_document_id"):
|
|
document = DocumentService.update_document_with_dataset_id(dataset, document_data, account)
|
|
documents.append(document)
|
|
batch = document.batch
|
|
else:
|
|
batch = time.strftime("%Y%m%d%H%M%S") + str(random.randint(100000, 999999))
|
|
# save process rule
|
|
if not dataset_process_rule:
|
|
process_rule = document_data["process_rule"]
|
|
if process_rule["mode"] == "custom":
|
|
dataset_process_rule = DatasetProcessRule(
|
|
dataset_id=dataset.id,
|
|
mode=process_rule["mode"],
|
|
rules=json.dumps(process_rule["rules"]),
|
|
created_by=account.id,
|
|
)
|
|
elif process_rule["mode"] == "automatic":
|
|
dataset_process_rule = DatasetProcessRule(
|
|
dataset_id=dataset.id,
|
|
mode=process_rule["mode"],
|
|
rules=json.dumps(DatasetProcessRule.AUTOMATIC_RULES),
|
|
created_by=account.id,
|
|
)
|
|
db.session.add(dataset_process_rule)
|
|
db.session.commit()
|
|
lock_name = "add_document_lock_dataset_id_{}".format(dataset.id)
|
|
with redis_client.lock(lock_name, timeout=600):
|
|
position = DocumentService.get_documents_position(dataset.id)
|
|
document_ids = []
|
|
duplicate_document_ids = []
|
|
if document_data["data_source"]["type"] == "upload_file":
|
|
upload_file_list = document_data["data_source"]["info_list"]["file_info_list"]["file_ids"]
|
|
for file_id in upload_file_list:
|
|
file = (
|
|
db.session.query(UploadFile)
|
|
.filter(UploadFile.tenant_id == dataset.tenant_id, UploadFile.id == file_id)
|
|
.first()
|
|
)
|
|
|
|
# raise error if file not found
|
|
if not file:
|
|
raise FileNotExistsError()
|
|
|
|
file_name = file.name
|
|
data_source_info = {
|
|
"upload_file_id": file_id,
|
|
}
|
|
# check duplicate
|
|
if document_data.get("duplicate", False):
|
|
document = Document.query.filter_by(
|
|
dataset_id=dataset.id,
|
|
tenant_id=current_user.current_tenant_id,
|
|
data_source_type="upload_file",
|
|
enabled=True,
|
|
name=file_name,
|
|
).first()
|
|
if document:
|
|
document.dataset_process_rule_id = dataset_process_rule.id
|
|
document.updated_at = datetime.datetime.utcnow()
|
|
document.created_from = created_from
|
|
document.doc_form = document_data["doc_form"]
|
|
document.doc_language = document_data["doc_language"]
|
|
document.data_source_info = json.dumps(data_source_info)
|
|
document.batch = batch
|
|
document.indexing_status = "waiting"
|
|
db.session.add(document)
|
|
documents.append(document)
|
|
duplicate_document_ids.append(document.id)
|
|
continue
|
|
document = DocumentService.build_document(
|
|
dataset,
|
|
dataset_process_rule.id,
|
|
document_data["data_source"]["type"],
|
|
document_data["doc_form"],
|
|
document_data["doc_language"],
|
|
data_source_info,
|
|
created_from,
|
|
position,
|
|
account,
|
|
file_name,
|
|
batch,
|
|
)
|
|
db.session.add(document)
|
|
db.session.flush()
|
|
document_ids.append(document.id)
|
|
documents.append(document)
|
|
position += 1
|
|
elif document_data["data_source"]["type"] == "notion_import":
|
|
notion_info_list = document_data["data_source"]["info_list"]["notion_info_list"]
|
|
exist_page_ids = []
|
|
exist_document = {}
|
|
documents = Document.query.filter_by(
|
|
dataset_id=dataset.id,
|
|
tenant_id=current_user.current_tenant_id,
|
|
data_source_type="notion_import",
|
|
enabled=True,
|
|
).all()
|
|
if documents:
|
|
for document in documents:
|
|
data_source_info = json.loads(document.data_source_info)
|
|
exist_page_ids.append(data_source_info["notion_page_id"])
|
|
exist_document[data_source_info["notion_page_id"]] = document.id
|
|
for notion_info in notion_info_list:
|
|
workspace_id = notion_info["workspace_id"]
|
|
data_source_binding = DataSourceOauthBinding.query.filter(
|
|
db.and_(
|
|
DataSourceOauthBinding.tenant_id == current_user.current_tenant_id,
|
|
DataSourceOauthBinding.provider == "notion",
|
|
DataSourceOauthBinding.disabled == False,
|
|
DataSourceOauthBinding.source_info["workspace_id"] == f'"{workspace_id}"',
|
|
)
|
|
).first()
|
|
if not data_source_binding:
|
|
raise ValueError("Data source binding not found.")
|
|
for page in notion_info["pages"]:
|
|
if page["page_id"] not in exist_page_ids:
|
|
data_source_info = {
|
|
"notion_workspace_id": workspace_id,
|
|
"notion_page_id": page["page_id"],
|
|
"notion_page_icon": page["page_icon"],
|
|
"type": page["type"],
|
|
}
|
|
document = DocumentService.build_document(
|
|
dataset,
|
|
dataset_process_rule.id,
|
|
document_data["data_source"]["type"],
|
|
document_data["doc_form"],
|
|
document_data["doc_language"],
|
|
data_source_info,
|
|
created_from,
|
|
position,
|
|
account,
|
|
page["page_name"],
|
|
batch,
|
|
)
|
|
db.session.add(document)
|
|
db.session.flush()
|
|
document_ids.append(document.id)
|
|
documents.append(document)
|
|
position += 1
|
|
else:
|
|
exist_document.pop(page["page_id"])
|
|
# delete not selected documents
|
|
if len(exist_document) > 0:
|
|
clean_notion_document_task.delay(list(exist_document.values()), dataset.id)
|
|
elif document_data["data_source"]["type"] == "website_crawl":
|
|
website_info = document_data["data_source"]["info_list"]["website_info_list"]
|
|
urls = website_info["urls"]
|
|
for url in urls:
|
|
data_source_info = {
|
|
"url": url,
|
|
"provider": website_info["provider"],
|
|
"job_id": website_info["job_id"],
|
|
"only_main_content": website_info.get("only_main_content", False),
|
|
"mode": "crawl",
|
|
}
|
|
if len(url) > 255:
|
|
document_name = url[:200] + "..."
|
|
else:
|
|
document_name = url
|
|
document = DocumentService.build_document(
|
|
dataset,
|
|
dataset_process_rule.id,
|
|
document_data["data_source"]["type"],
|
|
document_data["doc_form"],
|
|
document_data["doc_language"],
|
|
data_source_info,
|
|
created_from,
|
|
position,
|
|
account,
|
|
document_name,
|
|
batch,
|
|
)
|
|
db.session.add(document)
|
|
db.session.flush()
|
|
document_ids.append(document.id)
|
|
documents.append(document)
|
|
position += 1
|
|
db.session.commit()
|
|
|
|
# trigger async task
|
|
if document_ids:
|
|
document_indexing_task.delay(dataset.id, document_ids)
|
|
if duplicate_document_ids:
|
|
duplicate_document_indexing_task.delay(dataset.id, duplicate_document_ids)
|
|
|
|
return documents, batch
|
|
|
|
@staticmethod
|
|
def check_documents_upload_quota(count: int, features: FeatureModel):
|
|
can_upload_size = features.documents_upload_quota.limit - features.documents_upload_quota.size
|
|
if count > can_upload_size:
|
|
raise ValueError(
|
|
f"You have reached the limit of your subscription. Only {can_upload_size} documents can be uploaded."
|
|
)
|
|
|
|
@staticmethod
|
|
def build_document(
|
|
dataset: Dataset,
|
|
process_rule_id: str,
|
|
data_source_type: str,
|
|
document_form: str,
|
|
document_language: str,
|
|
data_source_info: dict,
|
|
created_from: str,
|
|
position: int,
|
|
account: Account,
|
|
name: str,
|
|
batch: str,
|
|
):
|
|
document = Document(
|
|
tenant_id=dataset.tenant_id,
|
|
dataset_id=dataset.id,
|
|
position=position,
|
|
data_source_type=data_source_type,
|
|
data_source_info=json.dumps(data_source_info),
|
|
dataset_process_rule_id=process_rule_id,
|
|
batch=batch,
|
|
name=name,
|
|
created_from=created_from,
|
|
created_by=account.id,
|
|
doc_form=document_form,
|
|
doc_language=document_language,
|
|
)
|
|
return document
|
|
|
|
@staticmethod
|
|
def get_tenant_documents_count():
|
|
documents_count = Document.query.filter(
|
|
Document.completed_at.isnot(None),
|
|
Document.enabled == True,
|
|
Document.archived == False,
|
|
Document.tenant_id == current_user.current_tenant_id,
|
|
).count()
|
|
return documents_count
|
|
|
|
@staticmethod
|
|
def update_document_with_dataset_id(
|
|
dataset: Dataset,
|
|
document_data: dict,
|
|
account: Account,
|
|
dataset_process_rule: Optional[DatasetProcessRule] = None,
|
|
created_from: str = "web",
|
|
):
|
|
DatasetService.check_dataset_model_setting(dataset)
|
|
document = DocumentService.get_document(dataset.id, document_data["original_document_id"])
|
|
if document is None:
|
|
raise NotFound("Document not found")
|
|
if document.display_status != "available":
|
|
raise ValueError("Document is not available")
|
|
# update document name
|
|
if document_data.get("name"):
|
|
document.name = document_data["name"]
|
|
# save process rule
|
|
if document_data.get("process_rule"):
|
|
process_rule = document_data["process_rule"]
|
|
if process_rule["mode"] == "custom":
|
|
dataset_process_rule = DatasetProcessRule(
|
|
dataset_id=dataset.id,
|
|
mode=process_rule["mode"],
|
|
rules=json.dumps(process_rule["rules"]),
|
|
created_by=account.id,
|
|
)
|
|
elif process_rule["mode"] == "automatic":
|
|
dataset_process_rule = DatasetProcessRule(
|
|
dataset_id=dataset.id,
|
|
mode=process_rule["mode"],
|
|
rules=json.dumps(DatasetProcessRule.AUTOMATIC_RULES),
|
|
created_by=account.id,
|
|
)
|
|
db.session.add(dataset_process_rule)
|
|
db.session.commit()
|
|
document.dataset_process_rule_id = dataset_process_rule.id
|
|
# update document data source
|
|
if document_data.get("data_source"):
|
|
file_name = ""
|
|
data_source_info = {}
|
|
if document_data["data_source"]["type"] == "upload_file":
|
|
upload_file_list = document_data["data_source"]["info_list"]["file_info_list"]["file_ids"]
|
|
for file_id in upload_file_list:
|
|
file = (
|
|
db.session.query(UploadFile)
|
|
.filter(UploadFile.tenant_id == dataset.tenant_id, UploadFile.id == file_id)
|
|
.first()
|
|
)
|
|
|
|
# raise error if file not found
|
|
if not file:
|
|
raise FileNotExistsError()
|
|
|
|
file_name = file.name
|
|
data_source_info = {
|
|
"upload_file_id": file_id,
|
|
}
|
|
elif document_data["data_source"]["type"] == "notion_import":
|
|
notion_info_list = document_data["data_source"]["info_list"]["notion_info_list"]
|
|
for notion_info in notion_info_list:
|
|
workspace_id = notion_info["workspace_id"]
|
|
data_source_binding = DataSourceOauthBinding.query.filter(
|
|
db.and_(
|
|
DataSourceOauthBinding.tenant_id == current_user.current_tenant_id,
|
|
DataSourceOauthBinding.provider == "notion",
|
|
DataSourceOauthBinding.disabled == False,
|
|
DataSourceOauthBinding.source_info["workspace_id"] == f'"{workspace_id}"',
|
|
)
|
|
).first()
|
|
if not data_source_binding:
|
|
raise ValueError("Data source binding not found.")
|
|
for page in notion_info["pages"]:
|
|
data_source_info = {
|
|
"notion_workspace_id": workspace_id,
|
|
"notion_page_id": page["page_id"],
|
|
"notion_page_icon": page["page_icon"],
|
|
"type": page["type"],
|
|
}
|
|
elif document_data["data_source"]["type"] == "website_crawl":
|
|
website_info = document_data["data_source"]["info_list"]["website_info_list"]
|
|
urls = website_info["urls"]
|
|
for url in urls:
|
|
data_source_info = {
|
|
"url": url,
|
|
"provider": website_info["provider"],
|
|
"job_id": website_info["job_id"],
|
|
"only_main_content": website_info.get("only_main_content", False),
|
|
"mode": "crawl",
|
|
}
|
|
document.data_source_type = document_data["data_source"]["type"]
|
|
document.data_source_info = json.dumps(data_source_info)
|
|
document.name = file_name
|
|
# update document to be waiting
|
|
document.indexing_status = "waiting"
|
|
document.completed_at = None
|
|
document.processing_started_at = None
|
|
document.parsing_completed_at = None
|
|
document.cleaning_completed_at = None
|
|
document.splitting_completed_at = None
|
|
document.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
document.created_from = created_from
|
|
document.doc_form = document_data["doc_form"]
|
|
db.session.add(document)
|
|
db.session.commit()
|
|
# update document segment
|
|
update_params = {DocumentSegment.status: "re_segment"}
|
|
DocumentSegment.query.filter_by(document_id=document.id).update(update_params)
|
|
db.session.commit()
|
|
# trigger async task
|
|
document_indexing_update_task.delay(document.dataset_id, document.id)
|
|
return document
|
|
|
|
@staticmethod
|
|
def save_document_without_dataset_id(tenant_id: str, document_data: dict, account: Account):
|
|
features = FeatureService.get_features(current_user.current_tenant_id)
|
|
|
|
if features.billing.enabled:
|
|
count = 0
|
|
if document_data["data_source"]["type"] == "upload_file":
|
|
upload_file_list = document_data["data_source"]["info_list"]["file_info_list"]["file_ids"]
|
|
count = len(upload_file_list)
|
|
elif document_data["data_source"]["type"] == "notion_import":
|
|
notion_info_list = document_data["data_source"]["info_list"]["notion_info_list"]
|
|
for notion_info in notion_info_list:
|
|
count = count + len(notion_info["pages"])
|
|
elif document_data["data_source"]["type"] == "website_crawl":
|
|
website_info = document_data["data_source"]["info_list"]["website_info_list"]
|
|
count = len(website_info["urls"])
|
|
batch_upload_limit = int(dify_config.BATCH_UPLOAD_LIMIT)
|
|
if count > batch_upload_limit:
|
|
raise ValueError(f"You have reached the batch upload limit of {batch_upload_limit}.")
|
|
|
|
DocumentService.check_documents_upload_quota(count, features)
|
|
|
|
dataset_collection_binding_id = None
|
|
retrieval_model = None
|
|
if document_data["indexing_technique"] == "high_quality":
|
|
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
|
|
document_data["embedding_model_provider"], document_data["embedding_model"]
|
|
)
|
|
dataset_collection_binding_id = dataset_collection_binding.id
|
|
if document_data.get("retrieval_model"):
|
|
retrieval_model = document_data["retrieval_model"]
|
|
else:
|
|
default_retrieval_model = {
|
|
"search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
|
|
"reranking_enable": False,
|
|
"reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
|
|
"top_k": 2,
|
|
"score_threshold_enabled": False,
|
|
}
|
|
retrieval_model = default_retrieval_model
|
|
# save dataset
|
|
dataset = Dataset(
|
|
tenant_id=tenant_id,
|
|
name="",
|
|
data_source_type=document_data["data_source"]["type"],
|
|
indexing_technique=document_data.get("indexing_technique", "high_quality"),
|
|
created_by=account.id,
|
|
embedding_model=document_data.get("embedding_model"),
|
|
embedding_model_provider=document_data.get("embedding_model_provider"),
|
|
collection_binding_id=dataset_collection_binding_id,
|
|
retrieval_model=retrieval_model,
|
|
)
|
|
|
|
db.session.add(dataset)
|
|
db.session.flush()
|
|
|
|
documents, batch = DocumentService.save_document_with_dataset_id(dataset, document_data, account)
|
|
|
|
cut_length = 18
|
|
cut_name = documents[0].name[:cut_length]
|
|
dataset.name = cut_name + "..."
|
|
dataset.description = "useful for when you want to answer queries about the " + documents[0].name
|
|
db.session.commit()
|
|
|
|
return dataset, documents, batch
|
|
|
|
@classmethod
|
|
def document_create_args_validate(cls, args: dict):
|
|
if "original_document_id" not in args or not args["original_document_id"]:
|
|
DocumentService.data_source_args_validate(args)
|
|
DocumentService.process_rule_args_validate(args)
|
|
else:
|
|
if ("data_source" not in args or not args["data_source"]) and (
|
|
"process_rule" not in args or not args["process_rule"]
|
|
):
|
|
raise ValueError("Data source or Process rule is required")
|
|
else:
|
|
if args.get("data_source"):
|
|
DocumentService.data_source_args_validate(args)
|
|
if args.get("process_rule"):
|
|
DocumentService.process_rule_args_validate(args)
|
|
|
|
@classmethod
|
|
def data_source_args_validate(cls, args: dict):
|
|
if "data_source" not in args or not args["data_source"]:
|
|
raise ValueError("Data source is required")
|
|
|
|
if not isinstance(args["data_source"], dict):
|
|
raise ValueError("Data source is invalid")
|
|
|
|
if "type" not in args["data_source"] or not args["data_source"]["type"]:
|
|
raise ValueError("Data source type is required")
|
|
|
|
if args["data_source"]["type"] not in Document.DATA_SOURCES:
|
|
raise ValueError("Data source type is invalid")
|
|
|
|
if "info_list" not in args["data_source"] or not args["data_source"]["info_list"]:
|
|
raise ValueError("Data source info is required")
|
|
|
|
if args["data_source"]["type"] == "upload_file":
|
|
if (
|
|
"file_info_list" not in args["data_source"]["info_list"]
|
|
or not args["data_source"]["info_list"]["file_info_list"]
|
|
):
|
|
raise ValueError("File source info is required")
|
|
if args["data_source"]["type"] == "notion_import":
|
|
if (
|
|
"notion_info_list" not in args["data_source"]["info_list"]
|
|
or not args["data_source"]["info_list"]["notion_info_list"]
|
|
):
|
|
raise ValueError("Notion source info is required")
|
|
if args["data_source"]["type"] == "website_crawl":
|
|
if (
|
|
"website_info_list" not in args["data_source"]["info_list"]
|
|
or not args["data_source"]["info_list"]["website_info_list"]
|
|
):
|
|
raise ValueError("Website source info is required")
|
|
|
|
@classmethod
|
|
def process_rule_args_validate(cls, args: dict):
|
|
if "process_rule" not in args or not args["process_rule"]:
|
|
raise ValueError("Process rule is required")
|
|
|
|
if not isinstance(args["process_rule"], dict):
|
|
raise ValueError("Process rule is invalid")
|
|
|
|
if "mode" not in args["process_rule"] or not args["process_rule"]["mode"]:
|
|
raise ValueError("Process rule mode is required")
|
|
|
|
if args["process_rule"]["mode"] not in DatasetProcessRule.MODES:
|
|
raise ValueError("Process rule mode is invalid")
|
|
|
|
if args["process_rule"]["mode"] == "automatic":
|
|
args["process_rule"]["rules"] = {}
|
|
else:
|
|
if "rules" not in args["process_rule"] or not args["process_rule"]["rules"]:
|
|
raise ValueError("Process rule rules is required")
|
|
|
|
if not isinstance(args["process_rule"]["rules"], dict):
|
|
raise ValueError("Process rule rules is invalid")
|
|
|
|
if (
|
|
"pre_processing_rules" not in args["process_rule"]["rules"]
|
|
or args["process_rule"]["rules"]["pre_processing_rules"] is None
|
|
):
|
|
raise ValueError("Process rule pre_processing_rules is required")
|
|
|
|
if not isinstance(args["process_rule"]["rules"]["pre_processing_rules"], list):
|
|
raise ValueError("Process rule pre_processing_rules is invalid")
|
|
|
|
unique_pre_processing_rule_dicts = {}
|
|
for pre_processing_rule in args["process_rule"]["rules"]["pre_processing_rules"]:
|
|
if "id" not in pre_processing_rule or not pre_processing_rule["id"]:
|
|
raise ValueError("Process rule pre_processing_rules id is required")
|
|
|
|
if pre_processing_rule["id"] not in DatasetProcessRule.PRE_PROCESSING_RULES:
|
|
raise ValueError("Process rule pre_processing_rules id is invalid")
|
|
|
|
if "enabled" not in pre_processing_rule or pre_processing_rule["enabled"] is None:
|
|
raise ValueError("Process rule pre_processing_rules enabled is required")
|
|
|
|
if not isinstance(pre_processing_rule["enabled"], bool):
|
|
raise ValueError("Process rule pre_processing_rules enabled is invalid")
|
|
|
|
unique_pre_processing_rule_dicts[pre_processing_rule["id"]] = pre_processing_rule
|
|
|
|
args["process_rule"]["rules"]["pre_processing_rules"] = list(unique_pre_processing_rule_dicts.values())
|
|
|
|
if (
|
|
"segmentation" not in args["process_rule"]["rules"]
|
|
or args["process_rule"]["rules"]["segmentation"] is None
|
|
):
|
|
raise ValueError("Process rule segmentation is required")
|
|
|
|
if not isinstance(args["process_rule"]["rules"]["segmentation"], dict):
|
|
raise ValueError("Process rule segmentation is invalid")
|
|
|
|
if (
|
|
"separator" not in args["process_rule"]["rules"]["segmentation"]
|
|
or not args["process_rule"]["rules"]["segmentation"]["separator"]
|
|
):
|
|
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")
|
|
|
|
@classmethod
|
|
def estimate_args_validate(cls, args: dict):
|
|
if "info_list" not in args or not args["info_list"]:
|
|
raise ValueError("Data source info is required")
|
|
|
|
if not isinstance(args["info_list"], dict):
|
|
raise ValueError("Data info is invalid")
|
|
|
|
if "process_rule" not in args or not args["process_rule"]:
|
|
raise ValueError("Process rule is required")
|
|
|
|
if not isinstance(args["process_rule"], dict):
|
|
raise ValueError("Process rule is invalid")
|
|
|
|
if "mode" not in args["process_rule"] or not args["process_rule"]["mode"]:
|
|
raise ValueError("Process rule mode is required")
|
|
|
|
if args["process_rule"]["mode"] not in DatasetProcessRule.MODES:
|
|
raise ValueError("Process rule mode is invalid")
|
|
|
|
if args["process_rule"]["mode"] == "automatic":
|
|
args["process_rule"]["rules"] = {}
|
|
else:
|
|
if "rules" not in args["process_rule"] or not args["process_rule"]["rules"]:
|
|
raise ValueError("Process rule rules is required")
|
|
|
|
if not isinstance(args["process_rule"]["rules"], dict):
|
|
raise ValueError("Process rule rules is invalid")
|
|
|
|
if (
|
|
"pre_processing_rules" not in args["process_rule"]["rules"]
|
|
or args["process_rule"]["rules"]["pre_processing_rules"] is None
|
|
):
|
|
raise ValueError("Process rule pre_processing_rules is required")
|
|
|
|
if not isinstance(args["process_rule"]["rules"]["pre_processing_rules"], list):
|
|
raise ValueError("Process rule pre_processing_rules is invalid")
|
|
|
|
unique_pre_processing_rule_dicts = {}
|
|
for pre_processing_rule in args["process_rule"]["rules"]["pre_processing_rules"]:
|
|
if "id" not in pre_processing_rule or not pre_processing_rule["id"]:
|
|
raise ValueError("Process rule pre_processing_rules id is required")
|
|
|
|
if pre_processing_rule["id"] not in DatasetProcessRule.PRE_PROCESSING_RULES:
|
|
raise ValueError("Process rule pre_processing_rules id is invalid")
|
|
|
|
if "enabled" not in pre_processing_rule or pre_processing_rule["enabled"] is None:
|
|
raise ValueError("Process rule pre_processing_rules enabled is required")
|
|
|
|
if not isinstance(pre_processing_rule["enabled"], bool):
|
|
raise ValueError("Process rule pre_processing_rules enabled is invalid")
|
|
|
|
unique_pre_processing_rule_dicts[pre_processing_rule["id"]] = pre_processing_rule
|
|
|
|
args["process_rule"]["rules"]["pre_processing_rules"] = list(unique_pre_processing_rule_dicts.values())
|
|
|
|
if (
|
|
"segmentation" not in args["process_rule"]["rules"]
|
|
or args["process_rule"]["rules"]["segmentation"] is None
|
|
):
|
|
raise ValueError("Process rule segmentation is required")
|
|
|
|
if not isinstance(args["process_rule"]["rules"]["segmentation"], dict):
|
|
raise ValueError("Process rule segmentation is invalid")
|
|
|
|
if (
|
|
"separator" not in args["process_rule"]["rules"]["segmentation"]
|
|
or not args["process_rule"]["rules"]["segmentation"]["separator"]
|
|
):
|
|
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")
|
|
|
|
|
|
class SegmentService:
|
|
@classmethod
|
|
def segment_create_args_validate(cls, args: dict, document: Document):
|
|
if document.doc_form == "qa_model":
|
|
if "answer" not in args or not args["answer"]:
|
|
raise ValueError("Answer is required")
|
|
if not args["answer"].strip():
|
|
raise ValueError("Answer is empty")
|
|
if "content" not in args or not args["content"] or not args["content"].strip():
|
|
raise ValueError("Content is empty")
|
|
|
|
@classmethod
|
|
def create_segment(cls, args: dict, document: Document, dataset: Dataset):
|
|
content = args["content"]
|
|
doc_id = str(uuid.uuid4())
|
|
segment_hash = helper.generate_text_hash(content)
|
|
tokens = 0
|
|
if dataset.indexing_technique == "high_quality":
|
|
model_manager = ModelManager()
|
|
embedding_model = model_manager.get_model_instance(
|
|
tenant_id=current_user.current_tenant_id,
|
|
provider=dataset.embedding_model_provider,
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
model=dataset.embedding_model,
|
|
)
|
|
# calc embedding use tokens
|
|
tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])
|
|
lock_name = "add_segment_lock_document_id_{}".format(document.id)
|
|
with redis_client.lock(lock_name, timeout=600):
|
|
max_position = (
|
|
db.session.query(func.max(DocumentSegment.position))
|
|
.filter(DocumentSegment.document_id == document.id)
|
|
.scalar()
|
|
)
|
|
segment_document = DocumentSegment(
|
|
tenant_id=current_user.current_tenant_id,
|
|
dataset_id=document.dataset_id,
|
|
document_id=document.id,
|
|
index_node_id=doc_id,
|
|
index_node_hash=segment_hash,
|
|
position=max_position + 1 if max_position else 1,
|
|
content=content,
|
|
word_count=len(content),
|
|
tokens=tokens,
|
|
status="completed",
|
|
indexing_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
|
|
completed_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
|
|
created_by=current_user.id,
|
|
)
|
|
if document.doc_form == "qa_model":
|
|
segment_document.answer = args["answer"]
|
|
|
|
db.session.add(segment_document)
|
|
db.session.commit()
|
|
|
|
# save vector index
|
|
try:
|
|
VectorService.create_segments_vector([args["keywords"]], [segment_document], dataset)
|
|
except Exception as e:
|
|
logging.exception("create segment index failed")
|
|
segment_document.enabled = False
|
|
segment_document.disabled_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
segment_document.status = "error"
|
|
segment_document.error = str(e)
|
|
db.session.commit()
|
|
segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment_document.id).first()
|
|
return segment
|
|
|
|
@classmethod
|
|
def multi_create_segment(cls, segments: list, document: Document, dataset: Dataset):
|
|
lock_name = "multi_add_segment_lock_document_id_{}".format(document.id)
|
|
with redis_client.lock(lock_name, timeout=600):
|
|
embedding_model = None
|
|
if dataset.indexing_technique == "high_quality":
|
|
model_manager = ModelManager()
|
|
embedding_model = model_manager.get_model_instance(
|
|
tenant_id=current_user.current_tenant_id,
|
|
provider=dataset.embedding_model_provider,
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
model=dataset.embedding_model,
|
|
)
|
|
max_position = (
|
|
db.session.query(func.max(DocumentSegment.position))
|
|
.filter(DocumentSegment.document_id == document.id)
|
|
.scalar()
|
|
)
|
|
pre_segment_data_list = []
|
|
segment_data_list = []
|
|
keywords_list = []
|
|
for segment_item in segments:
|
|
content = segment_item["content"]
|
|
doc_id = str(uuid.uuid4())
|
|
segment_hash = helper.generate_text_hash(content)
|
|
tokens = 0
|
|
if dataset.indexing_technique == "high_quality" and embedding_model:
|
|
# calc embedding use tokens
|
|
tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])
|
|
segment_document = DocumentSegment(
|
|
tenant_id=current_user.current_tenant_id,
|
|
dataset_id=document.dataset_id,
|
|
document_id=document.id,
|
|
index_node_id=doc_id,
|
|
index_node_hash=segment_hash,
|
|
position=max_position + 1 if max_position else 1,
|
|
content=content,
|
|
word_count=len(content),
|
|
tokens=tokens,
|
|
status="completed",
|
|
indexing_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
|
|
completed_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
|
|
created_by=current_user.id,
|
|
)
|
|
if document.doc_form == "qa_model":
|
|
segment_document.answer = segment_item["answer"]
|
|
db.session.add(segment_document)
|
|
segment_data_list.append(segment_document)
|
|
|
|
pre_segment_data_list.append(segment_document)
|
|
if "keywords" in segment_item:
|
|
keywords_list.append(segment_item["keywords"])
|
|
else:
|
|
keywords_list.append(None)
|
|
|
|
try:
|
|
# save vector index
|
|
VectorService.create_segments_vector(keywords_list, pre_segment_data_list, dataset)
|
|
except Exception as e:
|
|
logging.exception("create segment index failed")
|
|
for segment_document in segment_data_list:
|
|
segment_document.enabled = False
|
|
segment_document.disabled_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
segment_document.status = "error"
|
|
segment_document.error = str(e)
|
|
db.session.commit()
|
|
return segment_data_list
|
|
|
|
@classmethod
|
|
def update_segment(cls, args: dict, segment: DocumentSegment, document: Document, dataset: Dataset):
|
|
indexing_cache_key = "segment_{}_indexing".format(segment.id)
|
|
cache_result = redis_client.get(indexing_cache_key)
|
|
if cache_result is not None:
|
|
raise ValueError("Segment is indexing, please try again later")
|
|
if "enabled" in args and args["enabled"] is not None:
|
|
action = args["enabled"]
|
|
if segment.enabled != action:
|
|
if not action:
|
|
segment.enabled = action
|
|
segment.disabled_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
segment.disabled_by = current_user.id
|
|
db.session.add(segment)
|
|
db.session.commit()
|
|
# Set cache to prevent indexing the same segment multiple times
|
|
redis_client.setex(indexing_cache_key, 600, 1)
|
|
disable_segment_from_index_task.delay(segment.id)
|
|
return segment
|
|
if not segment.enabled:
|
|
if "enabled" in args and args["enabled"] is not None:
|
|
if not args["enabled"]:
|
|
raise ValueError("Can't update disabled segment")
|
|
else:
|
|
raise ValueError("Can't update disabled segment")
|
|
try:
|
|
content = args["content"]
|
|
if segment.content == content:
|
|
if document.doc_form == "qa_model":
|
|
segment.answer = args["answer"]
|
|
if args.get("keywords"):
|
|
segment.keywords = args["keywords"]
|
|
segment.enabled = True
|
|
segment.disabled_at = None
|
|
segment.disabled_by = None
|
|
db.session.add(segment)
|
|
db.session.commit()
|
|
# update segment index task
|
|
if "keywords" in args:
|
|
keyword = Keyword(dataset)
|
|
keyword.delete_by_ids([segment.index_node_id])
|
|
document = RAGDocument(
|
|
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,
|
|
},
|
|
)
|
|
keyword.add_texts([document], keywords_list=[args["keywords"]])
|
|
else:
|
|
segment_hash = helper.generate_text_hash(content)
|
|
tokens = 0
|
|
if dataset.indexing_technique == "high_quality":
|
|
model_manager = ModelManager()
|
|
embedding_model = model_manager.get_model_instance(
|
|
tenant_id=current_user.current_tenant_id,
|
|
provider=dataset.embedding_model_provider,
|
|
model_type=ModelType.TEXT_EMBEDDING,
|
|
model=dataset.embedding_model,
|
|
)
|
|
|
|
# calc embedding use tokens
|
|
tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])
|
|
segment.content = content
|
|
segment.index_node_hash = segment_hash
|
|
segment.word_count = len(content)
|
|
segment.tokens = tokens
|
|
segment.status = "completed"
|
|
segment.indexing_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
segment.completed_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
segment.updated_by = current_user.id
|
|
segment.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
segment.enabled = True
|
|
segment.disabled_at = None
|
|
segment.disabled_by = None
|
|
if document.doc_form == "qa_model":
|
|
segment.answer = args["answer"]
|
|
db.session.add(segment)
|
|
db.session.commit()
|
|
# update segment vector index
|
|
VectorService.update_segment_vector(args["keywords"], segment, dataset)
|
|
|
|
except Exception as e:
|
|
logging.exception("update segment index failed")
|
|
segment.enabled = False
|
|
segment.disabled_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
|
|
segment.status = "error"
|
|
segment.error = str(e)
|
|
db.session.commit()
|
|
segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment.id).first()
|
|
return segment
|
|
|
|
@classmethod
|
|
def delete_segment(cls, segment: DocumentSegment, document: Document, dataset: Dataset):
|
|
indexing_cache_key = "segment_{}_delete_indexing".format(segment.id)
|
|
cache_result = redis_client.get(indexing_cache_key)
|
|
if cache_result is not None:
|
|
raise ValueError("Segment is deleting.")
|
|
|
|
# enabled segment need to delete index
|
|
if segment.enabled:
|
|
# send delete segment index task
|
|
redis_client.setex(indexing_cache_key, 600, 1)
|
|
delete_segment_from_index_task.delay(segment.id, segment.index_node_id, dataset.id, document.id)
|
|
db.session.delete(segment)
|
|
db.session.commit()
|
|
|
|
|
|
class DatasetCollectionBindingService:
|
|
@classmethod
|
|
def get_dataset_collection_binding(
|
|
cls, provider_name: str, model_name: str, collection_type: str = "dataset"
|
|
) -> DatasetCollectionBinding:
|
|
dataset_collection_binding = (
|
|
db.session.query(DatasetCollectionBinding)
|
|
.filter(
|
|
DatasetCollectionBinding.provider_name == provider_name,
|
|
DatasetCollectionBinding.model_name == model_name,
|
|
DatasetCollectionBinding.type == collection_type,
|
|
)
|
|
.order_by(DatasetCollectionBinding.created_at)
|
|
.first()
|
|
)
|
|
|
|
if not dataset_collection_binding:
|
|
dataset_collection_binding = DatasetCollectionBinding(
|
|
provider_name=provider_name,
|
|
model_name=model_name,
|
|
collection_name=Dataset.gen_collection_name_by_id(str(uuid.uuid4())),
|
|
type=collection_type,
|
|
)
|
|
db.session.add(dataset_collection_binding)
|
|
db.session.commit()
|
|
return dataset_collection_binding
|
|
|
|
@classmethod
|
|
def get_dataset_collection_binding_by_id_and_type(
|
|
cls, collection_binding_id: str, collection_type: str = "dataset"
|
|
) -> DatasetCollectionBinding:
|
|
dataset_collection_binding = (
|
|
db.session.query(DatasetCollectionBinding)
|
|
.filter(
|
|
DatasetCollectionBinding.id == collection_binding_id, DatasetCollectionBinding.type == collection_type
|
|
)
|
|
.order_by(DatasetCollectionBinding.created_at)
|
|
.first()
|
|
)
|
|
|
|
return dataset_collection_binding
|
|
|
|
|
|
class DatasetPermissionService:
|
|
@classmethod
|
|
def get_dataset_partial_member_list(cls, dataset_id):
|
|
user_list_query = (
|
|
db.session.query(
|
|
DatasetPermission.account_id,
|
|
)
|
|
.filter(DatasetPermission.dataset_id == dataset_id)
|
|
.all()
|
|
)
|
|
|
|
user_list = []
|
|
for user in user_list_query:
|
|
user_list.append(user.account_id)
|
|
|
|
return user_list
|
|
|
|
@classmethod
|
|
def update_partial_member_list(cls, tenant_id, dataset_id, user_list):
|
|
try:
|
|
db.session.query(DatasetPermission).filter(DatasetPermission.dataset_id == dataset_id).delete()
|
|
permissions = []
|
|
for user in user_list:
|
|
permission = DatasetPermission(
|
|
tenant_id=tenant_id,
|
|
dataset_id=dataset_id,
|
|
account_id=user["user_id"],
|
|
)
|
|
permissions.append(permission)
|
|
|
|
db.session.add_all(permissions)
|
|
db.session.commit()
|
|
except Exception as e:
|
|
db.session.rollback()
|
|
raise e
|
|
|
|
@classmethod
|
|
def check_permission(cls, user, dataset, requested_permission, requested_partial_member_list):
|
|
if not user.is_dataset_editor:
|
|
raise NoPermissionError("User does not have permission to edit this dataset.")
|
|
|
|
if user.is_dataset_operator and dataset.permission != requested_permission:
|
|
raise NoPermissionError("Dataset operators cannot change the dataset permissions.")
|
|
|
|
if user.is_dataset_operator and requested_permission == "partial_members":
|
|
if not requested_partial_member_list:
|
|
raise ValueError("Partial member list is required when setting to partial members.")
|
|
|
|
local_member_list = cls.get_dataset_partial_member_list(dataset.id)
|
|
request_member_list = [user["user_id"] for user in requested_partial_member_list]
|
|
if set(local_member_list) != set(request_member_list):
|
|
raise ValueError("Dataset operators cannot change the dataset permissions.")
|
|
|
|
@classmethod
|
|
def clear_partial_member_list(cls, dataset_id):
|
|
try:
|
|
db.session.query(DatasetPermission).filter(DatasetPermission.dataset_id == dataset_id).delete()
|
|
db.session.commit()
|
|
except Exception as e:
|
|
db.session.rollback()
|
|
raise e
|