import copy
import datetime
import json
import logging
import random
import time
import uuid
from collections import Counter
from typing import Any, Optional
from flask_login import current_user # type: ignore
from sqlalchemy import func
from sqlalchemy.orm import Session
from werkzeug.exceptions import NotFound
from configs import dify_config
from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
from core.model_manager import ModelManager
from core.model_runtime.entities.model_entities import ModelType
from core.plugin.entities.plugin import ModelProviderID
from core.rag.index_processor.constant.built_in_field import BuiltInField
from core.rag.index_processor.constant.index_type import IndexType
from core.rag.retrieval.retrieval_methods import RetrievalMethod
from events.dataset_event import dataset_was_deleted
from events.document_event import document_was_deleted
from extensions.ext_database import db
from extensions.ext_redis import redis_client
from libs import helper
from models.account import Account, TenantAccountRole
from models.dataset import (
AppDatasetJoin,
ChildChunk,
Dataset,
DatasetAutoDisableLog,
DatasetCollectionBinding,
DatasetPermission,
DatasetPermissionEnum,
DatasetProcessRule,
DatasetQuery,
Document,
DocumentSegment,
ExternalKnowledgeBindings,
)
from models.model import UploadFile
from models.source import DataSourceOauthBinding
from services.entities.knowledge_entities.knowledge_entities import (
ChildChunkUpdateArgs,
KnowledgeConfig,
RerankingModel,
RetrievalModel,
SegmentUpdateArgs,
)
from services.errors.account import InvalidActionError, NoPermissionError
from services.errors.chunk import ChildChunkDeleteIndexError, ChildChunkIndexingError
from services.errors.dataset import DatasetNameDuplicateError
from services.errors.document import DocumentIndexingError
from services.errors.file import FileNotExistsError
from services.external_knowledge_service import ExternalDatasetService
from services.feature_service import FeatureModel, FeatureService
from services.tag_service import TagService
from services.vector_service import VectorService
from tasks.batch_clean_document_task import batch_clean_document_task
from tasks.clean_notion_document_task import clean_notion_document_task
from tasks.deal_dataset_vector_index_task import deal_dataset_vector_index_task
from tasks.delete_segment_from_index_task import delete_segment_from_index_task
from tasks.disable_segment_from_index_task import disable_segment_from_index_task
from tasks.disable_segments_from_index_task import disable_segments_from_index_task
from tasks.document_indexing_task import document_indexing_task
from tasks.document_indexing_update_task import document_indexing_update_task
from tasks.duplicate_document_indexing_task import duplicate_document_indexing_task
from tasks.enable_segments_to_index_task import enable_segments_to_index_task
from tasks.recover_document_indexing_task import recover_document_indexing_task
from tasks.retry_document_indexing_task import retry_document_indexing_task
from tasks.sync_website_document_indexing_task import sync_website_document_indexing_task
class DatasetService:
@staticmethod
def get_datasets(page, per_page, tenant_id=None, user=None, search=None, tag_ids=None, include_all=False):
query = Dataset.query.filter(Dataset.tenant_id == tenant_id).order_by(Dataset.created_at.desc())
if user:
# get permitted dataset ids
dataset_permission = DatasetPermission.query.filter_by(account_id=user.id, tenant_id=tenant_id).all()
permitted_dataset_ids = {dp.dataset_id for dp in dataset_permission} if dataset_permission else None
if user.current_role == TenantAccountRole.DATASET_OPERATOR:
# only show datasets that the user has permission to access
if permitted_dataset_ids:
query = query.filter(Dataset.id.in_(permitted_dataset_ids))
else:
return [], 0
else:
if user.current_role != TenantAccountRole.OWNER or not include_all:
# show all datasets that the user has permission to access
if permitted_dataset_ids:
query = query.filter(
db.or_(
Dataset.permission == DatasetPermissionEnum.ALL_TEAM,
db.and_(
Dataset.permission == DatasetPermissionEnum.ONLY_ME, Dataset.created_by == user.id
),
db.and_(
Dataset.permission == DatasetPermissionEnum.PARTIAL_TEAM,
Dataset.id.in_(permitted_dataset_ids),
),
)
)
else:
query = query.filter(
db.or_(
Dataset.permission == DatasetPermissionEnum.ALL_TEAM,
db.and_(
Dataset.permission == DatasetPermissionEnum.ONLY_ME, Dataset.created_by == user.id
),
)
)
else:
# if no user, only show datasets that are shared with all team members
query = query.filter(Dataset.permission == DatasetPermissionEnum.ALL_TEAM)
if search:
query = query.filter(Dataset.name.ilike(f"%{search}%"))
if tag_ids:
target_ids = TagService.get_target_ids_by_tag_ids("knowledge", tenant_id, tag_ids)
if target_ids:
query = query.filter(Dataset.id.in_(target_ids))
else:
return [], 0
datasets = query.paginate(page=page, per_page=per_page, max_per_page=100, error_out=False)
return datasets.items, datasets.total
@staticmethod
def get_process_rules(dataset_id):
# get the latest process rule
dataset_process_rule = (
db.session.query(DatasetProcessRule)
.filter(DatasetProcessRule.dataset_id == dataset_id)
.order_by(DatasetProcessRule.created_at.desc())
.limit(1)
.one_or_none()
)
if dataset_process_rule:
mode = dataset_process_rule.mode
rules = dataset_process_rule.rules_dict
else:
mode = DocumentService.DEFAULT_RULES["mode"]
rules = DocumentService.DEFAULT_RULES["rules"]
return {"mode": mode, "rules": rules}
@staticmethod
def get_datasets_by_ids(ids, tenant_id):
datasets = Dataset.query.filter(Dataset.id.in_(ids), Dataset.tenant_id == tenant_id).paginate(
page=1, per_page=len(ids), max_per_page=len(ids), error_out=False
)
return datasets.items, datasets.total
@staticmethod
def create_empty_dataset(
tenant_id: str,
name: str,
description: Optional[str],
indexing_technique: Optional[str],
account: Account,
permission: Optional[str] = None,
provider: str = "vendor",
external_knowledge_api_id: Optional[str] = None,
external_knowledge_id: Optional[str] = None,
):
# check if dataset name already exists
if Dataset.query.filter_by(name=name, tenant_id=tenant_id).first():
raise DatasetNameDuplicateError(f"Dataset with name {name} already exists.")
embedding_model = None
if indexing_technique == "high_quality":
model_manager = ModelManager()
embedding_model = model_manager.get_default_model_instance(
tenant_id=tenant_id, model_type=ModelType.TEXT_EMBEDDING
)
dataset = Dataset(name=name, indexing_technique=indexing_technique)
# dataset = Dataset(name=name, provider=provider, config=config)
dataset.description = description
dataset.created_by = account.id
dataset.updated_by = account.id
dataset.tenant_id = tenant_id
dataset.embedding_model_provider = embedding_model.provider if embedding_model else None
dataset.embedding_model = embedding_model.model if embedding_model else None
dataset.permission = permission or DatasetPermissionEnum.ONLY_ME
dataset.provider = provider
db.session.add(dataset)
db.session.flush()
if provider == "external" and external_knowledge_api_id:
external_knowledge_api = ExternalDatasetService.get_external_knowledge_api(external_knowledge_api_id)
if not external_knowledge_api:
raise ValueError("External API template not found.")
external_knowledge_binding = ExternalKnowledgeBindings(
tenant_id=tenant_id,
dataset_id=dataset.id,
external_knowledge_api_id=external_knowledge_api_id,
external_knowledge_id=external_knowledge_id,
created_by=account.id,
)
db.session.add(external_knowledge_binding)
db.session.commit()
return dataset
@staticmethod
def get_dataset(dataset_id) -> Optional[Dataset]:
dataset: Optional[Dataset] = Dataset.query.filter_by(id=dataset_id).first()
return dataset
@staticmethod
def check_dataset_model_setting(dataset):
if dataset.indexing_technique == "high_quality":
try:
model_manager = ModelManager()
model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model,
)
except LLMBadRequestError:
raise ValueError(
"No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
)
except ProviderTokenNotInitError as ex:
raise ValueError(f"The dataset in unavailable, due to: {ex.description}")
@staticmethod
def check_embedding_model_setting(tenant_id: str, embedding_model_provider: str, embedding_model: str):
try:
model_manager = ModelManager()
model_manager.get_model_instance(
tenant_id=tenant_id,
provider=embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=embedding_model,
)
except LLMBadRequestError:
raise ValueError(
"No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
)
except ProviderTokenNotInitError as ex:
raise ValueError(ex.description)
@staticmethod
def update_dataset(dataset_id, data, user):
dataset = DatasetService.get_dataset(dataset_id)
if not dataset:
raise ValueError("Dataset not found")
DatasetService.check_dataset_permission(dataset, user)
if dataset.provider == "external":
external_retrieval_model = data.get("external_retrieval_model", None)
if external_retrieval_model:
dataset.retrieval_model = external_retrieval_model
dataset.name = data.get("name", dataset.name)
dataset.description = data.get("description", "")
permission = data.get("permission")
if permission:
dataset.permission = permission
external_knowledge_id = data.get("external_knowledge_id", None)
db.session.add(dataset)
if not external_knowledge_id:
raise ValueError("External knowledge id is required.")
external_knowledge_api_id = data.get("external_knowledge_api_id", None)
if not external_knowledge_api_id:
raise ValueError("External knowledge api id is required.")
with Session(db.engine) as session:
external_knowledge_binding = (
session.query(ExternalKnowledgeBindings).filter_by(dataset_id=dataset_id).first()
)
if not external_knowledge_binding:
raise ValueError("External knowledge binding not found.")
if (
external_knowledge_binding.external_knowledge_id != external_knowledge_id
or external_knowledge_binding.external_knowledge_api_id != external_knowledge_api_id
):
external_knowledge_binding.external_knowledge_id = external_knowledge_id
external_knowledge_binding.external_knowledge_api_id = external_knowledge_api_id
db.session.add(external_knowledge_binding)
db.session.commit()
else:
data.pop("partial_member_list", None)
data.pop("external_knowledge_api_id", None)
data.pop("external_knowledge_id", None)
data.pop("external_retrieval_model", None)
filtered_data = {k: v for k, v in data.items() if v is not None or k == "description"}
action = None
if dataset.indexing_technique != data["indexing_technique"]:
# if update indexing_technique
if data["indexing_technique"] == "economy":
action = "remove"
filtered_data["embedding_model"] = None
filtered_data["embedding_model_provider"] = None
filtered_data["collection_binding_id"] = None
elif data["indexing_technique"] == "high_quality":
action = "add"
# get embedding model setting
try:
model_manager = ModelManager()
embedding_model = model_manager.get_model_instance(
tenant_id=current_user.current_tenant_id,
provider=data["embedding_model_provider"],
model_type=ModelType.TEXT_EMBEDDING,
model=data["embedding_model"],
)
filtered_data["embedding_model"] = embedding_model.model
filtered_data["embedding_model_provider"] = embedding_model.provider
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
embedding_model.provider, embedding_model.model
)
filtered_data["collection_binding_id"] = dataset_collection_binding.id
except LLMBadRequestError:
raise ValueError(
"No Embedding Model available. Please configure a valid provider "
"in the Settings -> Model Provider."
)
except ProviderTokenNotInitError as ex:
raise ValueError(ex.description)
else:
# add default plugin id to both setting sets, to make sure the plugin model provider is consistent
# Skip embedding model checks if not provided in the update request
if (
"embedding_model_provider" not in data
or "embedding_model" not in data
or not data.get("embedding_model_provider")
or not data.get("embedding_model")
):
# If the dataset already has embedding model settings, use those
if dataset.embedding_model_provider and dataset.embedding_model:
# Keep existing values
filtered_data["embedding_model_provider"] = dataset.embedding_model_provider
filtered_data["embedding_model"] = dataset.embedding_model
# If collection_binding_id exists, keep it too
if dataset.collection_binding_id:
filtered_data["collection_binding_id"] = dataset.collection_binding_id
# Otherwise, don't try to update embedding model settings at all
# Remove these fields from filtered_data if they exist but are None/empty
if "embedding_model_provider" in filtered_data and not filtered_data["embedding_model_provider"]:
del filtered_data["embedding_model_provider"]
if "embedding_model" in filtered_data and not filtered_data["embedding_model"]:
del filtered_data["embedding_model"]
else:
skip_embedding_update = False
try:
# Handle existing model provider
plugin_model_provider = dataset.embedding_model_provider
plugin_model_provider_str = None
if plugin_model_provider:
plugin_model_provider_str = str(ModelProviderID(plugin_model_provider))
# Handle new model provider from request
new_plugin_model_provider = data["embedding_model_provider"]
new_plugin_model_provider_str = None
if new_plugin_model_provider:
new_plugin_model_provider_str = str(ModelProviderID(new_plugin_model_provider))
# Only update embedding model if both values are provided and different from current
if (
plugin_model_provider_str != new_plugin_model_provider_str
or data["embedding_model"] != dataset.embedding_model
):
action = "update"
model_manager = ModelManager()
try:
embedding_model = model_manager.get_model_instance(
tenant_id=current_user.current_tenant_id,
provider=data["embedding_model_provider"],
model_type=ModelType.TEXT_EMBEDDING,
model=data["embedding_model"],
)
except ProviderTokenNotInitError:
# If we can't get the embedding model, skip updating it
# and keep the existing settings if available
if dataset.embedding_model_provider and dataset.embedding_model:
filtered_data["embedding_model_provider"] = dataset.embedding_model_provider
filtered_data["embedding_model"] = dataset.embedding_model
if dataset.collection_binding_id:
filtered_data["collection_binding_id"] = dataset.collection_binding_id
# Skip the rest of the embedding model update
skip_embedding_update = True
if not skip_embedding_update:
filtered_data["embedding_model"] = embedding_model.model
filtered_data["embedding_model_provider"] = embedding_model.provider
dataset_collection_binding = (
DatasetCollectionBindingService.get_dataset_collection_binding(
embedding_model.provider, embedding_model.model
)
)
filtered_data["collection_binding_id"] = dataset_collection_binding.id
except LLMBadRequestError:
raise ValueError(
"No Embedding Model available. Please configure a valid provider "
"in the Settings -> Model Provider."
)
except ProviderTokenNotInitError as ex:
raise ValueError(ex.description)
filtered_data["updated_by"] = user.id
filtered_data["updated_at"] = datetime.datetime.now()
# update Retrieval model
filtered_data["retrieval_model"] = data["retrieval_model"]
dataset.query.filter_by(id=dataset_id).update(filtered_data)
db.session.commit()
if action:
deal_dataset_vector_index_task.delay(dataset_id, action)
return dataset
@staticmethod
def delete_dataset(dataset_id, user):
dataset = DatasetService.get_dataset(dataset_id)
if dataset is None:
return False
DatasetService.check_dataset_permission(dataset, user)
dataset_was_deleted.send(dataset)
db.session.delete(dataset)
db.session.commit()
return True
@staticmethod
def dataset_use_check(dataset_id) -> bool:
count = AppDatasetJoin.query.filter_by(dataset_id=dataset_id).count()
if count > 0:
return True
return False
@staticmethod
def check_dataset_permission(dataset, user):
if dataset.tenant_id != user.current_tenant_id:
logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
raise NoPermissionError("You do not have permission to access this dataset.")
if user.current_role != TenantAccountRole.OWNER:
if dataset.permission == DatasetPermissionEnum.ONLY_ME and dataset.created_by != user.id:
logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
raise NoPermissionError("You do not have permission to access this dataset.")
if dataset.permission == "partial_members":
user_permission = DatasetPermission.query.filter_by(dataset_id=dataset.id, account_id=user.id).first()
if (
not user_permission
and dataset.tenant_id != user.current_tenant_id
and dataset.created_by != user.id
):
logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
raise NoPermissionError("You do not have permission to access this dataset.")
@staticmethod
def check_dataset_operator_permission(user: Optional[Account] = None, dataset: Optional[Dataset] = None):
if not dataset:
raise ValueError("Dataset not found")
if not user:
raise ValueError("User not found")
if user.current_role != TenantAccountRole.OWNER:
if dataset.permission == DatasetPermissionEnum.ONLY_ME:
if dataset.created_by != user.id:
raise NoPermissionError("You do not have permission to access this dataset.")
elif dataset.permission == DatasetPermissionEnum.PARTIAL_TEAM:
if not any(
dp.dataset_id == dataset.id for dp in DatasetPermission.query.filter_by(account_id=user.id).all()
):
raise NoPermissionError("You do not have permission to access this dataset.")
@staticmethod
def get_dataset_queries(dataset_id: str, page: int, per_page: int):
dataset_queries = (
DatasetQuery.query.filter_by(dataset_id=dataset_id)
.order_by(db.desc(DatasetQuery.created_at))
.paginate(page=page, per_page=per_page, max_per_page=100, error_out=False)
)
return dataset_queries.items, dataset_queries.total
@staticmethod
def get_related_apps(dataset_id: str):
return (
AppDatasetJoin.query.filter(AppDatasetJoin.dataset_id == dataset_id)
.order_by(db.desc(AppDatasetJoin.created_at))
.all()
)
@staticmethod
def get_dataset_auto_disable_logs(dataset_id: str) -> dict:
features = FeatureService.get_features(current_user.current_tenant_id)
if not features.billing.enabled or features.billing.subscription.plan == "sandbox":
return {
"document_ids": [],
"count": 0,
}
# get recent 30 days auto disable logs
start_date = datetime.datetime.now() - datetime.timedelta(days=30)
dataset_auto_disable_logs = DatasetAutoDisableLog.query.filter(
DatasetAutoDisableLog.dataset_id == dataset_id,
DatasetAutoDisableLog.created_at >= start_date,
).all()
if dataset_auto_disable_logs:
return {
"document_ids": [log.document_id for log in dataset_auto_disable_logs],
"count": len(dataset_auto_disable_logs),
}
return {
"document_ids": [],
"count": 0,
}
class DocumentService:
DEFAULT_RULES: dict[str, Any] = {
"mode": "custom",
"rules": {
"pre_processing_rules": [
{"id": "remove_extra_spaces", "enabled": True},
{"id": "remove_urls_emails", "enabled": False},
],
"segmentation": {"delimiter": "\n", "max_tokens": 500, "chunk_overlap": 50},
},
"limits": {
"indexing_max_segmentation_tokens_length": dify_config.INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH,
},
}
DOCUMENT_METADATA_SCHEMA: dict[str, Any] = {
"book": {
"title": str,
"language": str,
"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,
"author": str,
"publish_date": str,
"journal/conference_name": str,
"volume/issue/page_numbers": str,
"doi": str,
"topic/keywords": str,
"abstract": str,
},
"social_media_post": {
"platform": str,
"author/username": str,
"publish_date": str,
"post_url": str,
"topic/tags": str,
},
"wikipedia_entry": {
"title": str,
"language": str,
"web_page_url": str,
"last_edit_date": str,
"editor/contributor": str,
"summary/introduction": str,
},
"personal_document": {
"title": str,
"author": str,
"creation_date": str,
"last_modified_date": str,
"document_type": str,
"tags/category": str,
},
"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: Optional[str] = None) -> Optional[Document]:
if document_id:
document = (
db.session.query(Document).filter(Document.id == document_id, Document.dataset_id == dataset_id).first()
)
return document
else:
return None
@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_ids(document_ids: list[str]) -> list[Document]:
documents = (
db.session.query(Document)
.filter(
Document.id.in_(document_ids),
Document.enabled == True,
Document.indexing_status == "completed",
Document.archived == False,
)
.all()
)
return documents
@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_working_documents_by_dataset_id(dataset_id: str) -> list[Document]:
documents = (
db.session.query(Document)
.filter(
Document.dataset_id == dataset_id,
Document.enabled == True,
Document.indexing_status == "completed",
Document.archived == False,
)
.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 delete_documents(dataset: Dataset, document_ids: list[str]):
documents = db.session.query(Document).filter(Document.id.in_(document_ids)).all()
file_ids = [
document.data_source_info_dict["upload_file_id"]
for document in documents
if document.data_source_type == "upload_file"
]
batch_clean_document_task.delay(document_ids, dataset.id, dataset.doc_form, file_ids)
for document in documents:
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.")
if dataset.built_in_field_enabled:
if document.doc_metadata:
doc_metadata = copy.deepcopy(document.doc_metadata)
doc_metadata[BuiltInField.document_name.value] = name
document.doc_metadata = doc_metadata
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.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,
knowledge_config: KnowledgeConfig,
account: Account | Any,
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 not knowledge_config.original_document_id:
count = 0
if knowledge_config.data_source:
if knowledge_config.data_source.info_list.data_source_type == "upload_file":
upload_file_list = knowledge_config.data_source.info_list.file_info_list.file_ids # type: ignore
count = len(upload_file_list)
elif knowledge_config.data_source.info_list.data_source_type == "notion_import":
notion_info_list = knowledge_config.data_source.info_list.notion_info_list
for notion_info in notion_info_list: # type: ignore
count = count + len(notion_info.pages)
elif knowledge_config.data_source.info_list.data_source_type == "website_crawl":
website_info = knowledge_config.data_source.info_list.website_info_list
count = len(website_info.urls) # type: ignore
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 = knowledge_config.data_source.info_list.data_source_type # type: ignore
if not dataset.indexing_technique:
if knowledge_config.indexing_technique not in Dataset.INDEXING_TECHNIQUE_LIST:
raise ValueError("Indexing technique is invalid")
dataset.indexing_technique = knowledge_config.indexing_technique
if knowledge_config.indexing_technique == "high_quality":
model_manager = ModelManager()
if knowledge_config.embedding_model and knowledge_config.embedding_model_provider:
dataset_embedding_model = knowledge_config.embedding_model
dataset_embedding_model_provider = knowledge_config.embedding_model_provider
else:
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.embedding_model = dataset_embedding_model
dataset.embedding_model_provider = dataset_embedding_model_provider
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
dataset_embedding_model_provider, dataset_embedding_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 = (
knowledge_config.retrieval_model.model_dump()
if knowledge_config.retrieval_model
else default_retrieval_model
) # type: ignore
documents = []
if knowledge_config.original_document_id:
document = DocumentService.update_document_with_dataset_id(dataset, knowledge_config, 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 = knowledge_config.process_rule
if process_rule:
if process_rule.mode in ("custom", "hierarchical"):
dataset_process_rule = DatasetProcessRule(
dataset_id=dataset.id,
mode=process_rule.mode,
rules=process_rule.rules.model_dump_json() if process_rule.rules else None,
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,
)
else:
logging.warn(
f"Invalid process rule mode: {process_rule.mode}, can not find dataset process rule"
)
return
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 knowledge_config.data_source.info_list.data_source_type == "upload_file": # type: ignore
upload_file_list = knowledge_config.data_source.info_list.file_info_list.file_ids # type: ignore
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 knowledge_config.duplicate:
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 # type: ignore
document.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
document.created_from = created_from
document.doc_form = knowledge_config.doc_form
document.doc_language = knowledge_config.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, # type: ignore
knowledge_config.data_source.info_list.data_source_type, # type: ignore
knowledge_config.doc_form,
knowledge_config.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 knowledge_config.data_source.info_list.data_source_type == "notion_import": # type: ignore
notion_info_list = knowledge_config.data_source.info_list.notion_info_list # type: ignore
if not notion_info_list:
raise ValueError("No notion info list found.")
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.model_dump() if page.page_icon else None,
"type": page.type,
}
# Truncate page name to 255 characters to prevent DB field length errors
truncated_page_name = page.page_name[:255] if page.page_name else "nopagename"
document = DocumentService.build_document(
dataset,
dataset_process_rule.id, # type: ignore
knowledge_config.data_source.info_list.data_source_type, # type: ignore
knowledge_config.doc_form,
knowledge_config.doc_language,
data_source_info,
created_from,
position,
account,
truncated_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 knowledge_config.data_source.info_list.data_source_type == "website_crawl": # type: ignore
website_info = knowledge_config.data_source.info_list.website_info_list # type: ignore
if not website_info:
raise ValueError("No website info list found.")
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.only_main_content,
"mode": "crawl",
}
if len(url) > 255:
document_name = url[:200] + "..."
else:
document_name = url
document = DocumentService.build_document(
dataset,
dataset_process_rule.id, # type: ignore
knowledge_config.data_source.info_list.data_source_type, # type: ignore
knowledge_config.doc_form,
knowledge_config.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,
)
doc_metadata = {}
if dataset.built_in_field_enabled:
doc_metadata = {
BuiltInField.document_name: name,
BuiltInField.uploader: account.name,
BuiltInField.upload_date: datetime.datetime.now(datetime.UTC).strftime("%Y-%m-%d %H:%M:%S"),
BuiltInField.last_update_date: datetime.datetime.now(datetime.UTC).strftime("%Y-%m-%d %H:%M:%S"),
BuiltInField.source: data_source_type,
}
if doc_metadata:
document.doc_metadata = doc_metadata
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: KnowledgeConfig,
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")
# save process rule
if document_data.process_rule:
process_rule = document_data.process_rule
if process_rule.mode in {"custom", "hierarchical"}:
dataset_process_rule = DatasetProcessRule(
dataset_id=dataset.id,
mode=process_rule.mode,
rules=process_rule.rules.model_dump_json() if process_rule.rules else None,
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,
)
if dataset_process_rule is not None:
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.data_source:
file_name = ""
data_source_info = {}
if document_data.data_source.info_list.data_source_type == "upload_file":
if not document_data.data_source.info_list.file_info_list:
raise ValueError("No file info list found.")
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.info_list.data_source_type == "notion_import":
if not document_data.data_source.info_list.notion_info_list:
raise ValueError("No notion info list found.")
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.model_dump() if page.page_icon else None, # type: ignore
"type": page.type,
}
elif document_data.data_source.info_list.data_source_type == "website_crawl":
website_info = document_data.data_source.info_list.website_info_list
if website_info:
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.only_main_content, # type: ignore
"mode": "crawl",
}
document.data_source_type = document_data.data_source.info_list.data_source_type
document.data_source_info = json.dumps(data_source_info)
document.name = file_name
# update document name
if document_data.name:
document.name = document_data.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.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, knowledge_config: KnowledgeConfig, account: Account):
features = FeatureService.get_features(current_user.current_tenant_id)
if features.billing.enabled:
count = 0
if knowledge_config.data_source.info_list.data_source_type == "upload_file": # type: ignore
upload_file_list = (
knowledge_config.data_source.info_list.file_info_list.file_ids # type: ignore
if knowledge_config.data_source.info_list.file_info_list # type: ignore
else []
)
count = len(upload_file_list)
elif knowledge_config.data_source.info_list.data_source_type == "notion_import": # type: ignore
notion_info_list = knowledge_config.data_source.info_list.notion_info_list # type: ignore
if notion_info_list:
for notion_info in notion_info_list:
count = count + len(notion_info.pages)
elif knowledge_config.data_source.info_list.data_source_type == "website_crawl": # type: ignore
website_info = knowledge_config.data_source.info_list.website_info_list # type: ignore
if website_info:
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 knowledge_config.indexing_technique == "high_quality":
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
knowledge_config.embedding_model_provider, # type: ignore
knowledge_config.embedding_model, # type: ignore
)
dataset_collection_binding_id = dataset_collection_binding.id
if knowledge_config.retrieval_model:
retrieval_model = knowledge_config.retrieval_model
else:
retrieval_model = RetrievalModel(
search_method=RetrievalMethod.SEMANTIC_SEARCH.value,
reranking_enable=False,
reranking_model=RerankingModel(reranking_provider_name="", reranking_model_name=""),
top_k=2,
score_threshold_enabled=False,
)
# save dataset
dataset = Dataset(
tenant_id=tenant_id,
name="",
data_source_type=knowledge_config.data_source.info_list.data_source_type, # type: ignore
indexing_technique=knowledge_config.indexing_technique,
created_by=account.id,
embedding_model=knowledge_config.embedding_model,
embedding_model_provider=knowledge_config.embedding_model_provider,
collection_binding_id=dataset_collection_binding_id,
retrieval_model=retrieval_model.model_dump() if retrieval_model else None,
)
db.session.add(dataset) # type: ignore
db.session.flush()
documents, batch = DocumentService.save_document_with_dataset_id(dataset, knowledge_config, 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, knowledge_config: KnowledgeConfig):
if not knowledge_config.data_source and not knowledge_config.process_rule:
raise ValueError("Data source or Process rule is required")
else:
if knowledge_config.data_source:
DocumentService.data_source_args_validate(knowledge_config)
if knowledge_config.process_rule:
DocumentService.process_rule_args_validate(knowledge_config)
@classmethod
def data_source_args_validate(cls, knowledge_config: KnowledgeConfig):
if not knowledge_config.data_source:
raise ValueError("Data source is required")
if knowledge_config.data_source.info_list.data_source_type not in Document.DATA_SOURCES:
raise ValueError("Data source type is invalid")
if not knowledge_config.data_source.info_list:
raise ValueError("Data source info is required")
if knowledge_config.data_source.info_list.data_source_type == "upload_file":
if not knowledge_config.data_source.info_list.file_info_list:
raise ValueError("File source info is required")
if knowledge_config.data_source.info_list.data_source_type == "notion_import":
if not knowledge_config.data_source.info_list.notion_info_list:
raise ValueError("Notion source info is required")
if knowledge_config.data_source.info_list.data_source_type == "website_crawl":
if not knowledge_config.data_source.info_list.website_info_list:
raise ValueError("Website source info is required")
@classmethod
def process_rule_args_validate(cls, knowledge_config: KnowledgeConfig):
if not knowledge_config.process_rule:
raise ValueError("Process rule is required")
if not knowledge_config.process_rule.mode:
raise ValueError("Process rule mode is required")
if knowledge_config.process_rule.mode not in DatasetProcessRule.MODES:
raise ValueError("Process rule mode is invalid")
if knowledge_config.process_rule.mode == "automatic":
knowledge_config.process_rule.rules = None
else:
if not knowledge_config.process_rule.rules:
raise ValueError("Process rule rules is required")
if knowledge_config.process_rule.rules.pre_processing_rules is None:
raise ValueError("Process rule pre_processing_rules is required")
unique_pre_processing_rule_dicts = {}
for pre_processing_rule in knowledge_config.process_rule.rules.pre_processing_rules:
if not pre_processing_rule.id:
raise ValueError("Process rule pre_processing_rules id 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
knowledge_config.process_rule.rules.pre_processing_rules = list(unique_pre_processing_rule_dicts.values())
if not knowledge_config.process_rule.rules.segmentation:
raise ValueError("Process rule segmentation is required")
if not knowledge_config.process_rule.rules.segmentation.separator:
raise ValueError("Process rule segmentation separator is required")
if not isinstance(knowledge_config.process_rule.rules.segmentation.separator, str):
raise ValueError("Process rule segmentation separator is invalid")
if not (
knowledge_config.process_rule.mode == "hierarchical"
and knowledge_config.process_rule.rules.parent_mode == "full-doc"
):
if not knowledge_config.process_rule.rules.segmentation.max_tokens:
raise ValueError("Process rule segmentation max_tokens is required")
if not isinstance(knowledge_config.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])[0]
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.UTC).replace(tzinfo=None),
completed_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
created_by=current_user.id,
)
if document.doc_form == "qa_model":
segment_document.word_count += len(args["answer"])
segment_document.answer = args["answer"]
db.session.add(segment_document)
# update document word count
document.word_count += segment_document.word_count
db.session.add(document)
db.session.commit()
# save vector index
try:
VectorService.create_segments_vector([args["keywords"]], [segment_document], dataset, document.doc_form)
except Exception as e:
logging.exception("create segment index failed")
segment_document.enabled = False
segment_document.disabled_at = datetime.datetime.now(datetime.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)
increment_word_count = 0
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 = []
position = max_position + 1 if max_position else 1
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
if document.doc_form == "qa_model":
tokens = embedding_model.get_text_embedding_num_tokens(
texts=[content + segment_item["answer"]]
)[0]
else:
tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]
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=position,
content=content,
word_count=len(content),
tokens=tokens,
status="completed",
indexing_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
completed_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
created_by=current_user.id,
)
if document.doc_form == "qa_model":
segment_document.answer = segment_item["answer"]
segment_document.word_count += len(segment_item["answer"])
increment_word_count += segment_document.word_count
db.session.add(segment_document)
segment_data_list.append(segment_document)
position += 1
pre_segment_data_list.append(segment_document)
if "keywords" in segment_item:
keywords_list.append(segment_item["keywords"])
else:
keywords_list.append(None)
# update document word count
document.word_count += increment_word_count
db.session.add(document)
try:
# save vector index
VectorService.create_segments_vector(keywords_list, pre_segment_data_list, dataset, document.doc_form)
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.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: SegmentUpdateArgs, 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 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.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 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:
word_count_change = segment.word_count
content = args.content or segment.content
if segment.content == content:
segment.word_count = len(content)
if document.doc_form == "qa_model":
segment.answer = args.answer
segment.word_count += len(args.answer) if args.answer else 0
word_count_change = segment.word_count - word_count_change
keyword_changed = False
if args.keywords:
if Counter(segment.keywords) != Counter(args.keywords):
segment.keywords = args.keywords
keyword_changed = True
segment.enabled = True
segment.disabled_at = None
segment.disabled_by = None
db.session.add(segment)
db.session.commit()
# update document word count
if word_count_change != 0:
document.word_count = max(0, document.word_count + word_count_change)
db.session.add(document)
# update segment index task
if document.doc_form == IndexType.PARENT_CHILD_INDEX and args.regenerate_child_chunks:
# regenerate child chunks
# get embedding model instance
if dataset.indexing_technique == "high_quality":
# check embedding model setting
model_manager = ModelManager()
if dataset.embedding_model_provider:
embedding_model_instance = model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model,
)
else:
embedding_model_instance = model_manager.get_default_model_instance(
tenant_id=dataset.tenant_id,
model_type=ModelType.TEXT_EMBEDDING,
)
else:
raise ValueError("The knowledge base index technique is not high quality!")
# get the process rule
processing_rule = (
db.session.query(DatasetProcessRule)
.filter(DatasetProcessRule.id == document.dataset_process_rule_id)
.first()
)
if not processing_rule:
raise ValueError("No processing rule found.")
VectorService.generate_child_chunks(
segment, document, dataset, embedding_model_instance, processing_rule, True
)
elif document.doc_form in (IndexType.PARAGRAPH_INDEX, IndexType.QA_INDEX):
if args.enabled or keyword_changed:
VectorService.create_segments_vector(
[args.keywords] if args.keywords else None,
[segment],
dataset,
document.doc_form,
)
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
if document.doc_form == "qa_model":
tokens = embedding_model.get_text_embedding_num_tokens(texts=[content + segment.answer])[0]
else:
tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]
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.UTC).replace(tzinfo=None)
segment.completed_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
segment.updated_by = current_user.id
segment.updated_at = datetime.datetime.now(datetime.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
segment.word_count += len(args.answer) if args.answer else 0
word_count_change = segment.word_count - word_count_change
# update document word count
if word_count_change != 0:
document.word_count = max(0, document.word_count + word_count_change)
db.session.add(document)
db.session.add(segment)
db.session.commit()
if document.doc_form == IndexType.PARENT_CHILD_INDEX and args.regenerate_child_chunks:
# get embedding model instance
if dataset.indexing_technique == "high_quality":
# check embedding model setting
model_manager = ModelManager()
if dataset.embedding_model_provider:
embedding_model_instance = model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model,
)
else:
embedding_model_instance = model_manager.get_default_model_instance(
tenant_id=dataset.tenant_id,
model_type=ModelType.TEXT_EMBEDDING,
)
else:
raise ValueError("The knowledge base index technique is not high quality!")
# get the process rule
processing_rule = (
db.session.query(DatasetProcessRule)
.filter(DatasetProcessRule.id == document.dataset_process_rule_id)
.first()
)
if not processing_rule:
raise ValueError("No processing rule found.")
VectorService.generate_child_chunks(
segment, document, dataset, embedding_model_instance, processing_rule, True
)
elif document.doc_form in (IndexType.PARAGRAPH_INDEX, IndexType.QA_INDEX):
# 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.UTC).replace(tzinfo=None)
segment.status = "error"
segment.error = str(e)
db.session.commit()
new_segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment.id).first()
return new_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.index_node_id], dataset.id, document.id)
db.session.delete(segment)
# update document word count
document.word_count -= segment.word_count
db.session.add(document)
db.session.commit()
@classmethod
def delete_segments(cls, segment_ids: list, document: Document, dataset: Dataset):
index_node_ids = (
DocumentSegment.query.with_entities(DocumentSegment.index_node_id)
.filter(
DocumentSegment.id.in_(segment_ids),
DocumentSegment.dataset_id == dataset.id,
DocumentSegment.document_id == document.id,
DocumentSegment.tenant_id == current_user.current_tenant_id,
)
.all()
)
index_node_ids = [index_node_id[0] for index_node_id in index_node_ids]
delete_segment_from_index_task.delay(index_node_ids, dataset.id, document.id)
db.session.query(DocumentSegment).filter(DocumentSegment.id.in_(segment_ids)).delete()
db.session.commit()
@classmethod
def update_segments_status(cls, segment_ids: list, action: str, dataset: Dataset, document: Document):
if action == "enable":
segments = (
db.session.query(DocumentSegment)
.filter(
DocumentSegment.id.in_(segment_ids),
DocumentSegment.dataset_id == dataset.id,
DocumentSegment.document_id == document.id,
DocumentSegment.enabled == False,
)
.all()
)
if not segments:
return
real_deal_segmment_ids = []
for segment in segments:
indexing_cache_key = "segment_{}_indexing".format(segment.id)
cache_result = redis_client.get(indexing_cache_key)
if cache_result is not None:
continue
segment.enabled = True
segment.disabled_at = None
segment.disabled_by = None
db.session.add(segment)
real_deal_segmment_ids.append(segment.id)
db.session.commit()
enable_segments_to_index_task.delay(real_deal_segmment_ids, dataset.id, document.id)
elif action == "disable":
segments = (
db.session.query(DocumentSegment)
.filter(
DocumentSegment.id.in_(segment_ids),
DocumentSegment.dataset_id == dataset.id,
DocumentSegment.document_id == document.id,
DocumentSegment.enabled == True,
)
.all()
)
if not segments:
return
real_deal_segmment_ids = []
for segment in segments:
indexing_cache_key = "segment_{}_indexing".format(segment.id)
cache_result = redis_client.get(indexing_cache_key)
if cache_result is not None:
continue
segment.enabled = False
segment.disabled_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
segment.disabled_by = current_user.id
db.session.add(segment)
real_deal_segmment_ids.append(segment.id)
db.session.commit()
disable_segments_from_index_task.delay(real_deal_segmment_ids, dataset.id, document.id)
else:
raise InvalidActionError()
@classmethod
def create_child_chunk(
cls, content: str, segment: DocumentSegment, document: Document, dataset: Dataset
) -> ChildChunk:
lock_name = "add_child_lock_{}".format(segment.id)
with redis_client.lock(lock_name, timeout=20):
index_node_id = str(uuid.uuid4())
index_node_hash = helper.generate_text_hash(content)
child_chunk_count = (
db.session.query(ChildChunk)
.filter(
ChildChunk.tenant_id == current_user.current_tenant_id,
ChildChunk.dataset_id == dataset.id,
ChildChunk.document_id == document.id,
ChildChunk.segment_id == segment.id,
)
.count()
)
max_position = (
db.session.query(func.max(ChildChunk.position))
.filter(
ChildChunk.tenant_id == current_user.current_tenant_id,
ChildChunk.dataset_id == dataset.id,
ChildChunk.document_id == document.id,
ChildChunk.segment_id == segment.id,
)
.scalar()
)
child_chunk = ChildChunk(
tenant_id=current_user.current_tenant_id,
dataset_id=dataset.id,
document_id=document.id,
segment_id=segment.id,
position=max_position + 1,
index_node_id=index_node_id,
index_node_hash=index_node_hash,
content=content,
word_count=len(content),
type="customized",
created_by=current_user.id,
)
db.session.add(child_chunk)
# save vector index
try:
VectorService.create_child_chunk_vector(child_chunk, dataset)
except Exception as e:
logging.exception("create child chunk index failed")
db.session.rollback()
raise ChildChunkIndexingError(str(e))
db.session.commit()
return child_chunk
@classmethod
def update_child_chunks(
cls,
child_chunks_update_args: list[ChildChunkUpdateArgs],
segment: DocumentSegment,
document: Document,
dataset: Dataset,
) -> list[ChildChunk]:
child_chunks = (
db.session.query(ChildChunk)
.filter(
ChildChunk.dataset_id == dataset.id,
ChildChunk.document_id == document.id,
ChildChunk.segment_id == segment.id,
)
.all()
)
child_chunks_map = {chunk.id: chunk for chunk in child_chunks}
new_child_chunks, update_child_chunks, delete_child_chunks, new_child_chunks_args = [], [], [], []
for child_chunk_update_args in child_chunks_update_args:
if child_chunk_update_args.id:
child_chunk = child_chunks_map.pop(child_chunk_update_args.id, None)
if child_chunk:
if child_chunk.content != child_chunk_update_args.content:
child_chunk.content = child_chunk_update_args.content
child_chunk.word_count = len(child_chunk.content)
child_chunk.updated_by = current_user.id
child_chunk.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
child_chunk.type = "customized"
update_child_chunks.append(child_chunk)
else:
new_child_chunks_args.append(child_chunk_update_args)
if child_chunks_map:
delete_child_chunks = list(child_chunks_map.values())
try:
if update_child_chunks:
db.session.bulk_save_objects(update_child_chunks)
if delete_child_chunks:
for child_chunk in delete_child_chunks:
db.session.delete(child_chunk)
if new_child_chunks_args:
child_chunk_count = len(child_chunks)
for position, args in enumerate(new_child_chunks_args, start=child_chunk_count + 1):
index_node_id = str(uuid.uuid4())
index_node_hash = helper.generate_text_hash(args.content)
child_chunk = ChildChunk(
tenant_id=current_user.current_tenant_id,
dataset_id=dataset.id,
document_id=document.id,
segment_id=segment.id,
position=position,
index_node_id=index_node_id,
index_node_hash=index_node_hash,
content=args.content,
word_count=len(args.content),
type="customized",
created_by=current_user.id,
)
db.session.add(child_chunk)
db.session.flush()
new_child_chunks.append(child_chunk)
VectorService.update_child_chunk_vector(new_child_chunks, update_child_chunks, delete_child_chunks, dataset)
db.session.commit()
except Exception as e:
logging.exception("update child chunk index failed")
db.session.rollback()
raise ChildChunkIndexingError(str(e))
return sorted(new_child_chunks + update_child_chunks, key=lambda x: x.position)
@classmethod
def update_child_chunk(
cls,
content: str,
child_chunk: ChildChunk,
segment: DocumentSegment,
document: Document,
dataset: Dataset,
) -> ChildChunk:
try:
child_chunk.content = content
child_chunk.word_count = len(content)
child_chunk.updated_by = current_user.id
child_chunk.updated_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
child_chunk.type = "customized"
db.session.add(child_chunk)
VectorService.update_child_chunk_vector([], [child_chunk], [], dataset)
db.session.commit()
except Exception as e:
logging.exception("update child chunk index failed")
db.session.rollback()
raise ChildChunkIndexingError(str(e))
return child_chunk
@classmethod
def delete_child_chunk(cls, child_chunk: ChildChunk, dataset: Dataset):
db.session.delete(child_chunk)
try:
VectorService.delete_child_chunk_vector(child_chunk, dataset)
except Exception as e:
logging.exception("delete child chunk index failed")
db.session.rollback()
raise ChildChunkDeleteIndexError(str(e))
db.session.commit()
@classmethod
def get_child_chunks(
cls, segment_id: str, document_id: str, dataset_id: str, page: int, limit: int, keyword: Optional[str] = None
):
query = ChildChunk.query.filter_by(
tenant_id=current_user.current_tenant_id,
dataset_id=dataset_id,
document_id=document_id,
segment_id=segment_id,
).order_by(ChildChunk.position.asc())
if keyword:
query = query.where(ChildChunk.content.ilike(f"%{keyword}%"))
return query.paginate(page=page, per_page=limit, max_per_page=100, error_out=False)
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()
)
if not dataset_collection_binding:
raise ValueError("Dataset collection binding not found")
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