chore: ignore redis lock not owned error (#29064)
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
wangxiaolei
@@ -10,6 +10,7 @@ from collections.abc import Sequence
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from typing import Any, Literal
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import sqlalchemy as sa
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from redis.exceptions import LockNotOwnedError
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from sqlalchemy import exists, func, select
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from sqlalchemy.orm import Session
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from werkzeug.exceptions import NotFound
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@@ -1593,173 +1594,176 @@ class DocumentService:
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db.session.add(dataset_process_rule)
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db.session.flush()
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lock_name = f"add_document_lock_dataset_id_{dataset.id}"
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with redis_client.lock(lock_name, timeout=600):
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assert dataset_process_rule
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position = DocumentService.get_documents_position(dataset.id)
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document_ids = []
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duplicate_document_ids = []
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if knowledge_config.data_source.info_list.data_source_type == "upload_file":
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if not knowledge_config.data_source.info_list.file_info_list:
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raise ValueError("File source info is required")
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upload_file_list = knowledge_config.data_source.info_list.file_info_list.file_ids
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for file_id in upload_file_list:
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file = (
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db.session.query(UploadFile)
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.where(UploadFile.tenant_id == dataset.tenant_id, UploadFile.id == file_id)
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.first()
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)
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# raise error if file not found
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if not file:
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raise FileNotExistsError()
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file_name = file.name
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data_source_info: dict[str, str | bool] = {
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"upload_file_id": file_id,
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}
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# check duplicate
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if knowledge_config.duplicate:
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document = (
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db.session.query(Document)
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.filter_by(
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dataset_id=dataset.id,
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tenant_id=current_user.current_tenant_id,
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data_source_type="upload_file",
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enabled=True,
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name=file_name,
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)
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try:
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with redis_client.lock(lock_name, timeout=600):
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assert dataset_process_rule
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position = DocumentService.get_documents_position(dataset.id)
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document_ids = []
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duplicate_document_ids = []
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if knowledge_config.data_source.info_list.data_source_type == "upload_file":
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if not knowledge_config.data_source.info_list.file_info_list:
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raise ValueError("File source info is required")
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upload_file_list = knowledge_config.data_source.info_list.file_info_list.file_ids
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for file_id in upload_file_list:
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file = (
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db.session.query(UploadFile)
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.where(UploadFile.tenant_id == dataset.tenant_id, UploadFile.id == file_id)
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.first()
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)
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if document:
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document.dataset_process_rule_id = dataset_process_rule.id
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document.updated_at = naive_utc_now()
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document.created_from = created_from
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document.doc_form = knowledge_config.doc_form
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document.doc_language = knowledge_config.doc_language
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document.data_source_info = json.dumps(data_source_info)
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document.batch = batch
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document.indexing_status = "waiting"
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db.session.add(document)
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documents.append(document)
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duplicate_document_ids.append(document.id)
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continue
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document = DocumentService.build_document(
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dataset,
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dataset_process_rule.id,
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knowledge_config.data_source.info_list.data_source_type,
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knowledge_config.doc_form,
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knowledge_config.doc_language,
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data_source_info,
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created_from,
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position,
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account,
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file_name,
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batch,
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)
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db.session.add(document)
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db.session.flush()
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document_ids.append(document.id)
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documents.append(document)
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position += 1
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elif knowledge_config.data_source.info_list.data_source_type == "notion_import":
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notion_info_list = knowledge_config.data_source.info_list.notion_info_list # type: ignore
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if not notion_info_list:
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raise ValueError("No notion info list found.")
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exist_page_ids = []
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exist_document = {}
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documents = (
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db.session.query(Document)
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.filter_by(
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dataset_id=dataset.id,
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tenant_id=current_user.current_tenant_id,
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data_source_type="notion_import",
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enabled=True,
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)
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.all()
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)
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if documents:
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for document in documents:
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data_source_info = json.loads(document.data_source_info)
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exist_page_ids.append(data_source_info["notion_page_id"])
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exist_document[data_source_info["notion_page_id"]] = document.id
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for notion_info in notion_info_list:
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workspace_id = notion_info.workspace_id
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for page in notion_info.pages:
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if page.page_id not in exist_page_ids:
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data_source_info = {
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"credential_id": notion_info.credential_id,
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"notion_workspace_id": workspace_id,
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"notion_page_id": page.page_id,
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"notion_page_icon": page.page_icon.model_dump() if page.page_icon else None, # type: ignore
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"type": page.type,
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}
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# Truncate page name to 255 characters to prevent DB field length errors
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truncated_page_name = page.page_name[:255] if page.page_name else "nopagename"
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document = DocumentService.build_document(
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dataset,
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dataset_process_rule.id,
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knowledge_config.data_source.info_list.data_source_type,
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knowledge_config.doc_form,
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knowledge_config.doc_language,
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data_source_info,
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created_from,
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position,
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account,
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truncated_page_name,
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batch,
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)
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db.session.add(document)
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db.session.flush()
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document_ids.append(document.id)
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documents.append(document)
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position += 1
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else:
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exist_document.pop(page.page_id)
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# delete not selected documents
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if len(exist_document) > 0:
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clean_notion_document_task.delay(list(exist_document.values()), dataset.id)
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elif knowledge_config.data_source.info_list.data_source_type == "website_crawl":
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website_info = knowledge_config.data_source.info_list.website_info_list
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if not website_info:
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raise ValueError("No website info list found.")
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urls = website_info.urls
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for url in urls:
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data_source_info = {
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"url": url,
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"provider": website_info.provider,
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"job_id": website_info.job_id,
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"only_main_content": website_info.only_main_content,
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"mode": "crawl",
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}
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if len(url) > 255:
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document_name = url[:200] + "..."
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else:
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document_name = url
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document = DocumentService.build_document(
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dataset,
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dataset_process_rule.id,
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knowledge_config.data_source.info_list.data_source_type,
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knowledge_config.doc_form,
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knowledge_config.doc_language,
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data_source_info,
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created_from,
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position,
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account,
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document_name,
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batch,
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)
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db.session.add(document)
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db.session.flush()
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document_ids.append(document.id)
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documents.append(document)
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position += 1
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db.session.commit()
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# trigger async task
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if document_ids:
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DocumentIndexingTaskProxy(dataset.tenant_id, dataset.id, document_ids).delay()
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if duplicate_document_ids:
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duplicate_document_indexing_task.delay(dataset.id, duplicate_document_ids)
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# raise error if file not found
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if not file:
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raise FileNotExistsError()
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file_name = file.name
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data_source_info: dict[str, str | bool] = {
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"upload_file_id": file_id,
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}
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# check duplicate
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if knowledge_config.duplicate:
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document = (
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db.session.query(Document)
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.filter_by(
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dataset_id=dataset.id,
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tenant_id=current_user.current_tenant_id,
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data_source_type="upload_file",
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enabled=True,
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name=file_name,
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)
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.first()
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)
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if document:
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document.dataset_process_rule_id = dataset_process_rule.id
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document.updated_at = naive_utc_now()
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document.created_from = created_from
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document.doc_form = knowledge_config.doc_form
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document.doc_language = knowledge_config.doc_language
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document.data_source_info = json.dumps(data_source_info)
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document.batch = batch
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document.indexing_status = "waiting"
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db.session.add(document)
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documents.append(document)
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duplicate_document_ids.append(document.id)
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continue
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document = DocumentService.build_document(
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dataset,
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dataset_process_rule.id,
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knowledge_config.data_source.info_list.data_source_type,
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knowledge_config.doc_form,
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knowledge_config.doc_language,
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data_source_info,
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created_from,
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position,
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account,
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file_name,
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batch,
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)
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db.session.add(document)
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db.session.flush()
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document_ids.append(document.id)
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documents.append(document)
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position += 1
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elif knowledge_config.data_source.info_list.data_source_type == "notion_import":
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notion_info_list = knowledge_config.data_source.info_list.notion_info_list # type: ignore
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if not notion_info_list:
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raise ValueError("No notion info list found.")
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exist_page_ids = []
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exist_document = {}
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documents = (
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db.session.query(Document)
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.filter_by(
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dataset_id=dataset.id,
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tenant_id=current_user.current_tenant_id,
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data_source_type="notion_import",
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enabled=True,
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)
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.all()
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)
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if documents:
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for document in documents:
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data_source_info = json.loads(document.data_source_info)
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exist_page_ids.append(data_source_info["notion_page_id"])
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exist_document[data_source_info["notion_page_id"]] = document.id
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for notion_info in notion_info_list:
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workspace_id = notion_info.workspace_id
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for page in notion_info.pages:
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if page.page_id not in exist_page_ids:
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data_source_info = {
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"credential_id": notion_info.credential_id,
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"notion_workspace_id": workspace_id,
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"notion_page_id": page.page_id,
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"notion_page_icon": page.page_icon.model_dump() if page.page_icon else None, # type: ignore
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"type": page.type,
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}
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# Truncate page name to 255 characters to prevent DB field length errors
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truncated_page_name = page.page_name[:255] if page.page_name else "nopagename"
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document = DocumentService.build_document(
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dataset,
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dataset_process_rule.id,
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knowledge_config.data_source.info_list.data_source_type,
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knowledge_config.doc_form,
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knowledge_config.doc_language,
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data_source_info,
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created_from,
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position,
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account,
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truncated_page_name,
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batch,
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)
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db.session.add(document)
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db.session.flush()
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document_ids.append(document.id)
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documents.append(document)
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position += 1
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else:
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exist_document.pop(page.page_id)
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# delete not selected documents
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if len(exist_document) > 0:
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clean_notion_document_task.delay(list(exist_document.values()), dataset.id)
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elif knowledge_config.data_source.info_list.data_source_type == "website_crawl":
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website_info = knowledge_config.data_source.info_list.website_info_list
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if not website_info:
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raise ValueError("No website info list found.")
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urls = website_info.urls
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for url in urls:
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data_source_info = {
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"url": url,
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"provider": website_info.provider,
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"job_id": website_info.job_id,
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"only_main_content": website_info.only_main_content,
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"mode": "crawl",
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}
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if len(url) > 255:
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document_name = url[:200] + "..."
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else:
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document_name = url
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document = DocumentService.build_document(
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dataset,
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dataset_process_rule.id,
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knowledge_config.data_source.info_list.data_source_type,
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knowledge_config.doc_form,
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knowledge_config.doc_language,
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data_source_info,
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created_from,
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position,
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account,
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document_name,
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batch,
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)
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db.session.add(document)
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db.session.flush()
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document_ids.append(document.id)
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documents.append(document)
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position += 1
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db.session.commit()
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# trigger async task
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if document_ids:
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DocumentIndexingTaskProxy(dataset.tenant_id, dataset.id, document_ids).delay()
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if duplicate_document_ids:
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duplicate_document_indexing_task.delay(dataset.id, duplicate_document_ids)
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except LockNotOwnedError:
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pass
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return documents, batch
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@@ -2699,50 +2703,55 @@ class SegmentService:
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# calc embedding use tokens
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tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]
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lock_name = f"add_segment_lock_document_id_{document.id}"
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with redis_client.lock(lock_name, timeout=600):
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max_position = (
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db.session.query(func.max(DocumentSegment.position))
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.where(DocumentSegment.document_id == document.id)
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.scalar()
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)
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segment_document = DocumentSegment(
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tenant_id=current_user.current_tenant_id,
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dataset_id=document.dataset_id,
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document_id=document.id,
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index_node_id=doc_id,
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index_node_hash=segment_hash,
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position=max_position + 1 if max_position else 1,
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content=content,
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word_count=len(content),
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tokens=tokens,
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status="completed",
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indexing_at=naive_utc_now(),
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completed_at=naive_utc_now(),
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created_by=current_user.id,
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)
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if document.doc_form == "qa_model":
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segment_document.word_count += len(args["answer"])
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segment_document.answer = args["answer"]
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try:
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with redis_client.lock(lock_name, timeout=600):
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max_position = (
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db.session.query(func.max(DocumentSegment.position))
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.where(DocumentSegment.document_id == document.id)
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.scalar()
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)
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segment_document = DocumentSegment(
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tenant_id=current_user.current_tenant_id,
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dataset_id=document.dataset_id,
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document_id=document.id,
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index_node_id=doc_id,
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index_node_hash=segment_hash,
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position=max_position + 1 if max_position else 1,
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content=content,
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word_count=len(content),
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tokens=tokens,
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status="completed",
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indexing_at=naive_utc_now(),
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completed_at=naive_utc_now(),
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created_by=current_user.id,
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)
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if document.doc_form == "qa_model":
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segment_document.word_count += len(args["answer"])
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segment_document.answer = args["answer"]
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db.session.add(segment_document)
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# update document word count
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assert document.word_count is not None
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document.word_count += segment_document.word_count
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db.session.add(document)
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db.session.commit()
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# save vector index
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try:
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VectorService.create_segments_vector([args["keywords"]], [segment_document], dataset, document.doc_form)
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except Exception as e:
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logger.exception("create segment index failed")
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segment_document.enabled = False
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segment_document.disabled_at = naive_utc_now()
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segment_document.status = "error"
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segment_document.error = str(e)
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db.session.add(segment_document)
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# update document word count
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assert document.word_count is not None
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document.word_count += segment_document.word_count
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db.session.add(document)
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db.session.commit()
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segment = db.session.query(DocumentSegment).where(DocumentSegment.id == segment_document.id).first()
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return segment
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# save vector index
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try:
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VectorService.create_segments_vector(
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[args["keywords"]], [segment_document], dataset, document.doc_form
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)
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except Exception as e:
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logger.exception("create segment index failed")
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segment_document.enabled = False
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segment_document.disabled_at = naive_utc_now()
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segment_document.status = "error"
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segment_document.error = str(e)
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db.session.commit()
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segment = db.session.query(DocumentSegment).where(DocumentSegment.id == segment_document.id).first()
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return segment
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except LockNotOwnedError:
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pass
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@classmethod
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def multi_create_segment(cls, segments: list, document: Document, dataset: Dataset):
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@@ -2751,84 +2760,89 @@ class SegmentService:
|
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|
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lock_name = f"multi_add_segment_lock_document_id_{document.id}"
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increment_word_count = 0
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with redis_client.lock(lock_name, timeout=600):
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embedding_model = None
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if dataset.indexing_technique == "high_quality":
|
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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,
|
||||
provider=dataset.embedding_model_provider,
|
||||
model_type=ModelType.TEXT_EMBEDDING,
|
||||
model=dataset.embedding_model,
|
||||
try:
|
||||
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))
|
||||
.where(DocumentSegment.document_id == document.id)
|
||||
.scalar()
|
||||
)
|
||||
max_position = (
|
||||
db.session.query(func.max(DocumentSegment.position))
|
||||
.where(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
|
||||
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,
|
||||
keywords=segment_item.get("keywords", []),
|
||||
status="completed",
|
||||
indexing_at=naive_utc_now(),
|
||||
completed_at=naive_utc_now(),
|
||||
created_by=current_user.id,
|
||||
)
|
||||
if document.doc_form == "qa_model":
|
||||
tokens = embedding_model.get_text_embedding_num_tokens(
|
||||
texts=[content + segment_item["answer"]]
|
||||
)[0]
|
||||
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:
|
||||
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,
|
||||
keywords=segment_item.get("keywords", []),
|
||||
status="completed",
|
||||
indexing_at=naive_utc_now(),
|
||||
completed_at=naive_utc_now(),
|
||||
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
|
||||
assert document.word_count is not None
|
||||
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:
|
||||
logger.exception("create segment index failed")
|
||||
for segment_document in segment_data_list:
|
||||
segment_document.enabled = False
|
||||
segment_document.disabled_at = naive_utc_now()
|
||||
segment_document.status = "error"
|
||||
segment_document.error = str(e)
|
||||
db.session.commit()
|
||||
return segment_data_list
|
||||
keywords_list.append(None)
|
||||
# update document word count
|
||||
assert document.word_count is not None
|
||||
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:
|
||||
logger.exception("create segment index failed")
|
||||
for segment_document in segment_data_list:
|
||||
segment_document.enabled = False
|
||||
segment_document.disabled_at = naive_utc_now()
|
||||
segment_document.status = "error"
|
||||
segment_document.error = str(e)
|
||||
db.session.commit()
|
||||
return segment_data_list
|
||||
except LockNotOwnedError:
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def update_segment(cls, args: SegmentUpdateArgs, segment: DocumentSegment, document: Document, dataset: Dataset):
|
||||
|
||||
Reference in New Issue
Block a user