mirror of
https://github.com/infiniflow/ragflow.git
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bb3b99f0a5
### What problem does this PR solve? Feat: add button for remove header & footer in pipeline ### Type of change - [x] New Feature (non-breaking change which adds functionality)
1339 lines
55 KiB
Python
1339 lines
55 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import asyncio
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import io
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import json
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import os
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import random
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import re
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from functools import partial
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from litellm import logging
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import numpy as np
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from PIL import Image
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from api.db.services.file2document_service import File2DocumentService
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from api.db.services.file_service import FileService
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from api.db.services.llm_service import LLMBundle
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from api.db.joint_services.tenant_model_service import get_model_config_by_type_and_name, get_tenant_default_model_by_type
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from common import settings
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from common.constants import LLMType
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from common.misc_utils import get_uuid, thread_pool_exec
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from deepdoc.parser import ExcelParser, HtmlParser, TxtParser
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from deepdoc.parser.docling_parser import DoclingParser
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from deepdoc.parser.pdf_parser import PlainParser, RAGFlowPdfParser, VisionParser
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from deepdoc.parser.tcadp_parser import TCADPParser
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from rag.app.naive import Docx
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from rag.flow.base import ProcessBase, ProcessParamBase
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from rag.flow.parser.pdf_chunk_metadata import (
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extract_pdf_positions,
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normalize_pdf_items_metadata,
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reorder_multi_column_bboxes,
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)
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from rag.flow.parser.schema import ParserFromUpstream
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from rag.flow.parser.utils import (
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enhance_media_sections_with_vision,
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extract_word_outlines,
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extract_docx_header_footer_texts,
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remove_header_footer_docx_sections,
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remove_header_footer_html_blob,
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remove_toc,
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remove_toc_pdf,
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remove_toc_word,
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)
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from rag.llm.cv_model import Base as VLM
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from rag.utils.base64_image import image2id
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class ParserParam(ProcessParamBase):
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def __init__(self):
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super().__init__()
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self.allowed_output_format = {
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"pdf": [
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"json",
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"markdown",
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],
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"spreadsheet": [
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"json",
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"markdown",
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"html",
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],
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"doc": [
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"json",
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"markdown",
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],
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"docx": [
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"json",
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"markdown",
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],
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"slides": [
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"json",
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],
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"image": [
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"json",
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],
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"email": [
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"text",
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"json",
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],
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"markdown": [
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"text",
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"json",
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],
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"text&code": [
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"text",
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"json",
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],
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"html": [
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"text",
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"json",
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],
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"audio": [
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"json",
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],
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"video": [],
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"epub": [
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"text",
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"json",
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],
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}
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self.setups = {
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"pdf": {
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"parse_method": "deepdoc", # deepdoc/plain_text/tcadp_parser/vlm
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"lang": "Chinese",
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"flatten_media_to_text": False,
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"remove_toc": False,
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"remove_header_footer": False,
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"suffix": [
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"pdf",
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],
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"output_format": "json",
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},
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"spreadsheet": {
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"parse_method": "deepdoc", # deepdoc/tcadp_parser
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"flatten_media_to_text": False,
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"output_format": "html",
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"suffix": [
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"xls",
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"xlsx",
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"csv",
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],
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},
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"doc": {
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"remove_toc": False,
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"remove_header_footer": False,
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"suffix": [
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"doc",
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],
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"output_format": "json",
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},
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"docx": {
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"flatten_media_to_text": False,
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"remove_toc": False,
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"remove_header_footer": False,
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"suffix": [
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"docx",
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],
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"output_format": "json",
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},
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"markdown": {
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"flatten_media_to_text": False,
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"suffix": ["md", "markdown", "mdx"],
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"remove_toc": False,
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"output_format": "json",
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},
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"text&code": {
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"suffix": [
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"txt",
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"py",
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"js",
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"java",
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"c",
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"cpp",
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"h",
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"php",
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"go",
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"ts",
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"sh",
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"cs",
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"kt",
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"sql",
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],
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"output_format": "json",
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},
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"html": {
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"suffix": ["htm", "html"],
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"remove_toc": False,
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"remove_header_footer": False,
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"output_format": "json",
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},
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"slides": {
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"parse_method": "deepdoc", # deepdoc/tcadp_parser
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"suffix": [
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"pptx",
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"ppt",
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],
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"output_format": "json",
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},
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"image": {
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"parse_method": "ocr",
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"llm_id": "",
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"lang": "Chinese",
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"system_prompt": "",
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"suffix": ["jpg", "jpeg", "png", "gif"],
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"output_format": "json",
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},
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"email": {
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"suffix": [
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"eml",
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"msg",
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],
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"fields": ["from", "to", "cc", "bcc", "date", "subject", "body", "attachments", "metadata"],
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"output_format": "json",
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},
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"audio": {
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"suffix": [
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"da",
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"wave",
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"wav",
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"mp3",
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"aac",
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"flac",
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"ogg",
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"aiff",
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"au",
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"midi",
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"wma",
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"realaudio",
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"vqf",
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"oggvorbis",
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"ape",
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],
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"output_format": "text",
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},
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"video": {
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"suffix": [
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"mp4",
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"avi",
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"mkv",
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],
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"output_format": "text",
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"prompt": "",
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},
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"epub": {
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"suffix": [
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"epub",
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],
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"output_format": "json",
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},
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}
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def check(self):
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pdf_config = self.setups.get("pdf", {})
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if pdf_config:
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pdf_parse_method = pdf_config.get("parse_method", "")
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self.check_empty(pdf_parse_method, "Parse method abnormal.")
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if pdf_parse_method.lower() not in ["deepdoc", "plain_text", "mineru", "docling", "opendataloader", "tcadp parser", "paddleocr"]:
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self.check_empty(pdf_config.get("lang", ""), "PDF VLM language")
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pdf_output_format = pdf_config.get("output_format", "")
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self.check_valid_value(pdf_output_format, "PDF output format abnormal.", self.allowed_output_format["pdf"])
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spreadsheet_config = self.setups.get("spreadsheet", "")
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if spreadsheet_config:
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spreadsheet_output_format = spreadsheet_config.get("output_format", "")
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self.check_valid_value(spreadsheet_output_format, "Spreadsheet output format abnormal.", self.allowed_output_format["spreadsheet"])
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doc_config = self.setups.get("doc", "")
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if doc_config:
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doc_output_format = doc_config.get("output_format", "")
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self.check_valid_value(doc_output_format, "DOC output format abnormal.", self.allowed_output_format["doc"])
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docx_config = self.setups.get("docx", "")
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if docx_config:
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docx_output_format = docx_config.get("output_format", "")
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self.check_valid_value(docx_output_format, "DOCX output format abnormal.", self.allowed_output_format["docx"])
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slides_config = self.setups.get("slides", "")
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if slides_config:
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slides_output_format = slides_config.get("output_format", "")
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self.check_valid_value(slides_output_format, "Slides output format abnormal.", self.allowed_output_format["slides"])
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image_config = self.setups.get("image", "")
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if image_config:
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image_parse_method = image_config.get("parse_method", "")
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if image_parse_method not in ["ocr"]:
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self.check_empty(image_config.get("lang", ""), "Image VLM language")
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text_config = self.setups.get("markdown", "")
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if text_config:
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text_output_format = text_config.get("output_format", "")
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self.check_valid_value(text_output_format, "Markdown output format abnormal.", self.allowed_output_format["markdown"])
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code_config = self.setups.get("text&code", "")
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if code_config:
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code_output_format = code_config.get("output_format", "")
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self.check_valid_value(code_output_format, "Text&Code output format abnormal.", self.allowed_output_format["text&code"])
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html_config = self.setups.get("html", "")
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if html_config:
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html_output_format = html_config.get("output_format", "")
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self.check_valid_value(html_output_format, "HTML output format abnormal.", self.allowed_output_format["html"])
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audio_config = self.setups.get("audio", "")
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if audio_config:
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audio_vlm = audio_config.get("vlm") or {}
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self.check_empty(audio_vlm.get("llm_id"), "Audio VLM")
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video_config = self.setups.get("video", "")
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if video_config:
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video_vlm = video_config.get("vlm") or {}
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self.check_empty(video_vlm.get("llm_id"), "Video VLM")
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email_config = self.setups.get("email", "")
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if email_config:
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email_output_format = email_config.get("output_format", "")
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self.check_valid_value(email_output_format, "Email output format abnormal.", self.allowed_output_format["email"])
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epub_config = self.setups.get("epub", "")
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if epub_config:
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epub_output_format = epub_config.get("output_format", "")
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self.check_valid_value(epub_output_format, "EPUB output format abnormal.", self.allowed_output_format["epub"])
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def get_input_form(self) -> dict[str, dict]:
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return {}
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class Parser(ProcessBase):
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component_name = "Parser"
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def _pdf(self, name, blob, **kwargs):
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"""Parse PDF files into structured boxes or markdown/json output."""
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a PDF.")
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conf = self._param.setups["pdf"]
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self.set_output("output_format", conf["output_format"])
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flatten_media_to_text = conf.get("flatten_media_to_text")
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pdf_parser = None
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# Normalize parser selection and optional provider-specific model name.
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raw_parse_method = conf.get("parse_method", "")
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parser_model_name = None
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parse_method = raw_parse_method
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parse_method = parse_method or ""
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if isinstance(raw_parse_method, str):
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lowered = raw_parse_method.lower()
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if lowered.endswith("@mineru"):
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parser_model_name = raw_parse_method.rsplit("@", 1)[0]
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parse_method = "MinerU"
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elif lowered.endswith("@paddleocr"):
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parser_model_name = raw_parse_method.rsplit("@", 1)[0]
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parse_method = "PaddleOCR"
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# DeepDOC returns structured page boxes directly.
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if parse_method.lower() == "deepdoc":
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pdf_parser = RAGFlowPdfParser()
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bboxes = pdf_parser.parse_into_bboxes(blob, callback=self.callback)
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if conf.get("enable_multi_column"):
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bboxes = reorder_multi_column_bboxes(pdf_parser, bboxes)
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# Plain text only keeps extracted text lines.
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elif parse_method.lower() == "plain_text":
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pdf_parser = PlainParser()
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lines, _ = pdf_parser(blob)
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bboxes = [{"text": t, "layout_type": "text"} for t, _ in lines]
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# MinerU/PaddleOCR/Docling/TCADP all return line-like sections that need
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# to be converted into the shared bbox-like structure used below.
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elif parse_method.lower() == "mineru":
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def resolve_mineru_llm_name():
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configured = parser_model_name or conf.get("mineru_llm_name")
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if configured:
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return configured
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tenant_id = self._canvas._tenant_id
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if not tenant_id:
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return None
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from api.db.services.tenant_llm_service import TenantLLMService
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env_name = TenantLLMService.ensure_mineru_from_env(tenant_id)
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candidates = TenantLLMService.query(tenant_id=tenant_id, llm_factory="MinerU", model_type=LLMType.OCR.value)
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if candidates:
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return candidates[0].llm_name
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return env_name
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parser_model_name = resolve_mineru_llm_name()
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if not parser_model_name:
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raise RuntimeError("MinerU model not configured. Please add MinerU in Model Providers or set MINERU_* env.")
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tenant_id = self._canvas._tenant_id
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ocr_model_config = get_model_config_by_type_and_name(tenant_id, LLMType.OCR, parser_model_name)
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ocr_model = LLMBundle(tenant_id, ocr_model_config, lang=conf.get("lang", "Chinese"))
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pdf_parser = ocr_model.mdl
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lines, _ = pdf_parser.parse_pdf(
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filepath=name,
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binary=blob,
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callback=self.callback,
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parse_method="pipeline",
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lang=conf.get("lang", "Chinese"),
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)
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bboxes = []
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for line in lines or []:
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if not isinstance(line, tuple) or len(line) < 3:
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continue
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t, layout_type, poss = line[0], line[1], line[2]
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box = {
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"text": t,
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"layout_type": layout_type or "text",
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}
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positions = [[pos[0][-1] + 1, *pos[1:]] for pos in pdf_parser.extract_positions(poss)]
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if positions:
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box["positions"] = positions
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image = pdf_parser.crop(poss, 1)
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if image is not None:
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box["image"] = image
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bboxes.append(box)
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elif parse_method.lower() == "docling":
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pdf_parser = DoclingParser(docling_server_url=os.environ.get("DOCLING_SERVER_URL", ""))
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lines, _ = pdf_parser.parse_pdf(
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filepath=name,
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binary=blob,
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callback=self.callback,
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parse_method="pipeline",
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docling_server_url=os.environ.get("DOCLING_SERVER_URL", ""),
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)
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bboxes = []
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for item in lines or []:
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if not isinstance(item, tuple) or len(item) < 3:
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continue
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text, layout_type, poss = item[0], item[1], item[2]
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box = {
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"text": text,
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"layout_type": layout_type or "text",
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}
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if isinstance(poss, str) and poss:
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positions = [[pos[0][-1] + 1, *pos[1:]] for pos in pdf_parser.extract_positions(poss)]
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if positions:
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box["positions"] = positions
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image = pdf_parser.crop(poss, 1)
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if image is not None:
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box["image"] = image
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bboxes.append(box)
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elif parse_method.lower() == "opendataloader":
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def resolve_opendataloader_llm_name():
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configured = parser_model_name or conf.get("opendataloader_llm_name")
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if configured:
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return configured
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tenant_id = self._canvas._tenant_id
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if not tenant_id:
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return None
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from api.db.services.tenant_llm_service import TenantLLMService
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env_name = TenantLLMService.ensure_opendataloader_from_env(tenant_id)
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candidates = TenantLLMService.query(tenant_id=tenant_id, llm_factory="OpenDataLoader", model_type=LLMType.OCR.value)
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if candidates:
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return candidates[0].llm_name
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return env_name
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parser_model_name = resolve_opendataloader_llm_name()
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if not parser_model_name:
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raise RuntimeError("OpenDataLoader model not configured. Please add OpenDataLoader in Model Providers.")
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tenant_id = self._canvas._tenant_id
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ocr_model_config = get_model_config_by_type_and_name(tenant_id, LLMType.OCR, parser_model_name)
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ocr_model = LLMBundle(tenant_id, ocr_model_config)
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pdf_parser = ocr_model.mdl
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lines, odl_tables = pdf_parser.parse_pdf(
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filepath=name,
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binary=blob,
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callback=self.callback,
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parse_method="pipeline",
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)
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bboxes = []
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for item in lines or []:
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if not isinstance(item, tuple) or len(item) < 3:
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continue
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text, layout_type, poss = item[0], item[1], item[2]
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box = {
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"text": text,
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"layout_type": layout_type or "text",
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}
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if isinstance(poss, str) and poss:
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positions = [[pos[0][-1] + 1, *pos[1:]] for pos in pdf_parser.extract_positions(poss)]
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if positions:
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box["positions"] = positions
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image = pdf_parser.crop(poss, 1)
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if image is not None:
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box["image"] = image
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bboxes.append(box)
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# Merge tables and images from the second return value.
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for (img, html_or_caption), positions in odl_tables or []:
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box = {"layout_type": "table" if not isinstance(html_or_caption, list) else "figure"}
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if isinstance(html_or_caption, str):
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box["text"] = html_or_caption
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elif isinstance(html_or_caption, list):
|
|
box["text"] = html_or_caption[0] if html_or_caption else ""
|
|
if img is not None:
|
|
box["image"] = img
|
|
if positions:
|
|
try:
|
|
box["positions"] = [[p[0] + 1, p[1], p[2], p[3], p[4]] for p in positions]
|
|
except Exception:
|
|
pass
|
|
bboxes.append(box)
|
|
|
|
elif parse_method.lower() == "tcadp parser":
|
|
# ADP is a document parsing tool using Tencent Cloud API
|
|
table_result_type = conf.get("table_result_type", "1")
|
|
markdown_image_response_type = conf.get("markdown_image_response_type", "1")
|
|
pdf_parser = TCADPParser(
|
|
table_result_type=table_result_type,
|
|
markdown_image_response_type=markdown_image_response_type,
|
|
)
|
|
sections, _ = pdf_parser.parse_pdf(
|
|
filepath=name,
|
|
binary=blob,
|
|
callback=self.callback,
|
|
file_type="PDF",
|
|
file_start_page=1,
|
|
file_end_page=1000,
|
|
)
|
|
bboxes = []
|
|
for section, position_tag in sections:
|
|
if position_tag:
|
|
match = re.match(r"@@([0-9-]+)\t([0-9.]+)\t([0-9.]+)\t([0-9.]+)\t([0-9.]+)##", position_tag)
|
|
if match:
|
|
pn, x0, x1, top, bott = match.groups()
|
|
bboxes.append(
|
|
{
|
|
"page_number": int(pn.split("-")[0]),
|
|
"x0": float(x0),
|
|
"x1": float(x1),
|
|
"top": float(top),
|
|
"bottom": float(bott),
|
|
"text": section,
|
|
"layout_type": "text",
|
|
}
|
|
)
|
|
else:
|
|
bboxes.append({"text": section, "layout_type": "text"})
|
|
else:
|
|
bboxes.append({"text": section, "layout_type": "text"})
|
|
|
|
elif parse_method.lower() == "paddleocr":
|
|
|
|
def resolve_paddleocr_llm_name():
|
|
configured = parser_model_name or conf.get("paddleocr_llm_name")
|
|
if configured:
|
|
return configured
|
|
|
|
tenant_id = self._canvas._tenant_id
|
|
if not tenant_id:
|
|
return None
|
|
|
|
from api.db.services.tenant_llm_service import TenantLLMService
|
|
|
|
env_name = TenantLLMService.ensure_paddleocr_from_env(tenant_id)
|
|
candidates = TenantLLMService.query(tenant_id=tenant_id, llm_factory="PaddleOCR", model_type=LLMType.OCR.value)
|
|
if candidates:
|
|
return candidates[0].llm_name
|
|
return env_name
|
|
|
|
parser_model_name = resolve_paddleocr_llm_name()
|
|
if not parser_model_name:
|
|
raise RuntimeError("PaddleOCR model not configured. Please add PaddleOCR in Model Providers or set PADDLEOCR_* env.")
|
|
|
|
tenant_id = self._canvas._tenant_id
|
|
ocr_model_config = get_model_config_by_type_and_name(tenant_id, LLMType.OCR, parser_model_name)
|
|
ocr_model = LLMBundle(tenant_id, ocr_model_config)
|
|
pdf_parser = ocr_model.mdl
|
|
|
|
lines, _ = pdf_parser.parse_pdf(
|
|
filepath=name,
|
|
binary=blob,
|
|
callback=self.callback,
|
|
parse_method="pipeline",
|
|
)
|
|
bboxes = []
|
|
for line in lines or []:
|
|
if not isinstance(line, tuple) or len(line) < 3:
|
|
continue
|
|
|
|
t, layout_type, poss = line[0], line[1], line[2]
|
|
box = {
|
|
"text": t,
|
|
"layout_type": layout_type or "text",
|
|
}
|
|
positions = [[pos[0][-1] + 1, *pos[1:]] for pos in pdf_parser.extract_positions(poss)]
|
|
if positions:
|
|
box["positions"] = positions
|
|
image = pdf_parser.crop(poss)
|
|
if image is not None:
|
|
box["image"] = image
|
|
bboxes.append(box)
|
|
# Vision parser treats each page as a large image block.
|
|
else:
|
|
if conf.get("parse_method"):
|
|
vision_model_config = get_model_config_by_type_and_name(self._canvas._tenant_id, LLMType.IMAGE2TEXT, conf["parse_method"])
|
|
else:
|
|
vision_model_config = get_tenant_default_model_by_type(self._canvas._tenant_id, LLMType.IMAGE2TEXT)
|
|
vision_model = LLMBundle(self._canvas._tenant_id, vision_model_config, lang=self._param.setups["pdf"].get("lang"))
|
|
pdf_parser = VisionParser(vision_model=vision_model)
|
|
lines, _ = pdf_parser(blob, callback=self.callback)
|
|
bboxes = []
|
|
for t, poss in lines:
|
|
for pn, x0, x1, top, bott in RAGFlowPdfParser.extract_positions(poss):
|
|
bboxes.append(
|
|
{
|
|
"page_number": int(pn[0]) + 1,
|
|
"x0": float(x0),
|
|
"x1": float(x1),
|
|
"top": float(top),
|
|
"bottom": float(bott),
|
|
"text": t,
|
|
"layout_type": "text",
|
|
}
|
|
)
|
|
|
|
# Persist outlines and optionally remove TOC before normalizing metadata.
|
|
self.set_output("file", {**kwargs.get("file", {}), "outlines": pdf_parser.outlines})
|
|
if conf.get("remove_toc"):
|
|
if not pdf_parser.outlines:
|
|
bboxes, _ = remove_toc(bboxes)
|
|
elif pdf_parser.outlines[0][2] == 1:
|
|
bboxes = remove_toc_pdf(bboxes, pdf_parser.outlines)
|
|
else:
|
|
first_outline_page = pdf_parser.outlines[0][2]
|
|
split_at = len(bboxes)
|
|
for i, item in enumerate(bboxes):
|
|
page_number = item.get("page_number")
|
|
if page_number is None:
|
|
positions = extract_pdf_positions(item)
|
|
if positions:
|
|
page_number = positions[0][0]
|
|
if page_number is not None and page_number >= first_outline_page:
|
|
split_at = i
|
|
break
|
|
toc_bboxes, _ = remove_toc(bboxes[:split_at])
|
|
bboxes = toc_bboxes + bboxes[split_at:]
|
|
|
|
normalize_bboxes = []
|
|
# Normalize shared bbox fields for downstream consumers.
|
|
for b in bboxes:
|
|
raw_layout = str(b.get("layout_type") or "").strip()
|
|
has_layout = bool(raw_layout)
|
|
layout = re.sub(r"\s+", " ", raw_layout) if has_layout else "text"
|
|
b["layout_type"] = layout
|
|
if conf.get("remove_header_footer") and re.search(r"(header|footer|number)", raw_layout, re.I):
|
|
continue
|
|
if flatten_media_to_text:
|
|
b["doc_type_kwd"] = "text"
|
|
elif layout == "table":
|
|
b["doc_type_kwd"] = "table"
|
|
elif layout == "figure":
|
|
b["doc_type_kwd"] = "image"
|
|
elif not has_layout and b.get("image") is not None:
|
|
b["doc_type_kwd"] = "image"
|
|
else:
|
|
b["doc_type_kwd"] = "text"
|
|
normalize_bboxes.append(b)
|
|
bboxes = normalize_bboxes
|
|
|
|
enhance_media_sections_with_vision(
|
|
bboxes,
|
|
self._canvas._tenant_id,
|
|
conf.get("vlm"),
|
|
callback=self.callback,
|
|
)
|
|
|
|
# Emit the requested final PDF output format.
|
|
if conf.get("output_format") == "json":
|
|
normalize_pdf_items_metadata(bboxes)
|
|
self.set_output("json", bboxes)
|
|
if conf.get("output_format") == "markdown":
|
|
mkdn = ""
|
|
for b in bboxes:
|
|
if b.get("layout_type", "") == "title":
|
|
mkdn += "\n## "
|
|
if b.get("layout_type", "") == "figure":
|
|
mkdn += "\n".format(VLM.image2base64(b["image"]))
|
|
continue
|
|
mkdn += b.get("text", "") + "\n"
|
|
self.set_output("markdown", mkdn)
|
|
|
|
def _spreadsheet(self, name, blob, **kwargs):
|
|
"""Parse spreadsheet files and normalize them into html/json/markdown output."""
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on a Spreadsheet.")
|
|
conf = self._param.setups["spreadsheet"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
flatten_media_to_text = conf.get("flatten_media_to_text")
|
|
|
|
parse_method = conf.get("parse_method", "deepdoc")
|
|
|
|
# Handle TCADP parser
|
|
if parse_method.lower() == "tcadp parser":
|
|
table_result_type = conf.get("table_result_type", "1")
|
|
markdown_image_response_type = conf.get("markdown_image_response_type", "1")
|
|
tcadp_parser = TCADPParser(
|
|
table_result_type=table_result_type,
|
|
markdown_image_response_type=markdown_image_response_type,
|
|
)
|
|
if not tcadp_parser.check_installation():
|
|
raise RuntimeError("TCADP parser not available. Please check Tencent Cloud API configuration.")
|
|
|
|
# Determine file type based on extension
|
|
if re.search(r"\.xlsx?$", name, re.IGNORECASE):
|
|
file_type = "XLSX"
|
|
else:
|
|
file_type = "CSV"
|
|
|
|
self.callback(0.2, f"Using TCADP parser for {file_type} file.")
|
|
sections, tables = tcadp_parser.parse_pdf(
|
|
filepath=name,
|
|
binary=blob,
|
|
callback=self.callback,
|
|
file_type=file_type,
|
|
file_start_page=1,
|
|
file_end_page=1000,
|
|
)
|
|
|
|
# Process TCADP parser output based on configured output_format
|
|
output_format = conf.get("output_format", "html")
|
|
|
|
if output_format == "html":
|
|
# For HTML output, combine sections and tables into HTML
|
|
html_content = ""
|
|
for section, position_tag in sections:
|
|
if section:
|
|
html_content += section + "\n"
|
|
for table in tables:
|
|
if table:
|
|
html_content += table + "\n"
|
|
|
|
self.set_output("html", html_content)
|
|
|
|
elif output_format == "json":
|
|
# For JSON output, create a list of text items
|
|
result = []
|
|
# Add sections as text
|
|
for section, position_tag in sections:
|
|
if section:
|
|
result.append({"text": section, "doc_type_kwd": "text"})
|
|
# Add tables as text
|
|
for table in tables:
|
|
if table:
|
|
result.append(
|
|
{
|
|
"text": table,
|
|
"doc_type_kwd": "text" if flatten_media_to_text else "table",
|
|
}
|
|
)
|
|
|
|
self.set_output("json", result)
|
|
|
|
elif output_format == "markdown":
|
|
# For markdown output, combine into markdown
|
|
md_content = ""
|
|
for section, position_tag in sections:
|
|
if section:
|
|
md_content += section + "\n\n"
|
|
for table in tables:
|
|
if table:
|
|
md_content += table + "\n\n"
|
|
|
|
self.set_output("markdown", md_content)
|
|
else:
|
|
# Default DeepDOC parser
|
|
spreadsheet_parser = ExcelParser()
|
|
if conf.get("output_format") == "html":
|
|
htmls = spreadsheet_parser.html(blob, 1000000000)
|
|
self.set_output("html", htmls[0])
|
|
elif conf.get("output_format") == "json":
|
|
self.set_output("json", [{"text": txt, "doc_type_kwd": "text"} for txt in spreadsheet_parser(blob) if txt])
|
|
elif conf.get("output_format") == "markdown":
|
|
self.set_output("markdown", spreadsheet_parser.markdown(blob))
|
|
|
|
def _doc(self, name, blob, **kwargs):
|
|
"""Parse DOC files into text/json sections."""
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on a DOC document")
|
|
conf = self._param.setups["doc"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
|
|
from tika import parser as tika_parser
|
|
|
|
parsed = tika_parser.from_buffer(io.BytesIO(blob))
|
|
sections = [line for line in parsed["content"].split("\n") if line]
|
|
|
|
if conf.get("output_format") == "json":
|
|
self.set_output("json", [{"text": section, "doc_type_kwd": "text"} for section in sections])
|
|
return
|
|
|
|
self.set_output("markdown", "\n".join(sections))
|
|
|
|
def _docx(self, name, blob, **kwargs):
|
|
"""Parse DOCX files and optionally remove table-of-contents content."""
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on a DOCX document")
|
|
conf = self._param.setups["docx"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
flatten_media_to_text = conf.get("flatten_media_to_text")
|
|
|
|
if re.search(r"\.doc$", name, re.IGNORECASE):
|
|
self.set_output("file", {**kwargs.get("file", {}), "outlines": []})
|
|
try:
|
|
from tika import parser as tika_parser
|
|
except Exception as e:
|
|
msg = f"tika not available: {e}. Unsupported .doc parsing."
|
|
self.callback(0.8, msg)
|
|
logging.warning(f"{msg} for {name}.")
|
|
return
|
|
|
|
doc_parsed = tika_parser.from_buffer(io.BytesIO(blob))
|
|
content = doc_parsed.get("content")
|
|
if content is None:
|
|
msg = f"tika.parser got empty content from {name}."
|
|
self.callback(0.8, msg)
|
|
logging.warning(msg)
|
|
return
|
|
|
|
sections = [line.strip() for line in content.splitlines() if line and line.strip()]
|
|
if conf.get("remove_toc"):
|
|
sections = remove_toc_word(sections, [])
|
|
|
|
if conf.get("output_format") == "json":
|
|
self.set_output(
|
|
"json",
|
|
[{"text": line, "image": None, "doc_type_kwd": "text"} for line in sections],
|
|
)
|
|
elif conf.get("output_format") == "markdown":
|
|
# Tika gives us plain text lines, so join with blank lines to preserve paragraph boundaries in markdown.
|
|
self.set_output("markdown", "\n\n".join(sections))
|
|
|
|
self.callback(0.8, "Finish parsing.")
|
|
return
|
|
|
|
docx_parser = Docx()
|
|
|
|
# Extract heading-based outlines for metadata and TOC removal.
|
|
outlines = extract_word_outlines(name, blob)
|
|
self.set_output("file", {**kwargs.get("file", {}), "outlines": outlines})
|
|
|
|
# JSON output keeps text/image blocks and appends table HTML as table items.
|
|
if conf.get("output_format") == "json":
|
|
main_sections = docx_parser(name, binary=blob)
|
|
if conf.get("remove_header_footer"):
|
|
header_footer_texts = extract_docx_header_footer_texts(binary=blob)
|
|
main_sections = remove_header_footer_docx_sections(main_sections, header_footer_texts)
|
|
if conf.get("remove_toc"):
|
|
main_sections = remove_toc_word(main_sections, outlines)
|
|
sections = []
|
|
for text, image, html in main_sections:
|
|
sections.append(
|
|
{
|
|
"text": text,
|
|
"image": image,
|
|
"doc_type_kwd": "text" if flatten_media_to_text or image is None else "image",
|
|
}
|
|
)
|
|
if html:
|
|
sections.append(
|
|
{
|
|
"text": html,
|
|
"image": None,
|
|
"doc_type_kwd": "text" if flatten_media_to_text else "table",
|
|
}
|
|
)
|
|
enhance_media_sections_with_vision(
|
|
sections,
|
|
self._canvas._tenant_id,
|
|
conf.get("vlm"),
|
|
callback=self.callback,
|
|
)
|
|
|
|
self.set_output("json", sections)
|
|
|
|
# Markdown output removes TOC on plain markdown lines before writing back.
|
|
elif conf.get("output_format") == "markdown":
|
|
markdown_text = docx_parser.to_markdown(name, binary=blob)
|
|
if conf.get("remove_header_footer"):
|
|
header_footer_texts = extract_docx_header_footer_texts(binary=blob)
|
|
markdown_lines = remove_header_footer_docx_sections(markdown_text.split("\n"), header_footer_texts)
|
|
markdown_text = "\n".join(markdown_lines)
|
|
if conf.get("remove_toc"):
|
|
markdown_text = "\n".join(remove_toc_word(markdown_text.split("\n"), outlines))
|
|
|
|
self.set_output("markdown", markdown_text)
|
|
|
|
def _slides(self, name, blob, **kwargs):
|
|
"""Parse presentation files into json sections."""
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on a PowerPoint Document")
|
|
|
|
conf = self._param.setups["slides"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
|
|
parse_method = conf.get("parse_method", "deepdoc")
|
|
|
|
# Handle TCADP parser
|
|
if parse_method.lower() == "tcadp parser":
|
|
table_result_type = conf.get("table_result_type", "1")
|
|
markdown_image_response_type = conf.get("markdown_image_response_type", "1")
|
|
tcadp_parser = TCADPParser(
|
|
table_result_type=table_result_type,
|
|
markdown_image_response_type=markdown_image_response_type,
|
|
)
|
|
if not tcadp_parser.check_installation():
|
|
raise RuntimeError("TCADP parser not available. Please check Tencent Cloud API configuration.")
|
|
|
|
# Determine file type based on extension
|
|
if re.search(r"\.pptx?$", name, re.IGNORECASE):
|
|
file_type = "PPTX"
|
|
else:
|
|
file_type = "PPT"
|
|
|
|
self.callback(0.2, f"Using TCADP parser for {file_type} file.")
|
|
|
|
sections, tables = tcadp_parser.parse_pdf(
|
|
filepath=name,
|
|
binary=blob,
|
|
callback=self.callback,
|
|
file_type=file_type,
|
|
file_start_page=1,
|
|
file_end_page=1000,
|
|
)
|
|
|
|
# Process TCADP parser output - PPT only supports json format
|
|
output_format = conf.get("output_format", "json")
|
|
if output_format == "json":
|
|
# For JSON output, create a list of text items
|
|
result = []
|
|
# Add sections as text
|
|
for section, position_tag in sections:
|
|
if section:
|
|
result.append({"text": section, "doc_type_kwd": "text"})
|
|
# Add tables as text
|
|
for table in tables:
|
|
if table:
|
|
result.append({"text": table, "doc_type_kwd": "table"})
|
|
|
|
self.set_output("json", result)
|
|
else:
|
|
# Default DeepDOC parser (supports .pptx format)
|
|
from deepdoc.parser.ppt_parser import RAGFlowPptParser as ppt_parser
|
|
|
|
ppt_parser = ppt_parser()
|
|
txts = ppt_parser(blob, 0, 100000, None)
|
|
|
|
sections = [{"text": section, "doc_type_kwd": "text"} for section in txts if section.strip()]
|
|
|
|
# json
|
|
assert conf.get("output_format") == "json", "have to be json for ppt"
|
|
if conf.get("output_format") == "json":
|
|
self.set_output("json", sections)
|
|
|
|
def _markdown(self, name, blob, **kwargs):
|
|
"""Parse markdown files into text/json sections."""
|
|
from functools import reduce
|
|
|
|
from rag.app.naive import Markdown as naive_markdown_parser
|
|
from rag.nlp import concat_img
|
|
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on a markdown.")
|
|
conf = self._param.setups["markdown"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
flatten_media_to_text = conf.get("flatten_media_to_text")
|
|
|
|
markdown_parser = naive_markdown_parser()
|
|
sections, tables, section_images = markdown_parser(
|
|
name,
|
|
blob,
|
|
separate_tables=False,
|
|
delimiter=conf.get("delimiter"),
|
|
return_section_images=True,
|
|
)
|
|
|
|
if conf.get("output_format") == "json":
|
|
json_results = []
|
|
|
|
for idx, (section_text, _) in enumerate(sections):
|
|
json_result = {
|
|
"text": section_text,
|
|
}
|
|
|
|
images = []
|
|
if section_images and len(section_images) > idx and section_images[idx] is not None:
|
|
images.append(section_images[idx])
|
|
if images:
|
|
# If multiple images found, combine them using concat_img
|
|
combined_image = reduce(concat_img, images) if len(images) > 1 else images[0]
|
|
json_result["image"] = combined_image
|
|
json_result["doc_type_kwd"] = (
|
|
"text"
|
|
if flatten_media_to_text or json_result.get("image") is None
|
|
else "image"
|
|
)
|
|
json_results.append(json_result)
|
|
|
|
for table in tables:
|
|
table_text = table[0][1] if table and table[0] else ""
|
|
if table_text:
|
|
json_results.append(
|
|
{
|
|
"text": table_text,
|
|
"doc_type_kwd": "text" if flatten_media_to_text else "table",
|
|
}
|
|
)
|
|
|
|
enhance_media_sections_with_vision(
|
|
json_results,
|
|
self._canvas._tenant_id,
|
|
conf.get("vlm"),
|
|
callback=self.callback,
|
|
)
|
|
self.set_output("json", json_results)
|
|
else:
|
|
texts = [section_text for section_text, _ in sections if section_text]
|
|
texts.extend(table[0][1] for table in tables if table and table[0] and table[0][1])
|
|
self.set_output("text", "\n".join(texts))
|
|
|
|
def _code(self, name, blob, **kwargs):
|
|
"""Parse text and source code files as plain text chunks."""
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on a text or code file.")
|
|
conf = self._param.setups["text&code"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
|
|
sections = TxtParser()(
|
|
name,
|
|
blob,
|
|
conf.get("chunk_token_num", 128),
|
|
conf.get("delimiter", "\n!?;。；！？"),
|
|
)
|
|
if conf.get("output_format") == "json":
|
|
self.set_output("json", [{"text": section[0], "doc_type_kwd": "text"} for section in sections if section[0]])
|
|
return
|
|
|
|
self.set_output("text", "\n".join([section[0] for section in sections if section[0]]))
|
|
|
|
def _html(self, name, blob, **kwargs):
|
|
"""Parse HTML files into text/json sections."""
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on an HTML document.")
|
|
conf = self._param.setups["html"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
|
|
if conf.get("remove_header_footer"):
|
|
blob = remove_header_footer_html_blob(blob)
|
|
|
|
sections = HtmlParser()(name, blob, int(conf.get("chunk_token_num", 512)))
|
|
if conf.get("remove_toc"):
|
|
sections, _ = remove_toc(sections)
|
|
if conf.get("output_format") == "json":
|
|
self.set_output("json", [{"text": section, "doc_type_kwd": "text"} for section in sections if section])
|
|
return
|
|
|
|
self.set_output("text", "\n".join([section for section in sections if section]))
|
|
|
|
def _image(self, name, blob, **kwargs):
|
|
"""Parse images with OCR or image-to-text models."""
|
|
from deepdoc.vision import OCR
|
|
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on an image.")
|
|
conf = self._param.setups["image"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
|
|
img = Image.open(io.BytesIO(blob)).convert("RGB")
|
|
|
|
if conf["parse_method"] == "ocr":
|
|
# use ocr, recognize chars only
|
|
ocr = OCR()
|
|
bxs = ocr(np.array(img)) # return boxes and recognize result
|
|
txt = "\n".join([t[0] for _, t in bxs if t[0]])
|
|
else:
|
|
lang = conf["lang"]
|
|
# use VLM to describe the picture
|
|
cv_model_config = get_model_config_by_type_and_name(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, conf["parse_method"])
|
|
cv_model = LLMBundle(self._canvas.get_tenant_id(), cv_model_config, lang=lang)
|
|
img_binary = io.BytesIO()
|
|
img.save(img_binary, format="JPEG")
|
|
img_binary.seek(0)
|
|
|
|
system_prompt = conf.get("system_prompt")
|
|
if system_prompt:
|
|
txt = cv_model.describe_with_prompt(img_binary.read(), system_prompt)
|
|
else:
|
|
txt = cv_model.describe(img_binary.read())
|
|
|
|
json_result = [
|
|
{
|
|
"text": txt,
|
|
"image": img,
|
|
"doc_type_kwd": "image",
|
|
}
|
|
]
|
|
self.set_output("json", json_result)
|
|
|
|
def _audio(self, name, blob, **kwargs):
|
|
"""Parse audio files with speech-to-text models."""
|
|
import os
|
|
import tempfile
|
|
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on an audio.")
|
|
|
|
conf = self._param.setups["audio"]
|
|
vlm = conf.get("vlm")
|
|
self.set_output("output_format", conf["output_format"])
|
|
_, ext = os.path.splitext(name)
|
|
with tempfile.NamedTemporaryFile(suffix=ext) as tmpf:
|
|
tmpf.write(blob)
|
|
tmpf.flush()
|
|
tmp_path = os.path.abspath(tmpf.name)
|
|
seq2txt_model_config = get_model_config_by_type_and_name(self._canvas.get_tenant_id(), LLMType.SPEECH2TEXT, vlm["llm_id"])
|
|
seq2txt_mdl = LLMBundle(self._canvas.get_tenant_id(), seq2txt_model_config)
|
|
txt = seq2txt_mdl.transcription(tmp_path)
|
|
|
|
self.set_output("text", txt)
|
|
|
|
def _video(self, name, blob, **kwargs):
|
|
"""Parse video files with image-to-text models."""
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on an video.")
|
|
|
|
conf = self._param.setups["video"]
|
|
vlm = conf.get("vlm")
|
|
self.set_output("output_format", conf["output_format"])
|
|
cv_model_config = get_model_config_by_type_and_name(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, vlm["llm_id"])
|
|
cv_mdl = LLMBundle(self._canvas.get_tenant_id(), cv_model_config)
|
|
video_prompt = str(conf.get("prompt", "") or "")
|
|
txt = asyncio.run(cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=blob, filename=name, video_prompt=video_prompt))
|
|
|
|
self.set_output("text", txt)
|
|
|
|
def _email(self, name, blob, **kwargs):
|
|
"""Parse eml/msg files into structured email content."""
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on an email.")
|
|
|
|
email_content = {}
|
|
conf = self._param.setups["email"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
target_fields = conf["fields"]
|
|
|
|
_, ext = os.path.splitext(name)
|
|
if ext == ".eml":
|
|
# handle eml file
|
|
from email import policy
|
|
from email.parser import BytesParser
|
|
|
|
msg = BytesParser(policy=policy.default).parse(io.BytesIO(blob))
|
|
email_content["metadata"] = {}
|
|
# handle header info
|
|
for header, value in msg.items():
|
|
# get fields like from, to, cc, bcc, date, subject
|
|
if header.lower() in target_fields:
|
|
email_content[header.lower()] = value
|
|
# get metadata
|
|
elif header.lower() not in ["from", "to", "cc", "bcc", "date", "subject"]:
|
|
email_content["metadata"][header.lower()] = value
|
|
# get body
|
|
if "body" in target_fields:
|
|
body_text, body_html = [], []
|
|
|
|
def _add_content(m, content_type):
|
|
def _decode_payload(payload, charset, target_list):
|
|
try:
|
|
target_list.append(payload.decode(charset))
|
|
except (UnicodeDecodeError, LookupError):
|
|
for enc in ["utf-8", "gb2312", "gbk", "gb18030", "latin1"]:
|
|
try:
|
|
target_list.append(payload.decode(enc))
|
|
break
|
|
except UnicodeDecodeError:
|
|
continue
|
|
else:
|
|
target_list.append(payload.decode("utf-8", errors="ignore"))
|
|
|
|
if content_type == "text/plain":
|
|
payload = msg.get_payload(decode=True)
|
|
charset = msg.get_content_charset() or "utf-8"
|
|
_decode_payload(payload, charset, body_text)
|
|
elif content_type == "text/html":
|
|
payload = msg.get_payload(decode=True)
|
|
charset = msg.get_content_charset() or "utf-8"
|
|
_decode_payload(payload, charset, body_html)
|
|
elif "multipart" in content_type:
|
|
if m.is_multipart():
|
|
for part in m.iter_parts():
|
|
_add_content(part, part.get_content_type())
|
|
|
|
_add_content(msg, msg.get_content_type())
|
|
|
|
email_content["text"] = "\n".join(body_text)
|
|
email_content["text_html"] = "\n".join(body_html)
|
|
# get attachment
|
|
if "attachments" in target_fields:
|
|
attachments = []
|
|
for part in msg.iter_attachments():
|
|
content_disposition = part.get("Content-Disposition")
|
|
if content_disposition:
|
|
dispositions = content_disposition.strip().split(";")
|
|
if dispositions[0].lower() == "attachment":
|
|
filename = part.get_filename()
|
|
payload = part.get_payload(decode=True).decode(part.get_content_charset())
|
|
attachments.append(
|
|
{
|
|
"filename": filename,
|
|
"payload": payload,
|
|
}
|
|
)
|
|
email_content["attachments"] = attachments
|
|
else:
|
|
# handle msg file
|
|
import extract_msg
|
|
|
|
msg = extract_msg.Message(blob)
|
|
# handle header info
|
|
basic_content = {
|
|
"from": msg.sender,
|
|
"to": msg.to,
|
|
"cc": msg.cc,
|
|
"bcc": msg.bcc,
|
|
"date": msg.date,
|
|
"subject": msg.subject,
|
|
}
|
|
email_content.update({k: v for k, v in basic_content.items() if k in target_fields})
|
|
# get metadata
|
|
email_content["metadata"] = {
|
|
"message_id": msg.messageId,
|
|
"in_reply_to": msg.inReplyTo,
|
|
}
|
|
# get body
|
|
if "body" in target_fields:
|
|
email_content["text"] = msg.body[0] if isinstance(msg.body, list) and msg.body else msg.body
|
|
if not email_content["text"] and msg.htmlBody:
|
|
email_content["text"] = msg.htmlBody[0] if isinstance(msg.htmlBody, list) and msg.htmlBody else msg.htmlBody
|
|
# get attachments
|
|
if "attachments" in target_fields:
|
|
attachments = []
|
|
for t in msg.attachments:
|
|
attachments.append(
|
|
{
|
|
"filename": t.name,
|
|
"payload": t.data.decode("utf-8"),
|
|
}
|
|
)
|
|
email_content["attachments"] = attachments
|
|
|
|
if conf["output_format"] == "json":
|
|
email_content["doc_type_kwd"] = "text"
|
|
self.set_output("json", [email_content])
|
|
else:
|
|
content_txt = ""
|
|
for k, v in email_content.items():
|
|
if isinstance(v, str):
|
|
# basic info
|
|
content_txt += f"{k}:{v}" + "\n"
|
|
elif isinstance(v, dict):
|
|
# metadata
|
|
content_txt += f"{k}:{json.dumps(v)}" + "\n"
|
|
elif isinstance(v, list):
|
|
# attachments or others
|
|
for fb in v:
|
|
if isinstance(fb, dict):
|
|
# attachments
|
|
content_txt += f"{fb['filename']}:{fb['payload']}" + "\n"
|
|
else:
|
|
# str, usually plain text
|
|
content_txt += fb
|
|
self.set_output("text", content_txt)
|
|
|
|
def _epub(self, name, blob, **kwargs):
|
|
"""Parse EPUB files into text/json sections."""
|
|
from deepdoc.parser import EpubParser
|
|
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on an EPUB.")
|
|
conf = self._param.setups["epub"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
|
|
epub_parser = EpubParser()
|
|
sections = epub_parser(name, binary=blob)
|
|
|
|
if conf.get("output_format") == "json":
|
|
json_results = [{"text": s, "doc_type_kwd": "text"} for s in sections if s]
|
|
self.set_output("json", json_results)
|
|
else:
|
|
self.set_output("text", "\n".join(s for s in sections if s))
|
|
|
|
async def _invoke(self, **kwargs):
|
|
"""Dispatch the current file to the matching parser branch by suffix."""
|
|
function_map = {
|
|
"pdf": self._pdf,
|
|
"markdown": self._markdown,
|
|
"text&code": self._code,
|
|
"html": self._html,
|
|
"spreadsheet": self._spreadsheet,
|
|
"slides": self._slides,
|
|
"doc": self._doc,
|
|
"docx": self._docx,
|
|
"image": self._image,
|
|
"audio": self._audio,
|
|
"video": self._video,
|
|
"email": self._email,
|
|
"epub": self._epub,
|
|
}
|
|
|
|
try:
|
|
from_upstream = ParserFromUpstream.model_validate(kwargs)
|
|
except Exception as e:
|
|
self.set_output("_ERROR", f"Input error: {str(e)}")
|
|
return
|
|
|
|
name = from_upstream.name
|
|
if self._canvas._doc_id:
|
|
b, n = File2DocumentService.get_storage_address(doc_id=self._canvas._doc_id)
|
|
blob = settings.STORAGE_IMPL.get(b, n)
|
|
else:
|
|
blob = FileService.get_blob(from_upstream.file["created_by"], from_upstream.file["id"])
|
|
|
|
done = False
|
|
for p_type, conf in self._param.setups.items():
|
|
if from_upstream.name.split(".")[-1].lower() not in conf.get("suffix", []):
|
|
continue
|
|
call_kwargs = dict(kwargs)
|
|
call_kwargs.pop("name", None)
|
|
call_kwargs.pop("blob", None)
|
|
|
|
await thread_pool_exec(function_map[p_type], name, blob, **call_kwargs)
|
|
done = True
|
|
break
|
|
|
|
if not done:
|
|
raise Exception("No suitable for file extension: `.%s`" % from_upstream.name.split(".")[-1].lower())
|
|
|
|
outs = self.output()
|
|
tasks = []
|
|
for d in outs.get("json", []):
|
|
tasks.append(asyncio.create_task(image2id(d, partial(settings.STORAGE_IMPL.put, tenant_id=self._canvas._tenant_id), get_uuid())))
|
|
|
|
try:
|
|
await asyncio.gather(*tasks, return_exceptions=False)
|
|
except Exception as e:
|
|
logging.error("Error while parsing: %s" % e)
|
|
for t in tasks:
|
|
t.cancel()
|
|
await asyncio.gather(*tasks, return_exceptions=True)
|
|
raise
|