mirror of
https://github.com/infiniflow/ragflow.git
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7c20c964b4
### What problem does this PR solve? Fix incorrect image merging for naive markdown parser. #9349 [ragflow_readme.webm](https://github.com/user-attachments/assets/ca3f1e18-72b6-4a4c-80db-d03da9adf8dc) ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
747 lines
30 KiB
Python
747 lines
30 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 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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import trio
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import numpy as np
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from PIL import Image
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from common.constants import LLMType
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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 common.misc_utils import get_uuid
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from rag.utils.base64_image import image2id
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from deepdoc.parser import ExcelParser
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from deepdoc.parser.mineru_parser import MinerUParser
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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.schema import ParserFromUpstream
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from rag.llm.cv_model import Base as VLM
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from common import settings
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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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"word": [
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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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"text"
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],
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"email": ["text", "json"],
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"text&markdown": [
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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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}
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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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"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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"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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"word": {
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"suffix": [
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"doc",
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"docx",
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],
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"output_format": "json",
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},
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"text&markdown": {
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"suffix": ["md", "markdown", "mdx", "txt"],
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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": "text",
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},
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"email": {
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"suffix": [
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"eml", "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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},
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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", "tcadp parser"]:
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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("word", "")
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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, "Word processer document output format abnormal.", self.allowed_output_format["word"])
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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("text&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, "Text output format abnormal.", self.allowed_output_format["text&markdown"])
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audio_config = self.setups.get("audio", "")
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if audio_config:
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self.check_empty(audio_config.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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self.check_empty(video_config.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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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):
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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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if conf.get("parse_method").lower() == "deepdoc":
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bboxes = RAGFlowPdfParser().parse_into_bboxes(blob, callback=self.callback)
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elif conf.get("parse_method").lower() == "plain_text":
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lines, _ = PlainParser()(blob)
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bboxes = [{"text": t} for t, _ in lines]
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elif conf.get("parse_method").lower() == "mineru":
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mineru_executable = os.environ.get("MINERU_EXECUTABLE", "mineru")
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mineru_api = os.environ.get("MINERU_APISERVER", "http://host.docker.internal:9987")
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pdf_parser = MinerUParser(mineru_path=mineru_executable, mineru_api=mineru_api)
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ok, reason = pdf_parser.check_installation()
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if not ok:
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raise RuntimeError(f"MinerU not found or server not accessible: {reason}. Please install it via: pip install -U 'mineru[core]'.")
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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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output_dir=os.environ.get("MINERU_OUTPUT_DIR", ""),
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delete_output=bool(int(os.environ.get("MINERU_DELETE_OUTPUT", 1))),
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)
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bboxes = []
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for t, poss in lines:
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box = {
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"image": pdf_parser.crop(poss, 1),
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"positions": [[pos[0][-1], *pos[1:]] for pos in pdf_parser.extract_positions(poss)],
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"text": t,
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}
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bboxes.append(box)
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elif conf.get("parse_method").lower() == "tcadp parser":
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# ADP is a document parsing tool using Tencent Cloud API
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table_result_type = conf.get("table_result_type", "1")
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markdown_image_response_type = conf.get("markdown_image_response_type", "1")
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tcadp_parser = TCADPParser(
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table_result_type=table_result_type,
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markdown_image_response_type=markdown_image_response_type
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)
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sections, _ = tcadp_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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file_type="PDF",
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file_start_page=1,
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file_end_page=1000
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)
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bboxes = []
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for section, position_tag in sections:
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if position_tag:
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# Extract position information from TCADP's position tag
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# Format: @@{page_number}\t{x0}\t{x1}\t{top}\t{bottom}##
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import re
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match = re.match(r"@@([0-9-]+)\t([0-9.]+)\t([0-9.]+)\t([0-9.]+)\t([0-9.]+)##", position_tag)
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if match:
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pn, x0, x1, top, bott = match.groups()
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bboxes.append({
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"page_number": int(pn.split('-')[0]), # Take the first page number
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"x0": float(x0),
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"x1": float(x1),
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"top": float(top),
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"bottom": float(bott),
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"text": section
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})
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else:
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# If no position info, add as text without position
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bboxes.append({"text": section})
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else:
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bboxes.append({"text": section})
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else:
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vision_model = LLMBundle(self._canvas._tenant_id, LLMType.IMAGE2TEXT, llm_name=conf.get("parse_method"), lang=self._param.setups["pdf"].get("lang"))
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lines, _ = VisionParser(vision_model=vision_model)(blob, callback=self.callback)
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bboxes = []
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for t, poss in lines:
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for pn, x0, x1, top, bott in RAGFlowPdfParser.extract_positions(poss):
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bboxes.append({"page_number": int(pn[0]), "x0": float(x0), "x1": float(x1), "top": float(top), "bottom": float(bott), "text": t})
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if conf.get("output_format") == "json":
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self.set_output("json", bboxes)
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if conf.get("output_format") == "markdown":
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mkdn = ""
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for b in bboxes:
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if b.get("layout_type", "") == "title":
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mkdn += "\n## "
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if b.get("layout_type", "") == "figure":
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mkdn += "\n".format(VLM.image2base64(b["image"]))
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continue
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mkdn += b.get("text", "") + "\n"
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self.set_output("markdown", mkdn)
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def _spreadsheet(self, name, blob):
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a Spreadsheet.")
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conf = self._param.setups["spreadsheet"]
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self.set_output("output_format", conf["output_format"])
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parse_method = conf.get("parse_method", "deepdoc")
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# Handle TCADP parser
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if parse_method.lower() == "tcadp parser":
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table_result_type = conf.get("table_result_type", "1")
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markdown_image_response_type = conf.get("markdown_image_response_type", "1")
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tcadp_parser = TCADPParser(
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table_result_type=table_result_type,
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markdown_image_response_type=markdown_image_response_type
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)
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if not tcadp_parser.check_installation():
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raise RuntimeError("TCADP parser not available. Please check Tencent Cloud API configuration.")
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# Determine file type based on extension
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if re.search(r"\.xlsx?$", name, re.IGNORECASE):
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file_type = "XLSX"
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else:
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file_type = "CSV"
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self.callback(0.2, f"Using TCADP parser for {file_type} file.")
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sections, tables = tcadp_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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file_type=file_type,
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file_start_page=1,
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file_end_page=1000
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)
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# Process TCADP parser output based on configured output_format
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output_format = conf.get("output_format", "html")
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if output_format == "html":
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# For HTML output, combine sections and tables into HTML
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html_content = ""
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for section, position_tag in sections:
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if section:
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html_content += section + "\n"
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for table in tables:
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if table:
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html_content += table + "\n"
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self.set_output("html", html_content)
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elif output_format == "json":
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# For JSON output, create a list of text items
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result = []
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# Add sections as text
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for section, position_tag in sections:
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if section:
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result.append({"text": section})
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# Add tables as text
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for table in tables:
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if table:
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result.append({"text": table})
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self.set_output("json", result)
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elif output_format == "markdown":
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# For markdown output, combine into markdown
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md_content = ""
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for section, position_tag in sections:
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if section:
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md_content += section + "\n\n"
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for table in tables:
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if table:
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md_content += table + "\n\n"
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self.set_output("markdown", md_content)
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else:
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# Default DeepDOC parser
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spreadsheet_parser = ExcelParser()
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if conf.get("output_format") == "html":
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htmls = spreadsheet_parser.html(blob, 1000000000)
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self.set_output("html", htmls[0])
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elif conf.get("output_format") == "json":
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self.set_output("json", [{"text": txt} for txt in spreadsheet_parser(blob) if txt])
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elif conf.get("output_format") == "markdown":
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self.set_output("markdown", spreadsheet_parser.markdown(blob))
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def _word(self, name, blob):
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a Word Processor Document")
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conf = self._param.setups["word"]
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self.set_output("output_format", conf["output_format"])
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docx_parser = Docx()
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if conf.get("output_format") == "json":
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sections, tbls = docx_parser(name, binary=blob)
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sections = [{"text": section[0], "image": section[1]} for section in sections if section]
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sections.extend([{"text": tb, "image": None} for ((_,tb), _) in tbls])
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self.set_output("json", sections)
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elif conf.get("output_format") == "markdown":
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markdown_text = docx_parser.to_markdown(name, binary=blob)
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self.set_output("markdown", markdown_text)
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def _slides(self, name, blob):
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a PowerPoint Document")
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conf = self._param.setups["slides"]
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self.set_output("output_format", conf["output_format"])
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parse_method = conf.get("parse_method", "deepdoc")
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# Handle TCADP parser
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if parse_method.lower() == "tcadp parser":
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table_result_type = conf.get("table_result_type", "1")
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markdown_image_response_type = conf.get("markdown_image_response_type", "1")
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tcadp_parser = TCADPParser(
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table_result_type=table_result_type,
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markdown_image_response_type=markdown_image_response_type
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)
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if not tcadp_parser.check_installation():
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raise RuntimeError("TCADP parser not available. Please check Tencent Cloud API configuration.")
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# Determine file type based on extension
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if re.search(r"\.pptx?$", name, re.IGNORECASE):
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file_type = "PPTX"
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else:
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file_type = "PPT"
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self.callback(0.2, f"Using TCADP parser for {file_type} file.")
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sections, tables = tcadp_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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file_type=file_type,
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file_start_page=1,
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file_end_page=1000
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)
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# Process TCADP parser output - PPT only supports json format
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output_format = conf.get("output_format", "json")
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if output_format == "json":
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# For JSON output, create a list of text items
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result = []
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# Add sections as text
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for section, position_tag in sections:
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if section:
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result.append({"text": section})
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# Add tables as text
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for table in tables:
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if table:
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result.append({"text": table})
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self.set_output("json", result)
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else:
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# Default DeepDOC parser (supports .pptx format)
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from deepdoc.parser.ppt_parser import RAGFlowPptParser as ppt_parser
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ppt_parser = ppt_parser()
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txts = ppt_parser(blob, 0, 100000, None)
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sections = [{"text": section} for section in txts if section.strip()]
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# json
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assert conf.get("output_format") == "json", "have to be json for ppt"
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if conf.get("output_format") == "json":
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self.set_output("json", sections)
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def _markdown(self, name, blob):
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from functools import reduce
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from rag.app.naive import Markdown as naive_markdown_parser
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from rag.nlp import concat_img
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|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on a markdown.")
|
|
conf = self._param.setups["text&markdown"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
|
|
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_results.append(json_result)
|
|
|
|
self.set_output("json", json_results)
|
|
else:
|
|
self.set_output("text", "\n".join([section_text for section_text, _ in sections]))
|
|
|
|
|
|
def _image(self, name, blob):
|
|
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 = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, llm_name=conf["parse_method"], 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())
|
|
|
|
self.set_output("text", txt)
|
|
|
|
def _audio(self, name, blob):
|
|
import os
|
|
import tempfile
|
|
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on an audio.")
|
|
|
|
conf = self._param.setups["audio"]
|
|
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_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.SPEECH2TEXT)
|
|
txt = seq2txt_mdl.transcription(tmp_path)
|
|
|
|
self.set_output("text", txt)
|
|
|
|
def _video(self, name, blob):
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to work on an video.")
|
|
|
|
conf = self._param.setups["video"]
|
|
self.set_output("output_format", conf["output_format"])
|
|
|
|
cv_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, llm_name=conf["llm_id"])
|
|
txt = cv_mdl.chat(system="", history=[], gen_conf={}, video_bytes=blob, filename=name)
|
|
|
|
self.set_output("text", txt)
|
|
|
|
def _email(self, name, blob):
|
|
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
|
|
print("handle a msg file.")
|
|
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":
|
|
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)
|
|
|
|
async def _invoke(self, **kwargs):
|
|
function_map = {
|
|
"pdf": self._pdf,
|
|
"text&markdown": self._markdown,
|
|
"spreadsheet": self._spreadsheet,
|
|
"slides": self._slides,
|
|
"word": self._word,
|
|
"image": self._image,
|
|
"audio": self._audio,
|
|
"video": self._video,
|
|
"email": self._email,
|
|
}
|
|
|
|
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
|
|
await trio.to_thread.run_sync(function_map[p_type], name, blob)
|
|
done = True
|
|
break
|
|
|
|
if not done:
|
|
raise Exception("No suitable for file extension: `.%s`" % from_upstream.name.split(".")[-1].lower())
|
|
|
|
outs = self.output()
|
|
async with trio.open_nursery() as nursery:
|
|
for d in outs.get("json", []):
|
|
nursery.start_soon(image2id, d, partial(settings.STORAGE_IMPL.put, tenant_id=self._canvas._tenant_id), get_uuid())
|