mirror of
https://github.com/infiniflow/ragflow.git
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d353f7f7f8
### What problem does this PR solve? Dataflow support audio. And fix giteeAI's sequence2text model. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [x] New Feature (non-breaking change which adds functionality)
388 lines
14 KiB
Python
388 lines
14 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 logging
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import random
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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 api.db import LLMType
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from api.db.services.llm_service import LLMBundle
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from deepdoc.parser import ExcelParser
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from deepdoc.parser.pdf_parser import PlainParser, RAGFlowPdfParser, VisionParser
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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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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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],
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"ppt": [],
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"image": [
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"text"
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],
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"email": [],
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"text": [
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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/vlm
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"llm_id": "",
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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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"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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"markdown": {
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"suffix": ["md", "markdown"],
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"output_format": "json",
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},
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"ppt": {},
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"image": {
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"parse_method": ["ocr", "vlm"],
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"llm_id": "",
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"lang": "Chinese",
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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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"text": {
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"suffix": [
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"txt"
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],
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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": "json",
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},
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"video": {},
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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_valid_value(pdf_parse_method.lower(), "Parse method abnormal.", ["deepdoc", "plain_text", "vlm"])
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if pdf_parse_method not in ["deepdoc", "plain_text"]:
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self.check_empty(pdf_config.get("llm_id"), "VLM")
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pdf_language = pdf_config.get("lang", "")
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self.check_empty(pdf_language, "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, "Word processer document output format abnormal.", self.allowed_output_format["doc"])
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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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self.check_valid_value(image_parse_method.lower(), "Parse method abnormal.", ["ocr", "vlm"])
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if image_parse_method not in ["ocr"]:
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self.check_empty(image_config.get("llm_id"), "VLM")
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image_language = image_config.get("lang", "")
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self.check_empty(image_language, "Language")
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text_config = self.setups.get("text", "")
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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"])
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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"), "VLM")
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audio_language = audio_config.get("lang", "")
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self.check_empty(audio_language, "Language")
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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, from_upstream: ParserFromUpstream):
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a PDF.")
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blob = from_upstream.blob
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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") == "deepdoc":
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bboxes = RAGFlowPdfParser().parse_into_bboxes(blob, callback=self.callback)
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elif conf.get("parse_method") == "plain_text":
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lines, _ = PlainParser()(blob)
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bboxes = [{"text": t} for t, _ in lines]
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else:
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assert conf.get("llm_id")
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vision_model = LLMBundle(self._canvas._tenant_id, LLMType.IMAGE2TEXT, llm_name=conf.get("llm_id"), 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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pn, x0, x1, top, bott = poss.split(" ")
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bboxes.append({"page_number": int(pn), "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, from_upstream: ParserFromUpstream):
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a Spreadsheet.")
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blob = from_upstream.blob
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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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print("spreadsheet {conf=}", flush=True)
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spreadsheet_parser = ExcelParser()
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if conf.get("output_format") == "html":
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html = spreadsheet_parser.html(blob, 1000000000)
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self.set_output("html", html)
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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, from_upstream: ParserFromUpstream):
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from tika import parser as word_parser
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a Word Processor Document")
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blob = from_upstream.blob
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name = from_upstream.name
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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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print("word {conf=}", flush=True)
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doc_parsed = word_parser.from_buffer(blob)
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sections = []
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if doc_parsed.get("content"):
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sections = doc_parsed["content"].split("\n")
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sections = [{"text": section} for section in sections if section]
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else:
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logging.warning(f"tika.parser got empty content from {name}.")
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# json
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assert conf.get("output_format") == "json", "have to be json for doc"
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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, from_upstream: ParserFromUpstream):
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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.")
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blob = from_upstream.blob
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name = from_upstream.name
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conf = self._param.setups["markdown"]
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self.set_output("output_format", conf["output_format"])
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markdown_parser = naive_markdown_parser()
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sections, tables = markdown_parser(name, blob, separate_tables=False)
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# json
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assert conf.get("output_format") == "json", "have to be json for doc"
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if conf.get("output_format") == "json":
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json_results = []
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for section_text, _ in sections:
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json_result = {
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"text": section_text,
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}
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images = markdown_parser.get_pictures(section_text) if section_text else None
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if images:
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# If multiple images found, combine them using concat_img
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combined_image = reduce(concat_img, images) if len(images) > 1 else images[0]
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json_result["image"] = combined_image
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json_results.append(json_result)
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self.set_output("json", json_results)
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def _text(self, from_upstream: ParserFromUpstream):
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from deepdoc.parser.utils import get_text
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self.callback(random.randint(1, 5) / 100.0, "Start to work on a text.")
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blob = from_upstream.blob
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name = from_upstream.name
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conf = self._param.setups["text"]
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self.set_output("output_format", conf["output_format"])
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# parse binary to text
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text_content = get_text(name, binary=blob)
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if conf.get("output_format") == "json":
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result = [{"text": text_content}]
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self.set_output("json", result)
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else:
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result = text_content
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self.set_output("text", result)
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def _image(self, from_upstream: ParserFromUpstream):
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from deepdoc.vision import OCR
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self.callback(random.randint(1, 5) / 100.0, "Start to work on an image.")
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blob = from_upstream.blob
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conf = self._param.setups["image"]
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self.set_output("output_format", conf["output_format"])
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img = Image.open(io.BytesIO(blob)).convert("RGB")
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lang = conf["lang"]
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if conf["parse_method"] == "ocr":
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# use ocr, recognize chars only
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ocr = OCR()
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bxs = ocr(np.array(img)) # return boxes and recognize result
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txt = "\n".join([t[0] for _, t in bxs if t[0]])
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else:
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# use VLM to describe the picture
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cv_model = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, llm_name=conf["llm_id"],lang=lang)
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img_binary = io.BytesIO()
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img.save(img_binary, format="JPEG")
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img_binary.seek(0)
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txt = cv_model.describe(img_binary.read())
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self.set_output("text", txt)
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def _audio(self, from_upstream: ParserFromUpstream):
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import os
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import tempfile
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self.callback(random.randint(1, 5) / 100.0, "Start to work on an audio.")
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blob = from_upstream.blob
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name = from_upstream.name
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conf = self._param.setups["audio"]
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self.set_output("output_format", conf["output_format"])
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lang = conf["lang"]
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_, ext = os.path.splitext(name)
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tmp_path = ""
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with tempfile.NamedTemporaryFile(suffix=ext) as tmpf:
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tmpf.write(blob)
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tmpf.flush()
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tmp_path = os.path.abspath(tmpf.name)
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seq2txt_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.SPEECH2TEXT, lang=lang)
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txt = seq2txt_mdl.transcription(tmp_path)
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self.set_output("text", txt)
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async def _invoke(self, **kwargs):
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function_map = {
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"pdf": self._pdf,
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"markdown": self._markdown,
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"spreadsheet": self._spreadsheet,
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"word": self._word,
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"text": self._text,
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"image": self._image,
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"audio": self._audio,
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}
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try:
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from_upstream = ParserFromUpstream.model_validate(kwargs)
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except Exception as e:
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self.set_output("_ERROR", f"Input error: {str(e)}")
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return
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for p_type, conf in self._param.setups.items():
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if from_upstream.name.split(".")[-1].lower() not in conf.get("suffix", []):
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continue
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await trio.to_thread.run_sync(function_map[p_type], from_upstream)
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break
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