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2f768b96e8
### What problem does this PR solve? When parsing documents containing images, the current code uses a single-threaded approach to call the VL model, resulting in extremely slow parsing speed (e.g., parsing a Word document with dozens of images takes over 20 minutes). By switching to a multithreaded approach to call the VL model, the parsing speed can be improved to an acceptable level. ### Type of change - [x] Performance Improvement --------- Co-authored-by: liuzhenghua-jk <liuzhenghua-jk@360shuke.com>
98 lines
3.7 KiB
Python
98 lines
3.7 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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#
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from PIL import Image
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from rag.app.picture import vision_llm_chunk as picture_vision_llm_chunk
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from rag.prompts import vision_llm_figure_describe_prompt
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def vision_figure_parser_figure_data_wraper(figures_data_without_positions):
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return [(
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(figure_data[1], [figure_data[0]]),
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[(0, 0, 0, 0, 0)]
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) for figure_data in figures_data_without_positions if isinstance(figure_data[1], Image.Image)]
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shared_executor = ThreadPoolExecutor(max_workers=10)
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class VisionFigureParser:
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def __init__(self, vision_model, figures_data, *args, **kwargs):
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self.vision_model = vision_model
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self._extract_figures_info(figures_data)
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assert len(self.figures) == len(self.descriptions)
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assert not self.positions or (len(self.figures) == len(self.positions))
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def _extract_figures_info(self, figures_data):
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self.figures = []
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self.descriptions = []
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self.positions = []
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for item in figures_data:
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# position
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if len(item) == 2 and isinstance(item[1], list) and len(item[1]) == 1 and isinstance(item[1][0], tuple) and len(item[1][0]) == 5:
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img_desc = item[0]
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assert len(img_desc) == 2 and isinstance(img_desc[0], Image.Image) and isinstance(img_desc[1], list), "Should be (figure, [description])"
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self.figures.append(img_desc[0])
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self.descriptions.append(img_desc[1])
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self.positions.append(item[1])
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else:
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assert len(item) == 2 and isinstance(item, tuple) and isinstance(item[1], list), f"get {len(item)=}, {item=}"
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self.figures.append(item[0])
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self.descriptions.append(item[1])
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def _assemble(self):
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self.assembled = []
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self.has_positions = len(self.positions) != 0
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for i in range(len(self.figures)):
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figure = self.figures[i]
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desc = self.descriptions[i]
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pos = self.positions[i] if self.has_positions else None
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figure_desc = (figure, desc)
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if pos is not None:
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self.assembled.append((figure_desc, pos))
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else:
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self.assembled.append((figure_desc,))
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return self.assembled
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def __call__(self, **kwargs):
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callback = kwargs.get("callback", lambda prog, msg: None)
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def process(figure_idx, figure_binary):
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description_text = picture_vision_llm_chunk(
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binary=figure_binary,
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vision_model=self.vision_model,
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prompt=vision_llm_figure_describe_prompt(),
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callback=callback,
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)
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return figure_idx, description_text
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futures = []
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for idx, img_binary in enumerate(self.figures or []):
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futures.append(shared_executor.submit(process, idx, img_binary))
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for future in as_completed(futures):
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figure_num, txt = future.result()
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if txt:
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self.descriptions[figure_num] = txt + "\n".join(self.descriptions[figure_num])
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self._assemble()
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return self.assembled
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