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https://github.com/RYDE-WORK/Langchain-Chatchat.git
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9c525b7fa5
新功能： - 优化 PDF 文件的 OCR，过滤无意义的小图片 by @liunux4odoo #2525 - 支持 Gemini 在线模型 by @yhfgyyf #2630 - 支持 GLM4 在线模型 by @zRzRzRzRzRzRzR - elasticsearch更新https连接 by @xldistance #2390 - 增强对PPT、DOC知识库文件的OCR识别 by @596192804 #2013 - 更新 Agent 对话功能 by @zRzRzRzRzRzRzR - 每次创建对象时从连接池获取连接，避免每次执行方法时都新建连接 by @Lijia0 #2480 - 实现 ChatOpenAI 判断token有没有超过模型的context上下文长度 by @glide-the - 更新运行数据库报错和项目里程碑 by @zRzRzRzRzRzRzR #2659 - 更新配置文件/文档/依赖 by @imClumsyPanda @zRzRzRzRzRzRzR - 添加日文版 readme by @eltociear #2787 修复： - langchain 更新后，PGVector 向量库连接错误 by @HALIndex #2591 - Minimax's model worker 错误 by @xyhshen - ES库无法向量检索.添加mappings创建向量索引 by MSZheng20 #2688
60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
from langchain.document_loaders.unstructured import UnstructuredFileLoader
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from typing import List
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import tqdm
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class RapidOCRPPTLoader(UnstructuredFileLoader):
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def _get_elements(self) -> List:
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def ppt2text(filepath):
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from pptx import Presentation
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from PIL import Image
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import numpy as np
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from io import BytesIO
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from rapidocr_onnxruntime import RapidOCR
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ocr = RapidOCR()
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prs = Presentation(filepath)
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resp = ""
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def extract_text(shape):
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nonlocal resp
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if shape.has_text_frame:
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resp += shape.text.strip() + "\n"
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if shape.has_table:
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for row in shape.table.rows:
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for cell in row.cells:
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for paragraph in cell.text_frame.paragraphs:
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resp += paragraph.text.strip() + "\n"
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if shape.shape_type == 13: # 13 表示图片
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image = Image.open(BytesIO(shape.image.blob))
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result, _ = ocr(np.array(image))
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if result:
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ocr_result = [line[1] for line in result]
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resp += "\n".join(ocr_result)
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elif shape.shape_type == 6: # 6 表示组合
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for child_shape in shape.shapes:
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extract_text(child_shape)
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b_unit = tqdm.tqdm(total=len(prs.slides),
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desc="RapidOCRPPTLoader slide index: 1")
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# 遍历所有幻灯片
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for slide_number, slide in enumerate(prs.slides, start=1):
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b_unit.set_description(
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"RapidOCRPPTLoader slide index: {}".format(slide_number))
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b_unit.refresh()
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sorted_shapes = sorted(slide.shapes,
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key=lambda x: (x.top, x.left)) # 从上到下、从左到右遍历
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for shape in sorted_shapes:
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extract_text(shape)
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b_unit.update(1)
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return resp
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text = ppt2text(self.file_path)
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from unstructured.partition.text import partition_text
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return partition_text(text=text, **self.unstructured_kwargs)
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if __name__ == '__main__':
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loader = RapidOCRPPTLoader(file_path="../tests/samples/ocr_test.pptx")
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docs = loader.load()
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print(docs)
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