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如何加载PDF文件

可移植文档格式 (PDF),标准化为ISO 32000,是由Adobe于1992年开发的一种文件格式,用于以独立于应用软件、硬件和操作系统的方式呈现文档,包括文本格式和图像。

本指南涵盖如何将PDF文档加载到我们下游使用的LangChain 文档格式中。

PDF中的文本通常通过文本框表示。它们也可能包含图像。PDF解析器可能会执行以下某种组合:

  • 通过启发式或机器学习推断将文本框聚合成行、段落和其他结构;
  • 对图像运行光学字符识别 (OCR)以检测其中的文本;
  • 将文本分类为段落、列表、表格或其他结构;
  • 将文本结构化为表格行和列,或键值对。

LangChain与多种PDF解析器集成。一些解析器简单且相对低级;其他解析器将支持OCR和图像处理,或执行高级文档布局分析。正确的选择将取决于您的需求。以下是我们列举的可能性。

我们将在一个示例文件上演示这些方法:

file_path = (
"../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf"
)
A note on multimodal models

许多现代大型语言模型支持对多模态输入(例如图像)的推理。在某些应用中——例如对具有复杂布局、图表或扫描的PDF进行问答——跳过PDF解析可能是有利的,而是将PDF页面转换为图像并直接传递给模型。我们在下面的多模态模型的使用部分演示了一个示例。

简单快速的文本提取

如果您正在寻找嵌入在PDF中的文本的简单字符串表示,下面的方法是合适的。它将返回一个Document对象的列表——每页一个——包含文档的page_content属性中的页面文本的单个字符串。它不会解析图像或扫描PDF页面中的文本。在底层,它使用pypydf Python库。

LangChain 文档加载器实现了lazy_load及其异步变体alazy_load,返回Document对象的迭代器。我们将在下面使用这些。

%pip install -qU pypdf
<!--IMPORTS:[{"imported": "PyPDFLoader", "source": "langchain_community.document_loaders", "docs": "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html", "title": "How to load PDFs"}]-->
from langchain_community.document_loaders import PyPDFLoader

loader = PyPDFLoader(file_path)
pages = []
async for page in loader.alazy_load():
pages.append(page)
print(f"{pages[0].metadata}\n")
print(pages[0].page_content)
{'source': '../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf', 'page': 0}

LayoutParser : A Unified Toolkit for Deep
Learning Based Document Image Analysis
Zejiang Shen1( �), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain
Lee4, Jacob Carlson3, and Weining Li5
1Allen Institute for AI
shannons@allenai.org
2Brown University
ruochen zhang@brown.edu
3Harvard University
{melissadell,jacob carlson }@fas.harvard.edu
4University of Washington
bcgl@cs.washington.edu
5University of Waterloo
w422li@uwaterloo.ca
Abstract. Recent advances in document image analysis (DIA) have been
primarily driven by the application of neural networks. Ideally, research
outcomes could be easily deployed in production and extended for further
investigation. However, various factors like loosely organized codebases
and sophisticated model configurations complicate the easy reuse of im-
portant innovations by a wide audience. Though there have been on-going
efforts to improve reusability and simplify deep learning (DL) model
development in disciplines like natural language processing and computer
vision, none of them are optimized for challenges in the domain of DIA.
This represents a major gap in the existing toolkit, as DIA is central to
academic research across a wide range of disciplines in the social sciences
and humanities. This paper introduces LayoutParser , an open-source
library for streamlining the usage of DL in DIA research and applica-
tions. The core LayoutParser library comes with a set of simple and
intuitive interfaces for applying and customizing DL models for layout de-
tection, character recognition, and many other document processing tasks.
To promote extensibility, LayoutParser also incorporates a community
platform for sharing both pre-trained models and full document digiti-
zation pipelines. We demonstrate that LayoutParser is helpful for both
lightweight and large-scale digitization pipelines in real-word use cases.
The library is publicly available at https://layout-parser.github.io .
Keywords: Document Image Analysis ·Deep Learning ·Layout Analysis
·Character Recognition ·Open Source library ·Toolkit.
1 Introduction
Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of
document image analysis (DIA) tasks including document image classification [ 11,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021

请注意,每个文档的元数据存储了相应的页码。

PDF上的向量搜索

一旦我们将PDF加载到LangChain Document对象中,我们可以以通常的方式对其进行索引(例如,RAG应用)。下面我们使用OpenAI嵌入,尽管任何LangChain 嵌入模型都可以。

%pip install -qU langchain-openai
import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
<!--IMPORTS:[{"imported": "InMemoryVectorStore", "source": "langchain_core.vectorstores", "docs": "https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html", "title": "How to load PDFs"}, {"imported": "OpenAIEmbeddings", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html", "title": "How to load PDFs"}]-->
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings

vector_store = InMemoryVectorStore.from_documents(pages, OpenAIEmbeddings())
docs = vector_store.similarity_search("What is LayoutParser?", k=2)
for doc in docs:
print(f'Page {doc.metadata["page"]}: {doc.page_content[:300]}\n')
Page 13: 14 Z. Shen et al.
6 Conclusion
LayoutParser provides a comprehensive toolkit for deep learning-based document
image analysis. The off-the-shelf library is easy to install, and can be used to
build flexible and accurate pipelines for processing documents with complicated
structures. It also supports hi

Page 0: LayoutParser : A Unified Toolkit for Deep
Learning Based Document Image Analysis
Zejiang Shen1( �), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain
Lee4, Jacob Carlson3, and Weining Li5
1Allen Institute for AI
shannons@allenai.org
2Brown University
ruochen zhang@brown.edu
3Harvard University

布局分析和从图像中提取文本

如果您需要对文本进行更细粒度的分割(例如,分成不同的段落、标题、表格或其他结构)或需要从图像中提取文本,下面的方法是合适的。它将返回一个Document对象的列表,其中每个对象表示页面上的一个结构。文档的元数据存储了页码和与对象相关的其他信息(例如,在表格对象的情况下,它可能存储表格的行和列)。

在底层,它使用langchain-unstructured库。有关如何将Unstructured与LangChain一起使用的更多信息,请参见集成文档

Unstructured支持多个参数用于PDF解析:

  • strategy(例如,"fast""hi-res"
  • API或本地处理。您需要一个API密钥才能使用API。

hi-res策略提供文档布局分析和OCR的支持。我们将在下面通过API演示。有关本地运行时的注意事项,请参见下面的本地解析部分。

%pip install -qU langchain-unstructured
import getpass
import os

if "UNSTRUCTURED_API_KEY" not in os.environ:
os.environ["UNSTRUCTURED_API_KEY"] = getpass.getpass("Unstructured API Key:")
Unstructured API Key: ········

与之前一样,我们初始化一个加载器并懒加载文档:

<!--IMPORTS:[{"imported": "UnstructuredLoader", "source": "langchain_unstructured", "docs": "https://python.langchain.com/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html", "title": "How to load PDFs"}]-->
from langchain_unstructured import UnstructuredLoader

loader = UnstructuredLoader(
file_path=file_path,
strategy="hi_res",
partition_via_api=True,
coordinates=True,
)
docs = []
for doc in loader.lazy_load():
docs.append(doc)
INFO: Preparing to split document for partition.
INFO: Starting page number set to 1
INFO: Allow failed set to 0
INFO: Concurrency level set to 5
INFO: Splitting pages 1 to 16 (16 total)
INFO: Determined optimal split size of 4 pages.
INFO: Partitioning 4 files with 4 page(s) each.
INFO: Partitioning set #1 (pages 1-4).
INFO: Partitioning set #2 (pages 5-8).
INFO: Partitioning set #3 (pages 9-12).
INFO: Partitioning set #4 (pages 13-16).
INFO: HTTP Request: POST https://api.unstructuredapp.io/general/v0/general "HTTP/1.1 200 OK"
INFO: HTTP Request: POST https://api.unstructuredapp.io/general/v0/general "HTTP/1.1 200 OK"
INFO: HTTP Request: POST https://api.unstructuredapp.io/general/v0/general "HTTP/1.1 200 OK"
INFO: HTTP Request: POST https://api.unstructuredapp.io/general/v0/general "HTTP/1.1 200 OK"
INFO: Successfully partitioned set #1, elements added to the final result.
INFO: Successfully partitioned set #2, elements added to the final result.
INFO: Successfully partitioned set #3, elements added to the final result.
INFO: Successfully partitioned set #4, elements added to the final result.

在这16页的文档中,我们恢复了171个不同的结构:

print(len(docs))
171

我们可以使用文档元数据从单个页面恢复内容:

first_page_docs = [doc for doc in docs if doc.metadata.get("page_number") == 1]

for doc in first_page_docs:
print(doc.page_content)
LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis
1 2 0 2 n u J 1 2 ] V C . s c [ 2 v 8 4 3 5 1 . 3 0 1 2 : v i X r a
Zejiang Shen® (<), Ruochen Zhang?, Melissa Dell®, Benjamin Charles Germain Lee?, Jacob Carlson®, and Weining Li®
1 Allen Institute for AI shannons@allenai.org 2 Brown University ruochen zhang@brown.edu 3 Harvard University {melissadell,jacob carlson}@fas.harvard.edu 4 University of Washington bcgl@cs.washington.edu 5 University of Waterloo w422li@uwaterloo.ca
Abstract. Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks. Ideally, research outcomes could be easily deployed in production and extended for further investigation. However, various factors like loosely organized codebases and sophisticated model configurations complicate the easy reuse of im- portant innovations by a wide audience. Though there have been on-going efforts to improve reusability and simplify deep learning (DL) model development in disciplines like natural language processing and computer vision, none of them are optimized for challenges in the domain of DIA. This represents a major gap in the existing toolkit, as DIA is central to academic research across a wide range of disciplines in the social sciences and humanities. This paper introduces LayoutParser, an open-source library for streamlining the usage of DL in DIA research and applica- tions. The core LayoutParser library comes with a set of simple and intuitive interfaces for applying and customizing DL models for layout de- tection, character recognition, and many other document processing tasks. To promote extensibility, LayoutParser also incorporates a community platform for sharing both pre-trained models and full document digiti- zation pipelines. We demonstrate that LayoutParser is helpful for both lightweight and large-scale digitization pipelines in real-word use cases. The library is publicly available at https://layout-parser.github.io.
Keywords: Document Image Analysis · Deep Learning · Layout Analysis · Character Recognition · Open Source library · Toolkit.
1 Introduction
Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of document image analysis (DIA) tasks including document image classification [11,

提取表格和其他结构

我们加载的每个Document代表一个结构,如标题、段落或表格。

某些结构可能对索引或问答任务特别感兴趣。这些结构可能是:

  1. 分类以便于识别;
  2. 解析为更结构化的表示。

下面,我们识别并提取一个表格:

Click to expand code for rendering pages

%pip install -qU matplotlib PyMuPDF pillow

import fitz
import matplotlib.patches as patches
import matplotlib.pyplot as plt
from PIL import Image


def plot_pdf_with_boxes(pdf_page, segments):
pix = pdf_page.get_pixmap()
pil_image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)

fig, ax = plt.subplots(1, figsize=(10, 10))
ax.imshow(pil_image)
categories = set()
category_to_color = {
"Title": "orchid",
"Image": "forestgreen",
"Table": "tomato",
}
for segment in segments:
points = segment["coordinates"]["points"]
layout_width = segment["coordinates"]["layout_width"]
layout_height = segment["coordinates"]["layout_height"]
scaled_points = [
(x * pix.width / layout_width, y * pix.height / layout_height)
for x, y in points
]
box_color = category_to_color.get(segment["category"], "deepskyblue")
categories.add(segment["category"])
rect = patches.Polygon(
scaled_points, linewidth=1, edgecolor=box_color, facecolor="none"
)
ax.add_patch(rect)

# Make legend
legend_handles = [patches.Patch(color="deepskyblue", label="Text")]
for category in ["Title", "Image", "Table"]:
if category in categories:
legend_handles.append(
patches.Patch(color=category_to_color[category], label=category)
)
ax.axis("off")
ax.legend(handles=legend_handles, loc="upper right")
plt.tight_layout()
plt.show()


def render_page(doc_list: list, page_number: int, print_text=True) -> None:
pdf_page = fitz.open(file_path).load_page(page_number - 1)
page_docs = [
doc for doc in doc_list if doc.metadata.get("page_number") == page_number
]
segments = [doc.metadata for doc in page_docs]
plot_pdf_with_boxes(pdf_page, segments)
if print_text:
for doc in page_docs:
print(f"{doc.page_content}\n")
render_page(docs, 5)

LayoutParser: A Unified Toolkit for DL-Based DIA

5

Table 1: Current layout detection models in the LayoutParser model zoo

Dataset Base Model1 Large Model Notes PubLayNet [38] PRImA [3] Newspaper [17] TableBank [18] HJDataset [31] F / M M F F F / M M - - F - Layouts of modern scientific documents Layouts of scanned modern magazines and scientific reports Layouts of scanned US newspapers from the 20th century Table region on modern scientific and business document Layouts of history Japanese documents

1 For each dataset, we train several models of different sizes for different needs (the trade-off between accuracy vs. computational cost). For “base model” and “large model”, we refer to using the ResNet 50 or ResNet 101 backbones [13], respectively. One can train models of different architectures, like Faster R-CNN [28] (F) and Mask R-CNN [12] (M). For example, an F in the Large Model column indicates it has a Faster R-CNN model trained using the ResNet 101 backbone. The platform is maintained and a number of additions will be made to the model zoo in coming months.

layout data structures, which are optimized for efficiency and versatility. 3) When necessary, users can employ existing or customized OCR models via the unified API provided in the OCR module. 4) LayoutParser comes with a set of utility functions for the visualization and storage of the layout data. 5) LayoutParser is also highly customizable, via its integration with functions for layout data annotation and model training. We now provide detailed descriptions for each component.

3.1 Layout Detection Models

In LayoutParser, a layout model takes a document image as an input and generates a list of rectangular boxes for the target content regions. Different from traditional methods, it relies on deep convolutional neural networks rather than manually curated rules to identify content regions. It is formulated as an object detection problem and state-of-the-art models like Faster R-CNN [28] and Mask R-CNN [12] are used. This yields prediction results of high accuracy and makes it possible to build a concise, generalized interface for layout detection. LayoutParser, built upon Detectron2 [35], provides a minimal API that can perform layout detection with only four lines of code in Python:

1 import layoutparser as lp 2 image = cv2 . imread ( " image_file " ) # load images 3 model = lp . De t e c tro n2 Lay outM odel ( " lp :// PubLayNet / f as t er _ r c nn _ R _ 50 _ F P N_ 3 x / config " ) 4 5 layout = model . detect ( image )

LayoutParser provides a wealth of pre-trained model weights using various datasets covering different languages, time periods, and document types. Due to domain shift [7], the prediction performance can notably drop when models are ap- plied to target samples that are significantly different from the training dataset. As document structures and layouts vary greatly in different domains, it is important to select models trained on a dataset similar to the test samples. A semantic syntax is used for initializing the model weights in LayoutParser, using both the dataset name and model name lp://<dataset-name>/<model-architecture-name>.

请注意,尽管表格文本在文档内容中被压缩为一个字符串,但元数据包含其行和列的表示:

from IPython.display import HTML, display

segments = [
doc.metadata
for doc in docs
if doc.metadata.get("page_number") == 5 and doc.metadata.get("category") == "Table"
]

display(HTML(segments[0]["text_as_html"]))
able 1. LUllclll 1ayoul actCCLloll 1110AdCs 111 L1C LayoOulralsel 1110U4cl 200
Dataset| Base Model'|Notes
PubLayNet [38]F/MLayouts of modern scientific documents
PRImAMLayouts of scanned modern magazines and scientific reports
NewspaperFLayouts of scanned US newspapers from the 20th century
TableBank [18]FTable region on modern scientific and business document
HJDatasetF/MLayouts of history Japanese documents

从特定部分提取文本

结构可能具有父子关系——例如,一个段落可能属于一个有标题的部分。如果某个部分特别重要(例如,用于索引),我们可以隔离相应的 Document 对象。

下面,我们提取与文档的“结论”部分相关的所有文本:

render_page(docs, 14, print_text=False)

conclusion_docs = []
parent_id = -1
for doc in docs:
if doc.metadata["category"] == "Title" and "Conclusion" in doc.page_content:
parent_id = doc.metadata["element_id"]
if doc.metadata.get("parent_id") == parent_id:
conclusion_docs.append(doc)

for doc in conclusion_docs:
print(doc.page_content)
LayoutParser provides a comprehensive toolkit for deep learning-based document image analysis. The off-the-shelf library is easy to install, and can be used to build flexible and accurate pipelines for processing documents with complicated structures. It also supports high-level customization and enables easy labeling and training of DL models on unique document image datasets. The LayoutParser community platform facilitates sharing DL models and DIA pipelines, inviting discussion and promoting code reproducibility and reusability. The LayoutParser team is committed to keeping the library updated continuously and bringing the most recent advances in DL-based DIA, such as multi-modal document modeling [37, 36, 9] (an upcoming priority), to a diverse audience of end-users.
Acknowledgements We thank the anonymous reviewers for their comments and suggestions. This project is supported in part by NSF Grant OIA-2033558 and funding from the Harvard Data Science Initiative and Harvard Catalyst. Zejiang Shen thanks Doug Downey for suggestions.

从图像中提取文本

对图像运行OCR,从中提取文本:

render_page(docs, 11)