PyPDFLoader

本筆記本提供快速概觀，以開始使用 PyPDF 文件載入器。如需所有 DocumentLoader 功能和組態的詳細文件，請前往 API 參考。

概觀

整合詳細資訊

類別	套件	本機	可序列化	JS 支援
PyPDFLoader	langchain_community	✅	❌	❌

---

載入器功能

來源	文件延遲載入	原生非同步支援	擷取影像	擷取表格
PyPDFLoader	✅	❌	✅	❌

設定

憑證

使用 PyPDFLoader 不需要憑證。

如果您想要取得模型呼叫的自動化最佳類別追蹤，您也可以設定您的 LangSmith API 金鑰，方法是取消註解下方內容

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安裝

安裝 langchain_community 和 pypdf。

%pip install -qU langchain_community pypdf

Note: you may need to restart the kernel to use updated packages.

初始化

現在我們可以實例化我們的模型物件並載入文件

from langchain_community.document_loaders import PyPDFLoader

file_path = "./example_data/layout-parser-paper.pdf"
loader = PyPDFLoader(file_path)

API 參考：PyPDFLoader

載入

docs = loader.load()
docs[0]

Document(metadata={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': './example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}, page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (\x00 ), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\nshannons@allenai.org\n2 Brown University\nruochen zhang@brown.edu\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\nbcgl@cs.washington.edu\n5 University of Waterloo\nw422li@uwaterloo.ca\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\n· Character Recognition · Open Source library · Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021')

import pprint

pprint.pp(docs[0].metadata)

{'producer': 'pdfTeX-1.40.21',
 'creator': 'LaTeX with hyperref',
 'creationdate': '2021-06-22T01:27:10+00:00',
 'author': '',
 'keywords': '',
 'moddate': '2021-06-22T01:27:10+00:00',
 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live '
                    '2020) kpathsea version 6.3.2',
 'subject': '',
 'title': '',
 'trapped': '/False',
 'source': './example_data/layout-parser-paper.pdf',
 'total_pages': 16,
 'page': 0,
 'page_label': '1'}

延遲載入

pages = []
for doc in loader.lazy_load():
    pages.append(doc)
    if len(pages) >= 10:
        # do some paged operation, e.g.
        # index.upsert(page)

        pages = []
len(pages)

6

print(pages[0].page_content[:100])
pprint.pp(pages[0].metadata)

LayoutParser: A Unified Toolkit for DL-Based DIA 11
focuses on precision, efficiency, and robustness. T
{'producer': 'pdfTeX-1.40.21',
 'creator': 'LaTeX with hyperref',
 'creationdate': '2021-06-22T01:27:10+00:00',
 'author': '',
 'keywords': '',
 'moddate': '2021-06-22T01:27:10+00:00',
 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live '
                    '2020) kpathsea version 6.3.2',
 'subject': '',
 'title': '',
 'trapped': '/False',
 'source': './example_data/layout-parser-paper.pdf',
 'total_pages': 16,
 'page': 10,
 'page_label': '11'}

metadata 屬性至少包含下列索引鍵

• source
• page（如果在 page 模式中）
• total_page
• creationdate
• creator
• producer

其他 metadata 專屬於每個剖析器。這些資訊可能有助於（例如，將您的 PDF 分類）。

分割模式和自訂頁面分隔符號

載入 PDF 檔案時，您可以用兩種不同的方式分割它

• 依頁面
• 作為單一文字流

依預設，PyPDFLoader 會將 PDF 分割為單一文字流。

依頁面擷取 PDF。每個頁面都會擷取為 langchain Document 物件：

loader = PyPDFLoader(
    "./example_data/layout-parser-paper.pdf",
    mode="page",
)
docs = loader.load()
print(len(docs))
pprint.pp(docs[0].metadata)

16
{'producer': 'pdfTeX-1.40.21',
 'creator': 'LaTeX with hyperref',
 'creationdate': '2021-06-22T01:27:10+00:00',
 'author': '',
 'keywords': '',
 'moddate': '2021-06-22T01:27:10+00:00',
 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live '
                    '2020) kpathsea version 6.3.2',
 'subject': '',
 'title': '',
 'trapped': '/False',
 'source': './example_data/layout-parser-paper.pdf',
 'total_pages': 16,
 'page': 0,
 'page_label': '1'}

在此模式中，pdf 會依頁面分割，且產生的 Documents metadata 包含頁碼。但在某些情況下，我們可能希望將 pdf 作為單一文字流處理（這樣我們就不會將某些段落切成兩半）。在這種情況下，您可以使用 single 模式

將整個 PDF 擷取為單一 langchain Document 物件：

loader = PyPDFLoader(
    "./example_data/layout-parser-paper.pdf",
    mode="single",
)
docs = loader.load()
print(len(docs))
pprint.pp(docs[0].metadata)

1
{'producer': 'pdfTeX-1.40.21',
 'creator': 'LaTeX with hyperref',
 'creationdate': '2021-06-22T01:27:10+00:00',
 'author': '',
 'keywords': '',
 'moddate': '2021-06-22T01:27:10+00:00',
 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live '
                    '2020) kpathsea version 6.3.2',
 'subject': '',
 'title': '',
 'trapped': '/False',
 'source': './example_data/layout-parser-paper.pdf',
 'total_pages': 16}

邏輯上，在此模式中，「page_number」metadata 會消失。以下說明如何在文字流中清楚識別頁面結束的位置

新增自訂 pages_delimiter 以識別 single 模式中頁面結尾的位置：

loader = PyPDFLoader(
    "./example_data/layout-parser-paper.pdf",
    mode="single",
    pages_delimiter="\n-------THIS IS A CUSTOM END OF PAGE-------\n",
)
docs = loader.load()
print(docs[0].page_content[:5780])

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
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,
arXiv:2103.15348v2  [cs.CV]  21 Jun 2021
-------THIS IS A CUSTOM END OF PAGE-------
2 Z. Shen et al.
37], layout detection [38, 22], table detection [ 26], and scene text detection [ 4].
A generalized learning-based framework dramatically reduces the need for the
manual specification of complicated rules, which is the status quo with traditional
methods. DL has the potential to transform DIA pipelines and benefit a broad
spectrum of large-scale document digitization projects.
However, there are several practical difficulties for taking advantages of re-
cent advances in DL-based methods: 1) DL models are notoriously convoluted
for reuse and extension. Existing models are developed using distinct frame-
works like TensorFlow [1] or PyTorch [ 24], and the high-level parameters can
be obfuscated by implementation details [ 8]. It can be a time-consuming and
frustrating experience to debug, reproduce, and adapt existing models for DIA,
and many researchers who would benefit the most from using these methods lack
the technical background to implement them from scratch. 2) Document images
contain diverse and disparate patterns across domains, and customized training
is often required to achieve a desirable detection accuracy. Currently there is no
full-fledged infrastructure for easily curating the target document image datasets
and fine-tuning or re-training the models. 3) DIA usually requires a sequence of
models and other processing to obtain the final outputs. Often research teams use
DL models and then perform further document analyses in separate processes,
and these pipelines are not documented in any central location (and often not
documented at all). This makes it difficult for research teams to learn about how
full pipelines are implemented and leads them to invest significant resources in
reinventing the DIA wheel .
LayoutParser provides a unified toolkit to support DL-based document image
analysis and processing. To address the aforementioned challenges,LayoutParser
is built with the following components:
1. An off-the-shelf toolkit for applying DL models for layout detection, character
recognition, and other DIA tasks (Section 3)
2. A rich repository of pre-trained neural network models (Model Zoo) that
underlies the off-the-shelf usage
3. Comprehensive tools for efficient document image data annotation and model
tuning to support different levels of customization
4. A DL model hub and community platform for the easy sharing, distribu-
tion, and discussion of DIA models and pipelines, to promote reusability,
reproducibility, and extensibility (Section 4)
The library implements simple and intuitive Python APIs without sacrificing
generalizability and versatility, and can be easily installed via pip. Its convenient
functions for handling document image data can be seamlessly integrated with
existing DIA pipelines. With detailed documentations and carefully curated
tutorials, we hope this tool will benefit a variety of end-users, and will lead to
advances in applications in both industry and academic research.
LayoutParser is well aligned with recent efforts for improving DL model
reusability in other disciplines like natural language processing [ 8, 34] and com-
puter vision [ 35], but with a focus on unique challenges in DIA. We show
LayoutParser can be applied in sophisticated and large-scale digitization projects
-------THIS IS A CUSTOM END OF PAGE-------
LayoutParser: A Unified Toolkit for DL-Based DIA 3
that require precision, efficiency, and robustness, as well as simple and light

這可以只是 \n 或 \f，以清楚指示頁面變更，或是 <!-- PAGE BREAK -->，以便在 Markdown 檢視器中無縫注入而不會產生視覺效果。

從 PDF 擷取影像

您可以從 PDF 擷取影像，並從三種不同的解決方案中選擇

• rapidOCR（輕量型光學字元辨識工具）
• Tesseract（高精度的 OCR 工具）
• 多模態語言模型

您可以調整這些功能，以選擇擷取影像的輸出格式，包括 html、markdown 或 text

結果會插入在頁面文字的最後一段和倒數第二段之間。

使用 rapidOCR 從 PDF 擷取影像：

%pip install -qU rapidocr-onnxruntime

Note: you may need to restart the kernel to use updated packages.

from langchain_community.document_loaders.parsers import RapidOCRBlobParser

loader = PyPDFLoader(
    "./example_data/layout-parser-paper.pdf",
    mode="page",
    images_inner_format="markdown-img",
    images_parser=RapidOCRBlobParser(),
)
docs = loader.load()

print(docs[5].page_content)

API 參考：RapidOCRBlobParser

6 Z. Shen et al.
Fig. 2: The relationship between the three types of layout data structures.
Coordinate supports three kinds of variation; TextBlock consists of the co-
ordinate information and extra features like block text, types, and reading orders;
a Layout object is a list of all possible layout elements, including other Layout
objects. They all support the same set of transformation and operation APIs for
maximum flexibility.
Shown in Table 1, LayoutParser currently hosts 9 pre-trained models trained
on 5 different datasets. Description of the training dataset is provided alongside
with the trained models such that users can quickly identify the most suitable
models for their tasks. Additionally, when such a model is not readily available,
LayoutParser also supports training customized layout models and community
sharing of the models (detailed in Section 3.5).
3.2 Layout Data Structures
A critical feature of LayoutParser is the implementation of a series of data
structures and operations that can be used to efficiently process and manipulate
the layout elements. In document image analysis pipelines, various post-processing
on the layout analysis model outputs is usually required to obtain the final
outputs. Traditionally, this requires exporting DL model outputs and then loading
the results into other pipelines. All model outputs from LayoutParser will be
stored in carefully engineered data types optimized for further processing, which
makes it possible to build an end-to-end document digitization pipeline within
LayoutParser. There are three key components in the data structure, namely
the Coordinate system, the TextBlock, and the Layout. They provide different
levels of abstraction for the layout data, and a set of APIs are supported for
transformations or operations on these classes.



![Coordinate
(x1, y1)
(X1, y1)
(x2,y2)
APIS
x-interval
tart
end
Quadrilateral
operation
Rectangle
y-interval
ena
(x2, y2)
(x4, y4)
(x3, y3)
and
textblock
Coordinate
transformation
+
Block
Block
Reading
Extra features
Text
Type
Order
coordinatel
textblockl
layout
 same
textblock2
layoutl
The
A list of the layout elements](#)

請注意，RapidOCR 旨在與中文和英文搭配使用，而非其他語言。

使用 Tesseract 從 PDF 擷取影像：

%pip install -qU pytesseract

Note: you may need to restart the kernel to use updated packages.

from langchain_community.document_loaders.parsers import TesseractBlobParser

loader = PyPDFLoader(
    "./example_data/layout-parser-paper.pdf",
    mode="page",
    images_inner_format="html-img",
    images_parser=TesseractBlobParser(),
)
docs = loader.load()
print(docs[5].page_content)

API 參考：TesseractBlobParser

6 Z. Shen et al.
Fig. 2: The relationship between the three types of layout data structures.
Coordinate supports three kinds of variation; TextBlock consists of the co-
ordinate information and extra features like block text, types, and reading orders;
a Layout object is a list of all possible layout elements, including other Layout
objects. They all support the same set of transformation and operation APIs for
maximum flexibility.
Shown in Table 1, LayoutParser currently hosts 9 pre-trained models trained
on 5 different datasets. Description of the training dataset is provided alongside
with the trained models such that users can quickly identify the most suitable
models for their tasks. Additionally, when such a model is not readily available,
LayoutParser also supports training customized layout models and community
sharing of the models (detailed in Section 3.5).
3.2 Layout Data Structures
A critical feature of LayoutParser is the implementation of a series of data
structures and operations that can be used to efficiently process and manipulate
the layout elements. In document image analysis pipelines, various post-processing
on the layout analysis model outputs is usually required to obtain the final
outputs. Traditionally, this requires exporting DL model outputs and then loading
the results into other pipelines. All model outputs from LayoutParser will be
stored in carefully engineered data types optimized for further processing, which
makes it possible to build an end-to-end document digitization pipeline within
LayoutParser. There are three key components in the data structure, namely
the Coordinate system, the TextBlock, and the Layout. They provide different
levels of abstraction for the layout data, and a set of APIs are supported for
transformations or operations on these classes.



<img alt="Coordinate

textblock

x-interval

JeAsaqul-A

Coordinate
+

Extra features

Rectangle

Quadrilateral

Block
Text

Block
Type

Reading
Order

layout

[ coordinatel textblock1 |
&#x27;

“y textblock2 , layout1 ]

A list of the layout elements

The same transformation and operation APIs src="#" />

使用多模態模型從 PDF 擷取影像：

%pip install -qU langchain_openai

Note: you may need to restart the kernel to use updated packages.

import os

from dotenv import load_dotenv

load_dotenv()

True

from getpass import getpass

if not os.environ.get("OPENAI_API_KEY"):
    os.environ["OPENAI_API_KEY"] = getpass("OpenAI API key =")

from langchain_community.document_loaders.parsers import LLMImageBlobParser
from langchain_openai import ChatOpenAI

loader = PyPDFLoader(
    "./example_data/layout-parser-paper.pdf",
    mode="page",
    images_inner_format="markdown-img",
    images_parser=LLMImageBlobParser(model=ChatOpenAI(model="gpt-4o", max_tokens=1024)),
)
docs = loader.load()
print(docs[5].page_content)

API 參考：LLMImageBlobParser | ChatOpenAI

6 Z. Shen et al.
Fig. 2: The relationship between the three types of layout data structures.
Coordinate supports three kinds of variation; TextBlock consists of the co-
ordinate information and extra features like block text, types, and reading orders;
a Layout object is a list of all possible layout elements, including other Layout
objects. They all support the same set of transformation and operation APIs for
maximum flexibility.
Shown in Table 1, LayoutParser currently hosts 9 pre-trained models trained
on 5 different datasets. Description of the training dataset is provided alongside
with the trained models such that users can quickly identify the most suitable
models for their tasks. Additionally, when such a model is not readily available,
LayoutParser also supports training customized layout models and community
sharing of the models (detailed in Section 3.5).
3.2 Layout Data Structures
A critical feature of LayoutParser is the implementation of a series of data
structures and operations that can be used to efficiently process and manipulate
the layout elements. In document image analysis pipelines, various post-processing
on the layout analysis model outputs is usually required to obtain the final
outputs. Traditionally, this requires exporting DL model outputs and then loading
the results into other pipelines. All model outputs from LayoutParser will be
stored in carefully engineered data types optimized for further processing, which
makes it possible to build an end-to-end document digitization pipeline within
LayoutParser. There are three key components in the data structure, namely
the Coordinate system, the TextBlock, and the Layout. They provide different
levels of abstraction for the layout data, and a set of APIs are supported for
transformations or operations on these classes.



![**Image Summary:**  
Diagram explaining coordinate systems and layout elements with labels for coordinate intervals, rectangle, quadrilateral, textblock with extra features, and layout list. Includes transformation and operation APIs.

**Extracted Text:**  
Coordinate  
coordinate  
start  
start  
x-interval  
end  
y-interval  
end  
(x1, y1)  
Rectangle  
(x2, y2)  
(x1, y1)  
Quadrilateral  
(x2, y2)  
(x4, y4)  
(x3, y3)  
The same transformation and operation APIs  
textblock  
Coordinate  
Extra features  
Block Text  
Block Type  
Reading Order  
...  
layout  
coordinate1, textblock1, ...  
..., textblock2, layout1  
A list of the layout elements  ](#)

使用檔案

許多文件載入器都涉及剖析檔案。此類載入器之間的差異通常在於檔案的剖析方式，而不是檔案的載入方式。例如，您可以使用 open 讀取 PDF 或 markdown 檔案的二進位內容，但您需要不同的剖析邏輯才能將該二進位資料轉換為文字。

因此，將剖析邏輯與載入邏輯分離可能會很有幫助，這可讓您更輕鬆地重複使用給定的剖析器，而無論資料的載入方式為何。您可以使用此策略來分析不同的檔案，並使用相同的剖析參數。

from langchain_community.document_loaders import FileSystemBlobLoader
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import PyPDFParser

loader = GenericLoader(
    blob_loader=FileSystemBlobLoader(
        path="./example_data/",
        glob="*.pdf",
    ),
    blob_parser=PyPDFParser(),
)
docs = loader.load()
print(docs[0].page_content)
pprint.pp(docs[0].metadata)

API 參考：FileSystemBlobLoader | GenericLoader | PyPDFParser

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
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,
arXiv:2103.15348v2  [cs.CV]  21 Jun 2021
{'producer': 'pdfTeX-1.40.21',
 'creator': 'LaTeX with hyperref',
 'creationdate': '2021-06-22T01:27:10+00:00',
 'author': '',
 'keywords': '',
 'moddate': '2021-06-22T01:27:10+00:00',
 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live '
                    '2020) kpathsea version 6.3.2',
 'subject': '',
 'title': '',
 'trapped': '/False',
 'source': 'example_data/layout-parser-paper.pdf',
 'total_pages': 16,
 'page': 0,
 'page_label': '1'}

可以處理來自雲端儲存空間的檔案。

from langchain_community.document_loaders import CloudBlobLoader
from langchain_community.document_loaders.generic import GenericLoader

loader = GenericLoader(
    blob_loader=CloudBlobLoader(
        url="s3://mybucket",  # Supports s3://, az://, gs://, file:// schemes.
        glob="*.pdf",
    ),
    blob_parser=PyPDFParser(),
)
docs = loader.load()
print(docs[0].page_content)
pprint.pp(docs[0].metadata)

API 參考：CloudBlobLoader | GenericLoader

API 參考

如需所有 PyPDFLoader 功能和組態的詳細文件，請前往 API 參考：https://langchain-python.dev.org.tw/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html

相關內容

• 文件載入器 概念指南
• 文件載入器 操作指南