非结构化
此笔记本介绍如何使用Unstructured 文档加载器 加载多种类型的文件。Unstructured 目前支持加载文本文件、PowerPoint、HTML、PDF、图像等。
有关在本地设置 Unstructured（包括设置所需的系统依赖项）的更多说明，请参阅本指南。
概述
集成详细信息
| 类 | 包 | 本地 | 可序列化 | JS 支持 |
|---|---|---|---|---|
| UnstructuredLoader | langchain_unstructured | ✅ | ❌ | ✅ |
加载器功能
| 来源 | 文档延迟加载 | 原生异步支持 |
|---|---|---|
| UnstructuredLoader | ✅ | ❌ |
设置
凭据
默认情况下，langchain-unstructured 安装的占用空间较小，需要将分区逻辑卸载到 Unstructured API，这需要 API 密钥。如果您使用本地安装，则不需要 API 密钥。要获取您的 API 密钥，请访问此网站 并获取 API 密钥，然后在下面的单元格中设置它。
import getpass
import os
if "UNSTRUCTURED_API_KEY" not in os.environ:
os.environ["UNSTRUCTURED_API_KEY"] = getpass.getpass(
"Enter your Unstructured API key: "
)
安装
普通安装
运行此笔记本的其余部分需要以下软件包。
# Install package, compatible with API partitioning
%pip install --upgrade --quiet langchain-unstructured unstructured-client unstructured "unstructured[pdf]" python-magic
本地安装
如果您想在本地运行分区逻辑，则需要安装一些系统依赖项的组合，如此处的 Unstructured 文档中所述。
例如，在 Mac 上，您可以使用以下命令安装所需的依赖项：
# base dependencies
brew install libmagic poppler tesseract
# If parsing xml / html documents:
brew install libxml2 libxslt
您可以使用以下命令安装本地所需的 pip 依赖项：
pip install "langchain-unstructured[local]"
初始化
UnstructuredLoader 允许从各种不同的文件类型加载。要了解有关 unstructured 包的所有信息，请参阅其文档。在此示例中，我们展示了从文本文件和 PDF 文件中加载。
from langchain_unstructured import UnstructuredLoader
file_paths = [
"./example_data/layout-parser-paper.pdf",
"./example_data/state_of_the_union.txt",
]
loader = UnstructuredLoader(file_paths)
加载
docs = loader.load()
docs[0]
INFO: pikepdf C++ to Python logger bridge initialized
Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((16.34, 213.36), (16.34, 253.36), (36.34, 253.36), (36.34, 213.36)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'd3ce55f220dfb75891b4394a18bcb973'}, page_content='1 2 0 2')
print(docs[0].metadata)
{'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((16.34, 213.36), (16.34, 253.36), (36.34, 253.36), (36.34, 213.36)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'd3ce55f220dfb75891b4394a18bcb973'}
延迟加载
pages = []
for doc in loader.lazy_load():
pages.append(doc)
pages[0]
Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((16.34, 213.36), (16.34, 253.36), (36.34, 253.36), (36.34, 213.36)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'd3ce55f220dfb75891b4394a18bcb973'}, page_content='1 2 0 2')
后处理
如果您需要在提取后处理 unstructured 元素，则可以在实例化 UnstructuredLoader 时将 str -> str 函数列表传递给 post_processors 关键字参数。这也适用于其他 Unstructured 加载器。下面是一个示例。
from langchain_unstructured import UnstructuredLoader
from unstructured.cleaners.core import clean_extra_whitespace
loader = UnstructuredLoader(
"./example_data/layout-parser-paper.pdf",
post_processors=[clean_extra_whitespace],
)
docs = loader.load()
docs[5:10]
[Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((16.34, 393.9), (16.34, 560.0), (36.34, 560.0), (36.34, 393.9)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'parent_id': '89565df026a24279aaea20dc08cedbec', 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'e9fa370aef7ee5c05744eb7bb7d9981b'}, page_content='2 v 8 4 3 5 1 . 3 0 1 2 : v i X r a'),
Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((157.62199999999999, 114.23496279999995), (157.62199999999999, 146.5141628), (457.7358962799999, 146.5141628), (457.7358962799999, 114.23496279999995)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'filetype': 'application/pdf', 'category': 'Title', 'element_id': 'bde0b230a1aa488e3ce837d33015181b'}, page_content='LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis'),
Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((134.809, 168.64029940800003), (134.809, 192.2517444), (480.5464199080001, 192.2517444), (480.5464199080001, 168.64029940800003)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'parent_id': 'bde0b230a1aa488e3ce837d33015181b', 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': '54700f902899f0c8c90488fa8d825bce'}, page_content='Zejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain Lee4, Jacob Carlson3, and Weining Li5'),
Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((207.23000000000002, 202.57205439999996), (207.23000000000002, 311.8195408), (408.12676, 311.8195408), (408.12676, 202.57205439999996)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'page_number': 1, 'parent_id': 'bde0b230a1aa488e3ce837d33015181b', 'filetype': 'application/pdf', 'category': 'UncategorizedText', 'element_id': 'b650f5867bad9bb4e30384282c79bcfe'}, page_content='1 Allen Institute for AI [email protected] 2 Brown University ruochen [email protected] 3 Harvard University {melissadell,jacob carlson}@fas.harvard.edu 4 University of Washington [email protected] 5 University of Waterloo [email protected]'),
Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'coordinates': {'points': ((162.779, 338.45008160000003), (162.779, 566.8455408), (454.0372021523199, 566.8455408), (454.0372021523199, 338.45008160000003)), 'system': 'PixelSpace', 'layout_width': 612, 'layout_height': 792}, 'file_directory': './example_data', 'filename': 'layout-parser-paper.pdf', 'languages': ['eng'], 'last_modified': '2024-02-27T15:49:27', 'links': [{'text': ':// layout - parser . github . io', 'url': 'https://layout-parser.github.io', 'start_index': 1477}], 'page_number': 1, 'parent_id': 'bde0b230a1aa488e3ce837d33015181b', 'filetype': 'application/pdf', 'category': 'NarrativeText', 'element_id': 'cfc957c94fe63c8fd7c7f4bcb56e75a7'}, page_content='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.')]
Unstructured API
如果您想使用较小的软件包并获得最新的分区，您可以pip install unstructured-client 和 pip install langchain-unstructured。有关 UnstructuredLoader 的更多信息，请参阅Unstructured 提供程序页面。
当您传入 api_key 并设置 partition_via_api=True 时，加载器将使用托管的 Unstructured 无服务器 API 处理您的文档。您可以在此处生成免费的 Unstructured API 密钥。
如果您想自托管 Unstructured API 或在本地运行它，请查看此处的说明。
from langchain_unstructured import UnstructuredLoader
loader = UnstructuredLoader(
file_path="example_data/fake.docx",
api_key=os.getenv("UNSTRUCTURED_API_KEY"),
partition_via_api=True,
)
docs = loader.load()
docs[0]
INFO: Preparing to split document for partition.
INFO: Given file doesn't have '.pdf' extension, so splitting is not enabled.
INFO: Partitioning without split.
INFO: Successfully partitioned the document.
Document(metadata={'source': 'example_data/fake.docx', 'category_depth': 0, 'filename': 'fake.docx', 'languages': ['por', 'cat'], 'filetype': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'category': 'Title', 'element_id': '56d531394823d81787d77a04462ed096'}, page_content='Lorem ipsum dolor sit amet.')
您还可以使用 UnstructuredLoader 在单个 API 中通过 Unstructured API 批量处理多个文件。
loader = UnstructuredLoader(
file_path=["example_data/fake.docx", "example_data/fake-email.eml"],
api_key=os.getenv("UNSTRUCTURED_API_KEY"),
partition_via_api=True,
)
docs = loader.load()
print(docs[0].metadata["filename"], ": ", docs[0].page_content[:100])
print(docs[-1].metadata["filename"], ": ", docs[-1].page_content[:100])
INFO: Preparing to split document for partition.
INFO: Given file doesn't have '.pdf' extension, so splitting is not enabled.
INFO: Partitioning without split.
INFO: Successfully partitioned the document.
INFO: Preparing to split document for partition.
INFO: Given file doesn't have '.pdf' extension, so splitting is not enabled.
INFO: Partitioning without split.
INFO: Successfully partitioned the document.
``````output
fake.docx : Lorem ipsum dolor sit amet.
fake-email.eml : Violets are blue
Unstructured SDK 客户端
使用 Unstructured API 进行分区依赖于Unstructured SDK 客户端。
如果您想自定义客户端，则必须将 UnstructuredClient 实例传递给 UnstructuredLoader。以下是一个示例，说明如何自定义客户端的功能，例如使用您自己的 requests.Session()、传递替代的 server_url 和自定义 RetryConfig 对象。有关自定义客户端或 sdk 客户端接受的其他参数的更多信息，请参阅Unstructured Python SDK 文档和API 参数 文档的客户端部分。请注意，所有 API 参数都应传递给 UnstructuredLoader。
警告：以下示例可能未使用最新版本的 UnstructuredClient，并且在将来的版本中可能会发生重大更改。有关最新示例，请参阅Unstructured Python SDK 文档。
import requests
from langchain_unstructured import UnstructuredLoader
from unstructured_client import UnstructuredClient
from unstructured_client.utils import BackoffStrategy, RetryConfig
client = UnstructuredClient(
api_key_auth=os.getenv(
"UNSTRUCTURED_API_KEY"
), # Note: the client API param is "api_key_auth" instead of "api_key"
client=requests.Session(), # Define your own requests session
server_url="https://api.unstructuredapp.io/general/v0/general", # Define your own api url
retry_config=RetryConfig(
strategy="backoff",
retry_connection_errors=True,
backoff=BackoffStrategy(
initial_interval=500,
max_interval=60000,
exponent=1.5,
max_elapsed_time=900000,
),
), # Define your own retry config
)
loader = UnstructuredLoader(
"./example_data/layout-parser-paper.pdf",
partition_via_api=True,
client=client,
split_pdf_page=True,
split_pdf_page_range=[1, 10],
)
docs = loader.load()
print(docs[0].metadata["filename"], ": ", docs[0].page_content[:100])
INFO: Preparing to split document for partition.
INFO: Concurrency level set to 5
INFO: Splitting pages 1 to 10 (10 total)
INFO: Determined optimal split size of 2 pages.
INFO: Partitioning 5 files with 2 page(s) each.
INFO: Partitioning set #1 (pages 1-2).
INFO: Partitioning set #2 (pages 3-4).
INFO: Partitioning set #3 (pages 5-6).
INFO: Partitioning set #4 (pages 7-8).
INFO: Partitioning set #5 (pages 9-10).
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.
INFO: Successfully partitioned set #5, elements added to the final result.
INFO: Successfully partitioned the document.
``````output
layout-parser-paper.pdf : LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis
分块
UnstructuredLoader 不支持 mode 作为参数来对文本进行分组，就像旧的加载器 UnstructuredFileLoader 和其他加载器一样。它改为支持“分块”。Unstructured 中的分块与您可能熟悉的其他分块机制不同，后者基于纯文本特征（如“\n\n”或“\n”）形成块，这些特征可能表示段落边界或列表项边界。相反，所有文档都使用特定于每种文档格式的知识来将其划分为语义单元（文档元素），并且我们只需要在单个元素超过所需的最大块大小时才诉诸于文本分割。通常，分块会组合连续的元素以形成尽可能大的块，而不会超过最大块大小。分块会生成一系列 CompositeElement、Table 或 TableChunk 元素。每个“块”都是这三种类型之一的实例。
有关分块选项的更多详细信息，请参阅此页面，但要重现与 mode="single" 相同的行为，您可以设置 chunking_strategy="basic"、max_characters=<some-really-big-number> 和 include_orig_elements=False。
from langchain_unstructured import UnstructuredLoader
loader = UnstructuredLoader(
"./example_data/layout-parser-paper.pdf",
chunking_strategy="basic",
max_characters=1000000,
include_orig_elements=False,
)
docs = loader.load()
print("Number of LangChain documents:", len(docs))
print("Length of text in the document:", len(docs[0].page_content))
Number of LangChain documents: 1
Length of text in the document: 42772
加载网页
UnstructuredLoader 在本地运行时接受 web_url 关键字参数，该参数填充底层 Unstructured 分区 的 url 参数。这允许解析远程托管的文档，例如 HTML 网页。
使用示例
from langchain_unstructured import UnstructuredLoader
loader = UnstructuredLoader(web_url="https://www.example.com")
docs = loader.load()
for doc in docs:
print(f"{doc}\n")
page_content='Example Domain' metadata={'category_depth': 0, 'languages': ['eng'], 'filetype': 'text/html', 'url': 'https://www.example.com', 'category': 'Title', 'element_id': 'fdaa78d856f9d143aeeed85bf23f58f8'}
page_content='This domain is for use in illustrative examples in documents. You may use this domain in literature without prior coordination or asking for permission.' metadata={'languages': ['eng'], 'parent_id': 'fdaa78d856f9d143aeeed85bf23f58f8', 'filetype': 'text/html', 'url': 'https://www.example.com', 'category': 'NarrativeText', 'element_id': '3652b8458b0688639f973fe36253c992'}
page_content='More information...' metadata={'category_depth': 0, 'link_texts': ['More information...'], 'link_urls': ['https://www.iana.org/domains/example'], 'languages': ['eng'], 'filetype': 'text/html', 'url': 'https://www.example.com', 'category': 'Title', 'element_id': '793ab98565d6f6d6f3a6d614e3ace2a9'}
API 参考
有关所有 UnstructuredLoader 功能和配置的详细文档，请访问 API 参考：https://python.langchain.ac.cn/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html