Unstructured 集成

使用 LangChain Python 与 Unstructured 文档加载器集成

本笔记本介绍了如何使用 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。

请参阅 Unstructured 了解更多关于本地设置 Unstructured 的说明，包括设置所需的系统依赖项。

概述

集成细节

类	包	本地	可序列化	JS 支持
UnstructuredLoader	langchain-unstructured	✅	❌	✅

加载器功能

源	文档惰性加载	原生异步支持
UnstructuredLoader	✅	❌

设置

凭证

默认情况下，langchain-unstructured安装了一个较小的占用空间，需要将分区逻辑卸载到 Unstructured API，这需要一个API密钥。如果你使用本地安装，则不需要API密钥。要获取你的API密钥，请前往 此网站 获取API密钥，然后在下方单元格中设置它：

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

正常安装

运行此笔记本的其余部分需要以下包。

# Install package, compatible with API partitioning
pip install -qU langchain-unstructured unstructured-client unstructured "unstructured[pdf]" python-magic

本地安装

如果你希望本地运行分区逻辑，你需要安装一系列系统依赖项，如 Unstructured 文档中所述。

例如，在Mac上，你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

你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 unstructured 包的更多信息，请参阅其 文档。在本示例中，我们展示了从文本文件和PDF文件加载。

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'}

懒加载

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对 unstructured 元素进行后处理，可以在实例化 UnstructuredLoader 时通过 post_processors 参数传入一个 str -> str 函数列表。这同样适用于其他 Unstructured 加载器。以下是一个示例。

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 [e-mail] 2 Brown University ruochen [e-mail] 3 Harvard University {melissadell,jacob carlson}@fas.harvard.edu 4 University of Washington [e-mail] 5 University of Waterloo [e-mail]'),
 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

如果您想使用更小的包并获取最新的分区功能，可以执行 pip install unstructured and pip install langchain-unstructured。有关 UnstructuredLoader 的更多信息，请参阅 Unstructured 提供商页面。

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。您可以通过 Unstructured 平台 获取 API 密钥。

如果您想自托管 Unstructured API 或在本地运行它，请查看 自托管 Unstructured API 的 Docker 镜像说明。

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.')

您还可以通过 Unstructured API 在单个 API 调用中批量处理多个文件，使用 UnstructuredLoader。

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.

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，需要将一个 UnstructuredClient 实例传递给 UnstructuredLoader。以下示例展示了如何自定义客户端功能，例如使用您自己的 requests.Session()、传递替代的 server_url 以及自定义 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，未来版本中可能会有破坏性更改。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.

layout-parser-paper.pdf :  LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis

分块

UnstructuredLoader 不支持 mode 作为参数来分组文本，如旧版加载器 UnstructuredFileLoader 和其他加载器所做的那样。相反，它支持“分块”。Unstructured 中的分块与其他基于纯文本特征（如 “\n\n” 或 “\n” 等字符序列，这些可能表示段落边界或列表项边界）形成块的分块机制不同。相反，所有文档都使用关于每种文档格式的特定知识来分割文档，将其划分为语义单元（文档元素），并且仅在单个元素超过所需最大块大小时才需要使用文本分割。通常，分块将连续元素组合成尽可能大的块，而不会超过最大块大小。分块产生一系列 CompositeElement、Table 或 TableChunk 元素。每个“块”是这三种类型之一的一个实例。

有关分块选项的更多详细信息，请参阅此 页面，但要在 mode="single" 的行为相同，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 在本地运行时接受一个 web_url 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。

示例用法：

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

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官方原文：src/oss/python/integrations/document_loaders/unstructured_file.mdx · 非官方翻译，译文由 AI 生成，以英文原文为准。