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LLM Sherpa

本笔记本介绍了如何使用 LLM Sherpa 加载多种类型的文件。 LLM Sherpa 支持不同的文件格式，包括 DOCX、PPTX、HTML、TXT 和 XML。

LLMSherpaFileLoader 使用 LayoutPDFReader，它是 LLMSherpa 库的一部分。此工具旨在解析 PDF，同时保留其布局信息，而使用大多数 PDF 到文本解析器时通常会丢失这些信息。

以下是 LayoutPDFReader 的一些主要功能

  • 它可以识别并提取章节和小节及其级别。
  • 它将行组合成段落。
  • 它可以识别章节和段落之间的链接。
  • 它可以提取表格以及表格所在的章节。
  • 它可以识别并提取列表和嵌套列表。
  • 它可以连接跨页面的内容。
  • 它可以删除重复的页眉和页脚。
  • 它可以删除水印。

查看 llmsherpa 文档。

信息：此库在某些 pdf 文件上会失败，因此请谨慎使用。

# Install package
# !pip install --upgrade --quiet llmsherpa

LLMSherpaFileLoader​

在底层，LLMSherpaFileLoader 定义了一些策略来加载文件内容：["sections", "chunks", "html", "text"]，设置 nlm-ingestor 以获取 llmsherpa_api_url 或使用默认值。

sections 策略：返回解析为章节的文件​

from langchain_community.document_loaders.llmsherpa import LLMSherpaFileLoader

loader = LLMSherpaFileLoader(
    file_path="https://arxiv.org/pdf/2402.14207.pdf",
    new_indent_parser=True,
    apply_ocr=True,
    strategy="sections",
    llmsherpa_api_url="https://127.0.0.1:5010/api/parseDocument?renderFormat=all",
)
docs = loader.load()
API 参考：LLMSherpaFileLoader
docs[1]
Document(page_content='Abstract\nWe study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages.\nThis underexplored problem poses new challenges at the pre-writing stage, including how to research the topic and prepare an outline prior to writing.\nWe propose STORM, a writing system for the Synthesis of Topic Outlines through\nReferences\nFull-length Article\nTopic\nOutline\n2022 Winter Olympics\nOpening Ceremony\nResearch via Question Asking\nRetrieval and Multi-perspective Question Asking.\nSTORM models the pre-writing stage by\nLLM\n(1) discovering diverse perspectives in researching the given topic, (2) simulating conversations where writers carrying different perspectives pose questions to a topic expert grounded on trusted Internet sources, (3) curating the collected information to create an outline.\nFor evaluation, we curate FreshWiki, a dataset of recent high-quality Wikipedia articles, and formulate outline assessments to evaluate the pre-writing stage.\nWe further gather feedback from experienced Wikipedia editors.\nCompared to articles generated by an outlinedriven retrieval-augmented baseline, more of STORM’s articles are deemed to be organized (by a 25% absolute increase) and broad in coverage (by 10%).\nThe expert feedback also helps identify new challenges for generating grounded long articles, such as source bias transfer and over-association of unrelated facts.\n1. Can you provide any information about the transportation arrangements for the opening ceremony?\nLLM\n2. Can you provide any information about the budget for the 2022 Winter Olympics opening ceremony?…\nLLM- Role1\nLLM- Role2\nLLM- Role1', metadata={'source': 'https://arxiv.org/pdf/2402.14207.pdf', 'section_number': 1, 'section_title': 'Abstract'})
len(docs)
79

chunks 策略：返回解析为块的文件​

from langchain_community.document_loaders.llmsherpa import LLMSherpaFileLoader

loader = LLMSherpaFileLoader(
    file_path="https://arxiv.org/pdf/2402.14207.pdf",
    new_indent_parser=True,
    apply_ocr=True,
    strategy="chunks",
    llmsherpa_api_url="https://127.0.0.1:5010/api/parseDocument?renderFormat=all",
)
docs = loader.load()
API 参考：LLMSherpaFileLoader
docs[1]
Document(page_content='Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models\nStanford University {shaoyj, yuchengj, tkanell, peterxu, okhattab}@stanford.edu [email protected]', metadata={'source': 'https://arxiv.org/pdf/2402.14207.pdf', 'chunk_number': 1, 'chunk_type': 'para'})
len(docs)
306

html 策略：将文件作为一个 html 文档返回​

from langchain_community.document_loaders.llmsherpa import LLMSherpaFileLoader

loader = LLMSherpaFileLoader(
    file_path="https://arxiv.org/pdf/2402.14207.pdf",
    new_indent_parser=True,
    apply_ocr=True,
    strategy="html",
    llmsherpa_api_url="https://127.0.0.1:5010/api/parseDocument?renderFormat=all",
)
docs = loader.load()
API 参考：LLMSherpaFileLoader
docs[0].page_content[:400]
'<html><h1>Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models</h1><table><th><td colSpan=1>Yijia Shao</td><td colSpan=1>Yucheng Jiang</td><td colSpan=1>Theodore A. Kanell</td><td colSpan=1>Peter Xu</td></th><tr><td colSpan=1></td><td colSpan=1>Omar Khattab</td><td colSpan=1>Monica S. Lam</td><td colSpan=1></td></tr></table><p>Stanford University {shaoyj, yuchengj, '
len(docs)
1

text 策略：将文件作为一个文本文档返回​

from langchain_community.document_loaders.llmsherpa import LLMSherpaFileLoader

loader = LLMSherpaFileLoader(
    file_path="https://arxiv.org/pdf/2402.14207.pdf",
    new_indent_parser=True,
    apply_ocr=True,
    strategy="text",
    llmsherpa_api_url="https://127.0.0.1:5010/api/parseDocument?renderFormat=all",
)
docs = loader.load()
API 参考：LLMSherpaFileLoader
docs[0].page_content[:400]
'Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models\n | Yijia Shao | Yucheng Jiang | Theodore A. Kanell | Peter Xu\n | --- | --- | --- | ---\n |  | Omar Khattab | Monica S. Lam | \n\nStanford University {shaoyj, yuchengj, tkanell, peterxu, okhattab}@stanford.edu [email protected]\nAbstract\nWe study how to apply large language models to write grounded and organized long'
len(docs)
1

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