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LLM 向导

此笔记本介绍如何使用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，而大多数PDF转文本解析器在解析时会丢失这些信息。

以下是LayoutPDFReader的一些关键功能

  • 它可以识别并提取各个部分和小节及其级别。
  • 它将多行组合成段落。
  • 它可以识别部分和小节之间的链接。
  • 它可以提取表格以及表格所在的部分。
  • 它可以识别和提取列表和嵌套列表。
  • 它可以连接跨页的内容。
  • 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文件中会失败，因此请谨慎使用。

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

LLMSherpaFileLoader​

在幕后，LLMSherpaFileLoader定义了一些加载文件内容的策略：["sections", "chunks", "html", "text"]，设置nlm-ingestor以获取llmsherpa_api_url或使用默认值。

sections策略：将文件解析成部分并返回​

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="sections",
    llmsherpa_api_url="http://localhost: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：将文件解析成块并返回​

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="chunks",
    llmsherpa_api_url="http://localhost: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：将文件作为单个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="http://localhost: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：将文件作为单个文本文档返回​

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="http://localhost: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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