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In this lesson, we’ll walk through loading and splitting a PDF document—an employee handbook for Lakeside Bicycles—using LangChain’s PyPDFLoader. This process is a common first step in a Retrieval-Augmented Generation (RAG) pipeline, enabling your Q&A application to fetch answers directly from document content.

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Prerequisites

Before you begin, ensure you have the following:
RequirementInstall Command
Python 3.7+—
langchainpip install langchain
langchain-communitypip install langchain-community
You can install both packages at once:
pip install langchain langchain-community

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1. Import the PDF Loader

Start by importing PyPDFLoader from the community loaders: 
from langchain_community.document_loaders import PyPDFLoader

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2. Initialize the Loader

Point the loader at your PDF file (e.g., data/handbook.pdf): 
loader = PyPDFLoader("data/handbook.pdf")
Make sure the file path is correct and the PDF is not password-protected. Otherwise, the loader will raise an error.

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3. Load and Split into Pages

Use the load_and_split() method to read the PDF and split it by page: 
pages = loader.load_and_split()

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4. Verify the Page Count

Confirm you have the expected number of pages: 
print(len(pages))
# Output: 3
The image shows a Jupyter Notebook interface with text discussing performance appraisals, training, and grievance procedures. It includes details about online courses and disciplinary actions.
The output confirms three pages. You can inspect any page’s content by indexing into pages: 
print(pages[1].page_content)

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Next Steps

With your PDF now loaded and split, you can:
  • Embed page texts for semantic search
  • Build a vector store for similarity matching
  • Hook into a chat interface for RAG-powered Q&A

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