Loading PDFs

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.

Prerequisites

Before you begin, ensure you have the following:

pip install langchain langchain-community

1. Import the PDF Loader

Start by importing PyPDFLoader from the community loaders:

from langchain_community.document_loaders import PyPDFLoader

2. Initialize the Loader

Point the loader at your PDF file (e.g., data/handbook.pdf):

loader = PyPDFLoader("data/handbook.pdf")

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()

4. Verify the Page Count

Confirm you have the expected number of pages:

print(len(pages))
# Output: 3

The output confirms three pages. You can inspect any page’s content by indexing into pages:

print(pages[1].page_content)

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

Links and References

• LangChain Documentation
• PyPDFLoader Source Code
• Retrieval-Augmented Generation (RAG) Overview