Loading PDFs

In this lesson you’ll learn how to load a PDF and split it into pages as the first step of a Retrieval-Augmented Generation (RAG) pipeline. We’ll use a small, fictitious employee handbook for “Lakeside Bicycles” — a simple three-page PDF containing policies such as leave and discipline procedures. The goal is to extract the handbook text, split it into page-level documents, inspect the results, and prepare the output for downstream steps like embedding, indexing, and building a Q&A/chat interface.

Before running the examples, install the required packages. A typical install command is:

pip install langchain-community pypdf

See langchain-community on PyPI and pypdf on PyPI for details. For LangChain docs, visit LangChain Documentation.

Example dataset

From a notebook or shell, list the dataset directory:

!ls data

handbook.pdf

Load and split the PDF into page documents

We use the PyPDFLoader from the langchain-community package. The loader’s load_and_split() method extracts text and returns a list of LangChain Document objects (one per page by default).

from langchain_community.document_loaders import PyPDFLoader

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

Check how many page documents were produced:

len(pages)

What each page Document contains

Each item in pages is a LangChain Document with two primary attributes:

Attribute	Description	Example
`page_content`	Extracted text for that page	Short string containing page text
`metadata`	Metadata about the page (source, page number, etc.)	`{"source": "data/handbook.pdf", "page": 1}`

Inspect a page’s content and metadata

View the first page’s extracted text:

# First page content
pages[0].page_content

"complaint or concern about your work situation. You can contact the HR department for more information on the discipline and grievance procedures. We hope that this handbook has given you a clear and comprehensive overview of what it means to work at LakeSide Bicycles. If you have any questions or suggestions, please feel free to contact the HR department. We look forward to working with you and making LakeSide Bicycles a great place to work!"

View the first page’s metadata:

# First page metadata
pages[0].metadata

{"source": "data/handbook.pdf", "page": 1}

Iterate pages for inspection or processing

You can loop over pages to print metadata and a snippet of each page. This is useful for quick validation before moving to embeddings or indexing.

for i, p in enumerate(pages, start=1):
    print(f"Page {i} metadata: {p.metadata}")
    print(p.page_content[:200])  # print first 200 chars for a quick preview
    print("---")

Next steps in a RAG pipeline

After successfully loading and splitting the PDF, common next steps are:

Clean or normalize the text if necessary (remove headers/footers).
Create embeddings for each page using an embeddings model.
Store embeddings in a vector store (e.g., FAISS, Pinecone, Weaviate).
Build a retriever and attach a language model for Q&A/chat over the handbook.

References:

LangChain docs: https://langchain.readthedocs.io/en/latest/
Vector stores: FAISS, Pinecone, Weaviate

Scanned or image-based PDFs will not yield good text using PyPDFLoader alone — they need OCR (e.g., Tesseract, Amazon Textract, or other OCR services) before or during loading. Also, encrypted PDFs may require a decryption key or preprocessing.

Tips and common issues

If pages contain repeated header/footer text, consider removing those segments during preprocessing to improve retrieval relevance.
Verify encoding and whitespace issues on extraction; sometimes lines may be broken incorrectly and require normalization.
For large PDFs, consider splitting on semantic boundaries (sections or paragraphs) instead of fixed pages to get better retrieval granularity.

Now that the handbook is loaded and split into page-level Documents, you can proceed to embedding, indexing, and building your RAG-powered Q&A or chat application. Similar loader patterns apply to web pages and other document formats (use appropriate loaders and OCR tools where necessary).

Introduction

Overview of LangChain

Key Components of LangChain

Interacting with LLMs

Tips, Tricks, and Resources

Introduction to LCEL

Adding Memory to LLM Apps

Performing Retrieval

Implementing Chains

Using Tools

Building Agents

Conclusion

Building Blocks of LLM Apps

Example dataset

Load and split the PDF into page documents

What each page Document contains

Inspect a page’s content and metadata

Iterate pages for inspection or processing

Next steps in a RAG pipeline

Tips and common issues

Watch Video

​Example dataset

​Load and split the PDF into page documents

​What each page Document contains

​Inspect a page’s content and metadata

​Iterate pages for inspection or processing

​Next steps in a RAG pipeline

​Tips and common issues

Watch Video

Example dataset

Load and split the PDF into page documents

What each page Document contains

Inspect a page’s content and metadata

Iterate pages for inspection or processing

Next steps in a RAG pipeline

Tips and common issues