LangChain | Document Loader | PyPDFLoader: Extracting PDF Data for RAG | Video #25

Описание: Welcome back! 📄 In Video #25 of our LangChain Full Course, we dive into the most popular way to feed data into a RAG pipeline: the PyPDFLoader.

Most corporate and academic data is trapped in PDF format. As part of our Document Loader module, I will show you how to use the PyPDFLoader to extract text from multi-page documents. Unlike the simple TextLoader, this tool automatically handles page-level splitting and tracks page numbers in the metadata—which is essential for building AI chatbots that can cite their sources!

✅ In this practical tutorial, we cover:

Installation: Setting up the pypdf library required for LangChain to read PDF files.

The PyPDFLoader Class: How it inherits from the base Document Loader.

Automatic Page Splitting: Why PyPDFLoader creates a separate Document object for every page.

Advanced Metadata: How to access the page number and source automatically stored in the document object.

Coding Demo: Loading a complex PDF and inspecting the list of documents generated.

Why this matters: If you want to build a "Chat with your PDF" application, this is the most important loader to master. It transforms static documents into a structured format that your LLM can search and analyze.

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