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Exercise

PDF document loaders

To begin implementing Retrieval Augmented Generation (RAG), you'll first need to load the documents that the model will access. These documents can come from a variety of sources, and LangChain supports document loaders for many of them.

In this exercise, you'll use a document loader to load a PDF document containing the paper, RAG VS Fine-Tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture by Balaguer et al. (2024).

Note: pypdf, a dependency for loading PDF documents in LangChain, has already been installed for you.

Instructions

100 XP
  • Import the appropriate class for loading PDF documents in LangChain.
  • Create a document loader for the 'rag_vs_fine_tuning.pdf' document, which is available in the current directory.
  • Load the document into memory to view the contents of the first document, or page.