RAG with Multiple Complex PDF Files: Efficient Document Management with Vector Stores

Learn how to create a Retrieval-Augmented Generation (RAG) model using multiple PDFs in this tutorial. We’ll extract and store relevant information in a vector database, indexed by:

• Chunk_ID
• Unique Document ID
• Content

As illustrated in the figure below. We can then handle user queries and generate relevant responses with the source of information.

To build the Retrieval-Augmented Generator (RAG), we’ll begin by converting PDF pages into images, which will then be processed by GPT-4o, a vision-enabled Large Language Model to extract text. As a result, we can now obtain a rich and accurate representation of the PDF’s information, encompassing both text and images, with no loss of vital details, such as charts, graphs, and other visualizations.

Alternatively, ColPali can also be utilized for this task, but for the purpose of this demonstration, we’ll focus on leveraging GPT-4o’s vision-enabled LLM capabilities.