<ul><li>This article is the second part of a series on creating a Multimodal RAG (Retrieve, Aggregate, Generate) pipeline for chatbots from PDFs. The first part focused on extracting text, tables, and images from PDFs.</li><li>In this second part, the author discusses the process of summarizing the extracted data elements using text embeddings and storing them in a vector database.</li><li>The article outlines the flow of the process, starting with the creation of a standard prompt for the language model. The author also provides instructions for creating a prompt for image elements.</li><li>The article also mentions the use of the Gemini-2.0-flash model for cost-effectiveness. The importance of a .env file for storing the Google AI API key is highlighted.</li></ul>

Multimodal RAG From PDFs To Data Store For PDF Chatbots Part-2: Summarization

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