Imagine being able to ask a question using a picture, query instructional videos to receive a single targeted clip, or search through a diverse collection of multimedia files using your voice. All of this is possible with the Open Platform for Enterprise AI (OPEA™) Multimodal Question and Answer (MMQnA) chatbot. The MMQnA chatbot leverages the power of multimodal AI to deliver a flexible and intuitive way to interact with complex datasets. Whether you’re a developer, a data scientist, or an enterprise looking to enhance your information retrieval capabilities, this tool is designed to help you efficiently meet your needs.
In the era of Large Language Models (LLMs), we can now make use of robust and accurate models for complex datasets. Instead of being limited to a single modality, like text, we can leverage transformer architectures that support any modality type as an input. Here, we introduce a MMQnA chatbot capable of handling any mix of text, images, spoken audio, or video in a Retrieval-Augmented Generation (RAG) workflow.
This article will walk you through the steps to deploy and test drive OPEA’s MMQnA megaservice on the Intel® Gaudi® 2 AI accelerator using Intel® Tiber™ AI Cloud. From setup to execution, we’ll cover everything you need to know to get started with this multimodal GenAI application.
Read more at Intel.com.