Building Multimodal RAG Application #6: Large Vision Language Models (LVLMs) Inference

Author(s): Youssef Hosni

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

Multimodal retrieval-augmented generation (RAG) is transforming how AI applications handle complex information by merging retrieval and generation capabilities across diverse data types, such as text, images, and video.

Unlike traditional RAG, which typically focuses on text-based retrieval and generation, multimodal RAG systems can pull in relevant content from both text and visual sources to generate more contextually rich, comprehensive responses.

This article, the sixth installment in our Building Multimodal RAG Applications series, dives into inference with Large Vision Language Models (LVLMs) within an RAG framework.

We’ll cover setting up the environment, preparing data, and leveraging LVLMs across a variety of use cases. These include tasks like image captioning, visual question answering, and querying images based on embedded text or associated captions and transcripts, showcasing the full potential of LVLMs to unlock advanced multimodal interactions.

This article is the sixth in the ongoing series of Building Multimodal RAG Application:

Introduction to Multimodal RAG Applications (Published)Multimodal Embeddings (Published)Multimodal RAG Application Architecture (Published)Processing Videos for Multimodal RAG (Published)Multimodal Retrieval from Vector Stores (Published)Large Vision Language Models (LVLMs) (You are here!)Multimodal RAG with Multimodal LangChain (Coming soon!)Putting it All Together! Building Multimodal RAG Application (Coming soon!)

You can find… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI