Building Multimodal RAG Application #7: Multimodal RAG with Multimodal LangChain

Author(s): Youssef Hosni

Originally published on Towards AI.

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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 seventh installment in our Building Multimodal RAG Applications series, dives into building multimodal RAG systems with LangChain.

We will wrap all the modules created in the previous articles in LangChain chains using RunnableParallel, RunnablePassthrough, and RunnableLambda methods from LangChain.

This article is the seventh 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) (Published)Multimodal RAG with Multimodal LangChain (You are here!)Putting it All Together! Building Multimodal RAG Application (Coming soon!)

You can find the codes and datasets used in this series in this GitHub Repo

Setting Up Working EnvironmentInvoke the Multimodal RAG System with a QueryMultimodal RAG System Showing Retrieved Image/Frame

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Published via Towards AI