Building Multimodal RAG Application #3: Multimodal RAG System Architecture
In the third article of the Building Multimodal RAG Application series, we explore the system architecture of building a multimodal retrieval-augmented generation (RAG) application. We will start with the main components of multimodal RAG systems and how each of them functions in the context of an RAG system and we will end the article with the main functions of multimodal RAG systems.
This article is the third in the ongoing series of Building Multimodal RAG Application:
Multimodal RAG Application Architecture (You are here!)
Processing Videos for Multimodal RAG (Coming soon!)
Multimodal Retrieval from Vector Stores (Coming soon!)
Large Vision Language Models (LVLMs) (Coming soon!)
Multimodal RAG with Multimodal LangChain (Coming soon!)
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
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The multimodal RAG system combines retrieval mechanisms and generative models to create answers based on diverse sources of data, using embeddings for both textual and visual content. It efficiently handles multimodal queries, enabling in-depth, context-rich answers.
As shown in Figure 1 the multimodal RAG system is similar to that of traditional RAG, except for the fact of using multimodal data. We can summarize its working concept into eight main components: