Multimodal RAG Patterns Every AI Developer Should Know

Vectorize, co-founded by the author, focuses on developing applications using large language models (LLMs) and multimodal retrieval augmented generation (RAG) systems, which incorporate various data types like text, images, and audio. The article discusses three primary design patterns for building multimodal RAG systems: embedding text descriptions of non-text data, using multimodal embeddings with media storage, and employing text embeddings with raw media pointers stored as metadata. These patterns guide the architecture of RAG systems, depending on factors such as data complexity and scalability needs. The importance of metadata extraction and representation across different modalities is emphasized to enhance the quality of AI outputs. The text also highlights the need for careful selection of vector databases and discusses the challenges of preprocessing multimodal data, with Vectorize offering solutions to streamline these processes. The company provides a free tier to help developers optimize their vectorization strategies without incurring costs.

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