What is Multimodal RAG? Unlocking LLMs with Vector Databases

2026-02-16 · Source: IBM Technology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

Multimodal Retrieval Augmented Generation (RAG) extends traditional RAG to handle diverse data types beyond text, such as images, videos, and audio. While classic RAG converts text documents into vectors for storage in a vector database and retrieves relevant text chunks based on user queries, multimodal RAG addresses the limitation that much real-world data is not solely textual. The article outlines three approaches: "Text-ify everything RAG" converts all non-text modalities (e.g., images to captions, audio/video to transcripts) into text before applying standard RAG, though this can lose nuance. "Hybrid multimodal RAG" retrieves over text (captions, transcripts) but then passes the original non-text data alongside text to a multimodal LLM for reasoning. "Full multimodal RAG" employs a multimodal embedding stack that maps all modalities into a shared vector space, enabling direct cross-modal retrieval and reasoning, offering the richest grounding but with higher cost and complexity.

Key takeaway

For AI Engineers building advanced RAG systems, understanding the three multimodal RAG approaches is crucial for balancing capability and complexity. If your application requires nuanced reasoning over visual or audio data, consider moving beyond "Text-ify everything RAG" to "Hybrid" or "Full multimodal RAG" to preserve critical context and enable richer LLM interactions, despite increased computational demands.

Key insights

Multimodal RAG enables LLMs to reason over diverse data types by integrating non-textual information into retrieval and generation.

Principles

• Data modality dictates preprocessing needs
• Shared vector spaces enable cross-modal search

Method

Multimodal RAG can be implemented by text-ifying all data, using hybrid text-based retrieval with multimodal LLMs, or full multimodal embedding and retrieval.

In practice

• Use captioning for images in basic RAG
• Employ multimodal LLMs for richer context
• Align embeddings for cross-modal search

Topics

• Multimodal RAG
• Large Language Models
• Vector Databases
• Multimodal Embeddings
• Retrieval-Augmented Generation

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.