What are multimodal embeddings in RAG?

Multimodal embeddings represent different types of content—such as text, images, audio, or video—as vectors that can be compared in a shared or compatible embedding space.

In RAG, this allows retrieval to work across more than just text.

Why multimodal embeddings are useful

Traditional RAG often works like this:

Text document → text embedding User query → text embedding Vector search → retrieve relevant text

But many real documents contain information that text extraction alone cannot capture well.

Examples include:

• Diagrams
• Product images
• Charts
• Screenshots
• Scanned forms
• Slides
• Video frames

Multimodal embeddings let the retrieval system represent some of these non-text elements directly.

Example

Suppose a maintenance manual contains a diagram showing how a valve assembly is connected.

The user asks:

"Show me the diagram for the pressure-control valve."

A text-only RAG system may retrieve the surrounding paragraph.

A multimodal system can also embed the diagram itself and retrieve the image because its representation is semantically related to the query.

Shared embedding space

Some multimodal models place text and images into the same vector space.

That means:

Text query: "red sports car"

can be embedded and compared directly against image embeddings.

Images that visually represent a red sports car should appear close to the query vector.

This enables:

Text → image retrieval

It may also support:

Image → text retrieval Image → image retrieval

depending on the model.

How it fits into a RAG pipeline

A multimodal RAG ingestion pipeline may look like:

Document → extract text, images, charts, and other elements → generate embeddings for each modality → store vectors with source metadata

At query time:

User query → generate compatible embedding → search relevant vectors → retrieve text, images, or both → provide the selected evidence to a multimodal LLM

Do all modalities need one embedding model?

Not necessarily.

There are two common designs.

One approach uses a shared multimodal embedding model so text and images can be compared directly.

Another approach uses separate embedding models and separate indexes, then combines their retrieval results.

The right design depends on whether cross-modal similarity is required and how well the available models support the domain.

Important limitation

Multimodal embeddings help with retrieval, but they do not automatically understand every detail inside an image.

For example, a chart may require:

• OCR for labels
• Structured extraction of values
• Vision reasoning
• A caption or textual summary

So multimodal embeddings are often one part of a larger document-processing pipeline.

Interview takeaway

Multimodal embeddings convert different content types such as text and images into vector representations that can be searched semantically. In RAG, they enable retrieval across modalities—for example, using a text query to retrieve a relevant image or diagram. They are especially useful when important information cannot be represented reliably through extracted text alone.