Gemini Multimodal RAG - Text, Image and Document Retrieval

Multimodal RAG extends traditional text-only retrieval by indexing and retrieving across multiple content types - product images, PDF manuals, specification sheets, and text descriptions - in a unified pipeline. With Gemini, retrieved content from different modalities can be passed together in a single prompt, enabling richer and more accurate answers than text-only RAG.

The pattern works in three steps: embed all content (text and images) using the multimodal embedding model, store vectors with metadata identifying the source type, then at query time retrieve top-k results across all modalities and pass them to Gemini for synthesis. This gives ShopMax India a single search interface that works across product descriptions, user manuals, and product photos.

The below example shows how ShopMax India builds a multimodal product assistant that retrieves across text descriptions and image captions before generating a response.

It gives the following output,

Embedding functions ready

It gives the following output,

Index built: 3 items

With the index ready, queries retrieve the top-k most relevant chunks across all modalities and pass them to Gemini as context for the final answer.

It gives the following output,

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For production at ShopMax India, replace the in-memory index with Vertex AI Vector Search and store image captions alongside original image URLs so the agent can return both text answers and relevant product images. As the catalogue grows, the same pipeline handles thousands of indexed items with millisecond retrieval latency through the managed vector search endpoint.