What is a multimodal vector database?

A multimodal vector database is a system designed to store, index, and retrieve data from multiple modalities—such as text, images, audio, or video—using vector embeddings. Unlike traditional databases that rely on exact keyword matches or structured metadata, these databases convert unstructured data into numerical vectors (arrays of numbers) that capture semantic meaning. This allows developers to perform similarity searches across different data types. For example, a user could search for images similar to a text query like “a sunset over mountains” by comparing vector representations of both the text and images.

These databases work by using machine learning models to generate embeddings for each data type. For instance, a text embedding model like BERT converts sentences into vectors, while a vision model like CLIP does the same for images. The vectors are stored in the database and indexed using algorithms optimized for fast similarity comparisons, such as approximate nearest neighbor (ANN) search. When a query is made—whether it’s text, an image, or another format—the database converts it into a vector and retrieves the closest matches from the stored embeddings. This process enables cross-modal retrieval, like finding relevant images based on a text input or vice versa, by measuring vector similarity using metrics like cosine similarity.

Practical applications include recommendation systems, content moderation, and multimedia search. For example, an e-commerce platform could use a multimodal database to let users search for products using a combination of text descriptions and uploaded photos. A content moderation system might cross-reference uploaded images with banned text phrases to detect policy violations. Tools like FAISS, Milvus, or Pinecone are commonly used to implement such systems, often paired with pre-trained models like CLIP or Sentence-BERT to generate embeddings. The key advantage is flexibility: developers can unify search across diverse data types without relying solely on manual tagging or rigid schemas.