A multimodal vector database stores and indexes embeddings from multiple modalities, such as text, images, and audio, enabling efficient similarity searches across diverse data types. Unlike traditional vector databases designed for single-modality embeddings, multimodal vector databases are optimized for use cases that require cross-modal retrieval.
For example, a user might search for images by entering a text query like “red sports car.” The database stores both text and image embeddings in a shared space, allowing it to retrieve relevant images by comparing the semantic similarity between the text query and image embeddings.
These databases are often integrated with AI models like CLIP, which generate embeddings that align across modalities. Applications include multimedia search engines, recommendation systems, and augmented reality platforms.
Key features of multimodal vector databases include support for large-scale embeddings, low-latency retrieval, and compatibility with popular AI frameworks. They may also include indexing techniques like Hierarchical Navigable Small World (HNSW) graphs to ensure efficient queries even at scale.
Multimodal vector databases are critical for applications requiring seamless interaction between different data types, enabling richer and more dynamic user experiences.