Multimodal Cache
A Multimodal Cache is a specialized, high-speed data storage mechanism designed to store and retrieve representations of data from multiple modalities simultaneously. Unlike traditional caches that handle single data types (e.g., text strings or image files), a multimodal cache manages embeddings, feature vectors, and associated metadata derived from inputs like text, images, audio, and video.
In advanced AI applications, models rarely interact with just one type of data. A user might input an image and ask a question about it using text. A multimodal cache is crucial because it allows the system to quickly access pre-computed, semantically rich representations of both the image and the relevant knowledge base, drastically reducing latency.
The core function relies on embedding models. When data (e.g., an image) is processed, it is converted into a dense numerical vector (an embedding). The multimodal cache stores these vectors, often alongside metadata pointing to the original source. When a query arrives, the system converts the query into a vector and performs a nearest-neighbor search across the stored vectors, retrieving semantically similar content across different data types.
Vector Databases, Semantic Search, Retrieval-Augmented Generation (RAG), Embedding Models