What types of data can be used in vector search?

Vector search works by comparing numerical representations (vectors) of data to find similarities. The most common types of data used in vector search include text, images, audio, structured data, and multimodal combinations of these. Each type is converted into vectors using specialized models or algorithms that capture their unique features. For example, text embeddings might use models like BERT, while images could rely on convolutional neural networks (CNNs). The key is that any data type can be used as long as it can be meaningfully transformed into a vector representation.

Text is the most straightforward use case. Sentences, paragraphs, or documents are converted into vectors using embedding models like Word2Vec, GloVe, or transformer-based architectures (e.g., BERT). These embeddings capture semantic meaning, enabling tasks like document similarity checks or search engines that understand context. For images, pre-trained CNNs like ResNet or VGG extract features (e.g., edges, textures) to create vectors. This allows reverse image search or content-based recommendations. Audio data, such as speech or music, can be converted using models like VGGish or spectrogram analysis, enabling voice recognition or song similarity matching. Structured data, like user behavior logs or tabular data, can also be vectorized through techniques like entity embeddings or autoencoders, useful in recommendation systems.

A less obvious but powerful application is combining multiple data types into a single vector (multimodal search). For instance, a product listing might include text descriptions, images, and user reviews—all encoded into a unified vector. Tools like CLIP (which aligns text and images) demonstrate this approach. Developers must preprocess data appropriately (e.g., resizing images, tokenizing text) and choose embedding models that align with their use case. Performance considerations include balancing vector dimensionality (higher dimensions capture more details but increase computational cost) and selecting efficient search algorithms (e.g., approximate nearest neighbors via FAISS or HNSW). The flexibility of vector search makes it adaptable to nearly any data type, provided the embedding process retains meaningful patterns.