A vector search database is a specialized system designed to store, retrieve, and search for data vectors based on their similarity, providing a foundation for applications like image retrieval, natural language processing, and recommendation systems. Unlike traditional text-based search engines, vector databases use embeddings—high-dimensional vector representations of data items such as text, images, or audio—generated through techniques like convolutional neural networks for images and models like BERT for text. These embeddings are indexed and queried using algorithms like Approximate Nearest Neighbors (ANN), which include methods such as Locality-Sensitive Hashing and Hierarchical Navigable Small World graphs, to efficiently find similar vectors. Vector databases offer various implementations, including Google’s Vertex, Pinecone, and open-source solutions like Weaviate and Milvus, each with unique advantages depending on application needs. Labelbox Catalog further enhances vector search capabilities by allowing users to organize and query unstructured data using vector embeddings, enabling natural language searches and similarity searches to identify high-impact data efficiently.