Name a common algorithm used in vector-based search systems.

K-Nearest Neighbors (KNN) is a widely utilized algorithm in vector-based search systems due to its effectiveness in handling high-dimensional data. In vector-based searches, such as those used in machine learning and information retrieval, KNN operates by identifying the 'k' nearest data points in a multi-dimensional space, measured typically by distance metrics like Euclidean distance or cosine similarity. This is particularly beneficial in scenarios where the relationship between data points is crucial for determining relevance, making KNN suitable for various applications, such as recommendation systems and image recognition.

The algorithm's simplicity and efficiency in representing complex multi-dimensional relationships contribute to its popularity. When a search query is made, KNN retrieves the closest vectors (or data points) to the query vector, providing an intuitive and easily interpretable way to find relevant items based on proximity within the vector space. Its reliance on distance metrics aligns perfectly with the concept of vector embeddings, where items are represented in a high-dimensional feature space.

Other algorithms listed, such as Decision Trees, Support Vector Machines, and Naive Bayes, do not operate in the same manner as KNN in the context of vector searches. Decision Trees are primarily used for classification tasks by creating a model that predicts the class label based on