Optimizing Generative AI with Vector Databases: A Deep Dive into Search Algorithms and Techniques

The explosion of Generative AI (GenAI) models has significantly transformed fields like natural language processing, computer vision, and recommendation systems. These models often produce high-dimensional embeddings (vectors) to represent data such as text, images, or videos. To efficiently store, retrieve, and search through these embeddings, Vector Databases (Vector DBs) have emerged as crucial infrastructure.

In this article, we will explore what Vector Databases are, why they are essential in the context of GenAI, and take a closer look at several popular vector search algorithms like Locality Sensitive Hashing (LSH), Hierarchical Navigable Small World (HNSW), and ANNOY. We’ll also review which vector databases implement these methods and provide guidance on their use.

What is a Vector Database?

A Vector Database is a specialized database designed to store, index, and search high-dimensional vectors efficiently. Unlike traditional databases that store structured data (e.g., rows and columns), vector databases deal with unstructured data like images, text embeddings, or feature vectors, which are representations of data in a continuous vector space.

These databases are designed to facilitate vector similarity search, where the goal is to retrieve vectors that are closest (i.e., most similar) to a query vector based on metrics like cosine similarity or Euclidean…