Choosing a Vector Database for Your Gen AI Stack
Vector databases are designed for efficient storage, retrieval and similarity search of high-dimensional vector data. Using a process called embedding, vector data is represented in a continuous and meaningful high-dimensional vector space, usually referred to as an embedding space.In this article, we examine practical approaches for storing/retrieving vector data and performing similarity search, especially in light of generative AI applications. We will also highlight key capabilities where SingleStoreDB outshines other vector-capable databases.Before we dive deeper, let’s outline the capabilities that make up a vector database:Ability to perform similarity searchesWhen given a query vector, a vector database can retrieve the most similar vectors based on a specified similarity metric, such as cosine similarity or Euclidean distance. This allows applications to find relevant items or data points based on their similarity to a given query.Retrieve vector data with high performanceVector databases often employ indexing techniques, typically Approximate Nearest Neighbor (ANN) algorithms (e.g., Locality-Sensitive Hashing or Product Quantization), to accelerate the search process. These indexing methods aim to reduce the computational complexity of searching in high-dimensional vector spaces, where traditional methods like spatial decomposition become impractical due to high dimensionality.The landscape of vector databasesWe look at five approaches for persisting and retrieving vector dataPure vector databases like PineconeFull text search databases like ElasticSearchVector libraries like Faiss, Annoy and HnswlibVector-capable NoSQL databases like MongoDB, Cosmos DB and CassandraVector-capable SQL databases like SingleStoreDB or PostgreSQLApart from the five main approaches mentioned above, there are AI/ML platforms such as Vertex AI and Databricks whose capabilities go beyond databases and for this reason, I exclude them in this analysis.