What are vectors database?

What is a vector database?

A vector database is a data storage system used to manage, index, and query high-dimensional vector data. Vectors, in this context, represent data points in multi-dimensional space, often used in machine learning, data mining, and other advanced analytical applications.

Vector databases are useful in tasks involving the computation of distances or similarities between vectors, such as recommendation systems, image and video recognition, and natural language processing.

Unlike traditional databases that manage structured rows and columns, vector databases handle more complex, high-dimensional data representations, which are essential for applications requiring efficient similarity searches and pattern recognition. Some general purpose databases, such as PostgreSQL and Cassandra, now support both traditional data formats and vector data.

The five popular general purpose databases that provide vector database functionality.

Key features of open source vector databases

Open-source vector databases typically include the following features:

Efficient indexing: Indexing mechanisms such as Approximate Nearest Neighbor (ANN) searches reduce the time required to find similar vector representations, useful for applications involving real-time data analysis.
Similarity search: This feature finds vectors that are close to a given query vector in high-dimensional space, based on measures like Euclidean distance and cosine similarity. It is essential for applications like recommendation engines, where the system needs to identify items similar to the user’s preferences. Open-source vector databases often used algorithms to perform these searches accurately.
Scalability: As organizations collect more high-dimensional data, the database must efficiently manage this increase without compromising performance. Open-source solutions often offer distributed architectures that help in scaling out, ensuring consistent response times even as data volumes expand.
Integration with machine learning libraries: Open-source vector databases often work with popular machine learning frameworks, allowing for simple deployment of machine learning models directly on the database. This enables the direct application of learned models to the stored data for real-time analysis and predictions.
Community and support: An open-source community can provide assistance through forums, documentation, or contributions to the codebase. These databases often benefit from active communities that help in troubleshooting, feature enhancements, and providing comprehensive usage guides.