Why Vector Databases Are Having A Moment As The AI Hype Cycle Peaks
TechCrunch, Saturday, April 20th, 2024
Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and the generative AI (GenAI) movement have created fertile ground for vector database technologies to flourish.
While traditional relational databases such as Postgres or MySQL are well-suited to structured data - predefined data types that can be filed neatly in rows and columns - this doesn't work so well for unstructured data such as images, videos, emails, social media posts, and any data that doesn't adhere to a predefined data model.
Vector databases, on the other hand, store and process data in the form of vector embeddings, which convert text, documents, images, and other data into numerical representations that capture the meaning and relationships between the different data points. This is perfect for machine learning, as the database stores data spatially by how relevant each item is to the other, making it easier to retrieve semantically similar data.