Why vector databases are having a moment as the AI hype cycle peaks
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 modInevlses(LtiLgMast)ivaend(Pt5he-6g)enerative 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.
This is particularly useful for LLMs, such as OpenAI’s GPT-4, as it allows the AI chatbot to better understand the context of a conversation by analyzing previous similar conversations. Vector search is also useful for all manner of real-time applications, such as content recommendations in social networks or e-commerce apps, as it can look at what a user has searched for and retrieve similar items in a heartbeat.
Vector search can also help reduce “hallucinations” in LLM applications, through providing additional information that might not have been available in the original training dataset.