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Vector databases explained | Lantern Blog

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You might have heard the term “vector database” come up in the context of an AI like ChatGPT. You probably had a few questions. Why is a vector database useful for large language models? What benefits does it offer? What is a vector, anyway? In this article, we’ll walk through what exactly they are. We’ll define the major concepts - vector embeddings, vector indexing, and vector search. At the end, we’ll put them all together. It’s been pretty hard to ignore the buzz around ChatGPT. In November 2023, ChatGPT had over 100 million weekly active users. Large language models (LLMs) like ChatGPT are excellent at communicating with humans and explaining information in a way we understand. People are excited about the promise of LLMs to automate and improve work. For example, Github Copilot has over 1 million paying customers that use it to help write code. However, there are many things that ChatGPT can’t answer questions about – for example, non-public information that ChatGPT didn’t have a

Vector databases explained | Lantern Blog On this page First, why am I hearing about vectors and vector databases? What is a vector? How do we compare vectors to see if they’re similar? How are vectors useful? What is a vector database? How do vector databases relate to LLMs? How does a vector database work? End-to-end diagram for a vector database When to choose a vector database? What about relational databases? Share this post You might have heard the term “vector database” come up in the context of an AI like ChatGPT. You probably had a few questions. Why is a vector database useful for la

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