Vector Databases in AI

 

Overview

Vector databases have become popular with the rise of Generative AI. Advanced technologies such as CHAT-GPT and Gemini rely heavily on vector data. If you want to work with AI, understanding vector data is essential.

Embeddings

Embeddings are numerical vector representations of unstructured data such as text, images, and audio. They preserve semantic meaning, enabling related content to be located efficiently.

Example input:

“I love AI”  ---> 

The sentence is transformed into a vector that captures its semantic intent.

Embeddings are a key technique in AI. They transform text, images, and audio into numerical vectors so that machines can easily process and understand them.

Source 

How Do Vector Databases Work?

User Query

- The user enters a question into ChatGPT.

Embedding Creation

- ChatGPT converts the input data into a vector embedding that represents the core meaning of the question.

Database Comparison

- This vector embedding is compared with other embeddings stored in the vector database to identify the most semantically relevant ones.

Output Generation

- The database generates a response based on the embeddings that are most similar to the meaning of the user’s original question.

Source