A Gentle Introduction to Using a Vector Database | Steve Kinney
Let’s talk about vector databases by using Pinecone and OpenAI embeddings to build a simple script that allows you to search a set of what we’ll generously call “recipes.” We’re going to explore how vector databases differ from the usual SQL-style tables you might know—and potentially love/hate, learn how to generate embeddings with OpenAI to capture the “meaning” of text, and then put it all together in a simple TypeScript project—because I can’t be bothered to learn how virtualenvs work in Python. By the end, you’ll be ready to store and semantically query unhinged recipes—or anything else you fancy—with ease, grace, and poise.
If we’re going to spend the next little bit learning how to use a vector database, then it probably makes sense to spend a moment or two to quickly review what they are and why they’re potentialy useful.
A vector database is a specialized system for storing and searching high-dimensional representations (vectors) of data, rather than traditional rows and columns. When AI models (like large language models) convert text or images into numerical embeddings that capture semantic meaning, those embeddings can be efficiently stored in a vector database. This allows for “similarity searches” that retrieve the most relevant information based on how close two vectors are in high-dimensional space, rather than relying on exact keyword matches.
