Vector Embeddings — Storing AI-Generated Data
Understand what vector embeddings are, how to store them with pgvector, and how to build similarity search for AI-powered applications.
Full-text search matches words. But what if someone searches for "how to make my website faster" and your article is titled "Web Performance Optimization Techniques"? There's no word overlap, yet the content is exactly what they need.
This is where vector embeddings come in. Instead of matching keywords, you match meaning. An embedding converts text (or images, or audio) into a list of numbers that captures its semantic content. Similar meanings produce similar numbers, enabling a completely different kind of search.
If you're building AI-powered features — semantic search, recommendations, RAG (Retrieval-Augmented Generation), or chatbots with memory — you need to understand embeddings.
What Is a Vector Embedding?
An embedding is a list of numbers (a vector) that represents the
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