Every RAG app, semantic search feature, and “related to this” button depends on one hidden operation:

Find the nearest points among millions — fast.

That sounds simple until you realize each document is stored as a high-dimensional vector, often with more than a thousand numbers. If your app has two million help articles, the brute-force approach means comparing the query against every single vector, every time.

That is where normal SQL indexes break down.

A B-tree can help with IDs, prices, dates, and sorted values. But vector search asks a different question:

Which document is closest in meaning across 1,536 dimensions?

There is no single sorted line for that.

In this AI Concepts explainer, we look at why AI systems need vector databases, how approximate nearest-neighbor search works, and why HNSW-style indexes can search millions of vectors in milliseconds without checking every point.

https://youtu.be/7vOt3CJEOJA

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