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vector-databases

Choose and operate vector databases — indexing algorithms, filtering, scaling, and production concerns. Use when storing and querying embeddings at scale.

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# Vector Databases

A vector database stores embeddings and finds nearest neighbors fast at scale. The decisions: which 
index algorithm, how to filter, how to scale, and how to keep results good as data grows.

## Overview

Core operation: given a query vector, return the k most similar stored vectors — approximately 
(ANN) for speed at scale. HNSW graphs dominate for the quality/speed balance; IVF partitions for 
huge datasets. On top: metadata filtering (so "similar" also means "in the right category/date"), 
hybrid search hooks, and operational concerns — indexing throughput, recall tuning, and 
multi-tenancy.

## When to use