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Glossary·
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Vector database
A store built for embeddings: instead of matching exact values, it finds the records whose meaning sits closest to a query in high-dimensional space.
Why it matters
It is the retrieval half of every working AI assistant. Search quality here caps answer quality — the model cannot cite a document the search layer failed to return.
How we approach it
We size the choice to the data. Postgres with pgvector covers most projects honestly; a dedicated store earns its place at scale, with metadata filters and re-ranking doing more for quality than swapping engines.
