Vector database
Vektordatenbank
A database built to store embeddings and find the nearest ones fast. The retrieval engine behind most RAG systems.
01In short
A database built to store embeddings and find the nearest ones fast. The retrieval engine behind most RAG systems.
02Video
slot · videoVector database in 90 secondsadd: GUIDES['vector-database'].video
03Guide
slot · guide
A step-by-step guide for “Vector database” goes here. Suggested outline:
- What it is — in one paragraph
- Why it matters in production
- How to do it — 3 to 7 steps
- Pitfalls we see in the field
04Checklist
slot · checklist
Four to eight things a team can tick before go-live.
05FAQ
slot · FAQ
The three questions clients actually ask about “Vector database”.
06Related terms
BasicsEmbeddingA list of numbers that represents the meaning of text or an image, so that similar things end up close together. The basis of semantic search and RAG.Data & retrievalRAGBefore answering, the system searches your documents and hands the relevant passages to the model. Answers get grounded in your data without retraining.Data & retrievalSemantic searchSearch by meaning instead of exact words: “cancel my contract” finds the page titled “termination”.
Where we help · Build