AI Vocabulary · 03 Working with AI
What is Vector Database?
A database that stores embeddings, so you can search your documents by meaning instead of exact keywords.
A vector database stores embeddings: lists of numbers that represent the meaning of pieces of text. When a question comes in, it is turned into an embedding too, and the database quickly finds the stored passages whose meaning is closest. That retrieval step is the engine behind most RAG systems.
Before documents go in, they are usually split into smaller pieces through chunking. Vector search can be offered by dedicated databases or as a feature of ordinary databases and AI platforms.
Why it matters for you
If you want an assistant that answers from your company knowledge, you will need some form of vector search. When it returns the wrong passages, the setup of chunks and embeddings is usually the place to look.
Own it
A vector database holds a processed copy of your knowledge. Know where it is hosted, who can access it, and whether you can export it or rebuild it elsewhere. That is part of your data sovereignty.
Where it comes from
Searching by similarity is decades old, but two steps brought it to AI products. In 2017, Facebook released FAISS, a library for fast similarity search over billions of vectors. Around 2021, companies such as Pinecone began selling managed "vector databases" as their own product category, and the term took off with RAG after ChatGPT's launch.
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