Vector database
A vector database is a store built to hold embeddings (numeric representations of meaning) and to quickly find the entries most similar to a given query vector — the engine behind meaning-based search.
How it works
Ordinary databases are great at exact matches — find the row where the email equals this value. But “find the content that means roughly this” is a different problem, and that’s what a vector database is designed for.
It indexes embeddings so that, given a new query vector, it can return the closest matches in milliseconds even across large amounts of content.
How SiteMind uses it
SiteMind stores your content’s embeddings in a vector store (Postgres with the pgvector extension) so that every visitor question is matched to the most relevant passages from your knowledge base almost instantly.
Related terms
Embeddings
Embeddings turn text into lists of numbers that capture its meaning, so a computer can tell which pieces of content are about the same thing.
Semantic search
Semantic search finds content by meaning rather than exact keywords, so it can match a question to the right answer even when the words differ.
Chunking
Chunking is the process of splitting long content into smaller, self-contained pieces so an AI can retrieve exactly the relevant part instead of a whole page.
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