Definition: A vector database stores numerical representations of data, called embeddings, and quickly finds the items most similar to a query, which makes it useful for semantic search and RAG.
An embedding model converts text, images or other data into vectors, lists of numbers that place similar meanings close together. The database indexes these vectors so it can run approximate nearest-neighbor search at scale, usually combined with metadata filters such as customer, date or document type.
Options range from dedicated vector databases to extensions of existing databases, such as pgvector for Postgres. The choice depends on data volume, latency needs, filtering, cost and how much new infrastructure a team wants to operate.
A company needs vector search when it wants to find information by meaning rather than exact keywords: retrieval for AI assistants, recommendations, deduplication or similarity matching across large document sets.
Ryz vector database engineers design embedding pipelines and search indexes in your cloud, working on your team, often within a RAG-focused AI pod.
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