Vector database
A vector database indexes embeddings so that, given a query vector, it can quickly return the nearest ones by similarity, at a scale and speed a general-purpose relational database is not built for. It is the storage layer most retrieval-augmented generation is built on top of, holding the embedded chunks a retriever actually searches.
Because it holds the same sensitive content as the documents it was built from, everything about it, where it runs, who can reach it, how it is backed up, is part of the same governance question as the documents themselves, not a separate infrastructure decision.
A firm's policy manual, once embedded, lives in a vector index that a retrieval query searches in milliseconds, instead of scanning every document in the manual on every question.
In Dynamiq, Knowledge Bases provision managed vector storage by default, backed by Weaviate, or can point at 8 vector stores you operate yourself, including Pinecone, Qdrant, Milvus, pgvector, Elasticsearch, OpenSearch and Chroma, with the same retrieval and access control options either way.
See an agent on your own workflow.
Bring a process and its documents. Our engineers will show you how Dynamiq runs it, in your environment or ours.