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Vector database

As explained in Build Applications with Local AI Models on a Mac

A vector database stores vector representations and supports searching for vectors that are similar or close to a query vector.

A vector database stores vector representations and lets you search for vectors similar or close to a query vector. In retrieval-augmented generation (RAG), it can provide an AI with access to your documents. section 16.9 section 18.1

Example

ChromaDB is named as a vector database that can be used with RAG to give an AI access to your own documents. section 16.9

How Build Applications with Local AI Models on a Mac covers it

How deep it goes
Defines vector storage and similarity search, but does not explain implementation or internals.
Examples
Names ChromaDB as an example of a vector database used for RAG.
What you can do after
Understand vector databases as a component of RAG architectures.
How it connects
Connects vector databases with retrieval-augmented generation, embeddings, and retrieval pipelines.

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This explainer was generated by Manning's knowledge graph from the text of Build Applications with Local AI Models on a Mac. The section links point to the parts of the book it draws on.