Vector Database Setup

As soon as retrieval moves beyond a prototype and the questions become which store, which embedding model, and how to chunk the material.

The vector store is the substrate retrieval runs on. Selection matters less than the decisions around it: how content is chunked, what metadata travels with each chunk, and how permissions are represented so they can be enforced at query time.

Chunking is the choice most often made once and regretted later, because it determines whether retrieved passages are self-contained enough to be useful.

What the work includes

• Store selection against your scale, latency, and hosting constraints
• Chunking and metadata strategy suited to your document types
• Embedding model selection and re-embedding paths for when it changes
• Index maintenance as source material changes