What it does
Search infrastructure for AI applications, commonly used as a retrieval layer for agents, RAG apps, and local prototypes.
Why it belongs here
A practical way to keep context nearby and queryable instead of force-feeding the model everything every turn.
Best use case
Teams prototyping RAG apps, local AI tools, and agent memory systems that need a simple retrieval layer.
How to use it
Store embeddings for reusable knowledge, retrieve small task-relevant sets, and compare prompt size and answer quality against full-context baselines.
Limits
Easy setup does not remove the need for data hygiene, permissions, and retrieval evaluation before production use.
Tags
retrievalagentssearch
