RAG teams who outgrew nearest-neighbour search
Upgrade naive RAG to structured recall
Move past a bare vector store to retrieval that understands users, sessions, recency, and importance.
In short
A vector store returns nearest neighbours; it has no notion of who the memory belongs to, how recent it is, or how important. Memorg keeps the vector index but adds hierarchy and a deterministic blended score on top.
The problem
- ✕A plain vector DB gives nearest-neighbours with no ownership, no scoping, no recency, no importance.
- ✕Everything lands in one flat index, so retrieval cannot scope to a topic or a single user cleanly.
- ✕Ranking is pure similarity — a stale aside can outrank yesterday’s decision.
With Memorg
- ✓Keep semantic search (USearch over OpenAI embeddings) but attach it to a session/conversation/topic hierarchy.
- ✓Re-score nearest neighbours with a deterministic blend of similarity, recency, and importance.
- ✓Scope a query to a topic, a conversation, or a whole user history depending on what you need.
How it works
- 1
Point Memorg at your data
Store exchanges or typed memory items; each is embedded and indexed in USearch automatically.
- 2
Choose a retrieval scope
Query within a topic, a conversation, or across the user’s whole history.
- 3
Get a deterministic ranking
Results come back scored by the semantic + recency + importance blend — no LLM reranker, so it is reproducible.
Features it uses
See the full feature set or how it works.
FAQ
+ Do I have to drop my existing vector database?
Memorg ships USearch in the same SQLite file, so most teams do not need a separate vector DB at all. The store interface is small and pluggable if you have a reason to swap it.
+ Is the ranking reproducible?
Yes. Retrieval is a deterministic blend of semantic similarity, recency, and importance. There is no LLM reranker in the default path, so the same query returns the same ordering.
Related use cases
Conversation continuity for LLM chat
Keep a chat coherent across turns, sessions, and days without stuffing the whole history into every prompt.
→A memory layer for your agent
Drop deterministic recall next to any orchestrator without adopting a whole agent runtime.
→Support bots that remember the customer
Give a support assistant per-customer recall so it stops asking for context the customer already gave.
→Put it to work
Install Memorg and wire this pattern into your app in a few lines.