"Vector memory" and "RAG memory" get used interchangeably, but they name two halves of one mechanism: vectors are how meaning is stored, RAG is how it gets back into the model's context. Understanding the split explains most retrieval failures — and what to buy instead of build.
Vector memory: storage by meaning
Every memory or document chunk is embedded into a vector; similar meanings land near each other. That's what lets "what did we decide about login?" retrieve a memory that says "JWT with 15-minute expiry" — zero shared words, close vectors.
Where pure vectors fail
- Exact identifiers: error codes, invoice numbers, and product names embed poorly — keyword search finds them instantly.
- Compressed score ranges: everything looks vaguely similar, so thresholds can't separate on-topic from off-topic.
- Stale stores: vectors don't expire — without dedup and versioning, old truths outrank new ones.
RAG memory: retrieval into context
RAG completes the loop: retrieve the top chunks, pack them into the prompt, generate with citations. Done well it needs hybrid search (vectors + full-text fused), reranking, and a token budget — the details covered in our RAG pipeline guide.
The practical answer
Kit for AI runs hybrid retrieval (vector + keyword, rank-fused, reranked) over both memories and documents, behind remember/recall tools and a search API. You get vector memory and RAG memory as one system — without operating an embedding pipeline. Start free.