Vector Memory with Embeddings: Retrieval That Feels Like Recall in Agentic AI
Vector Memory with Embeddings: Retrieval That Feels Like Recall
Why vector memory exists
Users rarely say things the same way twice. Vector memory helps you retrieve relevant past context even when the wording changes.
How it works
- Convert text → embedding
- Store embedding + metadata
- On query, embed query and retrieve top-k similar memories
Metadata filtering is not optional
Always store metadata like user_id, tenant_id, topic, time. Then filter retrieval to avoid cross-user leakage.
Retrieval tuning
- Use top-k carefully (too high = noise)
- Prefer recent memories when scores are close
- Summarize retrieved chunks before injecting into prompt
Common mistake
People store raw chats in vector DB. Don’t. Store compact, well-formed memory cards.
