Kernel Memory leverages vector storage to save the meaning of the documents ingested into the service, solutions like Azure AI Search, Qdrant, Elastic Search, Redis etc.

Typically, storage solutions offer a maximum capacity for each collection, and often one needs to clearly separate data over distinct collections for security, privacy or other important reasons.

In KM terms, these collection are called “indexes”.

When storing information, when searching, and when asking questions, KM is always working within the boundaries of one index. Data in one index never leaks into other indexes.