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Welcome to the SERAPH wiki!

I will spare all of the you marketing stuff, you can find that on the website here https://www.seraph-db.com

What is SERAPH? SERAPH — Semantic Retrieval Architecture via Probabilistic Hashing

Its origins extend from knowledge graphs and vector databases, architecting components together to realize bounded propagation retrieval for agents, in order to provide a common memory substrate that is agnostic to use case. Many of you may have done the same thing, and have run into the same issues with that methodology. Constantly having to adjust taxonomy and the ontology of the system to fit different operating modes. Existing memory systems for AI agents make a structural compromise: the 'vector space' (where semantic similarity lives) and the 'graph' (where traversal lives) are separate layers that must be kept in sync. Vector stores answer "what is similar?" Graph databases answer "what is connected?" You query one to get candidates, then traverse the other for context. The seam between them is operational overhead, consistency risk, and a conceptual mismatch

SERAPH collapses this, the vector space IS the graph, semantic similarity and structural traversal become the same operation at different resolutions. Topology is created from content not declared (declared edges are supported, but not required) more on this later.

SERAPH is a append only storage layer, that may give one pause, as it did me adopting it as a architectural invariant but it provides several key advantages that make it invaluable to the realization of a attestable, verifiable, honest substate of knowledge. During initial design, as I said the goal was a agent memory substrate, it occurred to me, unlike my previous escapades where I was bounding propagation, confidence and retrieval by temporal factors, that a AI memory system shouldn't adopt the lossy, fragmented natures of the human concept of memory. It should have the ability to describe all conditions applicable to the specific moment of knowledge acquisition, thereby providing the proof of the knowledge, what makes it important and how its understanding evolved from that acquisition honestly.

These qualities make SERAPH an ideal substrate for a memory layer vs bolting together disjoint systems.