[silentsvn]

The inspectability angle is genuinely useful, being able to trace exactly why something was retrieved is something vector search can't offer, and the tag-receipt approach is clean for structured knowledge.

One thing I'm trying to understand: the README calls this "semantic" retrieval, but looking at the Unified Field Equation in the whitepaper, the core scoring is tag intersection with temporal decay: W(q,a) = (shared tags) × γ^(graph distance) × (recency). That's weighted keyword matching, which is deterministic precisely because it's lexical, not semantic.

The vector.ts also has MockSoulIndex as a no-op stub with a note saying dense vector search is "optional augmentation" that's currently disabled so no embeddings are running in practice.

I've been building in this space with hand-written TypeScript (no AI codegen) and the line between "semantic" and "keyword" matters a lot to users. If someone stores "the JWT conversation" they won't find it by querying "authentication."

Is the tag extraction smart enough to bridge that, or is explicit tagging on the user to handle?