Hi Jacob, thanks for sharing! Witchcraft looks super interesting—starring the repo now.

The late interaction (ColBERT / MaxSim token-level) approach is fascinating for local search. Retaining per-token interaction matrices gives incredible semantic retrieval precision without losing fine-grained token context.

From an architectural standpoint, the main trade-off I explored with Hillock was moving away from dense token-embedding matrices entirely toward an explicit neuro-symbolic split:

1. Symbolic Grounding: Hard Subject-Predicate-Object (SPO) triples in relational SQLite tables to eliminate vector drift for exact factual memory. 2. Subsymbolic VSA Gating: A 10,000-dimensional Vector Symbolic Architecture (HDC) hypervector space on CPU using subword n-grams and GloVe SimHash projections for <1ms gating and pronoun resolution. 3. Edge VRAM Footprint: Keeping the whole engine under 1.2 GB VRAM on a GTX 1070 by using a CUDA bi-encoder pipeline (Fastcoref + MiniLM + GLiREL) for non-generative document parsing.

I'm curious—how do you manage the index footprint and per-token memory overhead in Witchcraft when scaling to larger local document collections?

Excited to dig deeper into your codebase!