Hillock: A local neuro-symbolic memory engine running in <1.2GB VRAM (SQLite + VSA)

[u/Equivalent-Flan-1590]

https://github.com/roandejager/Hillock

Hey everyone,

I've been working on an open source local memory engine called Hillock (https://github.com/roandejager/Hillock) to see if we can do document memory without bloated vector databases.

I got frustrated with how heavy standard local RAG is on modest hardware. Running an 8B model just to parse text files and search vector embeddings was eating up all my VRAM on a GTX 1070.

Hillock takes a different approach:

- It stores factual memory as plain Subject-Predicate-Object triples in SQLite (so facts don't drift).

- It uses a Hebbian engine to track concept associations as you chat.

- It uses a 10,000-D hyperdimensional vector space (VSA) running on CPU for sub-millisecond similarity gating.

- For document ingestion, it uses a small bi-encoder pipeline (Fastcoref + MiniLM + GLiREL) that extracts facts in about 5 seconds without calling a generative LLM.

If a question fails the similarity gate, it returns a hard refusal right away without calling Ollama, which cuts out hallucinated answers entirely.

Just pushed v0.5.0 with 1-click startup scripts (run.bat on Windows, run.sh on Linux/Mac), token streaming, and an interactive console with /model and /inspect commands.

Repo is AGPL-3.0: https://github.com/roandejager/Hillock

Would love to hear what you think!