Local Ai
Built an SQLite memory engine for Ollama that does not eat all your VRAM
Wanted to share a local memory engine I built called Hillock. The main problem I had with standard local RAG was that running document parsing and vector search chewed up so much VRAM that my actual O
Wanted to share a local memory engine I built called Hillock. The main problem I had with standard local RAG was that running document parsing and vector search chewed up so much VRAM that my actual Ollama models ran painfully slow. With this setup, you feed it a document and it extracts facts in about five seconds using small models under 300MB instead of an LLM. Facts are saved in SQLite, and when you ask a question it runs a hyperdimensional vector check to see if the knowledge actually exists. If you ask something outside your notes, it blocks the query so Ollama does not hallucinate. There is a built in model switcher in the CLI that detects whatever Ollama models you have pulled locally and swaps between them on the fly. It also includes an OpenAI compatible API server on port 8000 with true token streaming and compatibility endpoints for Open WebUI and AnythingLLM. The entire engine stays under 1.2 GB of VRAM or runs on pure CPU, and we recently added bit packed CPU operations and published it to PyPI via pip install hillock. GitHub link: https://github.com/roandejager/Hillock Docs: https://hillock.mintlify.site/ Discord: https://discord.com/invite/BGUPNBcVdp submitted by /u/Equivalent-Flan-1590 [link] [comments]
Related
- Built an SQLite-based memory system for Ollama that doesn't eat all your VRAM
- Built a local memory engine for Ollama that runs in <1.2GB VRAM without vector DBs (Hillock v0.5)
- I got tired of flat RAG, so I built a 3D Multi-Agent Memory Core with 'Fading Gossip' (Open Source)
Source: r/ollama | 2026-10-07