In building a private, local-first context engine, semantic retrieval is non-negotiable. To know what you are working on, the system must search through thousands of chunks of meeting logs, active screens, and documents, returning matching vectors within milliseconds. Doing this locally on consumer hardware meant building a customized SQLite-VSS pipeline.
"Vector search in cloud databases is an orchestration problem. Vector search on a laptop is a cache-line and memory-bandwidth problem."
We achieved sub-100ms search across 250,000 embedded documents by applying scalar quantization and AVX2/NEON SIMD vector dot products. This post breaks down our SIMD kernel optimizations and distance ranking algorithms.