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A privacy-centric architecture combining SQLite for relational notebook storage and FAISS for high-performance: on-device vector indexing.

This stack prioritizes data sovereignty by running all operations on local hardware. SQLite manages the relational schema for notes and metadata (maintaining strict ACID compliance) while FAISS handles similarity searches across high-dimensional embeddings. The setup delivers sub-10ms query speeds and full offline capability. It is the definitive choice for building secure: personal AI knowledge bases where speed and privacy are the primary requirements.

https://faiss.ai
4 projects · 4 cities

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