ondevice-rag-engine

On-device semantic search uses Apple's NaturalLanguage framework to generate embeddings locally and SwiftData to store indexed chunks, enabling private retrieval of iOS notes and meetings without network calls.

Yes, offline retrieval-augmented generation is possible by storing embeddings in SwiftData and using Accelerate-based cosine similarity to retrieve relevant text chunks locally without any network connectivity.

Local vector similarity search computes cosine similarity between NaturalLanguage-generated embedding vectors using the Accelerate framework, matching user queries against locally indexed text chunks stored in SwiftData.

The best way to chunk notes for a private offline RAG pipeline is to break meeting records and notes into context-preserving segments before generating NaturalLanguage embeddings for accurate local semantic matching.

No network connection is needed to run semantic search on planner app data because the embedding generation, SwiftData vector storage, and Accelerate similarity retrieval operate entirely on-device for maximum privacy.

You should not use an on-device RAG engine for semantic search if your text corpus exceeds local storage limits or requires cross-device synchronization, as this pipeline is designed for offline, single-device SwiftData retrieval.