Building an AI-Powered Note-Taking App in React Native: Part 3 (Local RAG)
Article Summary
Jakub Mroz from Software Mansion just shipped a fully local RAG pipeline in React Native. No cloud APIs, no data leaks, just on-device AI that actually works.
This is Part 3 of a series building a privacy-first AI note-taking app. The team integrates LLaMA 3.2 1B SpinQuant with React Native ExecuTorch and React Native RAG to enable natural language chat with your notes, entirely offline.
Key Takeaways
- LLaMA 3.2 1B SpinQuant runs locally for mobile RAG
- RAG retrieves semantically similar notes, then generates grounded answers
- Token streaming provides smooth conversational UX without cloud latency
- Complete privacy: all embeddings, retrieval, and generation happen on-device
You can now build ChatGPT-style interfaces in React Native that run completely offline and keep user data private.
About This Article
Building conversational AI on mobile means grounding LLM responses in user data while keeping everything private. Software Mansion needed to retrieve semantically similar note chunks and filter them by similarity scores above 0.2 threshold without relying on cloud infrastructure.
The team built a RAG service using React Native ExecuTorch to run LLaMA 3.2 1B SpinQuant locally. They integrated the text vector store from Part 1 and created a prompt generator that categorizes retrieved results into three tiers: 'Highly relevant' (>0.6), 'Relevant' (>0.4), and 'Slightly relevant' (>0.2).
Users can now chat with their notes entirely on-device with token-by-token streaming. There's no cloud latency and no data leaves the phone. The AI assistant loads the full RAG pipeline on-demand and stops gracefully when the screen closes.