📘 Overview of LocalRAG
👉 Summary
Chatting with documents through AI has become a common professional expectation. However, most tools require sending files to the cloud, raising serious privacy concerns for lawyers, doctors, researchers or consultants handling sensitive data. LocalRAG tackles this head-on with a mobile app capable of running a language model fully on-device. The promise is bold: ask questions to your PDFs, Word, Excel or EPUBs without any data leaving the device. For anyone juggling contracts, patient files or confidential notes, this changes the game. This article reviews LocalRAG's positioning, features, use cases and pricing for privacy-demanding professionals worldwide today.
💡 What is LocalRAG?
LocalRAG is a mobile app (iOS, iPadOS and Android beta) dedicated to document chat with on-device language model execution. It supports 15 document formats including PDF, EPUB, Word, Excel, PowerPoint and images via OCR. Users add files to a collection and ask questions in natural language. LocalRAG returns answers with sourced citations and matching PDF highlight. The product's strength lies in its local Qwen3 4B LLM, running entirely on the device after a one-time download. For higher quality, a Claude API fallback is available.
🧩 Key features
LocalRAG includes a complete document processing chain. Extraction and indexing run 100% locally: BM25 for lexical search, multilingual E5 embeddings for semantic search. Questions are processed by local Qwen3 4B or by cloud Claude (Opus, Sonnet, Haiku) depending on user choice. Answers include [1][2] citations pointing to the exact page with highlighting. The app handles collection sort and custom ordering, OCR for photos and scans, layout-aware PDF table extraction and transparency on the search pipeline. Recent release notes mention inline citations, PDF highlight and cross-language search.
🚀 Use cases
Top use cases include legal professionals who must quickly identify clauses, risks and obligations in contracts. Researchers find valuable help summarizing academic papers and cross-referencing findings. Students chat with textbooks and prepare exams effectively. Consultants use LocalRAG to prepare client files without sending data to the cloud. Doctors can consult protocols or confidential publications. Consumer users leverage OCR to turn whiteboard or handwritten photos into indexable documents.
🤝 Benefits
The main benefit is full privacy when local mode is used: no data leaves the device. The second is offline availability: the app works without internet, valuable in mobility or restricted environments. The third is search quality: BM25 plus E5 plus Qwen3 4B deliver relevant answers with verifiable citations. The fourth is flexibility: optional Claude fallback for the most complex queries. Finally, OCR and 15-format support let users centralize their entire corpus in one app.
💰 Pricing
LocalRAG offers a one-week free trial, then a paid subscription for permanent use. The app is available on the iOS App Store and Android Play Store (beta). The local Qwen3 4B LLM is included, requiring a ~3GB download. Switching to Claude requires bringing your own API key from OpenAI/Anthropic and paying associated consumption. No hidden fees once subscribed.
📌 Conclusion
LocalRAG addresses a precise and growing need: a document AI assistant without compromising data privacy. On-device execution, feature richness and quality citations make it a reference for privacy-conscious mobile professionals across any sector handling sensitive content.
