Empowering Privacy: On-Device RAG for Secure Document Querying on Android

Smartphones now hold some of people’s most sensitive information: contracts, medical notes, financial records, private correspondence. At the same time, generative AI tools increasingly rely on cloud servers to process data. On-device Retrieval-Augmented Generation, or RAG, takes a different approach. It allows Android users to query their own documents locally, without sending text to remote servers.

In practical terms, this means searching PDFs, notes, or other private files directly on a phone, even without an internet connection. All processing stays on the device. Organizations and individual users can work with proprietary documents while keeping full control over their data. This model mirrors a broader shift in mobile AI, where generative systems increasingly access personal data without relying on the cloud.

What Retrieval-Augmented Generation does

Retrieval-Augmented Generation combines two systems. First, a retrieval layer searches a set of documents for passages relevant to a user’s question. Then a language model uses that retrieved text to produce an answer. Instead of relying only on what the model learned during training, RAG ties responses to specific sources.

This design reduces hallucinations by grounding outputs in actual documents and, in many implementations, by showing where the information came from. The retrieval step pulls in up-to-date or domain-specific material, which is especially useful when working with internal files rather than public data. RAG systems can handle a wide range of queries, from simple lookups to more specialized questions, including on mobile devices.

On Android, RAG is well suited to private document search. Semantic search over a curated set of files ensures answers stay relevant, while keeping the language model constrained by the user’s own data.

Why run AI on the device

Running RAG entirely on an Android device changes the trade-offs. The most obvious benefit is offline use. Queries work without a network connection, which matters when traveling or working in low-connectivity environments. Google’s ML Kit GenAI APIs reflect this direction, offering on-device generative features that avoid server calls.

Hardware improvements have made this practical. Google’s Gemini Nano model and similar small language models are designed for mobile inference, enabling real-time interactions on phones as of 2025. These models support use cases such as private question-and-answer sessions over personal files, without sending data to external servers.

The result is a class of applications that can analyze notes or PDFs locally, balancing performance with the limits of mobile hardware.

Tools and frameworks used today

A growing ecosystem supports on-device RAG on Android. React Native RAG is a local library that adds offline retrieval to language models, allowing document processing without external servers. Its modular design lets developers swap components such as text splitters or models, and it scales by pushing computation to the client device.

Google’s OnDevice-RAG-Android project provides a complete example. It combines an on-device vector database with embedding models and HuggingFace language models to answer questions over PDFs and DOCX files. The app can be installed from GitHub Releases or updated through tools like Obtainium.

Other components commonly appear in real projects. Developers experiment with local models such as Phi, Gemma, and Mistral, often sharing performance results on forums. The Cactus framework focuses on building personal AI assistants that run locally for confidential analysis. Google’s EmbeddingGemma 300M model generates text embeddings on device in more than 100 languages, enabling semantic search without network access. MediaPipe helps optimize inference on Android, while tools like Ollama simplify local model management. LangChain provides orchestration layers for RAG systems, including document loaders, embedding pipelines, and vector stores.

How an on-device RAG system is built

A typical Android RAG pipeline starts with document ingestion. Libraries such as iText Core extract text from PDFs directly on the device. The extracted text is then split into smaller chunks, often using the Deep Java Library API, which prepares data for efficient processing.

Those chunks are converted into embeddings using models such as EmbeddingGemma through frameworks like LiteRT. The embeddings are indexed in a vector store, which supports similarity search at runtime. LangChain’s components are often used here to manage chunking, indexing, and retrieval across unstructured documents.

When a user asks a question, the system retrieves the most relevant chunks and passes them to a local language model, such as Gemma 3, to generate an answer grounded in the document text. Projects like OnDevice-RAG-Android package this workflow into a ready-made pipeline for offline question answering. Developers can customize text handling with React Native RAG or use LangChain agents to orchestrate the full process.

Testing focuses on app size, memory use, and latency. These constraints determine whether a setup is viable on consumer Android devices.

Privacy, performance, and real use cases

The main privacy benefit of on-device RAG is straightforward: document text never leaves the phone during inference. This removes the risk of exposing sensitive material during cloud transmission. For tasks like reviewing financial documents or drafting confidential emails, keeping data local is a meaningful safeguard. React Native RAG explicitly enforces this by keeping retrieval and generation on the device.

Performance remains a balancing act. Local models consume memory and battery power, and developers must tune systems carefully. Still, recent advances allow near real-time interactions without network latency.

Concrete applications already exist. OnDevice-RAG-Android supports natural-language questions over PDFs and DOCX files using HuggingFace models. Google’s Gemma-based tutorials demonstrate full pipelines, from text extraction to similarity matching and answer generation, all on device. Frameworks like Cactus enable offline personal assistants for sensitive workflows. Based on current adoption trends, broader use in professional settings is expected through 2025 and 2026.

Limits and what comes next

On-device RAG is not without costs. Large models increase app size and can strain device storage. Performance varies widely across hardware, and memory usage, battery drain, and inference speed remain concerns. Developers often choose lightweight models such as Gemma or Phi, while experimenting with alternatives like Mistral or ONNX runtimes, as discussed in Android developer forums.

Accuracy can drop on complex queries over large document collections, making optimization critical. Cross-platform support also remains uneven. Still, improvements in mobile hardware and more efficient embeddings are steadily reducing these barriers. Based on current progress, widespread, privacy-preserving document querying on phones appears plausible by the second half of the decade.

Conclusion

On-device RAG shifts document querying on Android away from the cloud and back to the user’s device. By grounding language models in personal files, it produces answers tied to real sources while preserving data control. Tools such as React Native RAG, Gemma models, and LangChain show how quickly this ecosystem is maturing.

Performance limits remain, but the privacy gains and practical uses—from personal assistants to professional document analysis—are already clear. For users who care about keeping sensitive information off remote servers, on-device RAG is less a novelty than a necessary design choice.