On-device embeddings
Optional local embedding pipeline (WASM + small model packaged with the browser) for semantic features without calling a remote model hub—explain privacy benefit and limitations.
What this is
Some builds ship a local embedding stack (WASM + small model) so semantic features can run without sending text to a remote model hub—improving privacy for supported flows.
How to use it
- No user setup is required when packaged; features activate when enabled in that build.
- Expect larger download sizes on first install when embedding assets ship.
- Quality differs from cloud-scale models; use cloud features when you need maximum recall.
Notes and limits
Embedding coverage does not replace full-page understanding for arbitrary PDFs.
Related topics
Related Documentation
Performance and resource usage
Is Oasis too heavy? How CPU, memory, disk, and network are used—for browsing, the assistant, and optional on-device features.
Training and bonus tokens
How Oasis training works, whether anonymous training leaves the device, privacy-first trainable browser, personalized vs anonymous modes, and daily bonus tokens.
Import data from other browsers
How Oasis imports bookmarks, passwords, history, extensions, and autofill from Chrome, Edge, Safari, Brave, and more—and where that data lives relative to the cloud assistant.
About the Author
I'm the CEO of Kahana, bringing a unique perspective from my management consulting experience at Clarkston Consulting and biomedical engineering background from Duke University. I'm focused on making the future of work more elegant through innovative technology solutions that prioritize user well-being and productivity.