Chrome's local system that turns pages you visit into vectors, enabling natural-language search of your own browsing history on-device.
Chrome history embeddings are the vectors behind Chrome's AI-powered history search, which lets you find past pages with natural-language queries like "what was that ice cream shop I looked at last week?" It processes pages you visit into semantic passages, converts them to high-dimensional vectors, and stores them locally.
Decoded from Chromium's source, the pipeline centres on a DocumentChunker that walks the DOM with a recursive tree-walking algorithm, respecting semantic structure to aggregate content into meaningful passages before embedding them into 1540-dimensional vectors. Search then runs on-device via vector search.
It's a rare, concrete look at Google's real embedding and chunking choices — the same family of techniques used across search. Relevance is scored with a dot product over normalised vectors (see cosine similarity), and it's one of the many on-device models Chrome now runs.
