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Rank a short list with session-only embeddings, not a private search engine
A search box over your own notes is easy to treat as a local index that never leaves the laptop. It is not. Semantic Search sends each entry and the query to Google Gemini for embeddings, then ranks cosine similarity on this host. There is no database. Vectors live in an in-memory session that is deleted when you clear the page or when the session expires. DevNestro does not persist user embeddings to disk and does not use them for analytics of private content. Ranking is still a vector distance. It is not proof the top hit answers the question.
Why this is worth doing in the browser
People want semantic search because keyword find misses a paraphrase. Mixing that habit with a “processed locally” label is how a visitor indexes support tickets and believes Google never saw them. This page says the opposite: cloud embeddings, a text rate limit, session-only storage, and an instruction to read the top hit against the original line. Related tools include text similarity for a pair of passages and File Search when the corpus is uploaded documents rather than pasted lines.
How to use the tool
Paste at least two lines. Confirm cloud embeddings. Index to keep vectors in memory, or Search to embed the list and query in one pass. Read the ranked scores. Clear deletes the in-memory session.
- Do not index confidential notes unless you accept Google processing.
- Keep the list short. This is a temporary session, not a search product.
- A high score is not proof the line answers the query.
- Clear the session when you leave a shared computer.
Privacy
Entries and the query are sent to Google Gemini for embeddings. Rankings stay in memory for the session and are discarded. User embeddings are not written to disk. This is not processed only in your browser. Free-tier training language follows the configured tier.
Have a short list you are willing to send? Open Semantic Search, expect a session-only index, and read the ranking as a hint.