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Semantic Search Over Your Own Text, In the Browser

Embed a list of sentences with a real model and rank them by meaning — or find the near-duplicates in a list.

This tool embeds each line of your text with a sentence-transformer model running in your browser, then ranks lines against a query by cosine similarity, finds near-duplicate pairs above a threshold, or groups lines that mean the same thing. Because it compares meaning rather than words, it matches "when are you open?" to "what are your business hours?" — and none of the text is uploaded.

Runs in your browser
Uploads
None — the model is downloaded to you, not the reverse
Runs on
WebGPU where available, WebAssembly otherwise
Dimensions
384 on MiniLM, 768 on BGE-base, 1024 on mxbai-embed-large
Languages
English, or 50+ with the multilingual model
Practical limit
A few hundred lines per run
Privacy
Runs entirely in your browser — nothing is uploaded
Cost
Free, unlimited, no sign-up

Frequently asked questions

How is this different from Ctrl+F?

Ctrl+F matches characters. This matches meaning: "my card was declined" and "payment failed at checkout" have no words in common and are recognised as the same complaint. That is the whole difference between keyword search and semantic search.

Does it work across languages?

With the multilingual model, yes — it maps 50+ languages into one shared space, so a question in Spanish will match an answer written in English. The English-only models will not do that: they score a cross-language pair little better than an unrelated sentence.

Is the largest model always the best choice?

For retrieval quality it is the strongest here, but it is a 321 MB download and slower per sentence. MiniLM is enough for deduplicating a list or searching a few dozen items; reach for the larger models when near-misses matter and the small one is confusing things that merely share vocabulary.

What threshold should I use for duplicates?

Start at 0.75 and adjust while looking at the results. There is no universal figure — different models compress the similarity range differently, so a threshold copied from a tutorial written for another model will be wrong.

How to use the semantic search & similarity

  1. 1Enter or paste your text.
  2. 2Choose the do what.
  3. 3Enter or paste your query.
  4. 4Choose the model.
  5. 5Set the similarity threshold.
  6. 6Set the results.
  7. 7Press Run, then download the result when it is ready.

Sources & specifications

Embed this tool

Put the working semantic search & similarity on your own site. It runs in your visitors' browsers exactly as it does here — free, no account, nothing uploaded.

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