Where are my notes stored?

In your browser’s localStorage on this device, together with their embeddings. They are never uploaded anywhere, and clearing site data deletes them.

Why does it need to download a model?

Semantic search needs embeddings — numeric meaning-vectors for your text. The ~22 MB BGE model downloads once, runs on your device, and is cached for later visits.

Does it work in other languages?

Poorly. The embedding model is English-optimized; queries and notes in other languages rank unreliably. The tool discloses this up front.

Is this as good as cloud AI search?

No. A 22 MB on-device model is a lightweight similarity engine, not a large language model. It finds related notes well; it does not understand, summarize or answer questions.

What is a semantic search notes?

A semantic search notes is a free online tool that gives you quick, accurate results without spreadsheets or manual math. This version runs entirely in your browser for instant, private results.

How do I use this semantic note search: tool?

Enter your details in the fields above and get instant results. Everything runs in your browser — no signup, no waiting, no data uploaded.

Is this semantic note search: tool really free?

Yes, completely free with no account required. Use it as many times as you want — there are no usage limits or hidden paywalls.

Semantic Note Search: Free

What is Semantic search notes?

Semantic Note Search — Search your saved notes by meaning with free on-device embeddings. Notes stay in your browser — find ideas fast — absolutely nothing is uploaded.

Runs the Xenova/bge-small-en-v1.5 feature-extraction model (MIT, ~22 MB, 384 dimensions) directly in your browser via Transformers.js. Each saved note is embedded with mean pooling and L2 normalization and stored alongside the note in localStorage; the query is embedded with the model’s recommended retrieval prefix and ranked against notes by cosine similarity. Nothing is uploaded; the page is explicit that embeddings are English-optimized statistical similarity, not understanding.

Why use this semantic search notes?

🎯

Built for semantic search notes

Search your saved notes by meaning with free on-device embeddings. Notes stay in your browser — find ideas fast — absolutely nothing is uploaded.

📋

Transparent & honest

English-optimized embeddings — other languages rank poorly.

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Private by design

Everything runs in your browser. Your inputs never leave your device.

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Works everywhere

Fully responsive — perfect on desktop, tablet, and mobile. No app needed.

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Regularly updated

Regularly maintained and reviewed to ensure accuracy.

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Free forever

No accounts, no limits, no watermarks. Use Semantic Note Search as much as you need.

How to use Semantic Note Search

1Save notes with the form below — they are embedded on-device and kept in your browser (localStorage).
2Type a query describing what you are looking for, in your own words.
3Wait for the embedding model to download (~22 MB, once).
4Read your notes ranked by meaning-similarity, with scores.
5Best with English notes; skim the matched note — similarity is a statistical guess.

What can you do with Semantic Note Search?

Search notes by meaning — Search your saved notes by meaning with free on-device embeddings.
Ai note finder — Search your saved notes by meaning with free on-device embeddings.
Personal knowledge search — Search your saved notes by meaning with free on-device embeddings.

Good to know

English-optimized embeddings — other languages rank poorly.
Notes and embeddings live in this browser’s localStorage (up to 200 notes).
Similarity is a statistical guess — skim the matched note to confirm.

How it works

Runs the Xenova/bge-small-en-v1.5 feature-extraction model (MIT, ~22 MB, 384 dimensions) directly in your browser via Transformers.js. Each saved note is embedded with mean pooling and L2 normalization and stored alongside the note in localStorage; the query is embedded with the model’s recommended retrieval prefix and ranked against notes by cosine similarity. Nothing is uploaded; the page is explicit that embeddings are English-optimized statistical similarity, not understanding.

Examples

Finding a recipe note

A note titled "Breakfast ideas" containing a pancake recipe ranks at the top even without the word "fluffy".

Meeting follow-ups

Notes mentioning price decisions surface above unrelated meeting notes.

People also search for these — this tool handles them all:

semantic search notessearch notes by meaningai note finderpersonal knowledge search

Frequently asked questions

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