JSONToTable

Loading Text Embedding Generator...

How to Generate a Text Embedding

Step 1

Paste Your Text

Type or paste any text into the input box on the left — a single sentence, a paragraph, a product description, whatever you have. You can go up to 8,000 characters. The counter above the box tells you how much room is left.

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Any text works: sentences, paragraphs, questions, or short documents
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Try a sample: click Sample 1 or Sample 2 to drop in example text
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8,000 character limit: long enough for most real snippets
Step 2

Click Generate Embedding

Hit the green Generate Embedding button. Your text is sent to the backend, run through our embedding model, and comes back as a vector. It usually takes a second or two.

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One click: no config, no options to fiddle with
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Live status: the button shows "Generating…" while it runs
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Same model every time: so your vectors stay comparable
Step 3

See the Vector

The output panel shows the embedding — a list of 1,024 numbers that captures the meaning of your text. A summary line tells you the model and dimension count, and the full array is printed below as formatted JSON you can scroll through.

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1,024 numbers: that's the size of the vector this model returns
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Formatted JSON: a clean array, one value per line, ready to read
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Meaning, not words: similar text produces similar numbers
Step 4

Copy or Download the JSON

Copy the vector to your clipboard, or download it as an embedding.json file. Drop it into a vector database, a similarity search, or wherever your app needs it. You can also format or inspect it later with our JSON formatter.

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Copy to clipboard for a quick paste into your code
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Download embedding.json with the model, dimensions, and vector
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Use it anywhere: vector search, clustering, recommendations, or RAG

What is a Text Embedding?

An embedding is just a list of numbers that represents the meaning of a piece of text. Here, every input turns into 1,024 numbers. On their own the numbers don't mean much, but together they place your text at a specific point in a big "meaning space" — what's known as a vector space.

The useful part: text with similar meaning lands close together. "How do I reset my password?" and "I forgot my login" end up with similar vectors even though they share almost no words. That's what lets you compare two pieces of text by how close their numbers are — usually with cosine similarity — instead of matching exact keywords.

Developers use embeddings for semantic search, recommendations, clustering related items, deduplication, and feeding context into RAG pipelines. This tool gives you back the raw 1,024-number vector to use however you like. Need to store the result? Convert it to a readable table or keep it as JSON.

Frequently Asked Questions

Can I turn the numbers back into text?

No. An embedding is one-way. The model reads your text and boils it down to numbers that capture its meaning, but it throws away the exact words in the process. You can compare two vectors to see how similar their texts are, but you can't reconstruct the original sentence from the vector alone.

What do I get back?

A plain JSON array of numbers — the embedding vector. Every request returns 1,024 numbers, and that dimension count stays the same on every run, so your vectors are always consistent and comparable.

Is it free?

Yes, it's free and there's no signup. Paste text, generate the vector, copy or download it. Check out our other free tools like the JSON formatter and JSON validator.

What can I do with an embedding?

Plenty. The common ones are semantic search (find text by meaning, not keywords), recommendations, grouping similar items with clustering, spotting duplicates, and RAG — pulling the most relevant chunks to give an LLM better context. You compare vectors using cosine similarity or another distance measure.

Does it work for other languages?

Yes. The model is multilingual, so you can embed text in many languages, not just English. Text that means the same thing in different languages will even land near each other in the vector space.

What's the max length?

You can paste up to 8,000 characters per request. If you have a longer document, split it into smaller chunks and embed each one — that's the normal approach for search and RAG anyway.

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