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Text Chunker

Split long documents into overlapping chunks for embeddings and RAG.

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Paste a document above and it will be split here.

Quick Tool Overview

The Text Chunker splits a long document into overlapping chunks for embeddings and RAG pipelines, cutting at natural boundaries — paragraph, then sentence — while respecting the size you set. Where you cut matters: split mid-sentence and the embedding describes half an idea.

What it does
Splits documents into overlapping chunks for retrieval pipelines
You give it
A document, a chunk size and an overlap
You get back
Numbered chunks with sizes, exportable as JSON or text
Best for
Embeddings, vector databases and RAG ingestion
AI Tools Free, unlimited use No sign-up Runs in your browser

About the Text Chunker

Anything you feed to an embedding model or a retrieval pipeline has to be cut into pieces first, and where you cut matters more than people expect. Split mid-sentence and the embedding describes half an idea; split with no overlap and an answer that straddles a boundary becomes unretrievable. This splits your text at natural boundaries — paragraph, then sentence — while respecting the size you set, and carries an overlap from one chunk into the next.

Set the size in estimated tokens, characters or words, choose the overlap, and every chunk comes back numbered with its own size so you can spot the outliers. You can copy them individually, download the lot as JSON for a pipeline, or take a plain text file with separators. A sensible default to start from is around 500 tokens with 10 to 15 percent overlap, then tune from what your retrieval actually returns.

Key features of the Text Chunker

Boundary-aware splitting

Cuts fall on paragraph and then sentence boundaries rather than mid-sentence, so each chunk is a complete idea.

Configurable overlap

Carrying context from one chunk into the next stops an answer that straddles a boundary becoming unretrievable.

Size in tokens, characters or words

Set the limit in whichever unit your pipeline is specified in.

Per-chunk sizes shown

Every chunk is numbered with its own size, so outliers are easy to spot before you ingest them.

Export as JSON or text

Copy chunks individually, download the set as JSON for a pipeline, or take a plain text file with separators.

How to use the Text Chunker

  1. 1

    Paste your document

    Drop in the text you want to index. It stays in your browser.

  2. 2

    Set the chunk size

    Choose tokens, words or characters and a target size. Around 500 tokens suits most retrieval setups.

  3. 3

    Set the overlap

    Overlap carries the tail of one chunk into the next so an idea spanning a boundary is still findable. Ten to fifteen percent is a good default.

  4. 4

    Export the chunks

    Copy a single chunk, download them all as JSON for your pipeline, or take a plain text file with separators.

Frequently asked questions

Start near 500 tokens. Smaller chunks give sharper retrieval but lose surrounding context; larger ones keep context but dilute the embedding, so a query matches the chunk less precisely. Tune from what your retrieval actually returns.

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