Text Chunker for RAG

About the Text Chunker for RAG & Vector Databases

The explosion of AI has changed how software is built. To allow an AI to accurately answer questions based on your company's private data, developers use a process called Retrieval-Augmented Generation (RAG). However, you cannot just feed a 500-page PDF directly into an LLM because it exceeds the context window and destroys search accuracy. To fix this, you must split your document into small, mathematical pieces called "chunks". We built this online Text Chunker to give AI engineers and developers a fast, visual way to perfectly slice their documents before generating embeddings.

Standard text splitters are incredibly frustrating because they simply cut strings blindly by character count. If you split by 1,000 characters, it will chop the word "Artificial" exactly in half right at the 1,000th character mark. This destroys the semantic meaning for the AI! Our tool solves this with an advanced Semantic Sentence Chunking Engine. When you select the "Sentences" metric, the tool intelligently analyzes punctuation to ensure that every single chunk begins and ends with a complete, logical sentence, dramatically improving your vector database search results.

We also included a powerful Overlap Engine. In AI, overlaps ensure that context isn't lost between chunks. If chunk one ends mid-thought, chunk two repeats the last sentence of chunk one so the AI maintains the context. Finally, instead of returning messy raw text, our tool automatically outputs a perfectly formatted JSON Array of Strings. This allows you to instantly copy and paste the result directly into your Node.js or Python pipeline to send to the OpenAI Embeddings API or Pinecone database. And as always, processing happens 100% locally in your browser to keep your company's private documents completely secure.

Key Features

• Semantic Sentence Splitting: Intelligently slices documents at punctuation marks, ensuring words and sentences are never cut in half.
• Customizable Sliding Overlaps: Define exactly how many sentences, words, or characters should overlap between chunks to preserve AI context.
• Developer-Ready JSON Export: Outputs a perfectly formatted JSON array of strings, ready to drop straight into your code for embedding generation.
• Multiple Metrics: Choose between chunking by Sentences (best for RAG), Words, or raw Characters depending on your database limits.
• 100% Private Processing: Your sensitive corporate documents are never uploaded. All text chunking happens entirely on your local machine using JavaScript.

How to Chunk Text for Embeddings

• Paste your raw document, article, or text dump into the "Raw Source Document" box on the left, or upload a file.
• Select your preferred Chunking Strategy. We highly recommend "Semantic Sentences" for the best RAG retrieval results.
• Define your Chunk Size (how many items belong in a single block) and your Overlap Size (how many items to repeat to preserve context).
• The tool will automatically slice the text and output a JSON array in the right-side box.
• Check the green success message to verify exactly how many chunks were generated for your database.
• Click "Download" to export the JSON array, or "Copy" to move it to your script.

Frequently Asked Questions

What is text chunking in AI and RAG?

Text chunking is the process of breaking a massive document down into smaller, bite-sized pieces. In Retrieval-Augmented Generation (RAG), these chunks are converted into mathematical vectors (embeddings) and stored in a database. When a user asks a question, the AI only pulls the specific chunks relevant to the question, saving money and improving accuracy.

Why is "Sentence" chunking better than "Character" chunking?

If you slice a document purely by character count (e.g., 500 characters per chunk), you will almost certainly slice a word or a sentence directly in half. This destroys the semantic meaning of that sentence, making it harder for the AI to understand the context. Semantic Sentence chunking ensures the cuts only happen at logical punctuation marks.

What is Chunk Overlap?

Chunk Overlap is a sliding window technique used to preserve context. If Chunk A and Chunk B have zero overlap, a concept that spans across the cut line might lose its meaning. By enforcing an overlap (e.g., repeating the last sentence of Chunk A at the beginning of Chunk B), the AI maintains a seamless understanding of the narrative.

Why does the output format as a JSON Array?

To generate embeddings using the OpenAI API or insert data into vector databases like Pinecone, Milvus, or Weaviate, developers must pass the data as an array of strings in their code. By outputting a JSON Array, you bypass the annoying step of having to parse a raw text file in your Python or Node.js scripts.

Is it safe to paste private company documents here?

Yes, absolutely. We engineered this tool to operate 100% on the client-side using local web technologies. Your sensitive documents never leave your computer, and we do not use backend servers to process your text, meaning your data stays entirely secure on your own device.