RAG Text Chunking Visualizer
Visualize how documents split into chunks for RAG pipelines. Tune chunk size and overlap, see color-coded segments and overlaps, and export chunks as JSON.
Chunk Configuration
About RAG Text Chunking Visualizer
The RAG Text Chunking Visualizer is a free tool for developers building Retrieval-Augmented Generation applications. When preparing documents for vector databases like Pinecone, Weaviate, or ChromaDB, choosing the right chunk size and overlap is critical. This tool lets you visualize exactly how your text will be split before processing, helping you fine-tune parameters for optimal retrieval performance.
Features
- Color-coded chunk visualization with overlap indicators
- Adjustable chunk size and overlap via sliders
- Statistics panel: total chunks, avg size, overlap ratio
- Export chunks as JSON array for direct pipeline use
- 100% client-side — your documents stay private
How to Use
- Paste your document text into the input area
- Adjust the chunk size using the slider or input field
- Set the overlap to control context sharing between chunks
- View the color-coded visualization and export when satisfied
Frequently Asked Questions
What is text chunking in RAG?
Text chunking is the process of splitting large documents into smaller pieces (chunks) that can be embedded into vector databases for retrieval-augmented generation. The chunk size and overlap affect retrieval quality.
What chunk size should I use?
Typical chunk sizes range from 200-1000 characters. Smaller chunks give more precise retrieval but less context. Larger chunks preserve more context but may reduce relevance. An overlap of 10-20% helps maintain continuity.
Does this tool upload my document?
No. All text processing happens locally in your browser. Your document data never leaves your device.
Why is overlap necessary in chunking?
Overlap ensures that concepts crossing chunk boundaries aren't severed. If a sentence explaining a core concept spans two chunks, the overlap keeps the context intact for the LLM to understand.
How does chunk size affect vector databases?
Larger chunks use more memory and may hit embedding model limits (like OpenAI's token limits). Smaller chunks lead to more database entries, which can slightly increase search times but vastly improve accuracy.
Can I export the chunks for my code?
Yes! Once you are satisfied with the visual chunking, click 'Export as JSON' to download an array of the chunks, their index, start/end positions, and text content.