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RAG Chunking & Segmentation Visualizer

Visualize how text is split using character, word, sentence, or custom regex pattern strategies. Hover over chunks to inspect character/token metadata, boundaries, overlaps, and sizes.

analyzers

Select Chunking Strategy

Input Document

Segment Size (chars)150

Overlap Margin30

Total Chunks

Est. Tokens

204

Avg. Size

109

Overlap %

Interactive Boundaries Map

Retrieval-Augmented Generation (RAG) is an AI framework for retrieving facts from an external knowledge base to ground Large Language Models (LLMs) on the most accurate, up-to-date sources. RAG combines the intuitive reasoning capabilities of LLMs with the reliability of a database. By indexing documents as high-dimensional vector embeddings, a vector database can query relevant context matching a user's prompt in milliseconds. This utility slices documents into digestible segments. In the visualizer below, you can analyze overlapping bounds, adjust token limits, and simulate exactly what chunk parameters should be sent to your embedding model.

Generated Text Segments

#1 (chunk-c-0)

INDEX: 0-15038 TKNS

"Retrieval-Augmented Generation (RAG) is an AI framework for retrieving facts from an external knowledge base to ground Large Language Models (LLMs) on"

#2 (chunk-c-1)

INDEX: 120-27038 TKNS

"arge Language Models (LLMs) on the most accurate, up-to-date sources. RAG combines the intuitive reasoning capabilities of LLMs with the reliability "

#3 (chunk-c-2)

INDEX: 240-39038 TKNS

" of LLMs with the reliability of a database. By indexing documents as high-dimensional vector embeddings, a vector database can query relevant context"

#4 (chunk-c-3)

INDEX: 360-51038 TKNS

"ase can query relevant context matching a user's prompt in milliseconds. This utility slices documents into digestible segments. In the visualizer be"

#5 (chunk-c-4)

INDEX: 480-63038 TKNS

"segments. In the visualizer below, you can analyze overlapping bounds, adjust token limits, and simulate exactly what chunk parameters should be sent "

#6 (chunk-c-5)

INDEX: 600-65414 TKNS

"unk parameters should be sent to your embedding model."