Text Splitter / Chunker

Split text into chunks for RAG pipelines. Configure chunk size, overlap, and splitting mode.

What is a Text Splitter?

A text splitter divides large blocks of text into smaller, manageable chunks based on specified criteria like character count, word count, or delimiter. Text splitting is essential in AI and NLP workflows where documents need to be chunked to fit within model context windows. It's also useful for data processing, batch operations, and content management where text needs to be divided into consistent segments.

This tool splits text by character count, word count, sentence, paragraph, or custom delimiter, with options for overlap between chunks — a key feature for RAG (Retrieval-Augmented Generation) pipelines.

How to Use This Text Splitter

Paste your text — Enter the text you want to split into the input area.

Choose the split method — Select splitting by character count, word count, sentences, paragraphs, or a custom delimiter.

Set chunk size — Specify the maximum size for each chunk.

Configure overlap — Optionally set overlap between chunks to preserve context across boundaries.

View and copy chunks — See all generated chunks with their sizes, and copy individual chunks or all at once.

Common Use Cases

RAG pipeline preparation — Split documents into overlapping chunks for embedding and retrieval in AI applications.

LLM context management — Break long documents into chunks that fit within a model's context window for processing.

Batch processing — Divide large text files into smaller segments for parallel processing or API rate limit compliance.

Content management — Split articles, documentation, or datasets into consistent segments for publishing or analysis.

Frequently Asked Questions

What is text chunking for RAG?

Text chunking is the process of splitting documents into smaller pieces (chunks) for Retrieval-Augmented Generation (RAG) pipelines. Each chunk is embedded as a vector and stored in a vector database. When a user asks a question, relevant chunks are retrieved and provided as context to an LLM.

What is chunk overlap and why is it important?

Chunk overlap means that adjacent chunks share some content at their boundaries. This helps preserve context that spans chunk boundaries. Without overlap, important information that falls between two chunks might be lost during retrieval. A typical overlap is 10-20% of chunk size.

What chunk size should I use?

Optimal chunk size depends on your use case. Smaller chunks (200-500 tokens) work well for precise Q&A. Larger chunks (500-1500 tokens) are better for summarization or when context is important. Start with 500 tokens and adjust based on retrieval quality. The embedding model's max input size is also a constraint.