Text Chunker
Split long text into chunks that fit AI model context windows.
0 chunks · 0 tokens
How It Works
The chunker splits your text at sentence boundaries to create natural, readable chunks. It estimates token counts using language-aware tokenization and applies configurable overlap to preserve context at chunk boundaries. This is ideal for preparing documents for RAG, summarization, and long-context analysis.
Chunking Best Practices
- Small chunks:300-500 tokens
- Standard chunks:800-1000 tokens
- Overlap:10-20% of chunk size
- Leave room:Keep ~20% for answers
Frequently Asked Questions
Why do I need to chunk text for AI?
AI models have limited context windows. Chunking splits long documents into smaller pieces that fit within these limits, enabling RAG applications, summarization, and analysis of large texts.
What is chunk overlap?
Overlap is the number of tokens repeated between adjacent chunks. Overlap helps preserve context at chunk boundaries and prevents information loss during splitting, improving retrieval quality.
What is a good chunk size for RAG?
Common chunk sizes range from 500 to 1500 tokens with 50-200 tokens of overlap. The optimal size depends on your content type and retrieval strategy. Start with 800 tokens and adjust based on results.
How does the smart splitting work?
The chunker attempts to split at sentence boundaries (periods, question marks, exclamation points) to produce natural chunks. When sentences are too long, it falls back to word boundaries to keep chunks within the target size.