AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Specify Chunk Size
Enter your target chunk size in tokens (e.g. 256, 512, 1024 tokens).
2. Set Overlap Percentage or Tokens
Adjust the overlap slider between 0% and 50% (recommended: 10% to 20%).
3. Review Redundancy & Continuity
Inspect total duplicate tokens, effective step size (stride), and boundary retention score.
4. Get Splitter Config Snippet
Copy ready-to-use configuration parameters for LangChain and LlamaIndex recursive splitters.
Formula or logic used
Chunk Overlap & Stride Formulas
A sliding window text splitter advances through text in increments equal to the Stride. When overlap is too small, facts spanning two chunks are lost; when overlap is too large, storage and retrieval costs multiply unnecessarily.
Overlap Tokens = Chunk Size × (Overlap Percentage / 100)Stride (Advancement Step) = Chunk Size - Overlap TokensRedundancy Factor = Chunk Size / StrideDuplicate Token Storage Overhead (%) = ((Redundancy Factor - 1) × 100)Boundary Safety Score: < 5% Overlap = High Risk of Context Loss; 10-20% = Optimal; > 30% = Excessive Bloat
Examples
Example 1: Standard 512-Token Knowledge Base Chunk
77 tokens preserves 2 to 3 complete sentences across boundaries, providing sufficient context for semantic vector matching.
Example 2: Large 1,024-Token Legal Clause Chunk
Guarantees that complex conditional clauses in legal filings are never bisected without surrounding terms.
Common use cases
LangChain TextSplitter Tuning
Optimize chunk_overlap and chunk_size parameters for RecursiveCharacterTextSplitter.
LlamaIndex SentenceSplitter Sizing
Configure SentenceSplitter chunk_overlap to maintain semantic coherence across paragraphs.
Vector DB Storage Cost Containment
Prevent accidental 50% vector database inflation caused by unnecessarily large overlap settings.
Search Duplicate Result Elimination
Avoid retrieving identical information twice in Top-K results caused by excessive chunk overlap.
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Frequently asked questions
What is the recommended overlap percentage for RAG?
For most general RAG use cases, an overlap of 10% to 15% of the chunk size (e.g. 50 to 75 tokens for a 512-token chunk) offers the best tradeoff between semantic continuity and vector database storage efficiency.
What happens if chunk overlap is set to 0?
With zero overlap, sentences split precisely at the token threshold will have their subjects in chunk A and their predicates or conclusions in chunk B. Neither chunk will generate a strong embedding for queries addressing that specific relationship.
Can chunk overlap be too high?
Yes. Overlap higher than 30% generates significant token redundancy, inflates embedding API fees and vector database memory costs, and often leads to the Top-K retrieval algorithm returning nearly identical chunks.
Should overlap be measured in tokens or characters?
Always measure overlap in tokens when targeting LLMs, as context limits and model embeddings operate strictly on token boundaries. Character-based splitting often causes unpredictable token variance due to varying word lengths.
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