AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Specify Total Document Length
Enter total document words, tokens, or estimated character count across your corpus.
2. Set Target Chunk Size
Choose chunk size (e.g. 256, 512, 1024 tokens) based on your embedding model and retrieval needs.
3. Configure Overlap Percentage
Set chunk overlap (typically 10% to 20%) to preserve cross-boundary semantic context.
4. Review Total Chunks & Storage
Inspect total generated chunks, vector count, and estimated embedding API costs.
Formula or logic used
Sliding Window Chunking Mathematics
Document chunking with sliding window overlap calculates step stride, dividing total content by the non-overlapping window increment.
Chunk Overlap Tokens: $O = \text{Chunk Size} \times (\frac{\text{Overlap \%}}{100})$Step / Stride Size: $S = \text{Chunk Size} - O$Total Number of Chunks: $N = \max(1, \lceil \frac{\text{Total Tokens} - O}{S} \rceil)$Index Expansion Factor: $\text{Expansion} = \frac{N \times \text{Chunk Size}}{\text{Total Tokens}}$
Examples
Example 1: Technical Documentation Knowledge Base (250,000 tokens)
Provides high-resolution retrieval passages with 15% continuity between consecutive documentation sections.
Example 2: Legal Contract Repository (1,500,000 tokens)
Preserves complex legal definitions across clause boundaries while maintaining a compact vector index.
Common use cases
Vector Database Sizing
Determine exact vector row count before provisioning Pinecone pods, Qdrant clusters, or pgvector tables.
Embedding Cost Estimation
Calculate total API cost to embed an entire document corpus with text-embedding-3-small or large.
RAG Evaluation Benchmark Planning
Size golden dataset test samples relative to the overall document chunk index size.
Recursive Character Splitter Tuning
Model the impact of changing LangChain or LlamaIndex chunk sizes from 256 to 1024.
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Frequently asked questions
Why is chunk overlap necessary in RAG?
Without overlap, sentences or entities split across a chunk boundary lose their semantic context, causing vector search to miss relevant passages during retrieval.
What is the recommended chunk size for general RAG?
For general QA and documentation, 400 to 600 tokens with 10% to 20% overlap is the industry standard sweet spot, balancing semantic completeness with retrieval precision.
Does increasing overlap increase vector database storage?
Yes! A 20% overlap increases total generated chunks (and therefore vector database rows) by approximately 20% to 25%, increasing both embedding costs and index storage.
What happens if chunk size is too small?
Chunks under 150 tokens frequently lack enough semantic context to generate accurate embeddings, leading to noisy retrievals and hallucinated model answers.
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