About this RAG Chunk Calculator
RAG systems usually split documents into overlapping chunks before embedding. This calculator turns document count, words, chunk size, and overlap into an estimated chunk count.
Who this calculator is for
- AI builders sizing a retrieval pipeline before embedding a document set.
- Automation consultants estimating storage and indexing work for client knowledge bases.
- Developers comparing chunk size and overlap settings before ingestion.
When to use it
- Estimate how many vector records a document batch will create.
- Compare chunk sizes before running an embedding job.
- Check whether overlap settings are creating too many duplicate tokens.
How to use this calculator
- Enter the number of documents in the corpus.
- Estimate average words per document and words per token.
- Choose the chunk size and overlap in tokens.
- Review estimated tokens, effective chunk size, and total chunks.
Key assumptions
- The estimate uses average document length rather than parsing each file.
- Tokenizer behavior varies by model and language.
- Overlap must be smaller than chunk size.
Accuracy and testing
Review formula sources, default test cases, assumptions, unit handling, rounding rules, and changelog for this calculator.
How this calculator works
Inputs explained
Documents
The number of files, pages, records, or text units you plan to ingest.
Average words per document
A rough average is enough for early planning. Use a sample export for better estimates.
Average words per token
English text often lands near 0.75 words per token, but source material and tokenizer choice vary.
Chunk size and overlap
Chunk size controls retrieval granularity. Overlap repeats context between neighboring chunks.
Understanding your results
Example scenarios
Support knowledge base
500 documents x 900 words each at 0.75 words per token creates roughly 600,000 tokens. With 800-token chunks and 100-token overlap, the estimate is about 858 chunks.
Key Features
- Instant Calculation
- Private & Secure
- Mobile Optimized
Tips & Best Practices
- Start with a few representative documents and compare the estimate to your actual ingestion output.
- Larger chunks reduce vector count but can make retrieval less precise.
- Use the vector database cost calculator after estimating chunk count.