Count the Duplicate Tokens Created by Chunk Overlap

How to Use This Tool

Expose how a small overlap percentage compounds across a large corpus. Calculate duplicate RAG tokens from chunk count, tokens per chunk, overlap percentage and index replicas. Uses editable inputs and runs locally.

The failure RAG Overlap Cost is designed to catch

Repeated boundary text is stored and embedded again, so overlap carries both retrieval value and a measurable ingestion cost. The boundary is the job stated in Count the Duplicate Tokens Created by Chunk Overlap; RAG Overlap Cost is not intended to score or transform a different workflow.

The RAG Overlap Cost input contract

The fields used for this specific operation are Chunks in index, Tokens per chunk, Overlap %, Index replicas. Keep the source values beside the RAG Overlap Cost result, because replacing the original would remove the evidence needed to reproduce or reverse the operation.

• For RAG Overlap Cost, Chunks in index starts at 50000 in the worked case; replace that example with the matching source value.
• For RAG Overlap Cost, Tokens per chunk starts at 500 in the worked case; replace that example with the matching source value.
• For RAG Overlap Cost, Overlap % starts at 15 in the worked case; replace that example with the matching source value.
• For RAG Overlap Cost, Index replicas starts at 1 in the worked case; replace that example with the matching source value.

Worked result for RAG Overlap Cost

The executable case called Default decision scenario expects out: 3,750,000.0. Verify that observation before entering real material, and then change one RAG Overlap Cost field at a time so an unexpected direction or formatting change can be traced to a specific input.

Reading the RAG Overlap Cost output

It combines chunks in index, tokens per chunk, overlap % and index replicas into one decision result using the formula explained on the page. Apply that answer only when Chunks in index, Tokens per chunk, Overlap %, Index replicas describe the same scope and format as the worked operation. If the source uses different units, quoting, nesting, timing or account rules, a plausible-looking RAG Overlap Cost output is not sufficient validation.

Assumptions attached to RAG Overlap Cost

• RAG Overlap Cost assumes that all inputs describe the same unit or reporting period unless the field explicitly says otherwise.
• RAG Overlap Cost assumes that the model includes only the four visible inputs and does not infer hidden platform charges.
• RAG Overlap Cost assumes that the page never calls an AI model; token ratios, prices, limits and observed rates are user-supplied planning assumptions.

If one of these RAG Overlap Cost assumptions is false, keep the result as a diagnostic rather than production or decision data, and choose an implementation that explicitly supports the missing rule.

Evidence maintained for RAG Overlap Cost

The recorded reference is NIST — AI Risk Management Framework. Reopen that source when the definition, format, fee or policy behind RAG Overlap Cost changes; private configuration and downstream acceptance still have to be checked in the user's own system.

Where RAG Overlap Cost runs

The named operation executes in browser JavaScript without an ecech calculation API. For RAG Overlap Cost, local execution reduces transmission but does not control browser extensions, device security or the destination where the result is pasted, so sensitive inputs still require the user's normal handling rules.