I Tested Chunking on Docs, PDFs, and Code. The Winner Changed Every Time.

Description and use

This open‑source project provides a set of benchmarking tools that evaluate different text‑chunking strategies across various file types—plain documents, PDFs, and source code. By running the suite you can see which chunking method (e.g., fixed‑size, semantic, token‑based) yields the best performance for a given content type, with results that often flip depending on the data. The repository includes scripts, sample datasets, and visual reports to help developers choose the most effective chunking approach for their AI‑powered applications.

Use in a workflow

I Tested Chunking on Docs, PDFs, and Code. The Winner Changed Every Time. helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Early or unclear: treat as research material until maintenance, releases, docs, and issue activity are verified.