rag-chunk and adaptive-chunking
These tools are competitors, as both aim to identify optimal RAG chunking strategies for documents, with "messkan/rag-chunk" focusing on testing and benchmarking Markdown and "ekimetrics/adaptive-chunking" providing an automated selection mechanism for various document types.
About rag-chunk
messkan/rag-chunk
A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.
Implements six chunking strategies including header-aware and embedding-based semantic splitting, with token-accurate chunking via tiktoken for specific LLM models (GPT-3.5, GPT-4, etc.). Evaluates chunk quality through precision, recall, and F1-score metrics, and supports embedding-based semantic retrieval using sentence-transformers as an alternative to lexical matching. Exports results to JSON/CSV and integrates optional LangChain components for recursive character splitting.
About adaptive-chunking
ekimetrics/adaptive-chunking
Adaptive Chunking: automatically select the best chunking method per document for RAG. Accepted at LREC 2026.
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