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ujjucodes / RAG_Chunking_Strategies--research_paper

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Corpus-aware chunking for dense retrieval. Studies how chunk size and strategy affect RAG performance, and implements a rule-based chunker that adapts chunking to document type. This is an independant work of Ujjwal Deepak Sachdeva as a HD submission for Deakin University. No information is to be copied/infringed without knowledge.

nlp research nlp-machine-learning rag dense-retrieval llm rag-chunking
  • Updated Jul 15, 2026
  • Jupyter Notebook

instax-dutta / tooner

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100% client-side privacy-first converter that transforms docs, spreadsheets, code & data into token-optimized .toon files for LLMs. Powered by MarkItDownJS with RAG-ready chunking, semantic optimization & gzip packing — cutting tokens 15-40%. Zero server. Zero telemetry.

document-conversion file-optimization ai-optimization client-side-privacy gpt-tokenizer zero-server token-optimization llm-preprocessing rag-chunking markitdownjs
  • Updated Jul 3, 2026
  • HTML

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