chonkie and chunklet-py
These are competitors—both are chunking libraries designed to split documents into semantically meaningful pieces for RAG pipelines, with Chonkie offering more mature, production-tested functionality while Chunklet-py provides a simpler, multi-format alternative.
About chonkie
chonkie-inc/chonkie
🦛 CHONK docs with Chonkie ✨ — The lightweight ingestion library for fast, efficient and robust RAG pipelines
Provides pluggable chunking strategies—recursive, semantic, code-aware, and LLM-based—with composable pipeline workflows that chain multiple chunkers and refineries together. Integrates with 32+ tools across tokenizers (GPT-2, BPE), embeddings (OpenAI, Sentence Transformers), vector databases, and LLMs, while supporting 56 languages out-of-the-box through modular dependency installation.
About chunklet-py
speedyk-005/chunklet-py
One library to split them all: Sentence, Code, Docs. Chunk smarter, not harder — built for LLMs, RAG pipelines, and beyond.
Supports 50+ languages with automatic detection and offers composable constraints (sentences, tokens, sections, lines, functions) through a pluggable architecture with custom tokenizers and processors. Rich metadata annotations include source references, spans, and structural information—including AST details for code—making it well-suited for RAG and LLM applications. Handles diverse formats (PDF, DOCX, EPUB, Markdown, HTML, LaTeX, CSV, Excel) via optional document processing modules, with CLI, library, and web-based visualization interfaces.
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