10 ms·
Show HN: Chonkie – A Fast, Lightweight Text Chunking Library for RAG
I built Chonkie because I was tired of rewriting chunking code for RAG applications. Existing libraries were either too bloated (80MB+) or too basic, with no middle ground.
Core features:
- 21MB default install vs 80-171MB alternatives
- 33x faster token chunking than popular alternatives
- Supports multiple chunking strategies: token, word, sentence, and semantic
- Works with all major tokenizers (transformers, tokenizers, tiktoken)
- Zero external dependencies for basic functionality
Technical optimizations:
- Uses tiktoken with multi-threading for faster tokenization
- Implements aggressive caching and precomputation
- Running mean pooling for efficient semantic chunking
- Modular dependency system (install only what you need)
Benchmarks and code: https://github.com/bhavnicksm/chonkie https://github.com/bhavnicksm/chonkie
Looking for feedback on the architecture and performance optimizations. What other chunking strategies would be useful for RAG applications?
- will-burner 2y agoLove the name Chonkie and Moo Deng, the hippo, as the image/logo!! edit: Get some Moo Deng jokes in the docs!
- mixeden 2y ago> Token Chunking: 33x faster than the slowest alternative 1) what
- rkharsan64 2y agoThere's only 3 competitors in that particular benchmark, and the speedup compared to the 2nd is only 1.06x. Edit: Also, from the same table, it seems that only this library was ran after warming up, while others were not. https://github.com/bhavnicksm/chonkie/blob/main/benchmarks/README.md#-speed-benchmarks https://github.com/bhavnicksm/chonkie/blob/main/benchmarks/R...
- bhavnicksm 2y agoTokenChunking is really limited by the tokenizer and less by the Chunking algorithm. Tiktoken tokenizers seem to do better with warm-up which Chonkie defaults to -- which is also what the 2nd one is using. Algorithmically, there's not much difference in TokenChunking between Chonkie and LangChain or any other TokenChunking algorithm you might want to use. (except Llamaindex, I don't know what mess they made for 33x slower algo) If you only want TokenChunking (which I do not recommend completely), better than Chonkie or LangChain, just write your own for production :) At least don't install 80MiB packages for TokenChunking, Chonkie is 4x smaller than them. That's just my honest response... And these benchmarks are just the beginning, future optimizations on SemanticChunking which would increase the speed-up from the current 2nd (2.5x right now) to even higher.
- melony 2y agoHow does it compare with NLTK's chunking library? I have found that it works very well for sentence segmentation.
- samlinnfer 2y agoHow does it work for code? (Chunking code that is)
- nostrebored 2y agoPoorly, just like it does for text. Chunking is easily where all of these problems die beyond PoC scale. I’ve talked to multiple code generation companies in the past week — most are stuck with BM25 and taking in whole files.
- potatoman22 2y agoWhat do they use BM25 for? RAG?
- nostrebored 2y agoCorrect -- finding the correct functions and files to include
- bhavnicksm 2y agoRight now, we haven't worked on adding support for code -- some things like comments (#, //) have punctuations that adversely affect chunking, along with indentation and other issues. But, it's on the roadmap, so please hold on!
- bravura 2y agoOne thing I've been looking for, and was a bit tricky implementing myself to be very fast, is this: I have a particular max token length in mind, and I have a tokenizer like tiktoken. I have a string and I want to quickly find the maximum length truncation of the string that is <= target max token length. Does chonkie handle this?
- bhavnicksm 2y agoI don't fully understand what you mean by "maximum length truncation of the string"; but if you're talking about splitting the sentence into 'chunks' which have token counts less than a pre-specified max_token length then, yes! Is that what you meant?
- Eisenstein 2y agoI'm not sure if this is what they mean, but this is a use case that I have dealt with and had to roll my own code for: Given a list sentences, find the largest in order group of sentences which fit into a max token length such that the sentences contain a natural coherence. In my case I used a fuzzy token limit and the chunker would choose a smaller group of sentences that fit into a single paragraph or a single common structure instead of cramming every possible sentence until it ran out of room. It would do the same going over the limit if it would be beneficial to do so. A simple example would be having an alphabetized set and instead of making one chunk A items through part of B items it would end at A items with tokens to spare, or if it were only an extra 10% it would finish the B items. Most of the time it just decided to use paragraphs to end chunks instead of continuing into the middle of the next one.
- spullara 2y ago21MB? to split text? have you analyzed the footprint?
- bhavnicksm 2y agoJust to clarify, the 21MB is the size of the package itself! Other package sizes are way larger. Memory footprint of the chunking itself would vary widely based on the dataset, and it's not something we tested on... usually other providers don't test it either, as long as it doesn't bust up the computer/server. If saving memory during runtime is important for your application, let me know! I'd run some benchmarks for it... Thanks!
- ch1kkenm4ss4 2y agoChonkie and lightweight? Good naming!
- bhavnicksm 2y agoHaha~ thanks!
- opendang 2y ago[flagged]
- xivusr 2y agoIMO comments like this go against the spirit of HN - why not offer more constructive feedback? Implying defects without suggestions on how to improve (or proof) is low effort and what I expect on a YouTube comment thread, not HN.
- simonw 2y agoWould it make sense for this to offer a chunking strategy that doesn't need a tokenizer at all? I love the goal to keep it small, but "tokenizers" is still a pretty huge dependency (and one that isn't currently compatible with Python 3.13). I've been hoping to find an ultra light-weight chunking library that can do things like very simple regex-based sentence/paragraph/markdown-aware chunking with minimal additional dependencies.
- parhamn 2y agoAcross a broad enough dataset (char count / 4) is very close to the actual token count in english -- we verified across millions of queries. We had to switch to using an actual tokenizer for chinese and other unicode languages, as that simple formula misses the mark for context stuffing. The more complicated stuff is the effective bin-packing problem that emerges depending on how much different contextual sources you have.
- andai 2y agoI made a rudimentary semantic chunking in just a few lines of code. I just removed one sentence at a time from the left until there was a jump in the embedding distance. Then repeated for the right side.
- jimmySixDOF 2y agoFor a Regex approach take a look at the work from Jina.ai who among other things have a chunk/tokenizer [1] and now it's part of a bigger API service [2] also they developed an interesting late interaction (aka ColBERT like) chunking system that fits certain use cases. But the Regex is enough all by itself: [1] https://gist.github.com/LukasKriesch/e75a0132e93ca989f8870c4f95be734b https://gist.github.com/LukasKriesch/e75a0132e93ca989f8870c4... [2] https://jina.ai/segmenter/ https://jina.ai/segmenter/
- petesergeant 2y ago> What other chunking strategies would be useful for RAG applications? I’m using o1-preview for chunking, creating summary subdocuments.
- bhavnicksm 2y agoThat's pretty cool! I believe a research paper called LumberChunker recently evaluated that to be pretty decent as well. Thanks for responding, I'll try to make it easier to use something like that in Chonkie in the future!
- petesergeant 2y agoAh, that's an interesting paper, and a slightly different approach to what I'm doing, but possibly a superior one. Thanks!
- bhavnicksm 2y agoThank you so much for giving Chonkie a chance! Just to note Chonkie is still in beta mode (with v0.1.2 running) with a bunch of things planned for it. It's an initial working version, which seemed promising enough to present. I hope that you will stick with Chonkie for the journey of making the 'perfect' chunking library! Thanks again!
- vlovich123 2y agoOut of curiosity where does the 21 MiB come from? The codebase clone is 1.2 MiB and the src folder is only 68 KiB.
- ekianjo 2y agoDependencies in the venv?
- mattmein 2y agoAlso check out https://github.com/D-Star-AI/dsRAG/ https://github.com/D-Star-AI/dsRAG/ for a bit more involved chunking strategy.
- cadence- 2y agoThis looks pretty amazing. I will take it for a spin next week. I want to make a RAG that will answer questions related to my new car. The manual is huge and it is often hard to find answers in it, so I think this will be a big help to owners of the same car. I think your library can help me chunk that huge PDF easily.
- ilidur 2y agoReview: Chonkie is an MIT license project to help with chunking your sentences. It boasts fixed length, word length, sentence and semantic methods. The instructions for installing and usage are simple. The Benchmark numbers are massaged to look really impressive but upon scrutiny the improvements are at most <1.86x compared to the leading product LangChain in a further page describing the measurements. It claims to beat it on all aspects but where it gets close, the author's library uses a warmed up version so the numbers are not comparable. The author acknowledged this but didn't change the methodology to provide a direct comparison. The author is Bhavnick S. Minhas, an early career ML engineer with both research and industry experience and very prolific with his GitHub contributions.
- trwhite 2y agoWhat's RAG?
- adwf 2y agoRetrieval-Augmented Generation (AI). Think of it as if ChatGPT (or other models) didn't just have the embedded unstructured knowledge in their weights from learning, but also an extra DB on the side with specific structured knowledge that it can lookup on the fly.
- Dowwie 2y agoWhen would you ever want anything other than Semantic chunking? Cutting chunks into fixed lengths is fast, but it's arbitrarily encoding potentially dissimilar information.