This 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.

Across 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.

For 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/e75a0132e93ca989f8870c4...

[2] https://jina.ai/segmenter/

I 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.

There'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/R...

That'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!

Dependencies in the venv?

Poorly, 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.

Right 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!

I 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?

Just 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!

Retrieval-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.

Haha~ thanks!

IMO 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.