Launch HN: Chonkie (YC X25) – Open-Source Library for Advanced Chunking
17 comments
·June 9, 2025yawnxyz
I'm curious if chunking is different for embeddings vs. for "agentic retrieval" e.g. an AI or a person operates like a Librarian; they look up in an index at what resources to look up, get the relevant bits, then piece them together into a cohesive narrative whole — would we do any chunking at all for this, or does this purely rely on the way the DB is setup? I think for certain use cases, even a single DB record could be too large for context windows, so maybe chunking might need to be done to the record? (e.g. a db of research papers)
amir_karbasi
Looks great! I had looked at Chonkie a few months back, but didn't need it in our pipelines. I was just writing a POC for an agentic chunker this week to handle various formatting and chunking requirements. I'll give Chonkie a shot!
pj_mukh
Super cool!
It looks like size and speed is your major advantage. In our RAG pipeline we run the chunking process async as an onboarding type process. Is Chonkie primarily for people looking to process documents in some sort of real-time scenario?
snyy
In addition to size and speed we also offer the most variety of chunking strategies!
Typically, our current users fall into one of two categories:
- People who are running async chunking but need access to a strategy not supported in langchain/llamaIndex. Sometimes speed matters here too, especially if the user has a high volume of documents
- people who need real time chunking. Super useful for apps like codegen/code review tools.
elliot07
Chonkie is great software. Congrats on the launch! Has been a pleasure to use so far.
snyy
Thank you :)
_epps_
Excited to try this out! Also +1 for Moo Deng-ish mascot.
Andugal
Congratulations for the launch!
You said that Chonkie works with multiple vector stores. I was wondering what RAG database HN uses? Do you need a specialized one (like Chroma) or is Postgres just fine?
gavmor
Does HN even use a RAG database? What for? They don't even maintain their own search[0].
snyy
Not sure what HN uses :)
If you want agents/LLMs to be able to find relevant data based on similarity to queries, vectorDBs like Chroma (or even pgVector) are great.
greymalik
You’re part of YC but this is open source - how do you plan to make money off of it?
snyy
As mentioned in the other reply, we have a cloud/on-prem offering that comes with a managed ETL pipeline built on top of our OSS offering.
tevon
Looks like they will have a cloud offering, and mentioned in this post are on-prem and managed offerings
Hey HN! We're Shreyash and Bhavnick. We're building Chonkie (https://chonkie.ai), an open-source library for chunking and embedding data.
Python: https://github.com/chonkie-inc/chonkie
TypeScript: https://github.com/chonkie-inc/chonkie-ts
Here's a video showing our code chunker: https://youtu.be/Xclkh6bU1P0.
Bhavnick and I have been building personal projects with LLMs for a few years. For much of this time, we found ourselves writing our own chunking logic to support RAG applications. We often hesitated to use existing libraries because they either had only basic features or felt too bloated (some are 80MB+).
We built Chonkie to be lightweight, fast, extensible, and easy. The space is evolving rapidly, and we wanted Chonkie to be able to quickly support the newest strategies. We currently support: Token Chunking, Sentence Chunking, Recursive Chunking, Semantic Chunking, plus:
- Semantic Double Pass Chunking: Chunks text semantically first, then merges closely related chunks.
- Code Chunking: Chunks code files by creating an AST and finding ideal split points.
- Late Chunking: Based on the paper (https://arxiv.org/abs/2409.04701), where chunk embeddings are derived from embedding a longer document.
- Slumber Chunking: Based on the "Lumber Chunking" paper (https://arxiv.org/abs/2406.17526). It uses recursive chunking, then an LLM verifies split points, aiming for high-quality chunks with reduced token usage and LLM costs.
You can see how Chonkie compares to LangChain and LlamaIndex in our benchmarks: https://github.com/chonkie-inc/chonkie/blob/main/BENCHMARKS....
Some technical details about the Chonkie package: - ~15MB default install vs. ~80-170MB for some alternatives. - Up to 33x faster token chunking compared to LangChain and LlamaIndex in our tests. - Works with major tokenizers (transformers, tokenizers, tiktoken). - Zero external dependencies for basic functionality. - Implements aggressive caching and precomputation. - Uses running mean pooling for efficient semantic chunking. - Modular dependency system (install only what you need).
In addition to chunking, Chonkie also provides an easy way to create embeddings. For supported providers (SentenceTransformer, Model2Vec, OpenAI), you just specify the model name as a string. You can also create custom embedding handlers for other providers.
RAG is still the most common use case currently. However, Chonkie makes chunks that are optimized for creating high quality embeddings and vector retrieval, so it is not really tied to the "generation" part of RAG. In fact, We're seeing more and more people use Chonkie for implementing semantic search and/or setting context for agents.
We are currently focused on building integrations to simplify the retrieval process. We've created "handshakes" – thin functions that interact with vector DBs like pgVector, Chroma, TurboPuffer, and Qdrant, allowing you to interact with storage easily. If there's an integration you'd like to see (vector DB or otherwise), please let us know.
We also offer hosted and on-premise versions with OCR, extra metadata, all embedding providers, and managed vector databases for teams that want a fully managed pipeline. If you're interested, reach out at shreyash@chonkie.ai or book a demo: https://cal.com/shreyashn/chonkie-demo.
We're eager to hear your feedback and comments! Thanks!