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Advanced Chunking in JavaScript/TypeScript with Chonkie

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Advanced Chunking in JavaScript/TypeScript with Chonkie

Hi HN, We’re Shreyash and Bhavnick. We built Chonkie, an open-source library for advanced chunking and embedding of text and code. It was previously Python-only, but we just released a TypeScript version: https://github.com/chonkie-inc/chonkie-ts Many AI projects in JS/TS (like those using Vercel's AI SDK or Mastra) rely on basic text splitters. But better chunking = better retrieval = better performance. That’s what Chonkie is built for. Current native chunkers (in TS): - Code Chunker – handles Python, TypeScript, etc. - Recursive Chunker – rule-based, hierarchical splitting - Token Chunker – split by token count (fully customizable) - Sentence Chunker – split on sentence boundaries. Delimiters are customizable, so it works for multiple languages. All chunkers support custom tokenizers, chunk overlap, delimiters, and more. Coming soon in native TS (already available via the API client): - Semantic Chunker – splits texts wherever it detects a shift in meaning. - SDPM Chunker – merges semantically similar disjoint chunks - Late Chunker – generates context-aware embeddings for each chunk - Slumber Chunker – LLM-refined recursive chunks. Significantly reduces token usage (and thus cost) while maximizing chunk quality. - Embeddings Refinery - Embed chunks with any embedding model - Overlap Refinery – Create overlaps between consecutive chunks for better context preservation. Chonkie is free, open-source, and MIT licensed. GitHub: https://github.com/chonkie-inc/chonkie-ts We’d love your feedback, ideas, or contributions. Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, context, using · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: just released, ide, io · Missing: https docs, excited, exist
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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