blazechunk 0.13.0

The fastest semantic text chunking library

blazechunk

the fastest semantic text chunking library — up to 1 TB/s throughput

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blazechunk splits text at semantic boundaries and does it stupid fast: a SIMD-accelerated Rust core with a small, uniform Python API. It ships six chunkers — a zero-copy byte Chunker plus RecursiveChunker, SentenceChunker, TokenChunker, TableChunker, and CodeChunker — and every high-level chunker offers matching synchronous and asynchronous methods.

📖 Full documentation: https://blazechunk-documentation.vercel.app/

📦 installation

pip install blazechunk

🚀 usage

High-level chunkers (sync + async)

Every chunker exposes the same four methods, so once you know one you know them all: chunk / chunk_async and chunk_batch / chunk_batch_async.

from blazechunk import TokenChunker

chunker = TokenChunker(chunk_size=512, chunk_overlap=64)

# synchronous
chunks = chunker.chunk("... a long document ...")
for c in chunks:
    print(c.text, c.start_index, c.end_index, c.token_count)

# many documents at once
batches = chunker.chunk_batch(["doc one ...", "doc two ..."])

import asyncio
from blazechunk import RecursiveChunker

async def main() -> None:
    chunker = RecursiveChunker(chunk_size=2048)

    # await a single document — the work runs off the event loop
    chunks = await chunker.chunk_async("... a long document ...")

    # await many documents concurrently, with optional back-pressure
    batches = await chunker.chunk_batch_async(
        ["doc one ...", "doc two ..."], max_concurrency=8
    )

asyncio.run(main())

Other chunkers follow the same shape:

from blazechunk import SentenceChunker, TableChunker, CodeChunker

SentenceChunker(chunk_size=2048, chunk_overlap=128).chunk(prose)
TableChunker(chunk_size=3).chunk(markdown_or_html_table)   # header repeated per chunk
CodeChunker(chunk_size=2048, language="python").chunk(source_code)

Low-level byte chunker (zero-copy)

The Chunker primitive and the chunk() helper yield zero-copy memoryview slices for maximum throughput:

from blazechunk import chunk, chunk_async

# synchronous generator of zero-copy memoryviews
for view in chunk(b"Hello. World. Test.", size=10, delimiters=b"."):
    print(bytes(view))

# async variant returns owned bytes
chunks = await chunk_async(b"Hello. World.", size=10, delimiters=b".")

🔌 integrations

blazechunk plugs into popular RAG frameworks — install the matching extra.

pip install "blazechunk[langchain]"   # LangChain
pip install "blazechunk[agno]"        # Agno

# LangChain
from blazechunk import TokenChunker
from blazechunk.integrations.langchain import BlazechunkTextSplitter

splitter = BlazechunkTextSplitter(TokenChunker(chunk_size=512, chunk_overlap=64))
docs = splitter.create_documents([text])

# Agno
from blazechunk.integrations.agno import BlazechunkChunking

strategy = BlazechunkChunking(TokenChunker(chunk_size=512, chunk_overlap=64))

🙏 acknowledgements

blazechunk is a fork of the excellent chonkie-inc/chunk project, and builds on its SIMD chunking core. Licensed under either of Apache License, Version 2.0 or MIT license at your option.