snipsplit 0.1.1

Token-aware text chunker for RAG ingestion. Sentence-respecting, overlap-friendly.

snipsplit

Token-aware text chunker for RAG ingestion. Sentence-respecting, overlap-friendly. Rust core, Python frontend.

The problem

Your RAG ingestion pipeline needs to split long documents into chunks that fit a context budget. Naive token-window chunking cuts mid-sentence and degrades retrieval. Sentence-only splitting blows the token budget on legal/medical text. The right thing is to greedy-pack sentences into a token-budgeted window, with optional overlap, and fall back to token-level slicing only when a single sentence is genuinely too long.

snipsplit does exactly that, in Rust, fast enough that bulk ingestion of 100k documents runs in seconds rather than minutes.

Install

pip install snipsplit

30-second quickstart

from snipsplit import Chunker

chunker = Chunker(max_tokens=512, overlap_tokens=64, encoding="cl100k_base")

text = open("long_document.txt").read()
for chunk in chunker.split(text):
    print(chunk.token_count, chunk.start, chunk.end, chunk.text[:60])

For batch ingestion across many docs:

texts = [open(p).read() for p in paths]
all_chunks = chunker.split_many(texts, parallel=True)  # list[list[Chunk]]

API

class Chunker:
    def __init__(
        self,
        *,
        max_tokens: int = 512,
        overlap_tokens: int = 0,
        min_tokens: int = 1,
        encoding: str = "cl100k_base",       # or "o200k_base"
    ) -> None: ...

    def split(self, text: str) -> list[Chunk]: ...
    def split_many(self, texts: Sequence[str], *, parallel: bool = False) -> list[list[Chunk]]: ...

class Chunk:
    text: str
    start: int          # byte offset in the original text
    end: int            # byte offset (exclusive)
    token_count: int    # exact BPE token count

Algorithm

1. Split into paragraphs on \n{2,}, then sentences on [.!?]\s+ plus a handful of abbreviations (Mr., Dr., e.g., etc.).
2. Greedy-pack sentences into a chunk while the running token count is <= max_tokens.
3. If a single sentence exceeds max_tokens on its own, slice it at token boundaries (BPE) instead.
4. Apply overlap_tokens by re-prepending the last N tokens of each chunk to the next.
5. Drop chunks shorter than min_tokens.

License

Dual-licensed under MIT or Apache-2.0 at your option.