---
title: "ChonkieSentenceDocumentSplitter"
id: chonkiesentencedocumentsplitter
slug: "/chonkiesentencedocumentsplitter"
description: "Use `ChonkieSentenceDocumentSplitter` to split documents into sentence-aware chunks using the Chonkie library."
---

# ChonkieSentenceDocumentSplitter

`ChonkieSentenceDocumentSplitter` splits documents into chunks that respect sentence boundaries using [Chonkie](https://docs.chonkie.ai/)'s `SentenceChunker`.
Unlike pure token splitting, it avoids cutting mid-sentence, producing more coherent chunks.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In indexing pipelines after [Converters](../converters.mdx) and [`DocumentCleaner`](documentcleaner.mdx), before [Embedders](../embedders.mdx) |
| **Mandatory run variables**            | `documents`: A list of documents |
| **Output variables**                   | `documents`: A list of documents |
| **API reference**                      | [Chonkie](/reference/integrations-chonkie) |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/chonkie |

</div>

## Overview

`ChonkieSentenceDocumentSplitter` wraps Chonkie's `SentenceChunker` to split each input document into chunks whose boundaries align with sentence endings.
The chunker groups sentences together until the chunk size limit is reached.

Each output document includes the original document's metadata plus:
- `source_id`: ID of the original document
- `page_number`: Page number of the chunk within the original document
- `split_id`: Index of the chunk within the document
- `split_idx_start` / `split_idx_end`: Character offsets of the chunk in the original text
- `token_count`: Number of tokens in the chunk

## Installation

```bash
pip install chonkie-haystack
```

## Configuration

| Parameter | Default | Description |
| --- | --- | --- |
| `tokenizer` | `"character"` | Tokenizer to use. Common options: `"character"`, `"gpt2"`, `"cl100k_base"`. See [Chonkie docs](https://docs.chonkie.ai/) for all options. |
| `chunk_size` | `2048` | Maximum number of tokens per chunk. |
| `chunk_overlap` | `0` | Number of overlapping tokens between consecutive chunks. |
| `min_sentences_per_chunk` | `1` | Minimum number of sentences that must be included in each chunk. |
| `min_characters_per_sentence` | `12` | Minimum number of characters for a sentence to be considered valid. |
| `approximate` | `False` | Whether to use approximate chunking for faster processing. |
| `delim` | `None` | Custom sentence delimiters. If `None`, Chonkie's default delimiters are used. |
| `include_delim` | `"prev"` | Whether to attach the delimiter to the previous (`"prev"`) or next (`"next"`) chunk. |
| `skip_empty_documents` | `True` | Whether to skip documents with empty content. |
| `page_break_character` | `"\f"` | Character used to detect page breaks when tracking page numbers. |

## Usage

### On its own

```python
from haystack import Document
from haystack_integrations.components.preprocessors.chonkie import (
    ChonkieSentenceDocumentSplitter,
)

chunker = ChonkieSentenceDocumentSplitter(
    tokenizer="gpt2",
    chunk_size=512,
    chunk_overlap=0,
)
documents = [
    Document(
        content="Haystack is an open-source framework. It helps you build LLM applications.",
    ),
]
result = chunker.run(documents=documents)
print(result["documents"])
```

### In a pipeline

```python
from pathlib import Path

from haystack import Pipeline
from haystack.components.converters import TextFileToDocument
from haystack.components.preprocessors import DocumentCleaner
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.preprocessors.chonkie import (
    ChonkieSentenceDocumentSplitter,
)

document_store = InMemoryDocumentStore()

p = Pipeline()
p.add_component("converter", TextFileToDocument())
p.add_component("cleaner", DocumentCleaner())
p.add_component(
    "splitter",
    ChonkieSentenceDocumentSplitter(tokenizer="gpt2", chunk_size=512),
)
p.add_component("writer", DocumentWriter(document_store=document_store))

p.connect("converter.documents", "cleaner.documents")
p.connect("cleaner.documents", "splitter.documents")
p.connect("splitter.documents", "writer.documents")

files = list(Path("path/to/your/files").glob("*.txt"))
p.run({"converter": {"sources": files}})
```