---
title: "ChonkieSemanticDocumentSplitter"
id: chonkiesemanticdocumentsplitter
slug: "/chonkiesemanticdocumentsplitter"
description: "Use `ChonkieSemanticDocumentSplitter` to split documents at semantic topic boundaries using embedding similarity, powered by the Chonkie library."
---

# ChonkieSemanticDocumentSplitter

`ChonkieSemanticDocumentSplitter` splits documents at semantically meaningful boundaries using [Chonkie](https://docs.chonkie.ai/)'s `SemanticChunker`.
Rather than splitting by a fixed token count, it uses an embedding model to detect topic shifts and keeps related sentences together.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In indexing pipelines after [Converters](../converters.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

`ChonkieSemanticDocumentSplitter` wraps Chonkie's `SemanticChunker` to produce context-aware chunks by grouping sentences with similar semantic content.
It computes embeddings for sentences and uses cosine similarity to find natural topic boundaries.

The embedding model is loaded lazily — `warm_up()` is called automatically the first time `run()` is invoked, whether inside a pipeline or standalone.

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 |
| --- | --- | --- |
| `embedding_model` | `"minishlab/potion-base-32M"` | The embedding model used to compute sentence similarity. See [Chonkie docs](https://docs.chonkie.ai/) for supported models. |
| `threshold` | `0.8` | Cosine similarity threshold below which a sentence boundary becomes a split point. |
| `chunk_size` | `2048` | Maximum number of tokens per chunk (based on the embedding model's tokenizer). |
| `similarity_window` | `3` | Number of surrounding sentences to include when computing similarity. |
| `min_sentences_per_chunk` | `1` | Minimum number of sentences that must be included in each chunk. |
| `min_characters_per_sentence` | `24` | Minimum number of characters for a sentence to be considered valid. |
| `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_window` | `0` | Number of sentences to skip when computing similarity scores. |
| `filter_window` | `5` | Window size for the Savitzky-Golay smoothing filter applied to similarity scores. |
| `filter_polyorder` | `3` | Polynomial order for the Savitzky-Golay filter. |
| `filter_tolerance` | `0.2` | Tolerance used when filtering similarity scores. |
| `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 (
    ChonkieSemanticDocumentSplitter,
)

chunker = ChonkieSemanticDocumentSplitter(chunk_size=512, threshold=0.5)

documents = [
    Document(
        content="Haystack is an open-source framework for LLM applications. "
        "It makes building RAG pipelines easy. "
        "The Eiffel Tower is located in Paris. "
        "Paris is the capital of France.",
    ),
]
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 (
    ChonkieSemanticDocumentSplitter,
)

document_store = InMemoryDocumentStore()

p = Pipeline()
p.add_component("converter", TextFileToDocument())
p.add_component("cleaner", DocumentCleaner())
p.add_component(
    "splitter",
    ChonkieSemanticDocumentSplitter(chunk_size=512, threshold=0.5),
)
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}})
```
