---
title: "RecursiveDocumentSplitter"
id: recursivesplitter
slug: "/recursivesplitter"
description: "This component recursively breaks down text into smaller chunks by applying a given list of separators to the text."
---

# RecursiveDocumentSplitter

This component recursively breaks down text into smaller chunks by applying a given list of separators to the text.

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

|  |  |
| --- | --- |
| Most common position in a pipeline | In indexing pipelines after [Converters](../converters.mdx)   and [`DocumentCleaner`](documentcleaner.mdx)  , before [Classifiers](../classifiers.mdx) |
| Mandatory run variables            | `documents`: A list of documents                                                                                                                       |
| Output variables                   | `documents`: A list of documents                                                                                                                       |
| API reference                      | [PreProcessors](/reference/preprocessors-api)                                                                                                                 |
| Github link                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/preprocessors/recursive_splitter.py                                             |

</div>

## Overview

The `RecursiveDocumentSplitter` expects a list of documents as input and returns a list of documents with split texts. You can set the following parameters when initializing the component:

- `split_length`: The maximum length of each chunk, in words, by default. See the `split_units` parameter to change the the unit.
- `split_overlap`: The number of characters or words that overlap between consecutive chunks.
- `split_unit`: The unit of the `split_length` parameter. Can be either `"word"`, `"char"`, or `"token"`.
- `separators`: An optional list of separator strings to use for splitting the text. If you don’t provide any separators, the default ones are `["\n\n", "sentence", "\n", " "]`. The string separators will be treated as regular expressions. If the separator is `"sentence"`, the text will be split into sentences using a custom sentence tokenizer based on NLTK. See [SentenceSplitter](https://github.com/deepset-ai/haystack/blob/main/haystack/components/preprocessors/sentence_tokenizer.py#L116) code for more information.
- `sentence_splitter_params`: Optional parameters to pass to the [SentenceSplitter](https://github.com/deepset-ai/haystack/blob/main/haystack/components/preprocessors/sentence_tokenizer.py#L116).

The separators are applied in the same order as they are defined in the list. The first separator is used on the text; any resulting chunk that is within the specified `chunk_size` is retained. For chunks that exceed the defined `chunk_size`, the next separator in the list is applied. If all separators are used and the chunk still exceeds the `chunk_size`, a hard split occurs based on the `chunk_size`, taking into account whether words or characters are used as counting units. This process is repeated until all chunks are within the limits of the specified `chunk_size`.

## Usage

```python
from haystack import Document
from haystack.components.preprocessors import RecursiveDocumentSplitter

chunker = RecursiveDocumentSplitter(split_length=260, split_overlap=0, separators=["\n\n", "\n", ".", " "])
text = ('''Artificial intelligence (AI) - Introduction

AI, in its broadest sense, is intelligence exhibited by machines, particularly computer systems.
AI technology is widely used throughout industry, government, and science. Some high-profile applications include advanced web search engines; recommendation systems; interacting via human speech; autonomous vehicles; generative and creative tools; and superhuman play and analysis in strategy games.''')
doc = Document(content=text)
doc_chunks = chunker.run([doc])
print(doc_chunks["documents"])
>[
>Document(id=..., content: 'Artificial intelligence (AI) - Introduction\n\n', meta: {'original_id': '...', 'split_id': 0, 'split_idx_start': 0, '_split_overlap': []})
>Document(id=..., content: 'AI, in its broadest sense, is intelligence exhibited by machines, particularly computer systems.\n', meta: {'original_id': '...', 'split_id': 1, 'split_idx_start': 45, '_split_overlap': []})
>Document(id=..., content: 'AI technology is widely used throughout industry, government, and science.', meta: {'original_id': '...', 'split_id': 2, 'split_idx_start': 142, '_split_overlap': []})
>Document(id=..., content: ' Some high-profile applications include advanced web search engines; recommendation systems; interac...', meta: {'original_id': '...', 'split_id': 3, 'split_idx_start': 216, '_split_overlap': []})
>]
```

### In a pipeline

Here's how you can use `RecursiveSplitter` in an indexing pipeline:

```python
from pathlib import Path

from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.converters.txt import TextFileToDocument
from haystack.components.preprocessors import DocumentCleaner
from haystack.components.preprocessors import RecursiveDocumentSplitter
from haystack.components.writers import DocumentWriter

document_store = InMemoryDocumentStore()
p = Pipeline()
p.add_component(instance=TextFileToDocument(), name="text_file_converter")
p.add_component(instance=DocumentCleaner(), name="cleaner")
p.add_component(
    instance=RecursiveDocumentSplitter(
        split_length=400,
        split_overlap=0,
        split_unit="char",
        separators=["\n\n", "\n", "sentence", " "],
        sentence_splitter_params={
            "language": "en",
            "use_split_rules": True,
            "keep_white_spaces": False,
        },
    ),
    name="recursive_splitter",
)
p.add_component(instance=DocumentWriter(document_store=document_store), name="writer")
p.connect("text_file_converter.documents", "cleaner.documents")
p.connect("cleaner.documents", "splitter.documents")
p.connect("splitter.documents", "writer.documents")

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