Slumber Chunker

Efficient chunking with sliding window for long documents

Slumber Chunker

cURL

curl --request POST \
  --url https://api.chonkie.ai/v1/chunk/slumber

200

{
  "text": "<string>",
  "start_index": 123,
  "end_index": 123,
  "token_count": 123
}

The Slumber Chunker uses a sliding window approach with efficient processing for chunking long documents while maintaining context.

Examples

Text Input

Python

from chonkie.cloud import SlumberChunker

chunker = SlumberChunker(
    chunk_size=512,
    recipe="markdown"
)

text = "Your text here..."
chunks = chunker.chunk(text)

File Input

Python

from chonkie.cloud import SlumberChunker

chunker = SlumberChunker(
    chunk_size=512,
    recipe="markdown"
)

# Chunk from file
with open("document.txt", "rb") as f:
    chunks = chunker.chunk(file=f)

Request

Parameters

text · string | string[]

The text to chunk. Can be a single string or an array of strings for batch processing. Either text or file is required.

file · file

File to chunk. Use multipart/form-data encoding. Either text or file is required.

tokenizer · string · default: "gpt2"

Tokenizer to use for counting tokens.

chunk_size · integer · default: "512"

Maximum number of tokens per chunk.

recipe · string · default: "default"

Recursive rules recipe to follow. See all recipes on our Hugging Face repo

lang · string · default: "en"

Language of the document to chunk

candidate_size · integer · default: "128"

The size of the candidate splits that the chunker will consider.

min_characters_per_chunk · integer · default: "24"

Minimum number of characters per chunk

Response

Returns

Array of Chunk objects, each containing:

text · string

The chunk text content.

start_index · integer

Starting character position in the original text.

end_index · integer

Ending character position in the original text.

token_count · integer

Number of tokens in the chunk.