v0.7.0 - Markdown Support

What's New

Markdown Support! Both the Rust crate and Python package have a new MarkdownSplitter you can use to split markdown text. It leverages the great work of the pulldown-cmark crate to parse markdown according to the CommonMark spec, and allows for very fine-grained control over how to split the text.

In terms of use, the API is identical to the TextSplitter, so you should be able to just drop it in when you have Markdown available instead of just plain text.

Rust

use text_splitter::MarkdownSplitter;

// Default implementation uses character count for chunk size.
// Can also use all of the same tokenizer implementations as `TextSplitter`.
let splitter = MarkdownSplitter::default()
    // Optionally can also have the splitter trim whitespace for you. It
    // will preserve indentation if multiple lines are covered in a chunk.
    .with_trim_chunks(true);

let chunks = splitter.chunks("# Header\n\nyour document text", 1000)

Python

from semantic_text_splitter import MarkdownSplitter

# Default implementation uses character count for chunk size.
# Can also use all of the same tokenizer implementations as `TextSplitter`.
# By default it will also have trim whitespace for you.
# It will preserve indentation if multiple lines are covered in a chunk.
splitter = MarkdownSplitter()
chunks = splitter.chunks("# Header\n\nyour document text", 1000)

Breaking Changes

Rust

MSRV is now 1.75.0 since the ability to use impl Trait in trait methods allowed for much simpler internal APIs to enable the MarkdownSplitter.

Python

CharacterTextSplitter, HuggingFaceTextSplitter, TiktokenTextSplitter, and CustomTextSplitter classes have now all been consolidated into a single TextSplitter class. All of the previous use cases are still supported, you just need to instantiate the class with various class methods.

Below are the changes you need to make to your code to upgrade to v0.7.0:

CharacterTextSplitter

# Before
from semantic_text_splitter import CharacterTextSplitter
splitter = CharacterTextSplitter()

# After
from semantic_text_splitter import TextSplitter
splitter = TextSplitter()

HuggingFaceTextSplitter

# Before
from semantic_text_splitter import HuggingFaceTextSplitter
from tokenizers import Tokenizer

tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
splitter = HuggingFaceTextSplitter(tokenizer)

# After
from semantic_text_splitter import TextSplitter
from tokenizers import Tokenizer

tokenizer = Tokenizer.from_pretrained("bert-base-uncased")
splitter = TextSplitter.from_huggingface_tokenizer(tokenizer)

TiktokenTextSplitter

# Before
from semantic_text_splitter import TiktokenTextSplitter

splitter = TiktokenTextSplitter("gpt-3.5-turbo")

# After
from semantic_text_splitter import TextSplitter

splitter = TextSplitter.from_tiktoken_model("gpt-3.5-turbo")

CustomTextSplitter

# Before
from semantic_text_splitter import CustomTextSplitter

splitter = CustomTextSplitter(lambda text: len(text))

# After
from semantic_text_splitter import TextSplitter

splitter = TextSplitter.from_callback(lambda text: len(text))

New Contributors

• @SimonCW made their first contribution in #92

Full Changelog: v0.6.3...v0.7.0