Semantic Chunker

Semantic Chunker is a versatile library for dividing text into semantically meaningful chunks. It employs a BYOE (Bring Your Own Embedder) approach, allowing users to provide their own embedding function that maps text to a vector space.

Table of Contents

• Semantic Chunker
  • Table of Contents
  • Installation
  • Features
  • Usage
    • Semantic Chunker
    • Other Chunkers
      • Sentence Chunker
      • Full Chunker
  • API
    • semantic(options)
    • sentence(options)
    • full(options)
  • Embedding Functions
    • Example: Local Example
    • Example: External API Call
  • Demo
  • Contributing
  • Changelog
    • [0.0.2]
    • [0.0.1]
    • [0.0.0]
      • Added
  • License

Installation

To install Semantic Chunker, use npm:

npm install semantic-chunker

Features

• Flexible chunking based on semantic meaning
• Support for custom embedding functions
• Multiple chunking strategies: semantic, sentence-based, and full
• Adjustable parameters for fine-tuning chunk sizes and thresholds

Usage

Semantic Chunker

The main chunker that divides text based on semantic meaning:

import semantic from "semantic-chunker";

const embed = // ... your embedding function
const document = // ... your input text

const chunker = semantic({ embed, zScoreThreshold: 1 });

for await (const [text, embedding] of chunker(document)) {
  console.log({ text, embedding });
}

Other Chunkers

Sentence Chunker

Divides the text into sentence-level chunks:

import { sentence } from "semantic-chunker";

const embed = // ... your embedding function
const chunker = sentence({ embed });

// Usage same as semantic chunker

Full Chunker

Returns the entire document as a single chunk:

import { full } from "semantic-chunker";

const embed = // ... your embedding function
const chunker = full({ embed });

// Usage same as semantic chunker

API

semantic(options)

Creates a semantic chunker.

• options.embed: Function that takes a string and returns a vector (required)
• options.zScoreThreshold: Number that determines the threshold for creating new chunks (default: 2).
  • You will need to experiment with this value to get the best results.
• options.split: Force a splt after this many characters (optional)

sentence(options)

Creates a sentence chunker.

• options.embed: Function that takes a string and returns a vector (required)
• options.split: Force a splt after this many characters (optional)

full(options)

Creates a full chunker.

• options.embed: Function that takes a string and returns a vector (required)
• options.split: Force a splt after this many characters (optional)

Embedding Functions

For better or for worse, you'll need to supply your own embedding function with the following signature:

type Embed = (text: string) => Promise<number[]>;

We provide two examples that should cover most use cases:

Example: Local Example

In /embed/xenova.mjs we provide an example where we create an embedding function using a local transformer model.

For this example to work, in addition to the @xenova/transformers npm package, you will need to obtain a read-access token from Hugging Face and set it to the HF_ACCESS_TOKEN environment variable.

export HF_ACCESS_TOKEN=<your_access_token>

Upon first run, it will take a while to download the model Supabase/gte-small model, but subsequent runs will be much faster.

Example: External API Call

In /embed/ollama.mjs there is an example where we create an embedding function using an external API call.

For this example to work, in addition to the ollama npm package, you will need to install and run ollama and pull the latest nomic-embed-text model.

Demo

Run a demo with the following command:

node --run demo

Results in ./docs/demo-results.md.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Changelog

[0.0.2]

• Demo fixed minor issues with demo and docs

[0.0.1]

• Demo is more robust and outputs markdown
• Fix documentation for options.split
• Add options.split to ful chunker

[0.0.0]

Added

• Initial release of the semantic-chunker package
• Semantic chunker for semantic text division
• Sentence chunker for sentence-level text division
• Full chunker for processing entire documents
• README with usage examples and API documentation
• Basic test suite

License

This project is licensed under the MIT License - see the LICENSE file for details.