🍱 semantic-chunking

semantically create chunks from large text (useful for passing to LLM workflows)

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Install:

• npm install semantic-chunking

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Usage

import { chunkit } from 'semantic-chunking';

let text = "some long text...";
let myChunks = chunkit(text);

All Parameters

chunkit(
    text,
    { // options object
        logging,
        maxTokenSize,
        similarityThreshold,
        onnxEmbeddingModel,
        onnxEmbeddingModelQuantized,
        combineSimilarityChunks
    }
)

• text
  full string to split into chunks

• options object [optional]

  • logging [optional | boolean | default false ]
  • maxTokenSize [optional | int | default 500]
    max possible token size of each chunk
  • similarityThreshold [optional | float | default .567]
    threshold value used to determin if paired sentences are semantically close enough to be included in the same chunk
  • onnxEmbeddingModel [optional | string | default Xenova/paraphrase-multilingual-MiniLM-L12-v2]
    ONNX model to use for creating embeddings for similarity comparison (model name on huggingface)
  • onnxEmbeddingModelQuantized [optional | boolean | default true]
    if the quantized version of the model should be used
  • combineSimilarityChunks [optional | boolean | default true]
    if true, the initial round of smaller semantic similar chunks are combined to make larger chunks up to the defined max token limit

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Workflow

• text is split into an array of sentences
• a vector is created for each sentence
• a cosine similarity score is created for each sentence pair
• each sentence is added to a chunk until the similarity threshold or max token size for the chunk is exceeded
• after all similary chunks are created combine similary chunks into large chunks up to the max token size unless the combineSimilaryityChunks was set to false

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Example Calls

import { chunkit } from 'semantic-chunking';

const text = await fs.promises.readFile('./example.txt', 'utf8');
let myChunks = await chunkit(text, { logging: true, similarityThreshold: .9 });

myChunks.forEach((chunk, index) => {
    console.log("--------------------");
    console.log("Chunk " + (index + 1));
    console.log("--------------------");
    console.log(chunk);
    console.log("\n\n");
});

import { chunkit } from 'semantic-chunking';

let frogText = "A frog hops into a deli and croaks to the cashier, \"I'll have a sandwich, please.\" The cashier, surprised, quickly makes the sandwich and hands it over. The frog takes a big bite, looks around, and then asks, \"Do you have any flies to go with this?\" The cashier, taken aback, replies, \"Sorry, we're all out of flies today.\" The frog shrugs and continues munching on its sandwich, clearly unfazed by the lack of fly toppings. Just another day in the life of a sandwich-loving amphibian! 🐸🥪";

let myFrogChunks = await chunkit(frogText, { maxTokenSize: 65 });
console.log("myFrogChunks", myFrogChunks);