chunk-match
v1.1.1
Published
NodeJS library that semantically chunks text and matches it against a user query using cosine similarity for precise and relevant text retrieval
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🕵️♂️ chunk-match
A NodeJS library that semantically chunks text and matches it against a user query using cosine similarity for precise and relevant text retrieval.
Installation
npm install chunk-match
Features
- Semantic text chunking with configurable options
- Query matching using cosine similarity
- Configurable similarity thresholds and chunk sizes
- Returns chunks sorted by relevance with similarity scores
- Built on top of semantic-chunking for robust text processing
- Support for various ONNX embedding models
Usage
import { matchChunks } from 'chunk-match';
const documents = [
{
document_name: "doc1.txt",
document_text: "Your document text here..."
},
{
document_name: "doc2.txt",
document_text: "Another document text..."
}
];
const query = "What are the key points?";
const options = {
maxResults: 5,
minSimilarity: 0.5,
chunkingOptions: {
maxTokenSize: 500,
similarityThreshold: 0.5,
dynamicThresholdLowerBound: 0.4,
dynamicThresholdUpperBound: 0.8,
numSimilaritySentencesLookahead: 3,
combineChunks: true,
combineChunksSimilarityThreshold: 0.8,
onnxEmbeddingModel: "nomic-ai/nomic-embed-text-v1.5",
dtype: 'q8',
chunkPrefixDocument: "search_document",
chunkPrefixQuery: "search_query"
}
};
const results = await matchChunks(documents, query, options);
console.log(results);
API
matchChunks(documents, query, options)
Parameters
documents
required (Array): Array of document objects with properties:document_name
(string): Name/identifier of the documentdocument_text
(string): Text content to be chunked and matched
query
required (string): The search query to match against documentsoptions
optional (Object): Configuration optionsmaxResults
(number): Maximum number of results to return (default: 10)minSimilarity
(number): Minimum similarity threshold for matches (default: 0.475)chunkingOptions
(Object): Options for text chunkingmaxTokenSize
(number): Maximum token size for chunks (default: 500)similarityThreshold
(number): Threshold for semantic similarity (default: 0.5)dynamicThresholdLowerBound
(number): Lower bound for dynamic thresholding (default: 0.475)dynamicThresholdUpperBound
(number): Upper bound for dynamic thresholding (default: 0.8)numSimilaritySentencesLookahead
(number): Number of sentences to look ahead (default: 2)combineChunks
(boolean): Whether to combine similar chunks (default: true)combineChunksSimilarityThreshold
(number): Threshold for combining chunks (default: 0.6)onnxEmbeddingModel
(string): ONNX model to use for embeddings (see Models section below) (default:Xenova/all-MiniLM-L6-v2
)dtype
: String (optional, defaultfp32
) - Precision of the embedding model (options:fp32
,fp16
,q8
,q4
).chunkPrefixDocument
(string): Prefix for document chunks (for embedding models that support task prefixes) (default: null)chunkPrefixQuery
(string): Prefix for query chunk (for embedding models that support task prefixes) (default: null)
📗 For more details on the chunking options, see the semantic-chunking documentation
🚨 Note on Model Loading 🚨
The first time you use a specific embedding model, it will take longer to process as the model needs to be downloaded and cached locally, please be patient. Subsequent uses will be much faster since the cached model will be used.
Returns
Array of match results, each containing:
chunk
(string): The matched text chunkdocument_name
(string): Source document namedocument_id
(number): Document identifierchunk_number
(number): Chunk sequence numbertoken_length
(number): Length in tokenssimilarity
(number): Similarity score (0-1)
Embedding Models
This library supports various ONNX embedding models through the semantic-chunking
package.
Most models have a quantized version available (set onnxEmbeddingModelQuantized: true
), which offers better performance with minimal impact on accuracy.
For a complete list of supported models and their characteristics, see the semantic-chunking embedding models documentation.
onnxEmbeddingModel
- Type: String
- Default:
Xenova/all-MiniLM-L6-v2
- Description: Specifies the model used to generate sentence embeddings. Different models may yield different qualities of embeddings, affecting the chunking quality, especially in multilingual contexts.
- Resource Link: ONNX Embedding Models
Link to a filtered list of embedding models converted to ONNX library format by Xenova.
Refer to the Model table below for a list of suggested models and their sizes (choose a multilingual model if you need to chunk text other than English).
dtype
- Type: String
- Default:
fp32
- Description: Indicates the precision of the embedding model. Options are
fp32
,fp16
,q8
,q4
.fp32
is the highest precision but also the largest size and slowest to load.q8
is a good compromise between size and speed if the model supports it. All models supportfp32
, but only some supportfp16
,q8
, andq4
.
Curated ONNX Embedding Models
| Model | Precision (dtype) | Link | Size | | -------------------------------------------- | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------- | | nomic-ai/nomic-embed-text-v1.5 | fp32, q8 | https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 | 548 MB, 138 MB | | thenlper/gte-base | fp32 | https://huggingface.co/thenlper/gte-base | 436 MB | | Xenova/all-MiniLM-L6-v2 | fp32, fp16, q8 | https://huggingface.co/Xenova/all-MiniLM-L6-v2 | 23 MB, 45 MB, 90 MB | | Xenova/paraphrase-multilingual-MiniLM-L12-v2 | fp32, fp16, q8 | https://huggingface.co/Xenova/paraphrase-multilingual-MiniLM-L12-v2 | 470 MB, 235 MB, 118 MB | | Xenova/all-distilroberta-v1 | fp32, fp16, q8 | https://huggingface.co/Xenova/all-distilroberta-v1 | 326 MB, 163 MB, 82 MB | | BAAI/bge-base-en-v1.5 | fp32 | https://huggingface.co/BAAI/bge-base-en-v1.5 | 436 MB | | BAAI/bge-small-en-v1.5 | fp32 | https://huggingface.co/BAAI/bge-small-en-v1.5 | 133 MB | | yashvardhan7/snowflake-arctic-embed-m-onnx | fp32 | https://huggingface.co/yashvardhan7/snowflake-arctic-embed-m-onnx | 436 MB |
Each of these parameters allows you to customize the chunkit
function to better fit the text size, content complexity, and performance requirements of your application.
Web UI
Checkout the webui
folder for a web-based interface for experimenting with and tuning Chunk Match settings. This tool provides a visual way to test and configure the chunk-match
library's semantic text matching capabilities to get optimal results for your specific use case. Once you've found the best settings, you can generate code to implement them in your project.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Appreciation
If you enjoy this library please consider sending me a tip to support my work 😀