LSH Index

A TypeScript implementation of Locality-Sensitive Hashing for indexing similar items using random buckets.

References

- Random Projection for Locality Sensitive Hashing

Installation

Install the package using npm.

``bash
npm install lsh-index
`

Usage

Import and use the LSH class with your desired configuration:

`typescript
import { LSH } from "lsh-index";

const lsh = new LSH({
dimensions: 3,
numProjections: 10,
numBands: 5,
bucketSize: 4
});

// Insert vectors
lsh.insert({ id: "point1", vector: [1, 2, 3] });
lsh.insert({ id: "point2", vector: [1.1, 2.1, 3.1] });

// Query similar vectors
const results = lsh.query({ vector: [1, 2, 3], maxDistance: 0.5 });
`

API

$3

Creates a new LSH instance for similarity search.

#### options

- dimensions (number): Number of dimensions in your input vectors
- numProjections (number): Number of random projections to use. Must be a multiple of numBands
- numBands (number): Number of bands for LSH bucketing
- bucketSize (number, optional): Size of each bucket for quantization (default: 4)
- distanceMetric (function, optional): Custom distance metric function (default: Euclidean)

> Note: The numProjections must be a multiple of numBands to ensure even distribution of projections across bands. For example, if you have 5 bands, valid values for numProjections would be 5, 10, 15, etc.

$3

#### insert(params)

Inserts a vector into the LSH index.

- params.id (string): Unique identifier for the vector
- params.vector (number[]): Vector to insert

#### query(params)

Finds similar vectors within the specified distance.

- params.vector (number[]): Query vector
- params.maxDistance` (number): Maximum distance threshold
- Returns: Array of IDs of similar vectors

#### clear()

Removes all vectors from the index.

#### export()

Exports the current state of the LSH index.

License

MIT