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\n

Voy

\n A WASM vector similarity search engine written in Rust\n
\n\n![voy: a vector similarity search engine in WebAssembly][demo]\n\n[![npm version](https://badge.fury.io/js/voy-search.svg)](https://badge.fury.io/js/voy-search)\n\n- **Tiny**: 75KB gzipped, 69KB brotli.\n- **Fast**: Create the best search experience for the users. Voy uses [k-d tree][k-d-tree] to index and provide fast search\n- **Tree Shakable**: Optimize bundle size and enable asynchronous capabilities for modern Web API, such as [Web Workers](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API/Using_web_workers).\n- **Resumable**: Generate portable embeddings index anywhere, anytime.\n- **Worldwide**: Designed to deploy and run on CDN edge servers.\n\n> **🚜 Work in Progress**\n>\n> Voy is under active development. As a result, the API is not stable. Please be aware that there might be breaking changes before the upcoming 1.0 release.\n>\n> A sneak peek of what we are working on:\n>\n> - [ ] Built-in text transformation in WebAssembly: As of now, voy relies on JavaScript libraries like [`transformers.js`][transformers.js] to generate text embeddings. See [Usage](#usage) for more detail.\n> - [x] Index update: Currently it's required to [re-build the index](#indexresource-resource-serializedindex) when a resource update occurs.\n> - [x] TypeScript support: Due to the limitation of WASM tooling, complex data types are not auto-generated.\n\n## Installation\n\n```bash\n# with npm\nnpm i voy-search\n\n# with Yarn\nyarn add voy-search\n\n# with pnpm\npnpm add voy-search\n```\n\n## APIs\n\n### `class Voy`\n\nThe Voy class encapsulates an index and exposes all the public methods Voy has to offer.\n\n```ts\nclass Voy {\n /**\n * By instantiating with a resource, Voy will construct the index. If the resource is\n * absent, it will construct an empty index. Calling Voy.index() later on will override\n * the empty index.\n *\n * @param {Resource | undefined} resource\n */\n constructor(resource?: Resource);\n /**\n * Index given resource. Voy.index() is designed for the use case where a Voy instance\n * is instantiated without a resource. It will override the existing index. If you'd like\n * to keep the existing index, you can use Voy.add() to add your resource to the index.\n *\n * @param {Resource} resource\n */\n index(resource: Resource): void;\n /**\n * Search top k results with given query embedding.\n *\n * @param {Float32Array} query: Query Embedding\n * @param {number} k: Number of items in the search result\n * @returns {SearchResult}\n */\n search(query: Float32Array, k: number): SearchResult;\n /**\n * Add given resource to the index.\n *\n * @param {Resource} resource\n */\n add(resource: Resource): void;\n /**\n * Remove given resource from the index.\n *\n * @param {Resource} resource\n */\n remove(resource: Resource): void;\n /**\n * Remove all resources from the index.\n */\n clear(): void;\n}\n\ninterface Resource {\n embeddings: Array<{\n id: string; // id of the resource\n title: string; // title of the resource\n url: string; // url to the resource\n embeddings: number[]; // embeddings of the resource\n }>;\n}\n\ninterface SearchResult {\n neighbors: Array<{\n id: string; // id of the resource\n title: string; // title of the resource\n url: string; // url to the resource\n }>;\n}\n```\n\n### Individual Functions\n\nBesides the Voy class, Voy also exports all the instance methods as individual functions.\n\n#### `index(resource: Resource): SerializedIndex`\n\nIt indexes the given resource and returns a serialized index.\n\n**Parameters**\n\n```ts\ninterface Resource {\n embeddings: Array<{\n id: string; // id of the resource\n title: string; // title of the resource\n url: string; // url to the resource\n embeddings: number[]; // embeddings of the resource\n }>;\n}\n```\n\n**Return**\n\n```ts\ntype SerializedIndex = string;\n```\n\n#### `search(index: SerializedIndex, query: Query, k: NumberOfResult): SearchResult`\n\nIt deserializes the given index and search for the `k` nearest neighbors of the query.\n\n**Parameter**\n\n```ts\ntype SerializedIndex = string;\n\ntype Query = Float32Array; // embeddings of the search query\n\ntype NumberOfResult = number; // K top results to return\n```\n\n**Return**\n\n```ts\ninterface SearchResult {\n neighbors: Array<{\n id: string; // id of the resource\n title: string; // title of the resource\n url: string; // url to the resource\n }>;\n}\n```\n\n#### `add(index: SerializedIndex, resource: Resource): SerializedIndex`\n\nIt adds resources to the index and returns an updated serialized index.\n\n**Parameter**\n\n```ts\ntype SerializedIndex = string;\n\ninterface Resource {\n embeddings: Array<{\n id: string; // id of the resource\n title: string; // title of the resource\n url: string; // url to the resource\n embeddings: number[]; // embeddings of the resource\n }>;\n}\n```\n\n**Return**\n\n```ts\ntype SerializedIndex = string;\n```\n\n#### `remove(index: SerializedIndex, resource: Resource): SerializedIndex`\n\nIt removes resources from the index and returns an updated serialized index.\n\n**Parameter**\n\n```ts\ntype SerializedIndex = string;\n\ninterface Resource {\n embeddings: Array<{\n id: string; // id of the resource\n title: string; // title of the resource\n url: string; // url to the resource\n embeddings: number[]; // embeddings of the resource\n }>;\n}\n```\n\n**Return**\n\n```ts\ntype SerializedIndex = string;\n```\n\n#### `clear(index: SerializedIndex): SerializedIndex`\n\nIt removes all items from the index and returns an empty serialized index.\n\n**Parameter**\n\n```ts\ntype SerializedIndex = string;\n```\n\n**Return**\n\n```ts\ntype SerializedIndex = string;\n```\n\n## Usage\n\n### With Transformers\n\nAs of now, voy relies on libraries like [`transformers.js`][transformers.js] and [`web-ai`][web-ai] to generate embeddings for text:\n\n```js\nimport { TextModel } from \"@visheratin/web-ai\";\n\nconst { Voy } = await import(\"voy-search\");\n\nconst phrases = [\n \"That is a very happy Person\",\n \"That is a Happy Dog\",\n \"Today is a sunny day\",\n];\nconst query = \"That is a happy person\";\n\n// Create text embeddings\nconst model = await (await TextModel.create(\"gtr-t5-quant\")).model;\nconst processed = await Promise.all(phrases.map((q) => model.process(q)));\n\n// Index embeddings with voy\nconst data = processed.map(({ result }, i) => ({\n id: String(i),\n title: phrases[i],\n url: `/path/${i}`,\n embeddings: result,\n}));\nconst resource = { embeddings: data };\nconst index = new Voy(resource);\n\n// Perform similarity search for a query embeddings\nconst q = await model.process(query);\nconst result = index.search(q.result, 1);\n\n// Display search result\nresult.neighbors.forEach((result) =>\n console.log(`✨ voy similarity search result: \"${result.title}\"`)\n);\n```\n\n### Multiple Indexes\n\n```js\nimport { TextModel } from \"@visheratin/web-ai\";\n\nconst { Voy } = await import(\"voy-search\");\nconst phrases = [\n \"That is a very happy Person\",\n \"That is a Happy Dog\",\n \"Today is a sunny day\",\n \"Sun flowers are blooming\",\n];\nconst model = await (await TextModel.create(\"gtr-t5-quant\")).model;\nconst processed = await Promise.all(phrases.map((q) => model.process(q)));\n\nconst data = processed.map(({ result }, i) => ({\n id: String(i),\n title: phrases[i],\n url: `/path/${i}`,\n embeddings: result,\n}));\nconst resourceA = { embeddings: data.slice(0, 2) };\nconst resourceB = { embeddings: data.slice(2) };\n\nconst indexA = new Voy(resourceA);\nconst indexB = new Voy(resourceB);\n```\n\n## License\n\nLicensed under either of\n\n- Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)\n- MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)\n\nat your option.\n\n## Sponsor\n\n\n\n\n### Contribution\n\nUnless you explicitly state otherwise, any contribution intentionally\nsubmitted for inclusion in the work by you, as defined in the Apache-2.0\nlicense, shall be dual licensed as above, without any additional terms or\nconditions.\n\n[demo]: ./voy.gif \"voy demo\"\n[web-ai]: https://github.com/visheratin/web-ai\n[k-d-tree]: https://en.wikipedia.org/wiki/K-d_tree\n[transformers.js]: https://github.com/xenova/transformers.js\n","readmeFilename":"README.md"}