Orama Plugin Embeddings allows you to generate fast text embeddings at insert and search time offline, directly on your machine - no OpenAI needed!
To get started with Orama Plugin Embeddings, just install it with npm:
npm i @orama/plugin-embeddingsImportant note: to use this plugin, you'll also need to install one of the following TensorflowJS backend:
@tensorflow/tfjs@tensorflow/tfjs-node@tensorflow/tfjs-backend-webgl@tensorflow/tfjs-backend-cpu@tensorflow/tfjs-node-gpu@tensorflow/tfjs-backend-wasm
For example, if you're running Orama on the browser, we highly recommend using @tensorflow/tfjs-backend-webgl:
npm i @tensorflow/tfjs-backend-webglIf you're using Orama in Node.js, we recommend using @tensorflow/tfjs-node:
npm i @tensorflow/tfjs-nodeimport { create } from '@orama/orama'
import { pluginEmbeddings } from '@orama/plugin-embeddings'
import '@tensorflow/tfjs-node' // Or any other appropriate TensorflowJS backend
const plugin = await pluginEmbeddings({
embeddings: {
defaultProperty: 'embeddings', // Property used to store generated embeddings
onInsert: {
generate: true, // Generate embeddings at insert-time
properties: ['description'], // properties to use for generating embeddings at insert time
verbose: true,
}
}
})
const db = await create({
schema: {
description: 'string',
embeddings: 'vector[512]' // Orama generates 512-dimensions vectors
},
plugins: [plugin]
})Example usage at insert time:
await insert(db, {
description: 'Classroom Headphones Bulk 5 Pack, Student On Ear Color Varieties'
})
await insert(db, {
description: 'Kids Wired Headphones for School Students K-12'
})
await insert(db, {
description: 'Kids Headphones Bulk 5-Pack for K-12 School'
})
await insert(db, {
description: 'Bose QuietComfort Bluetooth Headphones'
})Orama will automatically generate text embeddings and store them into the embeddings property.
Then, you can use the vector or hybrid setting to perform hybrid or vector search at runtime:
await search(db, {
term: 'Headphones for 12th grade students',
mode: 'vector'
})Orama will generate embeddings at search time and perform vector or hybrid search for you.