@orama/orama
Advanced tools
Next generation full-text and vector search engine, written in TypeScript
Search, everywhere.
If you need more info, help, or want to provide general feedback on Orama, join the Orama Slack channel
You can install Orama using npm, yarn, pnpm:
npm i @orama/orama
yarn add @orama/orama
pnpm add @orama/orama
Or import it directly in a browser module:
<html>
<body>
<script type="module">
import { create, search, insert } from 'https://unpkg.com/@orama/orama@latest/dist/index.js'
// ...
</script>
</body>
</html>
Read the complete documentation at https://docs.oramasearch.com.
Orama is quite simple to use. The first thing to do is to create a new database instance and set an indexing schema:
import { create, insert, remove, search, searchVector } from '@orama/orama'
const db = await create({
schema: {
name: 'string',
description: 'string',
price: 'number',
embedding: 'vector[1536]', // Vector size must be expressed during schema initialization
meta: {
rating: 'number',
},
},
})
If you are using Node.js without ESM, please see the usage with CommonJS section below on how to properly require Orama.
Orama will only index string properties, but will allow you to set and store additional data if needed.
Once the db instance is created, you can start adding some documents:
await insert(db, {
name: 'Wireless Headphones',
description: 'Experience immersive sound quality with these noise-cancelling wireless headphones.',
price: 99.99,
embedding: [...],
meta: {
rating: 4.5,
},
})
await insert(db, {
name: 'Smart LED Bulb',
description: 'Control the lighting in your home with this energy-efficient smart LED bulb, compatible with most smart home systems.',
price: 24.99,
embedding: [...],
meta: {
rating: 4.3,
},
})
await insert(db, {
name: 'Portable Charger',
description: 'Never run out of power on-the-go with this compact and fast-charging portable charger for your devices.',
price: 29.99,
embedding: [...],
meta: {
rating: 3.6,
},
})
After the data has been inserted, you can finally start to query the database.
const searchResult = await search(db, {
term: 'headphones',
})
In the case above, you will be searching for all the documents containing the
word headphones, looking up in every schema property (AKA index):
{
elapsed: {
raw: 99512,
formatted: '99μs',
},
hits: [
{
id: '41013877-56',
score: 0.925085832971998432,
document: {
name: 'Wireless Headphones',
description: 'Experience immersive sound quality with these noise-cancelling wireless headphones.',
price: 99.99,
meta: {
rating: 4.5
}
}
}
],
count: 1
}
You can also restrict the lookup to a specific property:
const searchResult = await search(db, {
term: 'immersive sound quality',
properties: ['description'],
})
Result:
{
elapsed: {
raw: 21492,
formatted: '21μs',
},
hits: [
{
id: '41013877-56',
score: 0.925085832971998432,
document: {
name: 'Wireless Headphones',
description: 'Experience immersive sound quality with these noise-cancelling wireless headphones.',
price: 99.99,
meta: {
rating: 4.5
}
}
}
],
count: 1
}
If you want to perform a vector search, you can use the searchVector function:
const searchResult = await searchVector(db, {
vector: [...], // OpenAI embedding or similar vector to be used as an input
property: 'embedding' // Property to search through. Mandatory for vector search
})
Orama is packaged as ES modules, suitable for Node.js, Deno, Bun and modern browsers.
In most cases, simply import or @orama/orama will suffice ✨.
In Node.js, when not using ESM (with "type": "module" in the package.json), you have several ways to properly require Orama.
Starting with version 0.4.0 it becomes:
async function main() {
const { create, insert } = await import('@orama/orama')
const db = create(/* ... */)
insert(db, {
/* ... */
})
}
main().catch(console.error)
Orama methods can be required as CommonJS modules by requiring from @orama/orama.
const { create, insert } = require("@orama/orama")
create(/* ... */)
.then(db => insert(db, { /* ... */ })
.catch(console.error)
Note that only main methods are supported so for internals and other supported exports you still have to use await import.
Are you using Orama in production? Have you written an article or made a YouTube video on Orama? Contact us to get some Orama swag in return!
Read the complete documentation at https://docs.oramasearch.com.
Orama is licensed under the Apache 2.0 license.
FAQs
A complete search engine and RAG pipeline in your browser, server, or edge network with support for full-text, vector, and hybrid search in less than 2kb.
The npm package @orama/orama receives a total of 25,563 weekly downloads. As such, @orama/orama popularity was classified as popular.
We found that @orama/orama demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
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