New Case Study:See how Anthropic automated 95% of dependency reviews with Socket.Learn More →

@orama/orama

Package Overview

Dependencies

Advanced tools

Install Socket

Detect and block malicious and high-risk dependencies

Install

@orama/orama

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.

3.0.5
latest
Source
npm

Version published: 2 weeks ago

Weekly downloads: 22K; decreased by-21.55%

Maintainers: 0

Weekly downloads

Created: 2 years ago

Source

If you need more info, help, or want to provide general feedback on Orama, join the Orama Slack channel

Highlighted features

Installation

You can install Orama using npm, yarn, pnpm, bun:

npm i @orama/orama

Or import it directly in a browser module:

<html>
  <body>
    <script type="module">
      import { create, insert, search } from 'https://cdn.jsdelivr.net/npm/@orama/orama@latest/+esm'
    </script>
  </body>
</html>

With Deno, you can just use the same CDN URL or use npm specifiers:

import { create, search, insert } from 'npm:@orama/orama'

Read the complete documentation at https://docs.orama.com.

Orama Features

Usage

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 = create({
  schema: {
    name: 'string',
    description: 'string',
    price: 'number',
    embedding: 'vector[1536]', // Vector size must be expressed during schema initialization
    meta: {
      rating: 'number',
    },
  },
})

insert(db, {
  name: 'Noise cancelling headphones',
  description: 'Best noise cancelling headphones on the market',
  price: 99.99,
  embedding: [0.2432, 0.9431, 0.5322, 0.4234, ...],
  meta: {
    rating: 4.5
  }
})

const results = search(db, {
  term: 'Best headphones'
})

// {
//   elapsed: {
//     raw: 21492,
//     formatted: '21μs',
//   },
//   hits: [
//     {
//       id: '41013877-56',
//       score: 0.925085832971998432,
//       document: {
//         name: 'Noise cancelling headphones',
//         description: 'Best noise cancelling headphones on the market',
//         price: 99.99,
//         embedding: [0.2432, 0.9431, 0.5322, 0.4234, ...],
//         meta: {
//           rating: 4.5
//         }
//       }
//     }
//   ],
//   count: 1
// }

Orama currently supports 10 different data types:

Type	Description	Example
`string`	A string of characters.	`'Hello world'`
`number`	A numeric value, either float or integer.	`42`
`boolean`	A boolean value.	`true`
`enum`	An enum value.	`'drama'`
`geopoint`	A geopoint value.	`{ lat: 40.7128, lon: 74.0060 }`
`string[]`	An array of strings.	`['red', 'green', 'blue']`
`number[]`	An array of numbers.	`[42, 91, 28.5]`
`boolean[]`	An array of booleans.	`[true, false, false]`
`enum[]`	An array of enums.	`['comedy', 'action', 'romance']`
`vector[<size>]`	A vector of numbers to perform vector search on.	`[0.403, 0.192, 0.830]`

Vector and Hybrid Search Support

Orama supports both vector and hybrid search by just setting mode: 'vector' when performing search.

To perform this kind of search, you'll need to provide text embeddings at search time:

import { create, insertMultiple, search } from '@orama/orama'

const db = create({
  schema: {
    title: 'string',
    embedding: 'vector[5]'', // we are using a 5-dimensional vector.
  },
});

insertMultiple(db, [
  { title: 'The Prestige', embedding: [0.938293, 0.284951, 0.348264, 0.948276, 0.56472] },
  { title: 'Barbie', embedding: [0.192839, 0.028471, 0.284738, 0.937463, 0.092827] },
  { title: 'Oppenheimer', embedding: [0.827391, 0.927381, 0.001982, 0.983821, 0.294841] },
])

const results = search(db, {
  // Search mode. Can be 'vector', 'hybrid', or 'fulltext'
  mode: 'vector',
  vector: {
    // The vector (text embedding) to use for search
    value: [0.938292, 0.284961, 0.248264, 0.748276, 0.26472],
    // The schema property where Orama should compare embeddings
    property: 'embedding',
  },
  // Minimum similarity to determine a match. Defaults to `0.8`
  similarity: 0.85,
  // Defaults to `false`. Setting to 'true' will return the embeddings in the response (which can be very large).
  includeVectors: true,
})

Have trouble generating embeddings for vector and hybrid search? Try our @orama/plugin-embeddings plugin!

import { create } from '@orama/orama'
import { pluginEmbeddings } from '@orama/plugin-embeddings'
import '@tensorflow/tfjs-node' // Or any other appropriate TensorflowJS backend, like @tensorflow/tfjs-backend-webgl

const plugin = await pluginEmbeddings({
  embeddings: {
    // Schema property used to store generated embeddings
    defaultProperty: 'embeddings',
    onInsert: {
      // Generate embeddings at insert-time
      generate: true,
      // properties to use for generating embeddings at insert time.
      // Will be concatenated to generate a unique embedding.
      properties: ['description'],
      verbose: true,
    }
  }
})

const db = create({
  schema: {
    description: 'string',
    // Orama generates 512-dimensions vectors.
    // When using @orama/plugin-embeddings, set the property where you want to store embeddings as `vector[512]`.
    embeddings: 'vector[512]'
  },
  plugins: [plugin]
})

// Orama will generate and store embeddings 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 also generate and use embeddings at search time when search mode is set to "vector" or "hybrid"!
const searchResults = await search(db, {
  term: 'Headphones for 12th grade students',
  mode: 'vector'
})

Want to use OpenAI embedding models? Use our Secure Proxy plugin to call OpenAI from the client-side securely.

RAG and Chat Experiences with Orama

Since v3.0.0, Orama allows you to create your own ChatGPT/Perplexity/SearchGPT-like experience. You will need to call the OpenAI APIs, so we strongly recommend using the Secure Proxy Plugin to do that securely from your client side. It's free!

import { create, insert } from '@orama/orama'
import { pluginSecureProxy } from '@orama/plugin-secure-proxy'

const secureProxy = await pluginSecureProxy({
  apiKey: 'my-api-key',
  defaultProperty: 'embeddings',
  models: {
    // The chat model to use to generate the chat answer
    chat: 'openai/gpt-4o-mini'
  }
})

const db = create({
  schema: {
    name: 'string'
  },
  plugins: [secureProxy]
})

insert(db, { name: 'John Doe' })
insert(db, { name: 'Jane Doe' })

const session = new AnswerSession(db, {
  // Customize the prompt for the system
  systemPrompt: 'You will get a name as context, please provide a greeting message',
  events: {
    // Log all state changes. Useful to reactively update a UI on a new message chunk, sources, etc.
    onStateChange: console.log,
  }
})

const response = await session.ask({
  term: 'john'
})

console.log(response) // Hello, John Doe! How are you doing?

Read the complete documentation here.

Official Docs

Read the complete documentation at https://docs.orama.com/open-source.

Official Orama Plugins

Write your own plugin: https://docs.orama.com/open-source/plugins/writing-your-own-plugins

License

Orama is licensed under the Apache 2.0 license.

Keywords

FAQs

What is @orama/orama?

Is @orama/orama popular?

Is @orama/orama well maintained?

Package last updated on 20 Jan 2025

Did you know?

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install