If you need more info, help, or want to provide general feedback on Orama, join the Orama Slack channel
- Full-Text search
- Vector Search
- Hybrid Search
- GenAI Chat Sessions
- Search Filters
- Geosearch
- Pinning Rules (Merchandising)
- Facets
- Fields Boosting
- Typo Tolerance
- Exact Match
- BM25
- Stemming and tokenization in 30 languages
- Plugin System
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 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: '21ΞΌs', // }, // 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] |
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', similarity: 0.75, })
Want to use OpenAI embedding models? Use our Secure Proxy plugin to call OpenAI from the client-side securely.
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.
Read the complete documentation at https://docs.orama.com/open-source.
- Plugin Embeddings
- Plugin Secure Proxy
- Plugin Analytics
- Plugin Data Persistence
- Plugin QPS
- Plugin PT15
- Plugin Vitepress
- Plugin Docusaurus
- Plugin Astro
- Plugin Nextra
Write your own plugin: https://docs.orama.com/docs/orama-js/plugins/writing-your-own-plugins
Orama is licensed under the Apache 2.0 license.