npm package: @ibadus/orama
Description: 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.
Author: Michele Riva
Latest version: 3.1.18

<p align="center">
  <img src="https://raw.githubusercontent.com/oramasearch/orama/refs/heads/main/misc/readme/orama-readme-hero-light.png#gh-light-mode-only" />
</p>

[![Tests](https://github.com/oramasearch/orama/actions/workflows/turbo.yml/badge.svg)](https://github.com/oramasearch/orama/actions/workflows/turbo.yml)

If you need more info, help, or want to provide general feedback on Orama, join the [Orama Slack channel](https://orama.to/slack)

# Highlighted features

- [Full-Text search](https://docs.orama.com/docs/orama-js/search)
- [Vector Search](https://docs.orama.com/docs/orama-js/search/vector-search)
- [Hybrid Search](https://docs.orama.com/docs/orama-js/search/hybrid-search)
- [GenAI Chat Sessions](https://docs.orama.com/docs/orama-js/answer-engine)
- [Search Filters](https://docs.orama.com/docs/orama-js/search/filters)
- [Geosearch](https://docs.orama.com/docs/orama-js/search/geosearch)
- [Pinning Rules (Merchandising)](https://docs.orama.com/docs/orama-js/results-pinning)
- [Facets](https://docs.orama.com/docs/orama-js/search/facets)
- [Fields Boosting](https://docs.orama.com/docs/orama-js/search/fields-boosting)
- [Typo Tolerance](https://docs.orama.com/docs/orama-js/search#typo-tolerance)
- [Exact Match](https://docs.orama.com/docs/orama-js/search#exact-match)
- [BM25](https://docs.orama.com/docs/orama-js/search/bm25)
- [Stemming and tokenization in 30 languages](https://docs.orama.com/docs/orama-js/text-analysis/stemming)
- [Plugin System](https://docs.orama.com/docs/orama-js/plugins)

# Installation

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

```sh
npm i @orama/orama
```

Or import it directly in a browser module:

```html
<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:

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

Read the complete documentation at [https://docs.orama.com](https://docs.orama.com).

# Orama Features

<p align="center">
  <img src="https://raw.githubusercontent.com/oramasearch/orama/refs/heads/main/misc/readme/features-light.png#gh-light-mode-only" />
</p>

# Usage

Orama is quite simple to use. The first thing to do is to create a new database
instance and set an indexing schema:

```js
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](https://en.wikipedia.org/wiki/Word_embedding) at search time:

```js
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!

```js
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](https://docs.orama.com/docs/orama-js/plugins/plugin-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](https://docs.orama.com/docs/orama-js/plugins/plugin-secure-proxy) to do that securely from your client side. It's free!

```js
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](https://docs.orama.com/docs/orama-js/usage/answer-engine/introduction).

# Official Docs

Read the complete documentation at [https://docs.orama.com/open-source](https://docs.orama.com/open-source).

# Official Orama Plugins

- [Plugin Embeddings](https://docs.orama.com/docs/orama-js/plugins/plugin-embeddings)
- [Plugin Secure Proxy](https://docs.orama.com/docs/orama-js/plugins/plugin-secure-proxy)
- [Plugin Analytics](https://docs.orama.com/docs/orama-js/plugins/plugin-analytics)
- [Plugin Data Persistence](https://docs.orama.com/docs/orama-js/plugins/plugin-data-persistence)
- [Plugin QPS](https://docs.orama.com/docs/orama-js/plugins/plugin-qps)
- [Plugin PT15](https://docs.orama.com/docs/orama-js/plugins/plugin-pt15)
- [Plugin Vitepress](https://docs.orama.com/docs/orama-js/plugins/plugin-vitepress)
- [Plugin Docusaurus](https://docs.orama.com/docs/orama-js/plugins/plugin-docusaurus)
- [Plugin Astro](https://docs.orama.com/docs/orama-js/plugins/plugin-astro)
- [Plugin Nextra](https://docs.orama.com/docs/orama-js/plugins/plugin-nextra)

Write your own plugin: [https://docs.orama.com/docs/orama-js/plugins/writing-your-own-plugins](https://docs.orama.com/docs/orama-js/plugins/writing-your-own-plugins)

# License

Orama is licensed under the [Apache 2.0](/LICENSE.md) license.

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