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https://github.com/oramasearch/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.
https://github.com/oramasearch/orama

algiorithm data-structures full-text javascript node search search-algorithm search-engine typescript typo-tolerance vector vector-database vector-database-embedding vector-search vector-search-engine

Last synced: 6 days ago
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🌌 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.

Host: GitHub
URL: https://github.com/oramasearch/orama
Owner: oramasearch
License: other
Created: 2022-05-10T12:01:50.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-12-02T21:12:49.000Z (10 days ago)
Last Synced: 2024-12-04T06:38:01.356Z (8 days ago)
Topics: algiorithm, data-structures, full-text, javascript, node, search, search-algorithm, search-engine, typescript, typo-tolerance, vector, vector-database, vector-database-embedding, vector-search, vector-search-engine
Language: TypeScript
Homepage: https://docs.orama.com
Size: 122 MB
Stars: 8,769
Watchers: 40
Forks: 296
Open Issues: 29
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE.md

Awesome Lists containing this project

awesome-bun - Orama - 🌌 Fast, in-memory, typo-tolerant, full-text search engine written in TypeScript. (Extensions / Utilities)
my-awesome-list - orama - text, vector, and hybrid search in less than 2kb. | oramasearch | 8842 | (TypeScript)
StarryDivineSky - oramasearch/orama
awesome-repositories - oramasearch/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. (TypeScript)
awesome - oramasearch/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. (TypeScript)

README

        


  

  



[![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/open-source/usage/search/introduction)

- [Vector Search](https://docs.orama.com/open-source/usage/search/vector-search)

- [Hybrid Search](https://docs.orama.com/open-source/usage/search/hybrid-search)

- [GenAI Chat Sessions](https://docs.orama.com/open-source/usage/answer-engine/introduction)

- [Search Filters](https://docs.orama.com/open-source/usage/search/filters)

- [Geosearch](https://docs.orama.com/open-source/usage/search/geosearch)

- [Facets](https://docs.orama.com/open-source/usage/search/facets)

- [Fields Boosting](https://docs.orama.com/open-source/usage/search/fields-boosting)

- [Typo Tolerance](https://docs.orama.com/open-source/usage/search/introduction#typo-tolerance)

- [Exact Match](https://docs.orama.com/open-source/usage/search/introduction#exact-match)

- [BM25](https://docs.orama.com/open-source/usage/search/bm25-algorithm)

- [Stemming and tokenization in 30 languages](https://docs.orama.com/open-source/text-analysis/stemming)

- [Plugin System](https://docs.orama.com/open-source/plugins/introduction)

# Installation

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

```sh

npm i @orama/orama

```

Or import it directly in a browser module:

```html

  

    

      import { create, insert, search } from 'https://cdn.jsdelivr.net/npm/@orama/orama@latest/+esm'

    

  

```

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



  

  



# 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[]` | 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/open-source/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/open-source/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/open-source/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/open-source/plugins/plugin-embeddings)

- [Plugin Secure Proxy](https://docs.orama.com/open-source/plugins/plugin-secure-proxy)

- [Plugin Analytics](https://docs.orama.com/open-source/plugins/plugin-analytics)

- [Plugin Data Persistence](https://docs.orama.com/open-source/plugins/plugin-data-persistence)

- [Plugin QPS](https://docs.orama.com/open-source/plugins/plugin-qps)

- [Plugin PT15](https://docs.orama.com/open-source/plugins/plugin-pt15)

- [Plugin Vitepress](https://docs.orama.com/open-source/plugins/plugin-vitepress)

- [Plugin Docusaurus](https://docs.orama.com/open-source/plugins/plugin-docusaurus)

- [Plugin Astro](https://docs.orama.com/open-source/plugins/plugin-astro)

- [Plugin Nextra](https://docs.orama.com/open-source/plugins/plugin-nextra)

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

# License

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