AI prompts
base on 🌌 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. <p align="center">
<img src="https://raw.githubusercontent.com/oramasearch/orama/refs/heads/main/misc/readme/orama-readme-hero-dark.png#gh-dark-mode-only" />
<img src="https://raw.githubusercontent.com/oramasearch/orama/refs/heads/main/misc/readme/orama-readme-hero-light.png#gh-light-mode-only" />
</p>
[](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
<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-dark.png#gh-dark-mode-only" />
<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/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.
", Assign "at most 3 tags" to the expected json: {"id":"3560","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"