relevance-js-sdk

Install with npm using:

npm i @relevanceai/dataset

Features

Node and Browser support
Typescript definitions for almost all relevanceai.com apis
Insert millions of documents with one function call
Our SearchBuilder makes searching, filtering, and aggregating your data simple

Getting started

Get started by creating an account in cloud.relevanceai.com - select the Vector Database onboarding option. Once set up you can fetch your API key and use the below snippet.

import {Client,QueryBuilder} from "@relevanceai/dataset";

const discovery = new Client({
  project: '',
  api_key: '',
  endpoint: ''
});
const dataset = discovery.dataset('1000-movies');

const movies = [{ title: 'Lord of the Rings: The Fellowship of the Ring', grenre: 'action', budget: 100 }, ...]
await dataset.insertDocuments(movies, [{ model_name: 'text-embedding-ada-002', field: 'title' }]);

const {results} = await dataset.search(QueryBuilder().vector('title_vector_', { query: 'LOTR', model: 'text-embeddings-ada-002' }));

Set up your credentials

Option 1 - Use environment variables

First, set environment variables in your shell before you run your code.

set RELEVANCE_PROJECT to your project name.

set RELEVANCE_API_KEY to your api key. for more information, view the docs here: Authorization docs

Heres a template to copy and paste in for linux environments:

export RELEVANCE_PROJECT=#########
export RELEVANCE_API_KEY=#########

The SDK will use these variables when making api calls. You can then initialise your client like this:

import {Client} from "@relevanceai/dataset";
const client = new Client({});

Option 2 - Passing them in code.

import {Client} from "@relevanceai/dataset";
const client = new Client({
  project:'########',
  api_key:'########',
});

Examples

You can import builders and type definitions like this

import {QueryBuilder,Client,BulkInsertOutput} from "@relevanceai/dataset";

Insert millions of items with one function call

const discovery = new Client({ ... });
const dataset = discovery.dataset('tshirts-prod');
 // Here we create some demo data. Replace this with your real data
const fakeVector = [];
for (let i = 0; i < 768; i++) fakeVector.push(1);
const tshirtsData = [];
for (let i = 0; i < 10000; i++) {
  tshirtsData.push({_id:`tshirt-${i}1`,color:'red',price:i/1000,'title-fake_vector_':fakeVector});
  tshirtsData.push({_id:`tshirt-${i}2`,color:'blue',price:i/1000});
  tshirtsData.push({_id:`tshirt-${i}3`,color:'orange',price:i/1000});
}
const res = await dataset.insertDocuments(tshirtsData,{batchSize:10000});

insertDocuments will output:

{"inserted":30000,"failed_documents":[]}

Text Search and Vector Search

const builder = QueryBuilder();
builder.query('red').text().vector('title-fake_vector_',0.5).minimumRelevance(0.1);
// .text() searches all fields. alternatively, use .text(field1).text(field2)... to search specific fields
const searchResults = await dataset.search(builder);

Filter and retrieve items

const filters = QueryBuilder();
filters.match('color',['blue','red']).range('price',{lessThan:50});
const filteredItems = await dataset.search(filters);

search will output:

{
  results: [
    {
      color: 'red',
      price: 0,
      insert_date_: '2021-11-16T03:14:28.509Z',
      _id: 'tshirt-01',
      _relevance: 0
    }
    ...
  ],
  resultsSize: 10200,
  aggregations: {},
  aggregates: {},
  aggregateStats: {}
}

## Call raw api methods directly
```javascript
const discovery = new Client({ ... });
const dataset = discovery.dataset('tshirts-prod');
const {body} = await dataset.apiClient.FastSearch({filters:[{match:{key:'_id',value:`tshirt-01`}}]});
expect((body.results[0] as any).color).toBe('red')

RelevanceAI / relevance-js-sdk