https://github.com/relevanceai/relevance-js-sdk
Vector Database by Relevance AI. Fast and simple with support for chaining.
https://github.com/relevanceai/relevance-js-sdk
embeddings javascript nodejs vector-database vectors
Last synced: 6 months ago
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Vector Database by Relevance AI. Fast and simple with support for chaining.
- Host: GitHub
- URL: https://github.com/relevanceai/relevance-js-sdk
- Owner: RelevanceAI
- License: mit
- Created: 2021-10-26T22:28:25.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-12-13T00:10:01.000Z (almost 2 years ago)
- Last Synced: 2024-10-29T14:04:28.635Z (11 months ago)
- Topics: embeddings, javascript, nodejs, vector-database, vectors
- Language: TypeScript
- Homepage: https://relevanceai.com/vector-database
- Size: 1.84 MB
- Stars: 8
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# relevance-js-sdk
Install with npm using:
```
npm i @relevanceai/dataset
```
## Features
- Node and Browser support
- Typescript definitions for almost all [relevanceai.com](https://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](https://cloud.relevanceai.com) - select the Vector Database onboarding option. Once set up you can fetch your API key and use the below snippet.
```javascript
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](https://discovery.relevanceai.com/reference/api-usage)
Heres a template to copy and paste in for linux environments:
```bash
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:
```javascript
import {Client} from "@relevanceai/dataset";
const client = new Client({});
```
### Option 2 - Passing them in code.
```javascript
import {Client} from "@relevanceai/dataset";
const client = new Client({
project:'########',
api_key:'########',
});
```
# Examples
### You can import builders and type definitions like this
```javascript
import {QueryBuilder,Client,BulkInsertOutput} from "@relevanceai/dataset";
```
## Insert millions of items with one function call
```javascript
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:
```javascript
{"inserted":30000,"failed_documents":[]}
```
## Text Search and Vector Search
```javascript
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
```javascript
const filters = QueryBuilder();
filters.match('color',['blue','red']).range('price',{lessThan:50});
const filteredItems = await dataset.search(filters);
```
### search will output:
```javascript
{
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')
```