Firstbatch SDK

The FirstBatch SDK provides an interface for integrating vector databases and powering personalized AI experiences in your application.

Key Features

Seamlessly manage user sessions with persistent IDs or temporary sessions.
Send signal actions like likes, clicks, etc. to update user embeddings in real-time.
Fetch personalized batches of data tailored to each user's embeddings.
Support for multiple vector database integrations: Pinecone, Weaviate, etc.
Built-in algorithms for common personalization use cases.

Installation

Install the package from NPM:

yarn add firstbatch     # yarn
npm install firstbatch  # npm
pnpm add firstbatch     # pnpm

Usage

FirstBatch SDK is used together with an existing vector database client, where you register the client to our SDK and then create personalization sessions from it.

Adding a Client

First, initialize a Vector Database of your choice; our SDK supports Pinecone, Weaviate, Typesense, and soon Supabase. Let us go over an example using Pinecone.

import {Pinecone as PineconeClient} from '@pinecone-database/pinecone';
import {Pinecone, FirstBatch, UserAction, Signals} from 'firstbatch';

// create Pinecone client
const pinecone = new PineconeClient({apiKey: 'pinecone-api-key', environment: 'pinecone-env'});
await pinecone.describeIndex('index-name');
const index = pinecone.index('index-name');

Then, create a Vector Store with this index and pass it in the FirstBatch SDK.

// create SDK
const personalized = await FirstBatch.new('firstbatch-api-key');

// add client to SDK
const vectorStore = new Pinecone(index);
const vdbid = 'pinecone-example-db';
await personalized.addVdb(vdbid, vectorStore);

Personalization

Now, we can create a session with an algorithm that suits our use-case, and provide personalization to our users.

// create a session
const session = personalized.session('ALGORITHM_NAME', vdbid);

// make recommendations
const sessionId = session.data;
const [ids, batch] = personalized.batch(sessionId);

Suppose that the user has liked the first content from the batch above. We can provide personalization over this as follows:

const userPick = 0; // i.e. the first content
personalized.addSignal(sessionId, new UserAction(Signals.LIKE), ids[userPick]);

Custom Signals

The Signals in the code above contains a set of signals (i.e. labels and weights) that we have prepared for you, but you can also create a Signal with your own parameters:

import {Signal} from 'firstbatch';

const mySignal = new Signal('label', 0.5); // label and weight
const userAction = new UserAction(mySignal);

Building

Install the dependencies,

bun install

The following command will check the code with TSC and then build with Parcel.

bun run build

If you get an error like "expected content key ... to exist" simply delete the .parcel-cache folder and build again.

Testing

Run all tests via:

bun run test

Bun will look for .env.test during the tests.

Support

For any issues or queries contact support@firstbatch.xyz.

Resources

Feel free to dive into the technicalities and leverage FirstBatch SDK for highly personalized user experiences.

KerimRock/firstbatch-sdk-ts