firstbatch
v0.1.8
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- [x] Seamlessly manage user sessions with persistent IDs or temporary sessions.
- [x] Send signal actions like likes, clicks, etc. to update user embeddings in real-time.
- [x] Fetch personalized batches of data tailored to each user's embeddings.
- [x] Support for multiple vector database integrations: Pinecone, Weaviate, etc.
- [x] Built-in algorithms for common personalization use cases.
For more information, see the User Embedding Guide or the SDK Documentation.
Installation
Install the package from NPM:
npm install firstbatch # npm
yarn add firstbatch # yarn
pnpm add firstbatch # pnpm
Usage
FirstBatch SDK is used together with an existing vector store, where you register the vector store to our SDK and then create personalization sessions from it.
Adding a Client
First, initialize a Vector Database of your choice; our SDK supports:
Let us go over an example using Pinecone.
import {Pinecone as PineconeClient} from '@pinecone-database/pinecone';
import {Pinecone, FirstBatch, 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 vector store to SDK
const vectorStore = new Pinecone(index);
const vdbid = 'pinecone-example-db';
await personalized.addVectorStore(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 [ids, batch] = personalized.batch(session);
Suppose that the user has liked the first content from the batch
above. We can provide personalization over this as follows:
// the user liked the first content of the previous batch
const userPick = ids[0];
// signal this to the session
personalized.addSignal(sessionId, Signals.LIKE, userPick);
Here, LIKE
signal is one of the many preset signals provided by our SDK. You can also define your own signals:
import type {Signal} from 'firstbatch';
const mySignal: Signal = {label: 'SOME_USER_ACTION', weight: 0.5};
Building
Install the dependencies:
bun install
Then, build everything with:
bun run build
bun b # alternative
[!NOTE]
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 t # alternative
Bun will look for .env.test
during the tests.
Styling
Check formatting:
bun format
Lint everything:
bun lint