Usage
This example shows how to use the OpenAI text-embedding-ada-002 (opens in a new tab) embedding model and a Pinecone (opens in a new tab) datastore to index and query a set of documents.
import 'dotenv/config';
import { EmbeddingModel } from '@dexaai/dexter/model';
import { PineconeDatastore } from '@dexaai/dexter/datastore/pinecone';
async function example() {
const embeddingModel = new EmbeddingModel({
params: { model: 'text-embedding-ada-002' },
});
const store = new PineconeDatastore({
contentKey: 'content',
embeddingModel,
});
await store.upsert([
{ id: '1', metadata: { content: 'cat' } },
{ id: '2', metadata: { content: 'dog' } },
{ id: '3', metadata: { content: 'whale' } },
{ id: '4', metadata: { content: 'shark' } },
{ id: '5', metadata: { content: 'computer' } },
{ id: '6', metadata: { content: 'laptop' } },
{ id: '7', metadata: { content: 'phone' } },
{ id: '8', metadata: { content: 'tablet' } },
]);
const result = await store.query({ query: 'dolphin' });
console.log(result);
}
You can run this example by cloning this repo by following these instructions.