Embed, store, and search vectors on the client side!
Implement semantic search with only 5 lines of code
Compute embeddings outperforms OpenAI's text-embedding-ada-002
on client side
Search up to 100K vectors in less than 100ms, get rid of latency
Scale with our embedding API, pay only $20/mo to embed, store, and search 10M vectors
// npm i client-vector-search
import { getEmbedding, EmbeddingIndex } from 'client-vector-search';
// compute the embeddings of your data
const initialObjects = [
{ id: 1, name: "Apple", embedding: getEmbedding("Apple") },
...
]; // up to 100k embeddings // TODO:
// create an index
const index = new EmbeddingIndex(initialObjects);
// compute the embedding of your query
const queryEmbedding = await getEmbedding("pear");
// search the index
const results = await index.search(queryEmbedding, { topK: 5 });
// THAT'S IT!
// you can also save these indexes, and use our APIs to scale!
Playground
You can test how fast you can compute embeddings by pasting a long article and you can load a wikipedia dataset and search through it and see how fast search is.
You can find the dataset we use to search here.