Embeddings are used by large language models (LLMs) as vector representations of words or tokens. These vectors are saved as arrays of float numbers in databases such as Supabase Vector. These Vectors are later used to retrieve clusters of related items or search.

OpenAI generates embeddings with textual and other relevant data provided by your applications. Retrieving and preparing the data for embedding can take time. Defer’s remote background processing helps offload and run embedding generation reliably.

Use case

Let’s add an auto-tagging feature to a Cloud Drive product, enabling a Drive that is always sorted with dynamically created filters (e.g., “Legal and Finance”)!

Once your database is set up (with a documents table and an associated vector index), Defer will enable you to generate embeddings within your applications in no time, with no resource limitations issues.

import { defer } from "@defer/client";
import { createClient } from "@supabase/supabase-js";
import { Configuration, OpenAIApi } from "openai";

import { supabaseClient } from "./lib/supabase";
import { openAiClient } from "./lib/openai";
import { getRecentlyUpdatedDocuments } from "../lib";

import refreshCategories from "./refreshCategories";

async function generateEmbeddings(workspaceID: string) {
  // 1. retrieve the newly created or updated documents from the database
  const documents = await getRecentlyUpdatedDocuments(workspaceID);

  for (const document of documents) {
    // (OpenAI recommends replacing newlines with spaces for best results)
    const input = document.replace(/\n/g, " ");

    // 2. generate an embedding for each document
    const embeddingResponse = await openAiClient.createEmbedding({
      model: "text-embedding-ada-002",

    // 3. store the embedding as a vector in our Supabase Vector database
    await supabaseClient.from("documents").insert({
      content: document,
      embedding: embeddingResponse.data.data,

  // 4. Enqueue another execution that will extract clusters
  //   to refresh categories
  //   (see https://cookbook.openai.com/examples/clustering)
  await refreshCategories(workspaceID);

export default defer(generateEmbeddings, {
  concurrency: 5, // limit concurrency for OpenAI rate limiting
  retry: 2, // enable retry to handle external failures

Note: Dealing with large document embeddings impacts the database’s performance. Refer to Supabase’s documentation to allocate the appropriate resources for your use case.