Search

message: "The URL parameter is required for this end point",

const { OpenAIEmbeddings } = await import("npm:langchain/embeddings");

const { createClient } = await import(

splittedDocs,

new OpenAIEmbeddings({

openAIApiKey: process.env.OPEN_API_KEY,

client,

import { cronEvalLogger as logger } from "https://esm.town/v/nbbaier/cronLogger";

import { fetchOpenAiUsageData } from "https://esm.town/v/nbbaier/fetchOpenAiUsageData";

import { updateBlobUsageDB } from "https://esm.town/v/nbbaier/updateBlobUsageDB";

import { DateTime } from "npm:luxon";

const fetchAndStoreOpenAiUsage = async (interval: Interval) => {

const timeZone = "America/Chicago";

try {

const { data, whisper_api_data, dalle_api_data } = await fetchOpenAiUsageData(today);

const day_total = await createDayTotal(data, whisper_api_data, dalle_api_data);

console.error(error);

export default logger(fetchAndStoreOpenAiUsage);

export let openaiOpenAPI = `

openapi: 3.0.0

info:

import { Hono } from "npm:hono@3";

import { OpenAI } from "npm:openai";

const openai = new OpenAI();

function esmTown(url) {

try {

const response = await openai.chat.completions.create({

messages: [

import { cors } from 'npm:hono/cors';

import { stream, streamSSE } from "https://deno.land/x/hono@v4.3.11/helper.ts";

import { OpenAI } from "npm:openai";

import { ai } from "https://esm.town/v/yawnxyz/ai";

const app = new Hono();

const openai = new OpenAI();

app.use('*', cors({

origin: '*',

The app is set up so you can easily have a conversation between two people. The app will translate between the two selected languages, in each voice, as the speakers talk.

Add your OpenAI API Key, and make sure to open in a separate window for Mic to work.

import { cors } from 'npm:hono/cors';

import { OpenAI } from "npm:openai";

const app = new Hono();

const openai = new OpenAI(Deno.env.get("OPENAI_API_KEY_VOICE"));

class TranscriptionService {

try {

const transcription = await openai.audio.transcriptions.create({

file: audioFile,

} catch (error) {

console.error('OpenAI API error:', error);

throw error;

try {

const response = await openai.chat.completions.create({

model: "gpt-3.5-turbo",

} catch (error) {

console.error('OpenAI API error:', error);

return c.text('Error occurred during translation', 500);

try {

const mp3 = await openai.audio.speech.create({

model: "tts-1",

} catch (error) {

console.error('OpenAI API error:', error);

return c.text('Error occurred during speech generation', 500);

Uses [Neon](https://neon.tech/) to search embeddings of all vals, using the [pg_vector](https://neon.tech/docs/extensions/pgvector) extension.

- Call OpenAI to generate an embedding for the search query.

- Query the `vals_embeddings` table in Neon using the cosine similarity operator.

import { blob } from "https://esm.town/v/std/blob";

import OpenAI from "npm:openai";

const dimensions = 1536;

await client.connect();

const openai = new OpenAI();

const queryEmbedding = (await openai.embeddings.create({

model: "text-embedding-3-small",

export let autoGPT_Test2 = (async () => {

const { Configuration, OpenAIApi } = await import("npm:openai");

const configuration = new Configuration({

apiKey: process.env.openai,

const openai = new OpenAIApi(configuration);

const completion = await openai.createChatCompletion({

model: "gpt-3.5-turbo",

This val uses your OpenAI token if you have one, and the @std/openai if not, so it provides limited OpenAI usage for free.

import { chat } from "https://esm.town/v/stevekrouse/openai";

import type { ChatCompletion, ChatCompletionCreateParamsNonStreaming, Message } from "npm:@types/openai";

async function getOpenAI() {

if (Deno.env.get("OPENAI_API_KEY") === @std/openai) {

const { OpenAI } = await import("https://esm.town/v/std/openai");

return new OpenAI();

const { OpenAI } = await import("npm:openai");

return new OpenAI();

* Initiates a chat conversation with OpenAI's GPT model and retrieves the content of the first response.

const openai = await getOpenAI();

const completion = await openai.chat.completions.create(createParams);

import _ from "npm:lodash";

import OpenAI from "npm:openai";

export default async function semanticSearchPublicVals(query) {

db.add({ id, embedding, metadata: {} });

const openai = new OpenAI();

const embedding = await openai.embeddings.create({

model: "text-embedding-3-small",

This is an example of in-memory search, using a combination of lunr, OpenAI embeddings, and cosine similarity

import { embed, embedMany } from "npm:ai";

import { openai } from "npm:@ai-sdk/openai";

import lunr from "https://cdn.skypack.dev/lunr";

const { embedding } = await embed({

model: openai.embedding('text-embedding-3-small'),

value: text,

const { embeddings } = await embedMany({

model: openai.embedding('text-embedding-3-small'),

values: texts,

This val uses your OpenAI token if you have one, and the @std/openai if not, so it provides limited OpenAI usage for free.

import { chat } from "https://esm.town/v/stevekrouse/openai";

import type { ChatCompletion, ChatCompletionCreateParamsNonStreaming, Message } from "npm:@types/openai";

async function getOpenAI() {

if (Deno.env.get("OPENAI_API_KEY") === undefined) {

const { OpenAI } = await import("https://esm.town/v/std/openai");

return new OpenAI();

const { OpenAI } = await import("npm:openai");

return new OpenAI();

* Initiates a chat conversation with OpenAI's GPT model and retrieves the content of the first response.

const openai = await getOpenAI();

const completion = await openai.chat.completions.create(createParams);

import OpenAI from "https://esm.sh/openai";

import { VERSION } from "https://esm.sh/openai/version";

console.log(VERSION);

export { VERSION };

options = {},

// Initialize OpenAI API stub

const { OpenAI } = await import(

"https://esm.sh/openai"

const openai = new OpenAI();

const messages = typeof prompt === "string"

: prompt;

const completion = await openai.chat.completions.create({

messages: messages,

import process from "node:process";

const gpt3Example = async () => {

const response = await fetch("https://api.openai.com/v1/completions", {

method: "POST",

headers: {

"Content-Type": "application/json",

"Authorization": "Bearer " + process.env.OPENAI_API_KEY, // Replace with your OpenAI API Key

body: JSON.stringify({

"prompt": "send me a test message",