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Fork it and authenticate with your Val Town API token as the password. Needs an `OPENAI_API_KEY` env var to be set, and change the variables under "Set these to your own".

https://x.com/JanPaul123/status/1812957150559211918

import { OpenAI } from "https://esm.town/v/std/openai?v=4";

const prompt = "Tell me a dad joke. Format the response as JSON with 'setup' and 'punchline' keys.";

export default async function dailyDadJoke(req: Request): Response {

const openai = new OpenAI();

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

messages: [

Supports: checkbox, date, multi_select, number, rich_text, select, status, title, url, email

- Uses `NOTION_API_KEY`, `OPENAI_API_KEY` stored in env variables and uses [Valtown blob storage](https://esm.town/v/std/blob) to store information about the database.

- Use `get_notion_db_info` to use the stored blob if exists or create one, use `get_and_save_notion_db_info` to create a new blob (and replace an existing one if exists).

import { Client } from "npm:@notionhq/client";

import OpenAI from "npm:openai";

import { z } from "npm:zod";

"email": "string_email",

const oai = new OpenAI({

apiKey: process.env.OPENAI_API_KEY ?? undefined,

const client = Instructor({

import { Hono } from "npm:hono";

import { OpenAI } from "npm:openai";

const gistGPT = async (input: string, about?: boolean) => {

const chatInput = about ? input : await (await fetch(input)).text();

const openai = new OpenAI();

let chatCompletion = await openai.chat.completions.create({

messages: [

* Make a folder for the temporary vals that get generated, take the ID from the URL, and put it in `tempValsParentFolderId`.

* If you want to use OpenAI models you need to set the `OPENAI_API_KEY` [env var](https://www.val.town/settings/environment-variables).

* If you want to use Anthropic models you need to set the `ANTHROPIC_API_KEY` [env var](https://www.val.town/settings/environment-variables).

Demonstrated how to use assistants and threads with the OpenAI SDK and how to stream the response with Server-Sent Events

import OpenAI from "npm:openai";

import { renderToString } from "npm:react-dom/server";

const openai = new OpenAI();

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

app.get("/", async (c) => {

const thread = await openai.beta.threads.create();

const assistant = await openai.beta.assistants.create({

name: "",

const message = c.req.query("message");

await openai.beta.threads.messages.create(

threadId,

"data: " + JSON.stringify(str) + "\n\n",

const run = openai.beta.threads.runs.stream(threadId, {

assistant_id: assistantId,

* This app generates cute daily notes for a daughter using OpenAI's GPT model.

* It stores the generated notes in SQLite for persistence and displays them on a simple web interface.

async function server(request: Request): Promise<Response> {

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

const { sqlite } = await import("https://esm.town/v/stevekrouse/sqlite");

} else if (path === "/generate-note" && request.method === "POST") {

const openai = new OpenAI();

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

messages: [

url: "https://openrouter.ai/api/v1/models",

token: Deno.env.get("OPEN_ROUTER_API_KEY"),

url: "https://api.groq.com/openai/v1/models",

token: Deno.env.get("GROQ_API_KEY"),

// Create fetch promises for each API endpoint

if (provider === "groq") {

url.host = "api.groq.com";

url.pathname = url.pathname.replace("/api/v1", "/openai/v1");

url.port = "443";

url.protocol = "https";

Supports: checkbox, date, multi_select, number, rich_text, select, status, title, url, email

- Uses `NOTION_API_KEY`, `OPENAI_API_KEY` stored in env variables and uses [Valtown blob storage](https://esm.town/v/std/blob) to store information about the database.

- Use `get_notion_db_info` to use the stored blob if exists or create one, use `get_and_save_notion_db_info` to create a new blob (and replace an existing one if exists).

import { Client } from "npm:@notionhq/client";

import OpenAI from "npm:openai";

import { z } from "npm:zod";

"email": "string_email",

const oai = new OpenAI({

apiKey: process.env.OPENAI_API_KEY ?? undefined,

const client = Instructor({

const builder = await getModelBuilder({

type: "chat",

provider: "openai",

const model = await builder();

const { SystemMessage, HumanMessage } = await import("npm:langchain/schema");

Demonstrated how to use assistants and threads with the OpenAI SDK and how to stream the response with Server-Sent Events.

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

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

import { renderToString } from "npm:react-dom/server";

const openai = new OpenAI();

const jsxResponse = (jsx) => {

try {

thread = await openai.chat.completions.create({

model: "gpt-3.5-turbo", // Use a faster model

messages: [{ role: "system", content: "Start a new thread" }],

assistant = await openai.chat.completions.create({

model: "gpt-3.5-turbo", // Use a faster model

try {

await openai.chat.completions.create({

model: "gpt-3.5-turbo", // Use a faster model

"data: " + JSON.stringify(str) + "\n\n",

const run = openai.chat.completions.stream({

model: "gpt-3.5-turbo", // Use a faster model

export const rateArticleRelevance = async (interests: string, article: any) => {

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

const openai = new OpenAI({

apiKey: untitled_tealCoral.OPENAI_API_KEY,

try {

Give a score from 0 to 10. Why did you give this score? Respond with the score only.

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

messages: [

* Make a folder for the temporary vals that get generated, take the ID from the URL, and put it in `tempValsParentFolderId`.

* If you want to use OpenAI models you need to set the `OPENAI_API_KEY` [env var](https://www.val.town/settings/environment-variables).

* If you want to use Anthropic models you need to set the `ANTHROPIC_API_KEY` [env var](https://www.val.town/settings/environment-variables).

async function server(request: Request): Promise<Response> {

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

const pdfExtractText = await import("https://esm.town/v/pdebieamzn/pdfExtractText");

const fullText = await pdfExtractText.default(arrayBuffer);

const openai = new OpenAI();

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

messages: [

} catch (error) {

console.error('Error parsing OpenAI response:', error);

console.log('Raw response:', completion.choices[0].message.content);

if (!Array.isArray(events)) {

console.error('Unexpected response format from OpenAI');

return new Response(JSON.stringify({ error: 'Unexpected response format' }), { status: 500, headers: { 'Content-Type': 'application/json' } });

const cheerio = await import("npm:cheerio");

const html = await fetchText(

"https://en.wikipedia.org/wiki/OpenAI",

const $ = cheerio.load(html);

// Cheerio accepts a CSS selector, here we pick the second <p>