Search

import { parentReference } from "https://esm.town/v/stevekrouse/parentReference?v=3";

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

* OpenAI text completion. https://platform.openai.com/docs/api-reference/completions

* val.town and rlimit.com has generously provided a free daily quota. Until the quota is met, no need to provide an API key.

export const gpt4 = async (prompt: string, maxTokens?: number = 1000) => {

This val.town script fetches a user's recent GitHub activity and generates a summarized narrative overview using OpenAI's GPT model.

- Retrieves GitHub activity for a specified user from the past week

- Summarizes activity using OpenAI's GPT-3.5-turbo model

- Returns a concise, narrative summary of the user's GitHub contributions

Ensure you have necessary permissions and comply with GitHub's and OpenAI's terms of service when using this script.

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

const OPENAI_API_KEY = "your_openai_api_key"; // Replace with your actual OpenAI API key

export default async function main(req: Request): Promise<Response> {

const user2Summary = summarizeActivity(user2Data);

const openai = new OpenAI(OPENAI_API_KEY);

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

model: "gpt-3.5-turbo",

export let generateValCodeAPI = (description: string) =>

generateValCode(

process.env.OPENAI_API_KEY,

description,

export const braveAgent = (async () => {

const { ChatOpenAI } = await import(

"https://esm.sh/langchain/chat_models/openai"

const { BraveSearch } = await import("https://esm.sh/langchain/tools");

"https://esm.sh/langchain/agents"

const model = new ChatOpenAI({

temperature: 0,

openAIApiKey: process.env.OPENAI_API_KEY,

const tools = [

* 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).

This script exposes two endpoints, one for a form to capture information to utilize in creating a greeting card utilizing OpenAI, and the other to generate the greeting card based off the query parameters provided.

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

import { Hono } from "npm:hono";

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

const openai = new OpenAI(),

chat = openai.chat,

chatCompletions = chat.completions;

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";

body: msgDiv.textContent,

// Setup the SSE connection and stream back the response. OpenAI handles determining

// which message is the correct response based on what was last read from the

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

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

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

name: "",

let message = await c.req.text();

await openai.beta.threads.messages.create(

c.req.query("threadId"),

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

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

assistant_id: assistantId,

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

export async function cronprompt(prompt: string) {

const openai = new OpenAI();

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

messages: [

import { loadPageContent } from "https://esm.town/v/charlypoly/browserbaseUtils";

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

import { z } from "npm:zod";

.describe("Top 5 stories on Hacker News"),

const extractContentTool: any = {

parameters: zodToJsonSchema(schema),

const openai = new OpenAI();

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

model: "gpt-4-turbo",

tool_choice: "auto",

const result = completion.choices[0].message.tool_calls![0].function.arguments;

An example of using OpenAI to stream back a chat with an assistant. This example sends two messages to the assistant and streams back the responses when they come in.

Example response:

import OpenAI from "npm:openai";

const openai = new OpenAI();

import process from "node:process";

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

name: "Val Tutor",

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

}, 100);

const message = await openai.beta.threads.messages.create(

thread.id,

{ role: "user", content: messages[i] },

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

assistant_id: assistant.id,

browserWSEndpoint: `wss://chrome.browserless.io?token=${process.env.browserlessKey}`,

const page = await browser.newPage();

await page.goto("https://en.wikipedia.org/wiki/OpenAI");

const intro = await page.evaluate(

`document.querySelector('p:nth-of-type(2)').innerText`,

export let generateValCodeAPI = (description: string) =>

generateValCode(

process.env.VT_OPENAI_KEY,

description,

export const textToImageDalle = async (

openAIToken: string,

prompt: string,

} = await fetchJSON(

"https://api.openai.com/v1/images/generations",

method: "POST",

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

"Authorization": `Bearer ${openAIToken}`,

body: JSON.stringify({

export const getJoke = (async () => {

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

const result = await ChatCompletion.create({

model: "gpt-3.5-turbo",

6. Uses EPA's ranking to classify the severity of the score (ie "Unhealthy for Sensitive Groups")

It uses blob storage to cache the openai location id for your location string to skip a couple steps for the next time.