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verySalmonEmu
@tgrv
Cron
import { email } from "https://esm.town/v/std/email?v=11";
import { OpenAI } from "npm:openai";
let location = "brooklyn ny";
).then(r => r.json());
const openai = new OpenAI();
let chatCompletion = await openai.chat.completions.create({
messages: [{
longOliveGuppy
@sharanbabu
// This chatbot app will use a simple React frontend to display messages and allow user input.
HTTP
// This chatbot app will use a simple React frontend to display messages and allow user input.
// The backend will use OpenAI's GPT model to generate responses.
// We'll use SQLite to store conversation history.
/** @jsxImportSource https://esm.sh/react */
annoy
@rcurtiss
An interactive, runnable TypeScript val by rcurtiss
Script
Copying the example above, find a new word and do as above.
console.log({ prompt });
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": prompt,
weatherGPT
@sarfrazkhan18
Cron
import { email } from "https://esm.town/v/std/email?v=11";
import { OpenAI } from "npm:openai";
let location = "brooklyn ny";
).then(r => r.json());
const openai = new OpenAI();
let chatCompletion = await openai.chat.completions.create({
messages: [{
webgen
@yawnxyz
To-dos: Spruce up styles a bit Write this README ~Add a cache!~ ~Try moving style tag to the bottom by prompting so content appears immediately and then becomes styled~ didn't work b/c CSS parsing isn't progressive Need more prompting to get the model not to generate placeholder-y content Better root URL page / index page with links to some good sample generations
HTTP (deprecated)
import { blob } from "https://esm.town/v/std/blob?v=12";
import OpenAI from "npm:openai";
const openai = new OpenAI();
const getCacheKey = (url: string): string => {
let pageResult = "";
// // 2. Do one OpenAI inference to expand that URL to a longer page description
const pageDescriptionStream = await openai.chat.completions.create({
model: "gpt-4o",
// 3. Generate the page
const stream = await openai.chat.completions.create({
model: "gpt-4o",
webgen
@jonataaroeira
To-dos: Spruce up styles a bit Write this README ~Add a cache!~ ~Try moving style tag to the bottom by prompting so content appears immediately and then becomes styled~ didn't work b/c CSS parsing isn't progressive Need more prompting to get the model not to generate placeholder-y content Better root URL page / index page with links to some good sample generations
HTTP (deprecated)
let pageDescriptionInstructions = "";
let pageResult = "";
// // 2. Do one OpenAI inference to expand that URL to a longer page description
const pageDescriptionStream = await togetherAI.inference("mistralai/Mixtral-8x7B-Instruct-v0.1", {
prompt: `
multipleKeysAndMemoryConversationChainExample
@jacoblee93
An interactive, runnable TypeScript val by jacoblee93
Script
export const multipleKeysAndMemoryConversationChainExample = (async () => {
const { ChatOpenAI } = await import(
"https://esm.sh/langchain/chat_models/openai"
const { BufferMemory } = await import("https://esm.sh/langchain/memory");
const { ConversationChain } = await import("https://esm.sh/langchain/chains");
const llm = new ChatOpenAI({
modelName: "gpt-3.5-turbo",
openAIApiKey: process.env.OPENAI_API_KEY,
temperature: 0,
VALLE
@hubingkang
VALL-E LLM code generation for vals! Make apps with a frontend, backend, and database. It's a bit of work to get this running, but it's worth it. Fork this val to your own profile. 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 . If you want to use Anthropic models you need to set the ANTHROPIC_API_KEY env var . Create a Val Town API token , open the browser preview of this val, and use the API token as the password to log in.
HTTP (deprecated)
* 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).
browserbase_google_concerts
@kora
// Navigate to Google
Script
import puppeteer from "https://deno.land/x/puppeteer@16.2.0/mod.ts";
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
import { Browserbase } from "npm:@browserbasehq/sdk";
// ask chat gpt for list of concert dates
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
messages: [
VALLE
@jxnblk
VALL-E LLM code generation for vals! Make apps with a frontend, backend, and database. It's a bit of work to get this running, but it's worth it. Fork this val to your own profile. 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 . If you want to use Anthropic models you need to set the ANTHROPIC_API_KEY env var . Create a Val Town API token , open the browser preview of this val, and use the API token as the password to log in.
HTTP (deprecated)
* 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).
VALLE
@heaversm
VALL-E LLM code generation for vals! Make apps with a frontend, backend, and database. It's a bit of work to get this running, but it's worth it. Fork this val to your own profile. 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 . If you want to use Anthropic models you need to set the ANTHROPIC_API_KEY env var . Create a Val Town API token , open the browser preview of this val, and use the API token as the password to log in.
HTTP (deprecated)
* 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).
VALLE
@eugenechantk
VALL-E LLM code generation for vals! Make apps with a frontend, backend, and database. It's a bit of work to get this running, but it's worth it. Fork this val to your own profile. 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 . If you want to use Anthropic models you need to set the ANTHROPIC_API_KEY env var . Create a Val Town API token , open the browser preview of this val, and use the API token as the password to log in.
HTTP (deprecated)
* 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).
chat
@chatgpt
// Forked from @webup.chat
Script
options = {},
// Initialize OpenAI API stub
const { Configuration, OpenAIApi } = await import("https://esm.sh/openai");
const configuration = new Configuration({
apiKey: process.env.OPENAI,
const openai = new OpenAIApi(configuration);
// Request chat completion
: prompt;
const { data } = await openai.createChatCompletion({
model: "gpt-3.5-turbo-0613",
autoGPT_Test
@stevekrouse
An interactive, runnable TypeScript val by stevekrouse
Script
export let autoGPT_Test = (async () => {
const { Configuration, OpenAIApi } = await import("npm:openai@3.2.1");
const configuration = new Configuration({
apiKey: process.env.openai,
const openai = new OpenAIApi(configuration);
const completion = await openai.createChatCompletion({
model: "gpt-4",
OpenAISummary
@zzz
// Create a summary from a given text using GPT 4
Script
// Create a summary from a given text using GPT 4
export const OpenAISummary = async (text: string, config: {
apiKey?: string;
"anon",
"@zzz.OpenAISummary",
2,
const agent = await AIAgent(
apiKey || process.env.OPENAI_API_KEY_GPT4,
const response = await agent.summarize(text, modelName);