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newChatGPT35

@bingo16

An interactive, runnable TypeScript val by bingo16

Script

content: "请介绍一下你自己",

const getCompletion = async () => {

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

method: "POST",

headers: {

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

Authorization: `Bearer ${process.env.openaiKey}`,

body: JSON.stringify(postData),

const data = await response.json();

unserializeableLogEx

@stevekrouse

An interactive, runnable TypeScript val by stevekrouse

Script

export let unserializeableLogEx = (async () => {

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

const configuration = new Configuration({

apiKey: process.env.openai,

const openai = new OpenAIApi(configuration);

console.log(openai);

weatherGPT

@dantaeyoung

If you fork this, you'll need to set OPENAI_API_KEY in your Val Town Secrets .

Cron

If you fork this, you'll need to set `OPENAI_API_KEY` in your [Val Town Secrets](https://www.val.town/settings/secrets).

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: [{

resumeRecs

@iamseeley

An interactive, runnable TypeScript val by iamseeley

1 2

Script

if (!tokenBucket.consume()) {

throw new Error("Rate limit reached. Please try again later.");

const endpoint = 'https://api.openai.com/v1/chat/completions';

const model = 'gpt-4';

const messages = [

movieMashup

@dthyresson

Movie Mashup It's Blader Runner meets Pretty in Pink. OpenAI generated movie maship title, tagline and treatments. Fal generated movie posters.

HTTP

It's Blader Runner meets Pretty in Pink.

OpenAI generated movie maship title, tagline and treatments.

Fal generated movie posters.

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 */

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

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: `

sureVioletSlug

@arthrod

An interactive, runnable TypeScript val by arthrod

HTTP

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

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

const openai = new OpenAI();

const AUTH_TOKEN = Deno.env.get("valtown");

async function determineIntent(content: string): Promise<string> {

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

messages: [

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,

sendSassyEmail

@transcendr

// Configuration object for customizing the sassy message generation

Cron

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

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

// Configuration object for customizing the sassy message generation

subject: "Daily Sass Delivery 🔥",

// Prompt configuration for OpenAI

prompts: {

export default async function() {

const openai = new OpenAI();

// Randomly select a prompt style

Math.floor(Math.random() * CONFIG.prompts.styles.length)

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

* 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-v

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

healthtech4africa

@thomaskangah

AI Mental health support app for precision neuroscience

HTTP

export default 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");

const openai = new OpenAI();

const SCHEMA_VERSION = 2;

systemMessage = "You are an AI mental health therapist providing follow-up support for a Kenyan youth who may be experi

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

messages: [

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",

reasoninghelper

@prashamtrivedi

@jsxImportSource https://esm.sh/react@18.2.0

HTTP

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

const { zodResponseFormat } = await import("npm:openai/helpers/zod");

const openai = new OpenAI();

console.log("OpenAI API Request:", { prompt, topic });

completion = await openai.beta.chat.completions.parse({

console.log("OpenAI API Response:", response);

console.error("Error in OpenAI API call:", error);

Test00_getModelBuilder

@lisazz

241219 練習從 webup 而來

1 7

Script

provider?: "openai" | "huggingface";

} = { type: "llm", provider: "openai" }, options?: any) {

matches({ type: "llm", provider: "openai" }),

const { OpenAI } = await import("https://esm.sh/langchain/llms/openai");

return new OpenAI(args);

matches({ type: "chat", provider: "openai" }),

const { ChatOpenAI } = await import("https://esm.sh/langchain/chat_models/openai");

return new ChatOpenAI(args);

matches({ type: "embedding", provider: "openai" }),

const { OpenAIEmbeddings } = await import("https://esm.sh/langchain/embeddings/openai");

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