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ministerialLavenderSloth

@maxm

An interactive, runnable TypeScript val by maxm

HTTP (deprecated)

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

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

const openai = new OpenAI();

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

stream: true,

getOpenapiEmbedding

@wilt

* Call OpenAPI Embeddings api to vectorize a query string * Returns an array of 1536 numbers

Script

query: string;

}): Promise<number[]> =>

fetchJSON("https://api.openai.com/v1/embeddings", {

method: "POST",

headers: {

langchainEx

@weaverwhale

An interactive, runnable TypeScript val by weaverwhale

Script

export const langchainEx = (async () => {

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

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

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

const model = new OpenAI({

temperature: 0.9,

openAIApiKey: process.env.openai,

maxTokens: 100,

gpt4FunctionCallingExample

@stevekrouse

// TODO pull out function call and initial message

Script

import { OpenAI } from "npm:openai";

const openai = new OpenAI();

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

"messages": [

let functionCallResult = { "temperature": "22", "unit": "celsius", "description": "Sunny" };

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

"messages": [

openAIHonoChatStreamExample

@yawnxyz

Example of using Hono to stream OpenAI's streamed chat responses!

HTTP (deprecated)

Example of using Hono to stream OpenAI's streamed chat responses!

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

import { OpenAI } from "npm:openai";

const app = new Hono();

const openai = new OpenAI();

app.use('*', cors({

const SOURCE_URL = ""; // leave blank for deno deploy / native

// const SOURCE_URL = "https://yawnxyz-openAIHonoChatStreamSample.web.val.run"; // valtown as generator - no SSE

// const SOURCE_URL = "https://funny-crow-81.deno.dev"; // deno deploy as generator

<title>OpenAI Streaming Example</title>

<style>

<body>

<h1>OpenAI Streaming Example</h1>

<label for="prompt">Prompt:</label>

try {

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

model: "gpt-4",

blogPostEmbeddingsDimensionalityReduction

@janpaul123

An interactive, runnable TypeScript val by janpaul123

Script

import _ from "npm:lodash";

import OpenAI from "npm:openai";

export default async function blogPostEmbeddingsDimensionalityReduction() {

"chat server integration",

const openai = new OpenAI();

async function getEmbedding(str) {

return (await openai.embeddings.create({

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

langchainEx

@stevekrouse

An interactive, runnable TypeScript val by stevekrouse

Script

export const langchainEx = (async () => {

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

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

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

const model = new OpenAI({

temperature: 0.9,

openAIApiKey: process.env.openai,

maxTokens: 100,

BlogChatbotServer

@weaverwhale

// This approach will create a chatbot using OpenAI's SDK with access to a blog's content stored in a JSON.

HTTP

// This approach will create a chatbot using OpenAI's SDK with access to a blog's content stored in a JSON.

// We'll use Hono for routing, OpenAI for the chatbot, and stream the responses to the client.

// The blog content will be stored as JSON and used as a tool in the chatbot's chain.

import { streamSSE } from "https://esm.sh/hono/streaming";

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

const app = new Hono();

const { message } = await c.req.json();

const openai = new OpenAI();

return streamSSE(c, async (stream) => {

const blogSearchResults = searchBlogContent(message);

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

model: "gpt-4-mini",

chat

@mxis

OpenAI ChatGPT helper function 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"; const { content } = await chat("Hello, GPT!"); console.log(content); import { chat } from "https://esm.town/v/stevekrouse/openai"; const { content } = await chat( [ { role: "system", content: "You are Alan Kay" }, { role: "user", content: "What is the real computer revolution?"} ], { max_tokens: 50, model: "gpt-4" } ); console.log(content);

Script

# OpenAI ChatGPT helper function

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);

openAiModeration

@patrickjm

* Calls the OpenAI moderation model. Useful for determining if OpenAI will flag something you did. * https://platform.openai.com/docs/api-reference/moderations

Script

import { fetchJSON } from "https://esm.town/v/stevekrouse/fetchJSON?v=41";

* Calls the OpenAI moderation model. Useful for determining if OpenAI will flag something you did.

* https://platform.openai.com/docs/api-reference/moderations

export let openAiModeration = async ({

apiKey,

if (!apiKey) {

throw new Error("You must provide an OpenAI API Key");

const body: { model?: string, input: string|string[] } = {

const result = await fetchJSON(

"https://api.openai.com/v1/moderations",

method: "POST",

oddTanRoundworm

@l2046a

An interactive, runnable TypeScript val by l2046a

HTTP

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

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

status: 204,

const openai = new OpenAI();

try {

model: "gpt-4-turbo",

const stream = await openai.chat.completions.create(body);

if (!body.stream) {

weatherGPT

@stevekrouse

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: [{

gpt4Example

@kyutarou

GPT4 Example This uses the brand new gpt-4-1106-preview . To use this, set OPENAI_API_KEY in your Val Town Secrets .

Script

This uses the brand new `gpt-4-1106-preview`.

To use this, set `OPENAI_API_KEY` in your [Val Town Secrets](https://www.val.town/settings/secrets).

import { OpenAI } from "npm:openai";

Deno.env.get("OPENAI_API_KEY");

const openai = new OpenAI();

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

messages: [{

instructorExample

@inkpotmonkey

Example copied https://instructor-ai.github.io/instructor-js/#usage into val.town You will need to fork this and properly set the apiKey and organisation for it to work.

Script

import Instructor from "https://esm.sh/@instructor-ai/instructor";

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

import { z } from "https://esm.sh/zod";

const openAISecrets = {

apiKey: getApiKey(),

organization: getOrganisationKey(),

const oai = new OpenAI(openAISecrets);

const client = Instructor({

conversationalQAChainEx

@jacoblee93

An interactive, runnable TypeScript val by jacoblee93

Script

const { ChatOpenAI } = await import(

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

const { OpenAIEmbeddings } = await import(

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

const gpt35 = new ChatOpenAI({

openAIApiKey: process.env.OPENAI_API_KEY,

const gpt4 = new ChatOpenAI({

openAIApiKey: process.env.OPENAI_API_KEY,

new OpenAIEmbeddings({

openAIApiKey: process.env.OPENAI_API_KEY,

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