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import { weatherOfLatLon } from "https://esm.town/v/jdan/weatherOfLatLon";

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

const openai = new OpenAI();

const toolbox = {

"latLngOfCity": {

openAiTool: {

type: "function",

"weatherOfLatLon": {

openAiTool: {

type: "function",

"fetchWebpage": {

openAiTool: {

type: "function",

call: fetchWebpage

const tools = Object.values(toolbox).map(({ openAiTool }) => openAiTool);

const transcript = [

async function runConversation() {

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

messages: transcript,

export const chatAgentWithCustomPrompt = (async () => {

const { ChatOpenAI } = await import(

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

const { initializeAgentExecutorWithOptions } = await import(

"https://esm.sh/langchain/tools/calculator"

const model = new ChatOpenAI({

temperature: 0,

openAIApiKey: process.env.OPENAI_API_KEY,

const tools = [

<p>This schema defines several components that can be used to integrate OpenAI's services into a logging and configuration system. Here's how each component relates to potential OpenAI use cases:</p>

<li><strong>App</strong>: Represents an OpenAI-powered application with a name and URL.</li>

<li><strong>Config</strong>: Defines configuration options for OpenAI API integration and application settings.</li>

<li><strong>ConsoleLog</strong>: Captures console output from OpenAI model interactions and application processes.</li>

<li><strong>CronLog</strong>: Logs scheduled tasks related to OpenAI operations, such as model fine-tuning or dataset updates.</li>

<li><strong>HttpLog</strong>: Records HTTP requests made to and from the OpenAI API.</li>

<button class="collapsible-button">Key Components and OpenAI Use Cases</button>

Use Case: Store OpenAI API keys, model preferences, and application settings.

Example: Track rate limits, response times, and payload sizes for OpenAI API calls.

<p>By implementing this schema, developers can create robust, scalable applications that effectively integrate and manage OpenAI's powerful AI capabilities while maintaining comprehensive logging and configuration control.</p>

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.

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

<p align=center>

</p>

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

import OpenAI from "npm:openai";

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

eventSource.close();

const openai = new OpenAI();

const app = new Hono();

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,

export const conversationalRetrievalQAChainSummaryMemory = (async () => {

const { ChatOpenAI } = await import(

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

const { OpenAIEmbeddings } = await import(

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

const { ConversationSummaryMemory } = await import(

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

const chatModel = new ChatOpenAI({

openAIApiKey: process.env.OPENAI_API_KEY,

/* Create the vectorstore */

[{ id: 2 }, { id: 1 }, { id: 3 }],

new OpenAIEmbeddings({

openAIApiKey: process.env.OPENAI_API_KEY,

/* Create the chain */

import { getOpenAiResponse } from "https://esm.town/v/thomasatflexos/getOpenAiResponse";

import { getRelevantContent } from "https://esm.town/v/thomasatflexos/getRelevantContent";

import process from "node:process";

\n IF you think that the Linkedin post is about new job opportunities, just respond with the text "N/A" and stop immediately. Do not respond any further.

\n ELSE IF you think the Linkdedin post is not about new job opportunities, please proceed to a meaningful comment to the provided Linkedin post based on the provided context and include ${link} in the post in a clever and relevant way, (don't be too blatant).`;

let finalResponse = await getOpenAiResponse(PROMPT);

const { data1, error1 } = await supabase

.from("linkedin_seedings")

import { chat } from "https://esm.town/v/stevekrouse/openai?v=19";

export async function getGPT4oEmoji(url) {

const response = await chat([

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,

export const gpt3Unsafe = runVal("patrickjm.gpt3", {

prompt: "Write a haiku about being cool:",

openAiKey: process.env.openai,

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

import React, { useState } from "https://esm.sh/react";

import { systemPrompt } from "https://esm.town/v/weaverwhale/jtdPrompt";

let openai;

function App() {

const url = new URL(request.url);

// Initialize OpenAI here to avoid issues with Deno.env in browser context

if (!openai) {

const apiKey = Deno.env.get("OPENAI_API_KEY");

openai = new OpenAI({ apiKey });

if (request.method === "POST" && url.pathname === "/generate") {

// Generate DALL-E prompt using GPT

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

messages: [

// Generate DALL-E image

const response = await openai.images.generate({

model: "dall-e-3",

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";

const openai = new OpenAI();

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

messages: [

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

<p align=center>

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,

`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`,

const { question } = await request.json();

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

const openai = new OpenAI();

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

messages: [