Search results for "openai function calling"

gpt4FunctionCallingExample

@stevekrouse

// TODO pull out function call and initial message

1

Script

import { OpenAI } from "npm:openai";

const openai = new OpenAI();

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

"functions": [

console.log(functionExpression);

// TODO pull out function call and initial message

let args = functionExpression.choices[0].message.function_call.arguments;

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

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

"function_call": { "name": "get_current_weather", "arguments": "{ \"location\": \"Boston, MA\"}" },

ai

@yawnxyz

An http and class wrapper for Vercel's AI SDK Usage: Groq: https://yawnxyz-ai.web.val.run/generate?prompt="tell me a beer joke"&provider=groq&model=llama3-8b-8192 Perplexity: https://yawnxyz-ai.web.val.run/generate?prompt="what's the latest phage directory capsid & tail article about?"&provider=perplexity Mistral: https://yawnxyz-ai.web.val.run/generate?prompt="tell me a joke?"&provider=mistral&model="mistral-small-latest" async function calculateEmbeddings(text) { const url = `https://yawnxyz-ai.web.val.run/generate?embed=true&value=${encodeURIComponent(text)}`; try { const response = await fetch(url); const data = await response.json(); return data; } catch (error) { console.error('Error calculating embeddings:', error); return null; } }

2 37

1

HTTP

async function calculateEmbeddings(text) {

import { createOpenAI } from "npm:@ai-sdk/openai";

const openai = createOpenAI({

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

apiKey: Deno.env.get("OPENAI_API_KEY_COVERSHEET")

const groq = createOpenAI({

baseURL: 'https://api.groq.com/openai/v1',

const perplexity = createOpenAI({

this.defaultProvider = options.provider || 'openai';

case 'openai':

result = await this.generateOpenAIResponse({ model, prompt, maxTokens, temperature, streaming, schema, system, messag

grayWildfowl

@jdan

An interactive, runnable TypeScript val by jdan

0

Script

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

const openai = new OpenAI();

async function runConversation() {

const inputWord = "almond latte";

`.replaceAll(/\s+/g, "");

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

messages: [

// for (let i = 0; i < message.tool_calls.length; i++) {

// console.log("[CALLING]", message.tool_calls[i].function);

// const tool = toolbox[message.tool_calls[i].function.name];

// if (tool) {

// const result = await tool.call(JSON.parse(message.tool_calls[i].function.arguments));

// console.log("[RESULT]", truncate(result));

ai

@goode_bye

An http and class wrapper for Vercel's AI SDK Usage: Groq: https://yawnxyz-ai.web.val.run/generate?prompt="tell me a beer joke"&provider=groq&model=llama3-8b-8192 Perplexity: https://yawnxyz-ai.web.val.run/generate?prompt="what's the latest phage directory capsid & tail article about?"&provider=perplexity Mistral: https://yawnxyz-ai.web.val.run/generate?prompt="tell me a joke?"&provider=mistral&model="mistral-small-latest" async function calculateEmbeddings(text) { const url = `https://yawnxyz-ai.web.val.run/generate?embed=true&value=${encodeURIComponent(text)}`; try { const response = await fetch(url); const data = await response.json(); return data; } catch (error) { console.error('Error calculating embeddings:', error); return null; } }

0

HTTP

async function calculateEmbeddings(text) {

import { createOpenAI } from "npm:@ai-sdk/openai";

const openai = createOpenAI({

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

apiKey: Deno.env.get("OPENAI_API_KEY_COVERSHEET"),

const groq = createOpenAI({

baseURL: "https://api.groq.com/openai/v1",

const perplexity = createOpenAI({

this.defaultProvider = options.provider || "openai";

case "openai":

result = await this.generateOpenAIResponse({

weatherBot

@jdan

An interactive, runnable TypeScript val by jdan

2

0

Script

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

const openai = new OpenAI();

openAiTool: {

type: "function",

function: {

openAiTool: {

type: "function",

function: {

openAiTool: {

type: "function",

weatherBot

@charmaine

An interactive, runnable TypeScript val by charmaine

0

Script

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

const openai = new OpenAI();

openAiTool: {

type: "function",

function: {

openAiTool: {

type: "function",

function: {

openAiTool: {

type: "function",

openaiOpenAPI

@stevekrouse

An interactive, runnable TypeScript val by stevekrouse

3

0

Script

export let openaiOpenAPI = `

openapi: 3.0.0

description: |

evaluate JavaScript and TypeScript expressions, run vals as APIs, either as functions or Express handlers.

Learn more at [https://docs.val.town](https://docs.val.town)

The JavaScript or TypeScript expression to be evaluated.

This should be a single expression, like a single function

call, assignment operation, or calculation. If you need

to execute multiple expressions, wrap them in a function.

schema:

summary: Simple addition

functionCall:

value: "@stevekrouse.addOne(@stevekrouse.example1)"

summary: Calling a user's val function

handle:

description: |

This val is a function that returns its arguments. It is useful for testing how the API handles the arguments pas

View the val at [https://val.town/v/stevekrouse.id](https://val.town/v/stevekrouse.id)

summary: Simple addition result

functionCall:

value: 42

summary: Calling a function result

security:

cerebrasTemplate

@stevekrouse

Cerebras Inference template This val shows you how you can deploy an app using Cerebras Inference on Val Town in seconds. What is Cerebras? Cerebras is an American chip manufacturer that produces large wafer chips that deliver mind-blowing LLM inference speeds. As of this writing on Jan 17, 2025, Cerebras Inference provides Llama 3.18b, 3.1 70b, and 3.370b at a jaw-dropping 2k tokens per second – that's 50x faster than what the frontier labs produce. Llama 3.370b at 2k tokens per second is particularly noteworthy because it is a GPT-4-class model . This level of intelligence at that level of speed will unlock whole new classes of applications. Quick start Set up Cerebras Sign up for Cerebras Get a Cerebras API Key Save it in a Val Town environment variable called CEREBRAS_API_KEY Once Cerebras is set up in your Val Town account, there are two ways to get started: Fork this app and customize it (or ask Townie AI to customize it) Start a new chat with Townie AI and copy & paste the following instructions: . Use Cerebras for AI on the backend like so: const { OpenAI } = await import("https://esm.sh/openai"); const client = new OpenAI({ apiKey: Deno.env.get("CEREBRAS_API_KEY"), baseURL: "https://api.cerebras.ai/v1" }); const response = await client.chat.completions.create({ model: "llama-3.3-70b", messages: [], }); const generatedText = response.choices[0].message.content; For example, the val in this template was created by asking Townie AI to "Make a chatgpt clone", then I hit shift-enter twice, and then pasted in the instructions on how to use Cerebras from above, then hit enter. Townie built this app on its first try, in about 20 seconds. Sample apps Cerebras Searcher - a Perplexity clone that uses the SerpAPI to do RAG and summaries with Cerebras ( requires a SerpAPI key ) Cerebras Coder - an app that generates websites in a second with Cerebras Cerebras Debater - an app that truly shows Cerebras's speed: it's Cerebras talking to Cerebras in a debate

19

0

HTTP

Use Cerebras for AI on the backend like so:

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

const client = new OpenAI({

apiKey: Deno.env.get("CEREBRAS_API_KEY"),

import React, { useState } from "https://esm.sh/react@18.2.0";

function App() {

const [messages, setMessages] = useState([]);

</div>

function client() {

createRoot(document.getElementById("root")).render(<App />);

client();

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

if (request.method === "POST" && new URL(request.url).pathname === "/chat") {

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

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

// Cerebras Inference

// Keep these comments so we remember not to change this

const client = new OpenAI({

apiKey: Deno.env.get("CEREBRAS_API_KEY"),

} catch (error) {

console.error("Error calling Cerebras API:", error);

if (error.status === 429) {

promptScorer

@toowired

An interactive, runnable TypeScript val by toowired

0

Script

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

export async function promptScorer({

original,

evolved: string;

const openai = new OpenAI();

const scoringPrompt =

try {

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

model: "gpt-4o",

} catch (error) {

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

return { score: 5, error: "Failed to get score from OpenAI" };

byob

@vawogbemi

BYOB - Build Your Own Bot You can chat with llms over email, the email thread functions as memory. The biggest thing is that you can instantly create a chat like interface with llms. Pair that with back end data and functions and you got something really powerful. Take it further Custom domains Use cloudflare email workers or a similiar service to create a custom email domain and route any incoming emails to this val. Use any email api set up with that domain to send emails ie. Sendgrid, Resend, Postmark. Toolings Llms can uses tools , meaning you can make this an agent and a whole lot more useful.

2

Email

# BYOB - Build Your Own Bot

You can chat with llms over email, the email thread functions as memory. The biggest thing is that you can instantly create a

like interface with llms. Pair that with back end data and functions and you got something really powerful.

## Take it further

### Toolings

* Llms can uses [tools](https://platform.openai.com/docs/guides/function-calling), meaning you can make this an agent and a w

import { zodResponseFormat } from "https://esm.sh/openai/helpers/zod";

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

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

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

export default async function(e: Email) {

const client = new OpenAI();

const Messages = z.object({

generateBackendSchema

@toowired

// HTML content for testing

0

HTTP

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

interface SchemaResponse {

dependencies: string[];

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

// Set CORS headers

return new Response("Invalid input: description must be a non-empty string", { status: 400, headers });

const openai = new OpenAI();

let completion;

try {

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

messages: [

} catch (error) {

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

return new Response("Error generating schema. Please try again later.", { status: 500, headers });

} catch (error) {

console.error("Error parsing OpenAI response:", error);

return new Response("Error processing the generated schema. Please try again.", { status: 500, headers });

async function generateSchema() {

const description = document.getElementById('description').value;

egoBooster

@stevekrouse

* This ego booster app takes a selfie, sends it to GPT-4o-mini for analysis, * and streams funny, specific compliments about the user's appearance. * We use the WebRTC API for camera access, the OpenAI API for image analysis, * and server-sent events for real-time streaming of compliments.

0

HTTP

* We use the WebRTC API for camera access, the OpenAI API for image analysis,

function App() {

function client() {

console.log("Document is defined, calling client()");

console.log("Document is undefined, not calling client()");

async function server(request: Request): Promise<Response> {

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

const openai = new OpenAI();

console.log("Sending request to OpenAI");

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

cerebrasTemplate

@cory16457

Cerebras Inference template This val shows you how you can deploy an app using Cerebras Inference on Val Town in seconds. What is Cerebras? Cerebras is an American chip manufacturer that produces large wafer chips that deliver mind-blowing LLM inference speeds. As of this writing on Jan 17, 2025, Cerebras Inference provides Llama 3.18b, 3.1 70b, and 3.370b at a jaw-dropping 2k tokens per second – that's 50x faster than what the frontier labs produce. Llama 3.370b at 2k tokens per second is particularly noteworthy because it is a GPT-4-class model . This level of intelligence at that level of speed will unlock whole new classes of applications. Quick start Set up Cerebras Sign up for Cerebras Get a Cerebras API Key Save it in a Val Town environment variable called CEREBRAS_API_KEY Once Cerebras is set up in your Val Town account, there are two ways to get started: Fork this app and customize it (or ask Townie AI to customize it) Start a new chat with Townie AI and copy & paste the following instructions: . Use Cerebras for AI on the backend like so: const { OpenAI } = await import("https://esm.sh/openai"); const client = new OpenAI({ apiKey: Deno.env.get("CEREBRAS_API_KEY"), baseURL: "https://api.cerebras.ai/v1" }); const response = await client.chat.completions.create({ model: "llama-3.3-70b", messages: [], }); const generatedText = response.choices[0].message.content; For example, the val in this template was created by asking Townie AI to "Make a chatgpt clone", then I hit shift-enter twice, and then pasted in the instructions on how to use Cerebras from above, then hit enter. Townie built this app on its first try, in about 20 seconds. Sample apps Cerebras Searcher - a Perplexity clone that uses the SerpAPI to do RAG and summaries with Cerebras ( requires a SerpAPI key ) Cerebras Coder - an app that generates websites in a second with Cerebras Cerebras Debater - an app that truly shows Cerebras's speed: it's Cerebras talking to Cerebras in a debate

0

HTTP

Use Cerebras for AI on the backend like so:

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

const client = new OpenAI({

apiKey: Deno.env.get("CEREBRAS_API_KEY"),

import React, { useState } from "https://esm.sh/react@18.2.0";

function App() {

const [messages, setMessages] = useState([]);

</div>

function client() {

createRoot(document.getElementById("root")).render(<App />);

client();

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

if (request.method === "POST" && new URL(request.url).pathname === "/chat") {

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

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

// Cerebras Inference

// Keep these comments so we remember not to change this

const client = new OpenAI({

apiKey: Deno.env.get("csk-n552ehwetvcnykdcwc3wyffn33kw8yfcwddj6vw9cdvyxkpr"),

} catch (error) {

console.error("Error calling Cerebras API:", error);

if (error.status === 429) {

preciseScarletHerring

@stevekrouse

Cerebras Inference template This val shows you how you can deploy an app using Cerebras Inference on Val Town in seconds. What is Cerebras? Cerebras is an American chip manufacturer that produces large wafer chips that deliver mind-blowing LLM inference speeds. As of this writing on Jan 17, 2025, Cerebras Inference provides Llama 3.18b, 3.1 70b, and 3.370b at a jaw-dropping 2k tokens per second – that's 50x faster than what the frontier labs produce. Llama 3.370b at 2k tokens per second is particularly noteworthy because it is a GPT-4-class model . This level of intelligence at that level of speed will unlock whole new classes of applications. Quick start Set up Cerebras Sign up for Cerebras Get a Cerebras API Key Save it in a Val Town environment variable called CEREBRAS_API_KEY Once Cerebras is set up in your Val Town account, there are two ways to get started: Fork this app and customize it (or ask Townie AI to customize it) Start a new chat with Townie AI and copy & paste the following instructions: . Use Cerebras for AI on the backend like so: const { OpenAI } = await import("https://esm.sh/openai"); const client = new OpenAI({ apiKey: "YOUR_CEREBRAS_API_KEY", baseURL: "https://api.cerebras.ai/v1" }); const response = await client.chat.completions.create({ model: "llama-3.3-70b", messages: [], }); const generatedText = response.choices[0].message.content; For example, the val in this template was created by asking Townie AI to "Make a chatgpt clone", then I hit shift-enter twice, and then pasted in the instructions on how to use Cerebras from above, then hit enter. Townie built this app on its first try, in about 20 seconds. Sample apps Cerebras Searcher - a Perplexity clone that uses the SerpAPI to do RAG and summaries with Cerebras ( requires a SerpAPI key ) Cerebras Coder - an app that generates websites in a second with Cerebras Cerebras Debater - an app that truly shows Cerebras's speed: it's Cerebras talking to Cerebras in a debate

0

HTTP

Use Cerebras for AI on the backend like so:

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

const client = new OpenAI({

apiKey: "YOUR_CEREBRAS_API_KEY",

import React, { useState } from "https://esm.sh/react@18.2.0";

function App() {

const [messages, setMessages] = useState([]);

</div>

function client() {

createRoot(document.getElementById("root")).render(<App />);

client();

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

if (request.method === "POST" && new URL(request.url).pathname === "/chat") {

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

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

// Cerebras Inference

// Keep these comments so we remember not to change this

const client = new OpenAI({

apiKey: Deno.env.get("CEREBRAS_API_KEY"),

} catch (error) {

console.error("Error calling Cerebras API:", error);

if (error.status === 429) {

masterfulPeachHookworm

@stevekrouse

Cerebras Inference template This val shows you how you can deploy an app using Cerebras Inference on Val Town in seconds. What is Cerebras? Cerebras is an American chip manufacturer that produces large wafer chips that deliver mind-blowing LLM inference speeds. As of this writing on Jan 17, 2025, Cerebras Inference provides Llama 3.18b, 3.1 70b, and 3.370b at a jaw-dropping 2k tokens per second – that's 50x faster than what the frontier labs produce. Llama 3.370b at 2k tokens per second is particularly noteworthy because it is a GPT-4-class model . This level of intelligence at that level of speed will unlock whole new classes of applications. Quick start There are two ways to get started: Fork this app and customize it (or ask Townie AI to customize it) Start a new chat with Townie AI and copy & paste the following instructions: . Use Cerebras for AI on the backend like so: const { OpenAI } = await import("https://esm.sh/openai"); const client = new OpenAI({ apiKey: "YOUR_CEREBRAS_API_KEY", baseURL: "https://api.cerebras.ai/v1" }); const response = await client.chat.completions.create({ model: "llama-3.3-70b", messages: [], }); const generatedText = response.choices[0].message.content; For example, the val in this template was created by asking Townie AI to "Make a chatgpt clone", then I hit shift-enter twice, and then pasted in the instructions on how to use Cerebras from above, then hit enter. Townie built this app on its first try, in about 20 seconds. Sample apps Cerebras Searcher - a Perplexity clone that uses the SerpAPI to do RAG and summaries with Cerebras ( requires a SerpAPI key ) Cerebras Coder - an app that generates websites in a second with Cerebras Cerebras Debater - an app that truly shows Cerebras's speed: it's Cerebras talking to Cerebras in a debate

0

HTTP

Use Cerebras for AI on the backend like so:

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

const client = new OpenAI({

apiKey: "YOUR_CEREBRAS_API_KEY",

import React, { useState } from "https://esm.sh/react@18.2.0";

function App() {

const [messages, setMessages] = useState([]);

</div>

function client() {

createRoot(document.getElementById("root")).render(<App />);

client();

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

if (request.method === "POST" && new URL(request.url).pathname === "/chat") {

const { messages, sassy } = await request.json();

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

const client = new OpenAI({

apiKey: Deno.env.get("CEREBRAS_API_KEY"),

} catch (error) {

console.error("Error calling Cerebras API:", error);

if (error.status === 429) {