Search results for "openai"

ask_ai

@pomdtr

Ask gpt to update a val on your behalf Usage import { askAI } from "https://esm.town/v/pomdtr/ask_ai"; await askAI(`Add jsdoc comments for each exported function of the val @pomdtr/askAi`);

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1

Script

import { api } from "https://esm.town/v/pomdtr/api";

import { email } from "https://esm.town/v/std/email?v=12";

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

async function getValByAlias({ author, name }: { author: string; name: string }) {

const { id, code, readme } = await api(`/v1/alias/${author}/${name}`, {

type: "string";

export function askAI(content: string) {

const client = new OpenAI();

const runner = client.beta.chat.completions.runTools({

model: "gpt-3.5-turbo",

VALLE

@geltoob

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.

0

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-v

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

emojiGuessr

@jdan

Calorie Count via Photo Uploads your photo to ChatGPT's new vision model to automatically categorize the food and estimate the calories.

1

0

HTTP (deprecated)

import { fileToDataURL } from "https://esm.town/v/stevekrouse/fileToDataURL";

import { modifyImage } from "https://esm.town/v/stevekrouse/modifyImage";

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

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

function esmTown(url) {

rateArticleRelevance

@iakovos

An interactive, runnable TypeScript val by iakovos

1 4

0

Script

export const rateArticleRelevance = async (interests: string, article: any) => {

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

const openai = new OpenAI({ apiKey: process.env.OPENAI_KEY });

try {

Give a score from 0 to 10. Why did you give this score? Respond with the score only.

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

messages: [

greenCow

@willthereader

ChatGPT Implemented in Val Town Demonstrated how to use assistants and threads with the OpenAI SDK and how to stream the response with Server-Sent Events

0

HTTP (deprecated)

# ChatGPT Implemented in Val Town

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

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

import OpenAI from "npm:openai";

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

const openai = new OpenAI();

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

app.get("/", async (c) => {

const thread = await openai.beta.threads.create();

const assistant = await openai.beta.assistants.create({

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,

VALLE

@ttodosi

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.

0

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-v

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

VALLE

@davitchanturia

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.

0

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-v

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

readmeGPT

@nbbaier

Val Town AI Readme Writer This val provides a class ReadmeWriter for generating readmes for vals with OpenAI. It can both draft readmes and update them directly PRs welcome! See Todos below for some ideas I have. Usage To draft a readme for a given code, use the draftReadme method: import { ReadmeWriter } from "https://esm.town/v/nbbaier/readmeGPT"; const readmeWriter = new ReadmeWriter({}); const val = "https://www.val.town/v/:username/:valname"; const generatedReadme = await readmeWriter.draftReadme(val); To write and update a readme for a given code, use the writeReadme method: import { ReadmeWriter } from "https://esm.town/v/nbbaier/readmeGPT"; const readmeWriter = new ReadmeWriter({}); const val = "https://www.val.town/v/:username/:valname"; const successMessage = await readmeWriter.writeReadme(val); API Reference Class: ReadmeWriter The ReadmeWriter class represents a utility for generating and updating README files. Constructor Creates an instance of the ReadmeWriter class. Parameters: model (optional): The model to be used for generating the readme. Defaults to "gpt-3.5-turbo". apiKey (optional): An OpenAI API key. Defaults to Deno.env.get("OPENAI_API_KEY") . Methods draftReadme(val: string): Promise<string> : Generates a readme for the given val. Parameters: val : URL of the code repository. Returns: A promise that resolves to the generated readme. writeReadme(val: string): Promise<string> : Generates and updates a readme for the given val. Parameters: val : URL of the code repository. Returns: A promise that resolves to a success message if the update is successful. Todos [ ] Additional options to pass to the OpenAI model [ ] Ability to pass more instructions to the prompt to modify how the readme is constructed

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Script

his val provides a class `ReadmeWriter` for generating readmes for vals with OpenAI. It can both draft readmes and update the

- `apiKey` (optional): An OpenAI API key. Defaults to `Deno.env.get("OPENAI_API_KEY")`.

- [ ] Additional options to pass to the OpenAI model

import OpenAI, { type ClientOptions } from "npm:openai";

openai: OpenAI;

const { model, ...openaiOptions } = options;

this.openai = new OpenAI(openaiOptions);

private async performOpenAICall(prompt: string) {

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

throw new Error("No response from OpenAI");

throw new Error("No readme returned by OpenAI. Try again.");

const readme = await this.performOpenAICall(prompt);

aiSarcasticMotivationalMessage

@patrickjm

An interactive, runnable TypeScript val by patrickjm

0

Script

"Emphasize a morbid sense of humor.",

].join("\n"),

openAiKey: process.env.openai_key,

getChatgpt

@bingo16

An interactive, runnable TypeScript val by bingo16

3

0

Script

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

// Update your token in https://val.town/settings/secrets

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

const getCompelitoins = async (data) => {

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

method: "POST",

headers: {

poembuilder

@stevekrouse

@jsxImportSource npm:hono@3/jsx

1

0

HTTP (deprecated)

/** @jsxImportSource npm:hono@3/jsx */

import { OpenAI } from "https://esm.town/v/std/openai?v=2";

import { sqlite } from "https://esm.town/v/std/sqlite?v=5";

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

emeraldRaccoon

@willthereader

ChatGPT Implemented in Val Town Demonstrated how to use assistants and threads with the OpenAI SDK and how to stream the response with Server-Sent Events

0

HTTP (deprecated)

# ChatGPT Implemented in Val Town

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

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

import OpenAI from "npm:openai";

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

const openai = new OpenAI();

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

app.get("/", async (c) => {

const thread = await openai.beta.threads.create();

const assistant = await openai.beta.assistants.create({

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,

valTownChatGPT

@xuybin

ChatGPT Implemented in Val Town Demonstrated how to use assistants and threads with the OpenAI SDK and how to stream the response with Server-Sent Events.

0

HTTP (deprecated)

# ChatGPT Implemented in Val Town

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

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

import OpenAI from "npm:openai";

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

// This uses by personal API key, you'll need to provide your own if

// you fork this. We'll be adding support to the std/openai lib soon!

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({

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,

exampleTopHackerNewsDailyEmail

@charlypoly

An interactive, runnable TypeScript val by charlypoly

0

Cron

import { email } from "https://esm.town/v/std/email?v=12";

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

import { z } from "npm:zod";

.describe("Top 5 stories on Hacker News"),

// we create a OpenAI Tool that takes our schema as argument

const extractContentTool: any = {

parameters: zodToJsonSchema(schema),

const openai = new OpenAI();

// We ask OpenAI to extract the content from the given web page.

// The model will reach out to our `extract_content` tool and

// the requirement of `extract_content`s argument.

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

model: "gpt-4-turbo",

tool_choice: "auto",

// we retrieve the serialized arguments generated by OpenAI

const result = completion.choices[0].message.tool_calls![0].function.arguments;

const parsed = schema.parse(JSON.parse(result));

const completion2 = await openai.chat.completions.create({

model: "gpt-4-turbo",

add_to_notion_w_ai

@nerdymomocat

Uses instructor and open ai (with gpt-4-turbo) to process any content into a notion database entry. Use addToNotion with any database id and content. await addToNotion( "DB_ID_GOES_HERE", "CONTENT_GOES HERE"//"for example: $43.28 ordered malai kofta and kadhi (doordash) [me and mom] jan 3 2024" ); Prompts are created based on your database name, database description, property name, property type, property description, and if applicable, property options (and their descriptions). Supports: checkbox, date, multi_select, number, rich_text, select, status, title, url, email Uses NOTION_API_KEY , OPENAI_API_KEY stored in env variables and uses Valtown blob storage to store information about the database. Use get_notion_db_info to use the stored blob if exists or create one, use get_and_save_notion_db_info to create a new blob (and replace an existing one if exists).

2

0

Script

Supports: checkbox, date, multi_select, number, rich_text, select, status, title, url, email

- Uses `NOTION_API_KEY`, `OPENAI_API_KEY` stored in env variables and uses [Valtown blob storage](https://esm.town/v/std/blob

- Use `get_notion_db_info` to use the stored blob if exists or create one, use `get_and_save_notion_db_info` to create a new

import { Client } from "npm:@notionhq/client";

import OpenAI from "npm:openai";

import { z } from "npm:zod";

"email": "string_email",

const oai = new OpenAI({

apiKey: process.env.OPENAI_API_KEY ?? undefined,

const client = Instructor({