Search results for "openai"

emojiSearchBot

@stevekrouse

Emoji search bot Replies to mentions on twitter with the emojis your photo evokes. Inspired by Devon Zuegel .

1

Cron

import process from "node:process";

import OpenAI from "npm:openai";

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

export async function emojiSearchBot({ lastRunAt }: Interval) {

if (attachment.type !== "photo") return;

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

model: "gpt-4-vision-preview",

getContentFromUrl

@willthereader

getContentFromUrl Use this for summarizers. Combines https://r.jina.ai/URL and markdown.download's Youtube transcription getter to do its best to retrieve content from URLs. Usage: https://yawnxyz-getcontentfromurl.web.val.run/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187409/ https://yawnxyz-getcontentfromurl.web.val.run/https://www.youtube.com/watch?v=gzsczZnS84Y&ab_channel=PhageDirectory

0

HTTP (deprecated)

let result = await ai({

provider: "openai",

model: "gpt-3.5-turbo",

let result = await ai({

provider: "openai",

model: "gpt-3.5-turbo",

let result = await ai({

provider: "openai",

embed:true,

jadeGopher

@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({

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,

openAiFreeUsage

@patrickjm

// set at Sat Dec 09 2023 01:45:57 GMT+0000 (Coordinated Universal Time)

6

0

Script

// set at Sat Dec 09 2023 01:45:57 GMT+0000 (Coordinated Universal Time)

export let openAiFreeUsage = {"used_quota":12709400,"used_quota_usd":1.27094,"exceeded":false};

VALLE

@gitgrooves

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

chatGPTExample

@maxdrake

An interactive, runnable TypeScript val by maxdrake

0

Script

let repsonse_obj = await chatGPT(

"hello assistant",

tinue a conversation, you can pass in someting of the form: https://platform.openai.com/docs/guides/chat/introduction

API_KEY

let response_text = repsonse_obj.message;

CoverLetterGenerator

@shawnbasquiat

// This val creates a cover letter generator using OpenAI's GPT model

1

HTTP

// This val creates a cover letter generator using OpenAI's GPT model

// It takes a resume (as a PDF file) and job description as input and returns a concise cover letter

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

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

console.log("Entering generateCoverLetter function");

const openai = new OpenAI();

console.log("OpenAI instance created");

try {

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

model: "gpt-4o-mini",

max_tokens: 500,

console.log("OpenAI API call completed");

return completion.choices[0].message.content || "Unable to generate cover letter.";

} catch (error) {

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

throw error;

VALLE

@wlxiaozhzh

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

@starrnx

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

questionsWithGuidelinesChain

@jacoblee93

An interactive, runnable TypeScript val by jacoblee93

0

Script

export const questionsWithGuidelinesChain = (async () => {

const { ChatOpenAI } = await import(

"https://esm.sh/langchain@0.0.150/chat_models/openai"

const { LLMChain } = await import("https://esm.sh/langchain@0.0.150/chains");

const questionChain = questionPrompt

.pipe(new ChatOpenAI({

openAIApiKey: process.env.OPENAI_API_KEY,

.pipe(new StringOutputParser()));

.pipe(

new ChatOpenAI({

openAIApiKey: process.env.OPENAI_API_KEY,

.pipe(new StringOutputParser());

generateValCode

@yawnxyz

// import { openaiChatCompletion } from "https://esm.town/v/andreterron/openaiChatCompletion";

1

0

Script

// import { openaiChatCompletion } from "https://esm.town/v/andreterron/openaiChatCompletion";

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

const openai = new OpenAI();

export const generateValCode = async (

const lodash = await import('npm:lodash');

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

openaiKey: key,

organization: org,

VALLE

@tmcw

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

demoOpenAIGPTSummary

@zzz

An interactive, runnable TypeScript val by zzz

0

Script

import { confession } from "https://esm.town/v/alp/confession?v=2";

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

export let demoOpenAIGPTSummary = await runVal(

"zzz.OpenAISummary",

confession,

modelName: "gpt-3.5-turbo",

weatherGPT

@tgrv

0

Cron

import { email } from "https://esm.town/v/std/email?v=11";

import { OpenAI } from "npm:openai";

let location = "Wolfsburg";

).then(r => r.json());

const openai = new OpenAI();

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

messages: [{

sqliteWriter

@nbbaier

SQLite QueryWriter The QueryWriter class is a utility for generating and executing SQL queries using natural language and OpenAI. It provides a simplified interface for interacting with your Val Town SQLite database and generating SQL queries based on user inputs. This val is inspired by prisma-gpt . PRs welcome! See Todos below for some ideas I have. Usage Import the QueryWriter class into your script: import { QueryWriter } from "https://esm.town/v/nbbaier/sqliteWriter"; Create an instance of QueryWriter, providing the desired table and an optional model: const writer = new QueryWriter({ table: "my_table", model: "gpt-4-1106-preview" }); Call the writeQuery() method to generate an SQL query based on a user input string: const userInput = "Show me all the customers with more than $1000 in purchases."; const query = await writer.writeQuery(userInput); Alternatively, use the gptQuery() method to both generate and execute the SQL query: const userInput = "Show me all the customers with more than $1000 in purchases."; const result = await writer.gptQuery(userInput); Handle the generated query or query result according to your application's needs. API new QueryWriter(args: { table: string; model?: string }): QueryWriter Creates a new instance of the QueryWriter class. table : The name of the database table to operate on. model (optional): The model to use for generating SQL queries. Defaults to "gpt-3.5-turbo". apiKey (optional): An OpenAI API key. Defaults to Deno.env.get("OPENAI_API_KEY") . writeQuery(str: string): Promise<string> Generates an SQL query based on the provided user input string. str : The user input string describing the desired query. Returns a Promise that resolves to the generated SQL query. gptQuery(str: string): Promise<any> Generates and executes an SQL query based on the provided user input string. str : The user input string describing the desired query. Returns a Promise that resolves to the result of executing the generated SQL query. Todos [ ] Handle multiple tables for more complex use cases [ ] Edit prompt to allow for more than just SELECT queries [ ] Allow a user to add to the system prompt maybe? [ ] Expand usage beyond just Turso SQLite to integrate with other databases

1 3 2

0

Script

utility for generating and executing SQL queries using natural language and OpenAI. It provides a simplified interface for i

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

import OpenAI from "npm:openai";

openai: OpenAI;

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

// this.apiKey = openaiOptions.apiKey ? openaiOptions.apiKey : Deno.env.get("OPENAI_API_KEY");

this.openai = new OpenAI(openaiOptions);

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

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

throw new Error("No SQL returned from OpenAI. Try again.");

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

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