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compareEmbeddings

@janpaul123

An interactive, runnable TypeScript val by janpaul123

Script

import _ from "npm:lodash";

import OpenAI from "npm:openai";

const comparisons = [

["chat server integration", "discord bot"],

const openai = new OpenAI();

const cache = {};

async function getEmbedding(str) {

cache[str] = cache[str] || (await openai.embeddings.create({

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

getVectorStoreBuilder

@webup

An interactive, runnable TypeScript val by webup

1 11

Script

type: "memory" | "baas";

provider?: "pinecone" | "milvus";

} = { type: "memory" }, embed: "openai" | "huggingface" = "openai") {

const { cond, matches } = await import("npm:lodash-es");

const builder = await getModelBuilder({

audioManager

@yawnxyz

Usage: import { ai } from "https://esm.town/v/yawnxyz/ai"; import { AudioManager } from "https://esm.town/v/yawnxyz/audioManager"; let audio = new AudioManager(); let joke = await ai("tell me a joke in chinese!"); console.log('text', joke) let result = await audio.textToSpeechUpload(joke, {key: "random-joke.mp3"}); console.log('result:', result)

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Script

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

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

constructor(apiKey=null, uploadFunction = null, downloadFunction = null) {

this.openai = new OpenAI(apiKey);

this.uploadFunction = uploadFunction || this.blobUpload;

const mergedOptions = { ...defaultOptions, ...options };

const transcription = await this.openai.audio.transcriptions.create(mergedOptions);

return transcription;

const mergedOptions = { ...defaultOptions, ...options };

const translation = await this.openai.audio.translations.create(mergedOptions);

return translation;

// returns an openai speech object

async textToSpeech(text, options = {}) {

const mergedOptions = { ...defaultOptions, ...options };

const speech = await this.openai.audio.speech.create(mergedOptions);

const arrayBuffer = await speech.arrayBuffer();

const mergedOptions = { ...defaultOptions, ...options };

const speech = await this.openai.audio.speech.create(mergedOptions);

const arrayBuffer = await speech.arrayBuffer();

fineTuningJob1

@stevekrouse

An interactive, runnable TypeScript val by stevekrouse

Script

import process from "node:process";

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

export let fineTuningJob1 = openaiFineTuneData({

key: process.env.openai,

data: fineTuneExMispellings,

librarySecretEx

@stevekrouse

An interactive, runnable TypeScript val by stevekrouse

HTTP

export let librarySecretEx = gpt3({

prompt: "what is the meaning of life?",

openAiKey: process.env.openai,

weatherGPT

@liaolile

If you fork this, you'll need to set OPENAI_API_KEY in your Val Town Secrets .

Cron

If you fork this, you'll need to set `OPENAI_API_KEY` in your [Val Town Secrets](https://www.val.town/settings/secrets).

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

import { OpenAI } from "npm:openai";

let location = "shenzhen";

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

const openai = new OpenAI();

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

messages: [{

langchainEx

@jacoblee93

// Forked from @stevekrouse.langchainEx

Script

export const langchainEx = (async () => {

const { ChatOpenAI } = await import(

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

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

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

const model = new ChatOpenAI({

temperature: 0.9,

openAIApiKey: process.env.OPENAI_API_KEY,

verbose: true,

conversationalRetrievalQAChainStreamingExample

@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 streamingModel = new ChatOpenAI({

openAIApiKey: process.env.OPENAI_API_KEY,

const nonStreamingModel = new ChatOpenAI({

openAIApiKey: process.env.OPENAI_API_KEY,

new OpenAIEmbeddings({

openAIApiKey: process.env.OPENAI_API_KEY,

TokenizerDemo

@zzz

// Demo of tokenizer to mimic behavior of https://platform.openai.com/tokenizer

Script

import { Tokenizer } from "https://esm.town/v/zzz/Tokenizer";

// Demo of tokenizer to mimic behavior of https://platform.openai.com/tokenizer

// Tokenizer uses "gpt-3.5-turbo" model by default but this demo uses davinci to match the playground

export const TokenizerDemo = (async () => {

googlesearch

@lolocoo

An interactive, runnable TypeScript val by lolocoo

Express (deprecated)

dateRestrict: "m[1]",

const getSearch = async (data) => {

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

method: "GET",

body: JSON.stringify(data),

relationshipgenerator

@ejfox

// This val receives text input, sends it to OpenAI to generate relationships,

HTTP

// This val receives text input, sends it to OpenAI to generate relationships,

// and returns a newline-delimited list of relationships.

// It uses the OpenAI API to generate the relationships.

// Tradeoff: This approach relies on an external API, which may have rate limits or costs.

// curl -X POST -H "Content-Type: text/plain" -d "Your text here" https://your-val-url.web.val.run

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

const openai = new OpenAI();

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

const text = await req.text();

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

model: "gpt-3.5-turbo",

main

@henrique

An interactive, runnable TypeScript val by henrique

HTTP

export async function main(prompt: string) {

const { useCursive } = await import("npm:cursive");

const cursive = useCursive({ openAI: { apiKey: process.env.OPENAI_API_KEY } });

const res = await cursive.ask({

prompt,

openAiFreeQuotaExceeded

@patrickjm

An interactive, runnable TypeScript val by patrickjm

HTTP

import { openAiFreeUsage } from "https://esm.town/v/patrickjm/openAiFreeUsage";

export let openAiFreeQuotaExceeded = () =>

openAiFreeUsage.exceeded;

modelSampleChatGenerate

@webup

An interactive, runnable TypeScript val by webup

Script

const builder = await getModelBuilder({

type: "chat",

provider: "openai",

const model = await builder();

const { SystemMessage, HumanMessage } = await import("npm:langchain/schema");

VALLE

@oijoijcoiejoijce

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.

HTTP

* 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

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