Search

The app is set up so you can easily have a conversation between two people. The app will translate between the two selected languages, in each voice, as the speakers talk.

Add your OpenAI API Key, and make sure to open in a separate window for Mic to work.

import { html } from "npm:hono@3/html";

import { OpenAI } from "npm:openai";

const app = new Hono();

const openai = new OpenAI(Deno.env.get("OPENAI_API_KEY_VOICE"));

class TranscriptionService {

try {

const transcription = await openai.audio.transcriptions.create({

file: audioFile,

} catch (error) {

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

throw error;

try {

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

model: "gpt-3.5-turbo",

} catch (error) {

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

return c.text("Error occurred during translation", 500);

try {

const mp3 = await openai.audio.speech.create({

model: "tts-1",

} catch (error) {

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

return c.text("Error occurred during speech generation", 500);

* 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-variables).

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

import { extractValInfo } from "https://esm.town/v/pomdtr/extractValInfo?v=29";

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

const openai = new OpenAI();

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

messages: [

import process from "node:process";

export const elevenlabsTTS = async (req, res) => {

// https://platform.openai.com/docs/api-reference/images/create

// https://ale_annini-elevenlabstts.express.val.run/?args=[%22{\%22text\%22:\%22it%20beautiful\%22}%22]

const payload = {

export let kindness = async () => {

return await gpt3({

openAiKey: process.env.OPENAI_API_KEY,

prompt:

"Speaking as universal consciousness, say something short, true, uplifting, loving, and kind.",

Use the Vercel AI SDK in your Vals.

**Note**: you must add your OpenAI key to your Val Town [Env variables](https://www.val.town/settings/environment-variables) under `OPENAI_API_KEY`. If you would like to specify a different name for your API Key, you can [create a custom OpenAI provider](https://sdk.vercel.ai/providers/ai-sdk-providers/openai#provider-instance) with the `createOpenAI` function.

Prefer another AI provider? Use [any supported provider](https://sdk.vercel.ai/providers/ai-sdk-providers) by changing just two lines of code!

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

import { StreamingTextResponse, streamText } from "npm:ai";

const result = await streamText({

model: openai("gpt-4o"),

prompt: "Generate a fast recipe for Lasagna.",

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 = "manipal";

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

const openai = new OpenAI();

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

messages: [{

import _ from "npm:lodash";

import OpenAI from "npm:openai";

const openai = new OpenAI();

const queryEmbedding = (await openai.embeddings.create({

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

console.log(queryEmbedding.slice(0, 4));

const embedding = await openai.embeddings.create({

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

console.log("Hash is the same, no email sent.", { dynamiclandWebsiteHash });

const queryEmbeddingVal = (await openai.embeddings.create({

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

options = {},

// Initialize OpenAI API stub

const { Configuration, OpenAIApi } = await import(

"https://esm.sh/openai@3.3.0"

console.log(process.env);

// const configuration = new Configuration({

// apiKey: process.env.OPENAI,

// const openai = new OpenAIApi(configuration);

// // Request chat completion

// : prompt;

// const { data } = await openai.createChatCompletion({

// model: "gpt-3.5-turbo-0613",

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

const openai = new OpenAI();

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

"messages": [

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

export interface WriterOptions extends ClientOptions {

model?: string;

- Get all blobs with embeddings pointed to by the metadata, e.g. `allValsBlob1536EmbeddingsData_0` for `batchDataIndex` 0.

- Call OpenAI to generate an embedding for the search query.

- Go through all embeddings and compute cosine similarity with the embedding for the search query.

import _ from "npm:lodash";

import OpenAI from "npm:openai";

const dimensions = 1536;

await Promise.all(allBatchDataIndexesPromises);

const openai = new OpenAI();

const queryEmbedding = (await openai.embeddings.create({

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

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 = "san francisco ca";

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

const openai = new OpenAI();

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

messages: [{

export const streamingTest = (async () => {

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

// To enable streaming, we pass in `streaming: true` to the LLM constructor.

// Additionally, we pass in a handler for the `handleLLMNewToken` event.

const chat = new OpenAI({

maxTokens: 25,

streaming: true,

openAIApiKey: process.env.OPENAI_API_KEY,

const response = await chat.call("Tell me a joke.", undefined, [

Stream OpenAI API

Use the Vercel AI SDK in your Vals.

**Note**: you must add your OpenAI key to your Val Town [Env variables](https://www.val.town/settings/environment-variables) under `OPENAI_API_KEY`. If you would like to specify a different name for your API Key, you can [create a custom OpenAI provider](https://sdk.vercel.ai/providers/ai-sdk-providers/openai#provider-instance) with the `createOpenAI` function.

Prefer another AI provider? Use [any supported provider](https://sdk.vercel.ai/providers/ai-sdk-providers) by changing just two lines of code!

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

import { StreamingTextResponse, streamText } from "npm:ai";

const result = await streamText({

model: openai("gpt-4o"),

prompt: "Generate a fast recipe for Lasagna.",

We write openAI usage data to a `openai_usage` sqlite table. This script val is imported into the openai proxy. Use this val to run administrative scripts: https://www.val.town/v/std/OpenAIUsageScript

FROM

openai_usage,

params

model: string;

export class OpenAIUsage {

constructor() {}

async migrate() {

await sqlite.batch([`CREATE TABLE IF NOT EXISTS openai_usage (

id INTEGER PRIMARY KEY,

async drop() {

await sqlite.batch([`DROP TABLE IF EXISTS openai_usage`]);

async writeUsage(ur: UsageRow) {

sqlite.execute({

sql: "INSERT INTO openai_usage (user_id, handle, tier, tokens, model) VALUES (?, ?, ?, ?, ?)",

args: [ur.userId, ur.handle, ur.tier, ur.tokens, ur.model],

let resp = await sqlite.execute({

sql: `select count(*) from openai_usage where model LIKE 'gpt-4%' and model NOT LIKE '%mini%' and user_id = ? and timestamp > ?`,

args: [userId, now.toISOString()],