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3export async function getModelBuilder(spec: {
4  type?: "llm" | "chat" | "embedding";
5  provider?: "openai" | "huggingface";
6} = { type: "llm", provider: "openai" }, options?: any) {
7  const { extend, cond, matches, invoke } = await import("npm:lodash-es");
8  // Set up LangSmith tracer
17  // Set up API key for each providers
18  const args = extend({ callbacks }, options);
19  if (spec?.provider === "openai")
20    args.openAIApiKey = process.env.OPENAI;
21  else if (spec?.provider === "huggingface")
22    args.apiKey = process.env.HUGGINGFACE;
24  const setup = cond([
25    [
26      matches({ type: "llm", provider: "openai" }),
27      async () => {
28        const { OpenAI } = await import("npm:langchain/llms/openai");
29        return new OpenAI(args);
30      },
31    ],
32    [
33      matches({ type: "chat", provider: "openai" }),
34      async () => {
35        const { ChatOpenAI } = await import("npm:langchain/chat_models/openai");
36        return new ChatOpenAI(args);
37      },
38    ],
39    [
40      matches({ type: "embedding", provider: "openai" }),
41      async () => {
42        const { OpenAIEmbeddings } = await import(
43          "npm:langchain/embeddings/openai"
44        );
45        return new OpenAIEmbeddings(args);
46      },
47    ],

3export async function getModelBuilder(spec: {
4  type?: "llm" | "chat" | "embedding";
5  provider?: "openai" | "huggingface";
6} = { type: "llm", provider: "openai" }, options?: any) {
7  const { extend, cond, matches, invoke } = await import("npm:lodash-es");
8  // Set up LangSmith tracer
17  // Set up API key for each providers
18  const args = extend({ callbacks }, options);
19  if (spec?.provider === "openai")
20    args.openAIApiKey = process.env.OPENAI;
21  else if (spec?.provider === "huggingface")
22    args.apiKey = process.env.HUGGINGFACE;
24  const setup = cond([
25    [
26      matches({ type: "llm", provider: "openai" }),
27      async () => {
28        const { OpenAI } = await import("npm:langchain/llms/openai");
29        return new OpenAI(args);
30      },
31    ],
32    [
33      matches({ type: "chat", provider: "openai" }),
34      async () => {
35        const { ChatOpenAI } = await import("npm:langchain/chat_models/openai");
36        return new ChatOpenAI(args);
37      },
38    ],
39    [
40      matches({ type: "embedding", provider: "openai" }),
41      async () => {
42        const { OpenAIEmbeddings } = await import(
43          "npm:langchain/embeddings/openai"
44        );
45        return new OpenAIEmbeddings(args);
46      },
47    ],

19import { generateOpenGraphTags, OpenGraphData } from "https://esm.town/v/dthyresson/generateOpenGraphTags";
20import { ValTownLink } from "https://esm.town/v/dthyresson/viewOnValTownComponent";
21import { chat } from "https://esm.town/v/stevekrouse/openai";
22import * as fal from "npm:@fal-ai/serverless-client";
23

155    },
156    {
157      url: "https://api.groq.com/openai/v1/models",
158      token: Deno.env.get("GROQ_API_KEY"),
159    },
298    if (provider === "groq") {
299      url.host = "api.groq.com";
300      url.pathname = url.pathname.replace("/api/v1", "/openai/v1");
301      url.port = "443";
302      url.protocol = "https";

14Supports: checkbox, date, multi_select, number, rich_text, select, status, title, url, email
15
16- Uses `NOTION_API_KEY`, `OPENAI_API_KEY` stored in env variables and uses [Valtown blob storage](https://esm.town/v/std/blob) to store information about the database.
17- 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).

3import Instructor from "npm:@instructor-ai/instructor";
4import { Client } from "npm:@notionhq/client";
5import OpenAI from "npm:openai";
6import { z } from "npm:zod";
7
26};
27
28const oai = new OpenAI({
29  apiKey: process.env.OPENAI_API_KEY ?? undefined,
30});
31

133  if (request.method === "POST" && new URL(request.url).pathname === "/convert") {
134    try {
135      const { OpenAI } = await import("https://esm.town/v/std/openai");
136      const { blob } = await import("https://esm.town/v/std/blob");
137      const openai = new OpenAI();
138
139      const body = await request.json();
192
193        try {
194          const completion = await openai.chat.completions.create({
195            messages: [
196              {

1# OpenAI Structured Output Demo
2
3Ensure responses follow JSON Schema for structured outputs
5The following demo uses zod to describe and parse the response to JSON.
6
7Learn more in the [OpenAI Structured outputs
8 docs](https://platform.openai.com/docs/guides/structured-outputs/introduction).
9
10
11Migrated from folder: Archive/openai_structured_output_demo

27  try {
28    const imageURL = (await textToImageDalle(
29      process.env.openai,
30      text,
31      1,

1# Telegram DALLE Bot
2
3A personal telegram bot you can message to create images with OpenAI's [DALLE](https://openai.com/dall-e-2) ✨
4
5![DALLE: A Macintosh II sitting on a desk, painted by Picasso in his blue period.](https://i.imgur.com/uJrP5mE.png)