Merge branch 'main' into docs

.eslintrc.js

@@ -0,0 +1,10 @@
+module.exports = {
+  root: true,
+  // This tells ESLint to load the config from the package `eslint-config-vercel-ai`
+  extends: ['vercel-ai'],
+  settings: {
+    next: {
+      rootDir: ['apps/*/'],
+    },
+  },
+};

.github/workflows/release.yml

@@ -11,31 +11,35 @@ jobs:
   release:
     name: Release
     runs-on: ubuntu-latest
+    timeout-minutes: 10
     steps:
       - name: Checkout Repo
         uses: actions/checkout@v2
 
-      - name: Setup pnpm
-        uses: pnpm/action-setup@v2.2.2
+      - name: Setup pnpm 7
+        uses: pnpm/action-setup@v2
         with:
           version: 7
 
       - name: Setup Node.js 16.x
         uses: actions/setup-node@v2
         with:
           node-version: 16.x
-          cache: 'pnpm'
 
       - name: Install Dependencies
-        run: pnpm install
+        run: pnpm i
 
       - name: Create Release Pull Request or Publish to npm
         id: changesets
         uses: changesets/action@v1
         with:
           # This expects you to have a script called release which does a build for your packages and calls changeset publish
           publish: pnpm release
-          version: pnpm version-packages
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           NPM_TOKEN: ${{ secrets.NPM_TOKEN_ELEVATED }}
+
+      - name: Send a Slack notification if a publish happens
+        if: steps.changesets.outputs.published == 'true'
+        # You can do something when a publish happens.
+        run: my-slack-bot send-notification --message "A new version of ${GITHUB_REPOSITORY} was published!"

.gitignore

@@ -1,8 +1,13 @@
-# build
-dist/
-
-# dependencies
-node_modules/
-
-# logs
-npm-debug.log
+.DS_Store
+node_modules
+.turbo
+*.log
+.next
+dist
+dist-ssr
+*.local
+.env
+.cache
+server/dist
+public/dist
+.turbo

.npmrc

@@ -1,2 +1 @@
-save-exact = true
-strict-peer-dependencies=false
+auto-install-peers = true

README.md

@@ -46,21 +46,21 @@ The goal of this library lies in its commitment to work directly with each AI/Mo
 
 ```tsx
 // app/api/generate/route.ts
-import { Configuration, OpenAIApi } from 'openai-edge';
-import { OpenAITextStream, StreamingTextResponse } from '@vercel/ai-utils';
+import { Configuration, OpenAIApi } from "openai-edge";
+import { OpenAITextStream, StreamingTextResponse } from "@vercel/ai-utils";
 
 const config = new Configuration({
   apiKey: process.env.OPENAI_API_KEY,
 });
 const openai = new OpenAIApi(config);
 
-export const runtime = 'edge';
+export const runtime = "edge";
 
 export async function POST() {
   const response = await openai.createChatCompletion({
-    model: 'gpt-4',
+    model: "gpt-4",
     stream: true,
-    messages: [{ role: 'user', content: 'What is love?' }],
+    messages: [{ role: "user", content: "What is love?" }],
   });
   const stream = OpenAITextStream(response);
   return new StreamingTextResponse(stream);
@@ -99,11 +99,8 @@ Create a Next.js Route Handler that uses the Edge Runtime that we'll use to gene
 
 ```tsx
 // ./app/api/generate/route.ts
-import { Configuration, OpenAIApi } from 'openai-edge';
-import { 
-  OpenAITextStream, 
-  StreamingTextResponse 
-} from '@vercel/ai-utils';
+import { Configuration, OpenAIApi } from "openai-edge";
+import { OpenAITextStream, StreamingTextResponse } from "@vercel/ai-utils";
 
 // Create an OpenAI API client (that's edge friendly!)
 const config = new Configuration({
@@ -112,15 +109,15 @@ const config = new Configuration({
 const openai = new OpenAIApi(config);
 
 // IMPORTANT! Set the runtime to edge
-export const runtime = 'edge';
+export const runtime = "edge";
 
 export async function POST(req: Request) {
   // Extract the `prompt` from the body of the request
   const { prompt } = await req.json();
 
   // Ask OpenAI for a streaming chat completion given the prompt
   const response = await openai.createCompletion({
-    model: 'gpt-3.5-turbo',
+    model: "gpt-3.5-turbo",
     stream: true,
     prompt,
   });
@@ -139,21 +136,21 @@ Create a Client component with a form that we'll use to gather the prompt from t
 
 ```tsx
 // ./app/form.ts
-'use client';
+"use client";
 
-import { useState } from 'react';
-import { useCompletion } from '@vercel/ai-utils/react'; //@todo
+import { useState } from "react";
+import { useCompletion } from "@vercel/ai-utils/react"; //@todo
 
 export function Form() {
-  const [value, setValue] = useState('');
-  const { setPrompt, completion } = useCompletion('/api/generate');
+  const [value, setValue] = useState("");
+  const { setPrompt, completion } = useCompletion("/api/generate");
   return (
     <div>
       <form
         onSubmit={(e) => {
           e.preventDefault();
           setPrompt(value);
-          setValue('');
+          setValue("");
         }}
       >
         <textarea value={value} onChange={(e) => setValue(e.target.value)} />
@@ -211,16 +208,16 @@ A transform that will extract the text from _most_ chat and completion HuggingFa
 
 ```tsx
 // app/api/generate/route.ts
-import { HfInference } from '@huggingface/inference';
-import { HuggingFaceStream, StreamingTextResponse } from '@vercel/ai-utils';
+import { HfInference } from "@huggingface/inference";
+import { HuggingFaceStream, StreamingTextResponse } from "@vercel/ai-utils";
 
-export const runtime = 'edge';
+export const runtime = "edge";
 
 const Hf = new HfInference(process.env.HUGGINGFACE_API_KEY);
 
 export async function POST() {
   const response = await Hf.textGenerationStream({
-    model: 'OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
+    model: "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
     inputs: `<|prompter|>What's the Earth total population?<|endoftext|><|assistant|>`,
     parameters: {
       max_new_tokens: 200,
@@ -242,19 +239,19 @@ This is a tiny wrapper around `Response` class that makes returning `ReadableStr
 
 ```tsx
 // app/api/generate/route.ts
-import { OpenAITextStream, StreamingTextResponse } from '@vercel/ai-utils';
+import { OpenAITextStream, StreamingTextResponse } from "@vercel/ai-utils";
 
-export const runtime = 'edge';
+export const runtime = "edge";
 
 export async function POST() {
   const response = await openai.createChatCompletion({
-    model: 'gpt-4',
+    model: "gpt-4",
     stream: true,
-    messages: { role: 'user', content: 'What is love?' },
+    messages: { role: "user", content: "What is love?" },
   });
   const stream = OpenAITextStream(response);
   return new StreamingTextResponse(stream, {
-    'X-RATE-LIMIT': 'lol',
+    "X-RATE-LIMIT": "lol",
   }); // => new Response(stream, { status: 200, headers: { 'Content-Type': 'text/plain; charset=utf-8', 'X-RATE-LIMIT': 'lol' }})
 }
 ```

apps/docs/.eslintrc.js

@@ -0,0 +1,4 @@
+module.exports = {
+  root: true,
+  extends: ["vercel-ai"],
+};

apps/docs/next-env.d.ts

@@ -0,0 +1,5 @@
+/// <reference types="next" />
+/// <reference types="next/image-types/global" />
+
+// NOTE: This file should not be edited
+// see https://nextjs.org/docs/basic-features/typescript for more information.

apps/docs/next.config.js

@@ -3,4 +3,4 @@ const withNextra = require('nextra')({
   themeConfig: './theme.config.jsx'
 })
 
-module.exports = withNextra()
+module.exports = withNextra()

apps/docs/src/app/layout.tsx

@@ -0,0 +1,11 @@
+export default function RootLayout({
+  children,
+}: {
+  children: React.ReactNode;
+}) {
+  return (
+    <html lang="en">
+      <body>{children}</body>
+    </html>
+  );
+}

apps/docs/src/app/page.tsx

@@ -0,0 +1,8 @@
+export default function Page() {
+  return (
+    <>
+      <h1>Web</h1>
+      <Button>Boop</Button>
+    </>
+  );
+}

apps/docs/tsconfig.json

@@ -0,0 +1,14 @@
+{
+  "exclude": ["node_modules"],
+  "extends": "@vercel/ai-tsconfig/nextjs.json",
+  "compilerOptions": {
+    "outDir": "dist",
+    "incremental": true,
+    "plugins": [
+      {
+        "name": "next"
+      }
+    ]
+  },
+  "include": ["src", "next-env.d.ts", ".next/types/**/*.ts"]
+}