update to use @fern-api/ai

Author: dubwub

content/cookbook/01-next/10-generate-text.mdx

@@ -58,7 +58,7 @@ export default function Page() {
 Let's create a route handler for `/api/completion` that will generate text based on the input prompt. The route will call the `generateText` function from the `ai` module, which will then generate text based on the input prompt and return it.
 
 ```typescript filename='app/api/completion/route.ts'
-import { generateText } from 'ai';
+import { generateText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 export async function POST(req: Request) {

content/cookbook/01-next/11-generate-text-with-chat-prompt.mdx

@@ -31,7 +31,7 @@ Let's start by creating a simple chat interface with an input field that sends t
 ```tsx filename='app/page.tsx'
 'use client';
 
-import { ModelMessage } from 'ai';
+import { ModelMessage } from '@fern-api/ai';
 import { useState } from 'react';
 
 export default function Page() {
@@ -90,7 +90,7 @@ export default function Page() {
 Next, let's create the `/api/chat` endpoint that generates the assistant's response based on the conversation history.
 
 ```typescript filename='app/api/chat/route.ts'
-import { ModelMessage, generateText } from 'ai';
+import { ModelMessage, generateText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 export async function POST(req: Request) {

content/cookbook/01-next/12-generate-image-with-chat-prompt.mdx

@@ -14,7 +14,7 @@ Let's create an endpoint at `/api/chat` that generates the assistant's response
 
 ```typescript filename='app/api/chat/route.ts'
 import { openai } from '@ai-sdk/openai';
-import { experimental_generateImage, Message, streamText, tool } from 'ai';
+import { experimental_generateImage, Message, streamText, tool } from '@fern-api/ai';
 import { z } from 'zod';
 
 export async function POST(request: Request) {

content/cookbook/01-next/122-caching-middleware.mdx

@@ -69,7 +69,7 @@ import {
   type LanguageModelV2Middleware,
   type LanguageModelV1StreamPart,
   simulateReadableStream,
-} from 'ai';
+} from '@fern-api/ai';
 
 const redis = new Redis({
   url: process.env.KV_URL,
@@ -164,7 +164,7 @@ Finally, you will create an API route for `api/chat` to handle the assistant's m
 ```tsx filename='app/api/chat/route.ts'
 import { cacheMiddleware } from '@/ai/middleware';
 import { openai } from '@ai-sdk/openai';
-import { wrapLanguageModel, streamText, tool } from 'ai';
+import { wrapLanguageModel, streamText, tool } from '@fern-api/ai';
 import { z } from 'zod';
 
 const wrappedModel = wrapLanguageModel({

content/cookbook/01-next/20-stream-text.mdx

@@ -47,7 +47,7 @@ export default function Page() {
 Let's create a route handler for `/api/completion` that will generate text based on the input prompt. The route will call the `streamText` function from the `ai` module, which will then generate text based on the input prompt and stream it to the client.
 
 ```typescript filename='app/api/completion/route.ts'
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 export async function POST(req: Request) {

content/cookbook/01-next/21-stream-text-with-chat-prompt.mdx

@@ -62,7 +62,7 @@ export default function Page() {
 Next, let's create the `/api/chat` endpoint that generates the assistant's response based on the conversation history.
 
 ```typescript filename='app/api/chat/route.ts'
-import { streamText, UIMessage } from 'ai';
+import { streamText, UIMessage } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 export async function POST(req: Request) {

content/cookbook/01-next/22-stream-text-with-image-prompt.mdx

@@ -17,7 +17,7 @@ The last message is the user's message, and we add the image URL to it.
 
 ```tsx filename='app/api/chat/route.ts' highlight="8,9,23"
 import { openai } from '@ai-sdk/openai';
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 
 export const maxDuration = 60;
 

content/cookbook/01-next/23-chat-with-pdf.mdx

@@ -23,7 +23,7 @@ Create a route handler that will use Anthropic's Claude model to process message
 
 ```tsx filename="app/api/chat/route.ts"
 import { anthropic } from '@ai-sdk/anthropic';
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 
 export const maxDuration = 30;
 

content/cookbook/01-next/24-stream-text-multistep.mdx

@@ -17,7 +17,7 @@ allowing you to have different steps in a single assistant UI message.
 
 ```typescript filename='app/api/chat/route.ts'
 import { openai } from '@ai-sdk/openai';
-import { createDataStreamResponse, streamText, tool } from 'ai';
+import { createDataStreamResponse, streamText, tool } from '@fern-api/ai';
 import { z } from 'zod';
 
 export async function POST(req: Request) {

content/cookbook/01-next/25-markdown-chatbot-with-memoization.mdx

@@ -18,7 +18,7 @@ On the server, you use a simple route handler that streams the response from the
 
 ```tsx filename='app/api/chat/route.ts'
 import { openai } from '@ai-sdk/openai';
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 
 export const maxDuration = 60;
 

content/cookbook/01-next/30-generate-object.mdx

@@ -82,7 +82,7 @@ export default function Page() {
 Let's create a route handler for `/api/completion` that will generate an object based on the input prompt. The route will call the `generateObject` function from the `ai` module, which will then generate an object based on the input prompt and return it.
 
 ```typescript filename='app/api/completion/route.ts'
-import { generateObject } from 'ai';
+import { generateObject } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 import { z } from 'zod';
 

content/cookbook/01-next/31-generate-object-with-file-prompt.mdx

@@ -67,7 +67,7 @@ export default function Page() {
 On the server, create an API route that receives the PDF, sends it to the LLM, and returns the result. This example uses the [ `generateObject` ](/docs/reference/ai-sdk-core/generate-object) function to generate the summary as part of a structured output.
 
 ```typescript file="app/api/analyze/route.ts"
-import { generateObject } from 'ai';
+import { generateObject } from '@fern-api/ai';
 import { anthropic } from '@ai-sdk/anthropic';
 import { z } from 'zod';
 

content/cookbook/01-next/40-stream-object.mdx

@@ -102,7 +102,7 @@ On the server, we use [`streamObject`](/docs/reference/ai-sdk-core/stream-object
 
 ```typescript filename='app/api/use-object/route.ts'
 import { openai } from '@ai-sdk/openai';
-import { streamObject } from 'ai';
+import { streamObject } from '@fern-api/ai';
 import { notificationSchema } from './schema';
 
 // Allow streaming responses up to 30 seconds
@@ -237,7 +237,7 @@ On the server, specify `output: 'array'` to generate an array of objects.
 
 ```typescript filename='app/api/use-object/route.ts'
 import { openai } from '@ai-sdk/openai';
-import { streamObject } from 'ai';
+import { streamObject } from '@fern-api/ai';
 import { notificationSchema } from './schema';
 
 export const maxDuration = 30;
@@ -312,7 +312,7 @@ On the server, specify `output: 'no-schema'`.
 
 ```typescript filename='app/api/use-object/route.ts'
 import { openai } from '@ai-sdk/openai';
-import { streamObject } from 'ai';
+import { streamObject } from '@fern-api/ai';
 import { notificationSchema } from './schema';
 
 export const maxDuration = 30;

content/cookbook/01-next/70-call-tools.mdx

@@ -74,7 +74,7 @@ You will use the [`tools`](/docs/reference/ai-sdk-core/generate-text#tools) para
 You will also use zod to specify the schema for the `celsiusToFahrenheit` function's parameters.
 
 ```tsx filename='app/api/chat/route.ts'
-import { ToolInvocation, streamText } from 'ai';
+import { ToolInvocation, streamText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 import { z } from 'zod';
 

content/cookbook/01-next/71-call-tools-in-parallel.mdx

@@ -74,7 +74,7 @@ You will use the [`tools`](/docs/reference/ai-sdk-core/generate-text#tools) para
 You will add the `getWeather` function and use zod to specify the schema for its parameters.
 
 ```ts filename='app/api/chat/route.ts'
-import { ToolInvocation, streamText } from 'ai';
+import { ToolInvocation, streamText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 import { z } from 'zod';
 

content/cookbook/01-next/72-call-tools-multiple-steps.mdx

@@ -56,7 +56,7 @@ You will use the [`tools`](/docs/reference/ai-sdk-core/generate-text#tools) para
 You will add the two functions mentioned earlier and use zod to specify the schema for its parameters.
 
 ```ts filename='app/api/chat/route.ts'
-import { ToolInvocation, streamText } from 'ai';
+import { ToolInvocation, streamText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 import { z } from 'zod';
 

content/cookbook/01-next/73-mcp-tools.mdx

@@ -17,7 +17,7 @@ To use the `StreamableHTTPClientTransport`, you will need to install the officia
 <Snippet text="pnpm install @modelcontextprotocol/sdk" />
 
 ```ts filename="app/api/completion/route.ts"
-import { experimental_createMCPClient, streamText } from 'ai';
+import { experimental_createMCPClient, streamText } from '@fern-api/ai';
 import { Experimental_StdioMCPTransport } from 'ai/mcp-stdio';
 import { openai } from '@ai-sdk/openai';
 import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp';

content/cookbook/01-next/75-human-in-the-loop.mdx

@@ -55,7 +55,7 @@ On the backend, create a route handler (API Route) that returns a `DataStreamRes
 
 ```ts filename="api/chat/route.ts"
 import { openai } from '@ai-sdk/openai';
-import { createDataStreamResponse, streamText, tool } from 'ai';
+import { createDataStreamResponse, streamText, tool } from '@fern-api/ai';
 import { z } from 'zod';
 
 export async function POST(req: Request) {
@@ -110,7 +110,7 @@ To implement HITL functionality, you start by omitting the `execute` function fr
 
 ```ts filename="api/chat/route.ts" highlight="17"
 import { openai } from '@ai-sdk/openai';
-import { createDataStreamResponse, streamText, tool } from 'ai';
+import { createDataStreamResponse, streamText, tool } from '@fern-api/ai';
 import { z } from 'zod';
 
 export async function POST(req: Request) {
@@ -238,7 +238,7 @@ import {
   Message,
   streamText,
   tool,
-} from 'ai';
+} from '@fern-api/ai';
 import { z } from 'zod';
 
 export async function POST(req: Request) {
@@ -340,7 +340,7 @@ import {
   DataStreamWriter,
   ToolExecutionOptions,
   ToolSet,
-} from 'ai';
+} from '@fern-api/ai';
 import { z } from 'zod';
 
 // Approval string to be shared across frontend and backend
@@ -477,7 +477,7 @@ Finally, you declare a function called `getToolsRequiringConfirmation` that take
 In order to use these utility functions, you will need to move tool declarations to their own file:
 
 ```ts filename="tools.ts"
-import { tool } from 'ai';
+import { tool } from '@fern-api/ai';
 import { z } from 'zod';
 
 const getWeatherInformation = tool({
@@ -510,7 +510,7 @@ Update your route handler to use the `processToolCalls` utility function.
 
 ```ts filename="app/api/chat/route.ts"
 import { openai } from '@ai-sdk/openai';
-import { createDataStreamResponse, Message, streamText } from 'ai';
+import { createDataStreamResponse, Message, streamText } from '@fern-api/ai';
 import { processToolCalls } from './utils';
 import { tools } from './tools';
 

content/cookbook/01-next/80-send-custom-body-from-use-chat.mdx

@@ -66,7 +66,7 @@ The rest of the message history can be loaded from storage.
 
 ```tsx filename='app/api/chat/route.ts' highlight="8,9,23"
 import { openai } from '@ai-sdk/openai'
-import { streamText } from 'ai'
+import { streamText } from '@fern-api/ai'
 
 // Allow streaming responses up to 30 seconds
 export const maxDuration = 30

content/cookbook/01-next/90-render-visual-interface-in-chat.mdx

@@ -44,7 +44,7 @@ Let's build an assistant that gets the weather for any city by calling the `getW
 ```tsx filename='app/page.tsx'
 'use client';
 
-import { ToolInvocation } from 'ai';
+import { ToolInvocation } from '@fern-api/ai';
 import { Message, useChat } from '@ai-sdk/react';
 
 export default function Chat() {
@@ -186,7 +186,7 @@ export default function Chat() {
 
 ```tsx filename='api/chat.ts'
 import { openai } from '@ai-sdk/openai';
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 import { z } from 'zod';
 
 export default async function POST(request: Request) {

content/cookbook/05-node/10-generate-text.mdx

@@ -11,7 +11,7 @@ For example, you may want to generate a response to a question or summarize a bo
 The `generateText` function can be used to generate text based on the input prompt.
 
 ```ts file='index.ts'
-import { generateText } from 'ai';
+import { generateText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 const result = await generateText({

content/cookbook/05-node/100-retrieval-augmented-generation.mdx

@@ -16,7 +16,7 @@ import fs from 'fs';
 import path from 'path';
 import dotenv from 'dotenv';
 import { openai } from '@ai-sdk/openai';
-import { cosineSimilarity, embed, embedMany, generateText } from 'ai';
+import { cosineSimilarity, embed, embedMany, generateText } from '@fern-api/ai';
 
 dotenv.config();
 

content/cookbook/05-node/11-generate-text-with-chat-prompt.mdx

@@ -11,7 +11,7 @@ Previously, we were able to generate text and objects using either a single mess
 A chat completion allows you to generate text based on a series of messages. This series of messages can be any series of interactions between any number of systems, but the most popular and relatable use case has been a series of messages that represent a conversation between a user and a model.
 
 ```ts file='index.ts'
-import { generateText } from 'ai';
+import { generateText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 const result = await generateText({

content/cookbook/05-node/12-generate-text-with-image-prompt.mdx

@@ -11,7 +11,7 @@ Some language models that support vision capabilities accept images as part of t
 ## URL
 
 ```ts file='index.ts'
-import { generateText } from 'ai';
+import { generateText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 const result = await generateText({
@@ -42,7 +42,7 @@ console.log(result);
 ## File Buffer
 
 ```ts file='index.ts'
-import { generateText } from 'ai';
+import { generateText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 import fs from 'fs';
 

content/cookbook/05-node/20-stream-text.mdx

@@ -26,7 +26,7 @@ Overall, Joyful Hearts Day is a day to celebrate love, kindness, and positivity,
 ## Without reader
 
 ```ts file='index.ts'
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 const result = streamText({
@@ -45,7 +45,7 @@ for await (const textPart of result.textStream) {
 ## With reader
 
 ```ts file='index.ts'
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 const result = streamText({

content/cookbook/05-node/21-stream-text-with-chat-prompt.mdx

@@ -12,7 +12,7 @@ This allows the client to display the new message as it is being generated by th
 rather than have users wait for it to finish.
 
 ```ts file='index.ts'
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 
 const result = streamText({

content/cookbook/05-node/22-stream-text-with-image-prompt.mdx

@@ -10,7 +10,7 @@ Vision-language models can analyze images alongside text prompts to generate res
 
 ```ts
 import { anthropic } from '@ai-sdk/anthropic';
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 import 'dotenv/config';
 import fs from 'node:fs';
 

content/cookbook/05-node/23-stream-text-with-file-prompt.mdx

@@ -10,7 +10,7 @@ Working with files in AI applications often requires analyzing documents, proces
 
 ```ts
 import { anthropic } from '@ai-sdk/anthropic';
-import { streamText } from 'ai';
+import { streamText } from '@fern-api/ai';
 import 'dotenv/config';
 import fs from 'node:fs';
 

content/cookbook/05-node/30-generate-object-reasoning.mdx

@@ -14,7 +14,7 @@ One solution is to pass the output from a reasoning model through a smaller mode
 ```ts
 import { deepseek } from '@ai-sdk/deepseek';
 import { openai } from '@ai-sdk/openai';
-import { generateObject, generateText } from 'ai';
+import { generateObject, generateText } from '@fern-api/ai';
 import 'dotenv/config';
 import { z } from 'zod';
 

content/cookbook/05-node/30-generate-object.mdx

@@ -11,7 +11,7 @@ Earlier functions like `generateText` and `streamText` gave us the ability to ge
 The function requires you to provide a schema using [zod](https://zod.dev), a library for defining schemas for JavaScript objects. By using zod, you can also use it to validate the generated object and ensure that it conforms to the specified structure.
 
 ```ts file='index.ts'
-import { generateObject } from 'ai';
+import { generateObject } from '@fern-api/ai';
 import { openai } from '@ai-sdk/openai';
 import { z } from 'zod';
 

content/cookbook/05-node/40-stream-object.mdx

@@ -15,7 +15,7 @@ You can use the [`streamObject`](/docs/reference/ai-sdk-core/stream-object) func
 
 ```ts file='index.ts'
 import { openai } from '@ai-sdk/openai';
-import { streamObject } from 'ai';
+import { streamObject } from '@fern-api/ai';
 import { z } from 'zod';
 
 const { partialObjectStream } = streamObject({