prettier: enforce semi colons, add trailingComma (vercel#529)

Author: MaxLeiter

.changeset/popular-badgers-applaud.md

@@ -2,4 +2,4 @@
 'ai': patch
 ---
 
-ai/react: fix: handle partial chunks in react getStreamedResponse
+ai/react: fix:  handle partial chunks in react getStreamedResponse when using experimental_StreamData

.eslintrc.js

@@ -4,7 +4,7 @@ module.exports = {
   extends: ['vercel-ai'],
   settings: {
     next: {
-      rootDir: ['apps/*/']
-    }
-  }
-}
+      rootDir: ['apps/*/'],
+    },
+  },
+};

docs/pages/docs/api-reference/ai-stream.mdx

@@ -2,7 +2,7 @@
 title: AIStream
 ---
 
-import { OptionTable } from '@/components/table'
+import { OptionTable } from '@/components/table';
 
 # AIStream
 
@@ -32,7 +32,7 @@ This is an optional parameter which is an object that contains callback function
 
 ```tsx
 interface AIStreamParser {
-  (data: string): string | void
+  (data: string): string | void;
 }
 ```
 
@@ -45,23 +45,23 @@ This is an object that contains the following properties:
     [
       'onStart',
       '() => Promise<void>',
-      'An optional function that is called at the start of the stream processing.'
+      'An optional function that is called at the start of the stream processing.',
     ],
     [
       'onCompletion',
       '(completion: string) => Promise<void>',
-      "An optional function that is called for every completion. It's passed the completion as a string."
+      "An optional function that is called for every completion. It's passed the completion as a string.",
     ],
     [
       'onFinal',
       '(completion: string) => Promise<void>',
-      "An optional function that is called once for every request. It's passed the completion as a string. Differs from onCompletion when function calls are present."
+      "An optional function that is called once for every request. It's passed the completion as a string. Differs from onCompletion when function calls are present.",
     ],
     [
       'onToken',
       '(token: string) => Promise<void>',
-      "An optional function that is called for each token in the stream. It's passed the token as a string."
-    ]
+      "An optional function that is called for each token in the stream. It's passed the token as a string.",
+    ],
   ]}
 />
 

docs/pages/docs/api-reference/anthropic-stream.mdx

@@ -23,33 +23,33 @@ The `AnthropicStream` function can be coupled with a `fetch` call to the Anthrop
 Here's a step-by-step example of how to implement this in Next.js:
 
 ```js filename="app/api/completion/route.ts"
-import { AnthropicStream, StreamingTextResponse } from 'ai'
+import { AnthropicStream, StreamingTextResponse } from 'ai';
 
-export const runtime = 'edge'
+export const runtime = 'edge';
 
 export async function POST(req: Request) {
-  const { prompt } = await req.json()
+  const { prompt } = await req.json();
 
   const response = await fetch('https://api.anthropic.com/v1/complete', {
     method: 'POST',
     headers: {
       'Content-Type': 'application/json',
-      'x-api-key': process.env.ANTHROPIC_API_KEY
+      'x-api-key': process.env.ANTHROPIC_API_KEY,
     },
     body: JSON.stringify({
       prompt,
       model: 'claude-v1',
       max_tokens_to_sample: 300,
       temperature: 0.9,
-      stream: true
-    })
-  })
+      stream: true,
+    }),
+  });
 
   // Convert the response into a friendly text-stream
-  const stream = AnthropicStream(response)
+  const stream = AnthropicStream(response);
 
   // Respond with the stream
-  return new StreamingTextResponse(stream)
+  return new StreamingTextResponse(stream);
 }
 ```
 

docs/pages/docs/api-reference/huggingface-stream.mdx

@@ -37,15 +37,15 @@ The `HuggingFaceStream` function can be coupled with the Hugging Face Inference
 Here's a step-by-step example of how to implement `HuggingFaceStream`:
 
 ```tsx filename="app/api/completion/route.ts"
-import { HfInference } from '@huggingface/inference'
-import { HuggingFaceStream, StreamingTextResponse } from 'ai'
+import { HfInference } from '@huggingface/inference';
+import { HuggingFaceStream, StreamingTextResponse } from 'ai';
 
-export const runtime = 'edge'
+export const runtime = 'edge';
 
-const Hf = new HfInference(process.env.HUGGINGFACE_API_KEY)
+const Hf = new HfInference(process.env.HUGGINGFACE_API_KEY);
 
 export async function POST(req: Request) {
-  const { prompt } = await req.json()
+  const { prompt } = await req.json();
 
   // Initialize a text generation stream using Hugging Face Inference SDK
   const iter = await Hf.textGenerationStream({
@@ -55,15 +55,15 @@ export async function POST(req: Request) {
       max_new_tokens: 200,
       temperature: 0.5,
       repetition_penalty: 1,
-      return_full_text: false
-    }
-  })
+      return_full_text: false,
+    },
+  });
 
   // Convert the async generator into a readable stream
-  const stream = HuggingFaceStream(iter)
+  const stream = HuggingFaceStream(iter);
 
   // Return a StreamingTextResponse, enabling the client to consume the response
-  return new StreamingTextResponse(stream)
+  return new StreamingTextResponse(stream);
 }
 ```
 

docs/pages/docs/api-reference/langchain-stream.mdx

@@ -29,33 +29,33 @@ The `LangChainCallbacks` object has properties which are compatible with [LangCh
 ## Example
 
 ```tsx filename="app/api/chat/route.ts"
-import { StreamingTextResponse, LangChainStream } from 'ai'
-import { ChatOpenAI } from 'langchain/chat_models/openai'
-import { AIMessage, HumanMessage } from 'langchain/schema'
+import { StreamingTextResponse, LangChainStream } from 'ai';
+import { ChatOpenAI } from 'langchain/chat_models/openai';
+import { AIMessage, HumanMessage } from 'langchain/schema';
 
-export const runtime = 'edge'
+export const runtime = 'edge';
 
 export async function POST(req: Request) {
-  const { messages } = await req.json()
-  const { stream, handlers, writer } = LangChainStream()
+  const { messages } = await req.json();
+  const { stream, handlers, writer } = LangChainStream();
 
   const llm = new ChatOpenAI({
-    streaming: true
-  })
+    streaming: true,
+  });
 
   llm
     .call(
       messages.map(m =>
         m.role == 'user'
           ? new HumanMessage(m.content)
-          : new AIMessage(m.content)
+          : new AIMessage(m.content),
       ),
       {},
-      [handlers]
+      [handlers],
     )
-    .catch(console.error)
+    .catch(console.error);
 
-  return new StreamingTextResponse(stream)
+  return new StreamingTextResponse(stream);
 }
 ```
 

docs/pages/docs/api-reference/openai-stream.mdx

@@ -30,58 +30,58 @@ Below are some examples of how to use `OpenAIStream` with chat and completion mo
 ### Chat Model Example
 
 ```tsx
-import OpenAI from 'openai'
-import { OpenAIStream, StreamingTextResponse } from 'ai'
+import OpenAI from 'openai';
+import { OpenAIStream, StreamingTextResponse } from 'ai';
 
 const openai = new OpenAI({
-  apiKey: process.env.OPENAI_API_KEY
-})
+  apiKey: process.env.OPENAI_API_KEY,
+});
 
-export const runtime = 'edge'
+export const runtime = 'edge';
 
 export async function POST(req: Request) {
-  const { messages } = await req.json()
+  const { messages } = await req.json();
   // Create a chat completion using OpenAI
   const response = await openai.chat.completions.create({
     model: 'gpt-4',
     stream: true,
-    messages
-  })
+    messages,
+  });
 
   // Transform the response into a readable stream
-  const stream = OpenAIStream(response)
+  const stream = OpenAIStream(response);
 
   // Return a StreamingTextResponse, which can be consumed by the client
-  return new StreamingTextResponse(stream)
+  return new StreamingTextResponse(stream);
 }
 ```
 
 ### Completion Model Example
 
 ```tsx
-import OpenAI from 'openai'
-import { OpenAIStream, StreamingTextResponse } from 'ai'
+import OpenAI from 'openai';
+import { OpenAIStream, StreamingTextResponse } from 'ai';
 
 const openai = new OpenAI({
-  apiKey: process.env.OPENAI_API_KEY
-})
+  apiKey: process.env.OPENAI_API_KEY,
+});
 
-export const runtime = 'edge'
+export const runtime = 'edge';
 
 export async function POST(req: Request) {
-  const { prompt } = await req.json()
+  const { prompt } = await req.json();
   // Create a completion using OpenAI
   const response = await openai.completions.create({
     model: 'text-davinci-003',
     stream: true,
-    prompt
-  })
+    prompt,
+  });
 
   // Transform the response into a readable stream
-  const stream = OpenAIStream(response)
+  const stream = OpenAIStream(response);
 
   // Return a StreamingTextResponse, which can be consumed by the client
-  return new StreamingTextResponse(stream)
+  return new StreamingTextResponse(stream);
 }
 ```
 

docs/pages/docs/api-reference/prompts.mdx

@@ -2,7 +2,7 @@
 title: ai/prompts
 ---
 
-import { Callout } from 'nextra-theme-docs'
+import { Callout } from 'nextra-theme-docs';
 
 # `ai/prompts`
 
@@ -19,39 +19,39 @@ The `ai/prompts` module contains functions to assist with converting `Message`'s
 Uses `<|prompter|>`, `<|endoftext|>`, and `<|assistant>` tokens. If a `Message` with an unsupported `role` is passed, an error will be thrown.
 
 ```ts filename="route.ts" {6}
-import { experimental_buildOpenAssistantPrompt } from 'ai/prompts'
+import { experimental_buildOpenAssistantPrompt } from 'ai/prompts';
 
-const { messages } = await req.json()
+const { messages } = await req.json();
 const response = Hf.textGenerationStream({
   model: 'OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
-  inputs: experimental_buildOpenAssistantPrompt(messages)
-})
+  inputs: experimental_buildOpenAssistantPrompt(messages),
+});
 ```
 
 ## `experimental_buildStarChatBetaPrompt`
 
 Uses `<|user|>`, `<|end|>`, `<|system|>`, and `<|assistant>` tokens. If a `Message` with an unsupported `role` is passed, an error will be thrown.
 
 ```ts filename="route.ts" {6}
-import { experimental_buildStarChatBetaPrompt } from 'ai/prompts'
+import { experimental_buildStarChatBetaPrompt } from 'ai/prompts';
 
-const { messages } = await req.json()
+const { messages } = await req.json();
 const response = Hf.textGenerationStream({
   model: 'HuggingFaceH4/starchat-beta',
-  inputs: experimental_buildStarChatBetaPrompt(messages)
-})
+  inputs: experimental_buildStarChatBetaPrompt(messages),
+});
 ```
 
 ## `experimental_buildLlama2Prompt`
 
 Uses LLama 2 chat tokens (`[INST]`) to create a prompt, learn more in the [Hugging Face Blog on how to prompt Llama 2](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). If a `Message` with an unsupported `role` is passed, an error will be thrown.
 
 ```ts filename="route.ts" {6}
-import { experimental_buildLlama2Prompt } from 'ai/prompts'
+import { experimental_buildLlama2Prompt } from 'ai/prompts';
 
-const { messages } = await req.json()
+const { messages } = await req.json();
 const response = Hf.textGenerationStream({
   model: 'meta-llama/Llama-2-7b-chat-hf',
-  inputs: experimental_buildLlama2Prompt(messages)
-})
+  inputs: experimental_buildLlama2Prompt(messages),
+});
 ```