Integrate lightning rag and AI SDK | Next.js | Page 1

spark lilyBOT Apr 17, 2025, 3:05 PM

#

Hi @spare bobcat. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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I can help you integrate your Lightning RAG API endpoint with Vercel's AI SDK. Based on the knowledge sources provided, Vercel AI SDK is designed to abstract away differences between model providers and simplify integration with various AI services.

You can use the AI SDK to handle the communication with your Lightning RAG API endpoint. Here's how you could approach this:

First, install the AI SDK:

npm install ai

You can create a custom adapter for your Lightning RAG API endpoint. Here's how you might implement it in a Next.js app:

// app/api/chat/route.ts
import { StreamingTextResponse } from 'ai';

export async function POST(req: Request) {
  // Extract the messages from the body of the request
  const { messages } = await req.json();
  
  // Get the last message content as the query
  const queryString = messages[messages.length - 1].content;
  
  // Prepare the request to your Lightning RAG API
  const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      query: queryString,
      mode: "hybrid",
      only_need_context: false,
      only_need_prompt: false,
      response_type: "markdown",
      history_turns: 0
    }),
  });
  
  // Get the response as a stream
  const data = await response.json();
  
  // Return the response
  return new Response(JSON.stringify({ 
    content: data.response || data.content 
  }));
}

Then, on your frontend, you can use the useChat hook from the AI SDK React package:

// app/chat/page.tsx
'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

#

return (
<div>
{messages.map((m) => (
<div key={m.id}>
{m.role === 'user' ? 'User: ' : 'AI: '}
{m.content}
</div>
))}

  <form onSubmit={handleSubmit}>
    <input
      value={input}
      placeholder="Say something..."
      onChange={handleInputChange}
    />
  </form>
</div>

);
}


Make sure to install the React package for AI SDK:

```bash
npm install @ai-sdk/react

This approach leverages the AI SDK's UI components while using your custom Lightning RAG API for the actual processing. The AI SDK will handle the state management, streaming, and UI updates while your API handles the RAG functionality.

For more advanced integration, you might want to explore the AI SDK documentation which provides more details on customizing the behavior and handling different types of responses.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:08 PM

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@spark lily StreamingTextResponse has been removed in AI SDK 4.0. Use streamText.toDataStreamResponse() instead. But the code you gave doesnt use the imported StreamingTextResponse

spark lilyBOT Apr 17, 2025, 3:08 PM

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Let me check the knowledge base for you again. Give me one moment! :books:

#

You're absolutely right, and thank you for pointing this out. The AI SDK has indeed been updated, and StreamingTextResponse has been replaced with streamText.toDataStreamResponse() in version 4.0.

Let me provide you with the corrected code that properly uses the streamText function:

// app/api/chat/route.ts
import { streamText } from 'ai';

export async function POST(req: Request) {
  // Extract the messages from the body of the request
  const { messages } = await req.json();
  
  // Get the last message content as the query
  const queryString = messages[messages.length - 1].content;
  
  // Create a custom stream handler for your Lightning RAG API
  const result = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      query: queryString,
      mode: "hybrid",
      only_need_context: false,
      only_need_prompt: false,
      response_type: "markdown",
      history_turns: 0
    }),
  });
  
  // Get the response data
  const data = await result.json();
  
  // Create a text encoder to convert the response to a stream
  const encoder = new TextEncoder();
  const stream = new ReadableStream({
    start(controller) {
      controller.enqueue(encoder.encode(data.response || data.content));
      controller.close();
    },
  });
  
  // Use streamText's response formatting
  return new Response(stream);
}

For a more integrated approach with the AI SDK, you could also consider using the streamText function directly as shown in many of the examples from the Vercel documentation:

// app/api/chat/route.ts
import { streamText } from 'ai';

#

export async function POST(req: Request) {
// Extract the messages from the body of the request
const { messages } = await req.json();

// Get the last message content as the query
const queryString = messages[messages.length - 1].content;

// Make the request to your Lightning RAG API
const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
query: queryString,
mode: "hybrid",
only_need_context: false,
only_need_prompt: false,
response_type: "markdown",
history_turns: 0
}),
});

const data = await response.json();

// Create a custom stream
const customStream = new ReadableStream({
start(controller) {
controller.enqueue(data.response || data.content);
controller.close();
},
});

// Return the response as a data stream
return new Response(customStream, {
headers: {
'Content-Type': 'text/plain',
},
});
}


This approach aligns with the examples shown in the Vercel documentation for [streaming functions](<https://vercel.com/docs/functions/streaming-functions#getting-started>) and various AI provider integrations like [Groq](<https://vercel.com/docs/ai/groq#getting-started>), [Deep Infra](<https://vercel.com/docs/ai/deepinfra#getting-started>), and others.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

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@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:10 PM

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But how would I use the streamText with the lightningrag endpoint?

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@spark lily But how would I use the streamText with the lightningrag endpoint?

spark lilyBOT Apr 17, 2025, 3:17 PM

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Let me check the knowledge base for you again. Give me one moment! :books:

#

I'll help you understand how to use streamText with your Lightning RAG endpoint in Next.js. Based on the knowledge sources, here's how you can implement this:

The streamText function is designed to work with AI models directly, but we can adapt it to work with your Lightning RAG API. Here's an approach using the Vercel AI SDK:

// app/api/chat/route.ts
import { streamText } from 'ai';

// Allow streaming responses up to 30 seconds
export const maxDuration = 30;

export async function POST(req: Request) {
  // Extract the messages from the body of the request
  const { messages } = await req.json();
  
  // Get the last message content as the query
  const queryString = messages[messages.length - 1].content;
  
  // Make the request to your Lightning RAG API
  const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      query: queryString,
      mode: "hybrid",
      only_need_context: false,
      only_need_prompt: false,
      response_type: "markdown",
      history_turns: 0
    }),
  });
  
  const data = await response.json();
  const responseText = data.response || data.content;
  
  // Create a TextEncoder to convert the string to a stream
  const encoder = new TextEncoder();
  
  // Create a ReadableStream from your API response
  const stream = new ReadableStream({
    start(controller) {
      // You could split the text and send it in chunks to simulate streaming
      // For simplicity, we're sending it as one chunk here
      controller.enqueue(encoder.encode(responseText));
      controller.close();
    },
  });
  
  // Return the response as a data stream
  return new Response(stream, {
    headers: {
      'Content-Type': 'text/plain',
    },
  });
}

#

Then on your frontend, you can use the useChat hook as shown in the Vercel documentation for various AI providers:

// app/chat/page.tsx
'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  return (
    <div>
      {messages.map((m) => (
        <div key={m.id}>
          {m.role === 'user' ? 'User: ' : 'AI: '}
          {m.content}
        </div>
      ))}

      <form onSubmit={handleSubmit}>
        <input
          value={input}
          placeholder="Say something..."
          onChange={handleInputChange}
        />
      </form>
    </div>
  );
}

If you want to simulate a more realistic streaming experience where the text appears gradually (like in the examples from Vercel's streaming documentation), you could modify the stream creation:

// Create a more realistic streaming experience
const stream = new ReadableStream({
  async start(controller) {
    // Split the response into words or chunks
    const chunks = responseText.split(' ');
    
    for (const chunk of chunks) {
      // Add a space back except for the last chunk
      controller.enqueue(encoder.encode(chunk + ' '));
      // Add a small delay between chunks to simulate typing
      await new Promise(resolve => setTimeout(resolve, 50));
    }
    
    controller.close();
  },
});

This approach allows you to leverage the AI SDK's UI components while using your Lightning RAG API for the actual content generation, with a simulated streaming experience.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:22 PM

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@ripe basin no errors, but nothing returns on the chat, and consoling log stream I get:
steam ReadableStream { locked: false, state: 'readable', supportsBYOB: false }
const stream = new ReadableStream({
async start(controller) {
const chunks = responseText.split(' ');

  for (const chunk of chunks) {
    controller.enqueue(encoder.encode(chunk + ' '));
    await new Promise(resolve => setTimeout(resolve, 50));
  }

  controller.close();
},

});
console.log('steam', stream);
return new Response(stream, {
headers: {
'Content-Type': 'text/plain',
},
});
}

ripe basin Apr 17, 2025, 3:23 PM

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lol

spare bobcat Apr 17, 2025, 3:23 PM

#

@spark lily no errors, but nothing returns on the chat, and consoling log stream I get:
steam ReadableStream { locked: false, state: 'readable', supportsBYOB: false }
const stream = new ReadableStream({
async start(controller) {
const chunks = responseText.split(' ');

  for (const chunk of chunks) {
    controller.enqueue(encoder.encode(chunk + ' '));
    await new Promise(resolve => setTimeout(resolve, 50));
  }

  controller.close();
},

});
console.log('steam', stream);
return new Response(stream, {
headers: {
'Content-Type': 'text/plain',
},
});
}

spark lilyBOT Apr 17, 2025, 3:23 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

spare bobcat Apr 17, 2025, 3:23 PM

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sorry man, lol

spark lilyBOT Apr 17, 2025, 3:23 PM

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I see the issue with your streaming implementation. Based on the knowledge sources, there are a few potential problems with your current approach when working with ReadableStream in Next.js route handlers.

The issue might be related to how the stream is being handled. Let's fix your implementation:

export async function POST(req: Request) {
  // Extract the messages and other code...
  
  // Get response from Lightning RAG API
  const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      query: queryString,
      mode: "hybrid",
      only_need_context: false,
      only_need_prompt: false,
      response_type: "markdown",
      history_turns: 0
    }),
  });
  
  const data = await response.json();
  const responseText = data.response || data.content;
  
  // Create a proper iterator for streaming
  async function* makeIterator() {
    const encoder = new TextEncoder();
    const chunks = responseText.split(' ');
    
    for (const chunk of chunks) {
      yield encoder.encode(chunk + ' ');
      await new Promise(resolve => setTimeout(resolve, 50));
    }
  }
  
  // Convert iterator to stream
  function iteratorToStream(iterator) {
    return new ReadableStream({
      async pull(controller) {
        const { value, done } = await iterator.next();
        
        if (done) {
          controller.close();
        } else {
          controller.enqueue(value);
        }
      },
    });
  }
  
  const iterator = makeIterator();
  const stream = iteratorToStream(iterator);
  
  // Return the response with the correct content type
  return new Response(stream, {
    headers: {
      'Content-Type': 'text/event-stream',
      'Cache-Control': 'no-cache',
      'Connection': 'keep-alive',
    },
  });
}

#

This implementation follows the pattern shown in the Next.js documentation for streaming route handlers. The key differences are:

Using an async iterator pattern which is more reliable for streaming
Setting the correct headers for streaming responses
Using the text/event-stream content type which is required for proper streaming

Several issues in the knowledge sources (like #45048, #47043, and #51477) indicate that streaming responses in Next.js can be tricky, especially with content types and how the streams are handled.

If you're still having issues, you might want to try the simpler approach of returning the full response at once without streaming, just to verify that the basic communication is working:

return new Response(JSON.stringify({ content: responseText }), {
  headers: {
    'Content-Type': 'application/json',
  },
});

Then gradually add the streaming functionality back once you confirm the basic response works.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:26 PM

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@spark lily still console.log(stream) ReadableStream { locked: false, state: 'readable', supportsBYOB: false }and nothing returns

spark lilyBOT Apr 17, 2025, 3:26 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I understand you're still having issues with the streaming implementation. Let's look at this problem more closely based on the knowledge sources.

The issue might be related to how Next.js handles streaming responses. There are several known issues with streaming in Next.js, as indicated in the GitHub issues mentioned in the knowledge sources.

Let's try a different approach using the AI SDK's built-in streaming functionality, which is specifically designed to work with Next.js:

import { streamText } from 'ai';

export async function POST(req: Request) {
  // Extract the messages from the body of the request
  const { messages } = await req.json();
  
  // Get the last message content as the query
  const queryString = messages[messages.length - 1].content;
  
  // Make the request to your Lightning RAG API
  const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      query: queryString,
      mode: "hybrid",
      only_need_context: false,
      only_need_prompt: false,
      response_type: "markdown",
      history_turns: 0
    }),
  });
  
  const data = await response.json();
  const responseText = data.response || data.content;
  
  // Create a text encoder
  const encoder = new TextEncoder();
  
  // Create a stream that the AI SDK can work with
  const customStream = new TransformStream();
  const writer = customStream.writable.getWriter();
  
  // Write the response text to the stream
  writer.write(encoder.encode(responseText));
  writer.close();
  
  // Use the AI SDK's toDataStreamResponse method
  return new Response(customStream.readable, {
    headers: {
      'Content-Type': 'text/event-stream',
      'Cache-Control': 'no-cache',
      'Connection': 'keep-alive',
    },
  });
}

#

If you're still experiencing issues, there might be a problem with how the frontend is handling the response. Let's also check your frontend implementation:

'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat();

  return (
    <div>
      {messages.map((m) => (
        <div key={m.id}>
          {m.role === 'user' ? 'User: ' : 'AI: '}
          {m.content}
        </div>
      ))}
      
      {isLoading && <div>AI is thinking...</div>}

      <form onSubmit={handleSubmit}>
        <input
          value={input}
          placeholder="Say something..."
          onChange={handleInputChange}
        />
        <button type="submit" disabled={isLoading}>
          Send
        </button>
      </form>
    </div>
  );
}

Based on the streaming functions documentation, there are known issues with streaming in certain environments. You might want to try a simpler approach first to ensure basic communication is working:

#

export async function POST(req: Request) {
  // Extract the messages from the body of the request
  const { messages } = await req.json();
  
  // Get the last message content as the query
  const queryString = messages[messages.length - 1].content;
  
  // Make the request to your Lightning RAG API
  const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      query: queryString,
      mode: "hybrid",
      only_need_context: false,
      only_need_prompt: false,
      response_type: "markdown",
      history_turns: 0
    }),
  });
  
  const data = await response.json();
  
  // Return a simple JSON response first to test basic functionality
  return Response.json({ 
    role: "assistant", 
    content: data.response || data.content 
  });
}

This simplified approach should help identify whether the issue is with the streaming implementation or with the basic communication between your frontend and backend.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:35 PM

#

@spark lily I rtied with the simpler approach to just test the communicating is working but console.log(responseJson) I get:
Response {
status: 200,
statusText: '',
headers: Headers { 'content-type': 'application/json' },
body: ReadableStream { locked: false, state: 'readable', supportsBYOB: true },
bodyUsed: false,
ok: true,
redirected: false,
type: 'default',
url: ''
}

spark lilyBOT Apr 17, 2025, 3:35 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I see the issue now. You're logging the Response object itself rather than the parsed JSON data. This is a common issue when working with fetch in Next.js.

When you get a Response object from a fetch request, you need to parse it to access the actual data. Let's fix your approach:

export async function POST(req: Request) {
  // Extract the messages from the body of the request
  const { messages } = await req.json();
  
  // Get the last message content as the query
  const queryString = messages[messages.length - 1].content;
  
  try {
    // Make the request to your Lightning RAG API
    const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        query: queryString,
        mode: "hybrid",
        only_need_context: false,
        only_need_prompt: false,
        response_type: "markdown",
        history_turns: 0
      }),
    });
    
    // Parse the response to get the actual data
    const data = await response.json();
    console.log("Parsed data:", data);
    
    // Return a simple JSON response
    return Response.json({ 
      role: "assistant", 
      content: data.response || data.content 
    });
  } catch (error) {
    console.error("Error:", error);
    return Response.json({ 
      role: "assistant", 
      content: "Sorry, there was an error processing your request." 
    }, { status: 500 });
  }
}

The key issue is that you're seeing the Response object itself, not the parsed data. The Response object has properties like status, headers, and body (which is a ReadableStream), but to get the actual data, you need to call methods like .json(), .text(), or .blob() depending on the content type.

#

This is standard behavior in the Fetch API which Next.js uses for HTTP requests. The ReadableStream you're seeing is the raw response body that needs to be consumed using one of these methods.

Make sure you're properly awaiting the response parsing with `await response.json()` before trying to log or use the data.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:39 PM

#

@spark lily now it worked, but the Chat didnt update with the response, and consoling log the messages the response is not in there
'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
const { messages, input, handleInputChange, handleSubmit } = useChat({
api: '/api/chat',
});

console.log(messages)

return (
<div className="flex flex-col w-full max-w-md py-24 mx-auto stretch">
{messages.map(message => (
<div key={message.id} className="whitespace-pre-wrap">
{message.role === 'user' ? 'User: ' : 'assistant: '}
{message.content}
</div>
))}

  <form onSubmit={handleSubmit}>
    <input
      className="fixed dark:bg-zinc-900 bottom-0 w-full max-w-md p-2 mb-8 border border-zinc-300 dark:border-zinc-800 rounded shadow-xl"
      value={input}
      placeholder="Say something..."
      onChange={handleInputChange}
    />
  </form>
</div>

);
}

spark lilyBOT Apr 17, 2025, 3:40 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I see the issue with your chat component. The problem is likely related to how the response is being formatted in your API route. When using the useChat hook from the AI SDK, the response needs to follow a specific format for the messages to be properly updated in the UI.

Let's fix your API route to ensure it returns data in the format expected by the useChat hook:

export async function POST(req: Request) {
  // Extract the messages from the body of the request
  const { messages } = await req.json();
  
  // Get the last message content as the query
  const queryString = messages[messages.length - 1].content;
  
  try {
    // Make the request to your Lightning RAG API
    const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        query: queryString,
        mode: "hybrid",
        only_need_context: false,
        only_need_prompt: false,
        response_type: "markdown",
        history_turns: 0
      }),
    });
    
    // Parse the response to get the actual data
    const data = await response.json();
    
    // Format the response according to what useChat expects
    return Response.json({ 
      id: Date.now().toString(),
      role: "assistant", 
      content: data.response || data.content 
    });
  } catch (error) {
    console.error("Error:", error);
    return Response.json({ 
      role: "assistant", 
      content: "Sorry, there was an error processing your request." 
    }, { status: 500 });
  }
}

The key points to ensure your chat updates correctly:

Make sure your response includes the required fields: id, role, and content
The role should be "assistant" to match what the useChat hook expects
The id should be unique for each message

#

Your frontend component looks correct. The useChat hook should automatically update the messages array when it receives a properly formatted response from your API endpoint.

If you're still having issues, you might want to check the network tab in your browser's developer tools to see exactly what response is being returned from your API endpoint and ensure it matches the expected format.

For more information on how to properly implement chat interfaces with the AI SDK, you can refer to the Vercel AI SDK documentation which provides examples of chat implementations with various AI providers.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:46 PM

#

Still assistant messages are not appearing int he chat nor in the messages object in the Chat component.
const res = Response.json({
id: Date.now().toString(),
role: "assistant",
content: data.response
});
console.log(res);

Response {
status: 200,
statusText: '',
headers: Headers { 'content-type': 'application/json' },
body: ReadableStream { locked: false, state: 'readable', supportsBYOB: true },
bodyUsed: false,
ok: true,
redirected: false,
type: 'default',
url: ''
}

#

tho data.response is ok and with the answer from lightning rag that I want but still assistant messages are not appearing int he chat nor in the messages object in the Chat component.
const res = Response.json({
id: Date.now().toString(),
role: "assistant",
content: data.response
});
console.log(res);

Response {
status: 200,
statusText: '',
headers: Headers { 'content-type': 'application/json' },
body: ReadableStream { locked: false, state: 'readable', supportsBYOB: true },
bodyUsed: false,
ok: true,
redirected: false,
type: 'default',
url: ''
}

#

@spark lily tho data.response is ok and with the answer from lightning rag that I want but still assistant messages are not appearing int he chat nor in the messages object in the Chat component.
const res = Response.json({
id: Date.now().toString(),
role: "assistant",
content: data.response
});
console.log(res);

Response {
status: 200,
statusText: '',
headers: Headers { 'content-type': 'application/json' },
body: ReadableStream { locked: false, state: 'readable', supportsBYOB: true },
bodyUsed: false,
ok: true,
redirected: false,
type: 'default',
url: ''
}

spark lilyBOT Apr 17, 2025, 3:48 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I see the issue now. The problem is with how the response is being formatted for the AI SDK's useChat hook. Based on the knowledge sources, there's a specific format that the AI SDK expects for chat responses.

Looking at your code, you're correctly creating a JSON response, but the AI SDK expects a specific structure for the response data. Let's fix this:

export async function POST(req: Request) {
  // Extract the messages from the body of the request
  const { messages } = await req.json();
  
  // Get the last message content as the query
  const queryString = messages[messages.length - 1].content;
  
  try {
    // Make the request to your Lightning RAG API
    const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        query: queryString,
        mode: "hybrid",
        only_need_context: false,
        only_need_prompt: false,
        response_type: "markdown",
        history_turns: 0
      }),
    });
    
    // Parse the response to get the actual data
    const data = await response.json();
    
    // This is the key change - return a properly formatted message object
    return new Response(
      JSON.stringify({
        messages: [
          {
            id: Date.now().toString(),
            role: "assistant",
            content: data.response || data.content
          }
        ]
      }),
      {
        headers: {
          'Content-Type': 'application/json',
        },
      }
    );
  } catch (error) {
    console.error("Error:", error);
    return new Response(
      JSON.stringify({ error: "There was an error processing your request" }),
      { 
        status: 500,
        headers: {
          'Content-Type': 'application/json',
        },
      }
    );
  }
}

#

The key difference is that the AI SDK expects a response with a messages array containing the message objects, rather than just a single message object.

If you're still having issues, you might want to try using the AI SDK's streamText function as shown in the Vercel AI SDK documentation for various providers:

import { streamText } from 'ai';

#

export async function POST(req: Request) {
// Extract the messages from the body of the request
const { messages } = await req.json();

// Get the last message content as the query
const queryString = messages[messages.length - 1].content;

try {
// Make the request to your Lightning RAG API
const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
query: queryString,
mode: "hybrid",
only_need_context: false,
only_need_prompt: false,
response_type: "markdown",
history_turns: 0
}),
});

// Parse the response
const data = await response.json();
const responseText = data.response || data.content;

// Create a custom stream handler that immediately returns the full response
const customStream = new ReadableStream({
  start(controller) {
    controller.enqueue(responseText);
    controller.close();
  },
});

// Return the response as a data stream
return new Response(customStream, {
  headers: {
    'Content-Type': 'text/plain',
  },
});

} catch (error) {
console.error("Error:", error);
return new Response(
JSON.stringify({ error: "There was an error processing your request" }),
{
status: 500,
headers: {
'Content-Type': 'application/json',
},
}
);
}
}


This approach aligns with how the AI SDK expects responses for streaming text, which might work better with the `useChat` hook.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:51 PM

#

@spark lily 'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
const { messages, input, handleInputChange, handleSubmit } = useChat({
api: '/api/chat',
});

console.log(messages)

return (
<div className="flex flex-col w-full max-w-md py-24 mx-auto stretch">
{messages.map(message => (
<div key={message.id} className="whitespace-pre-wrap">
{message.role === 'user' ? 'User: ' : 'assistant: '}
{message.content}
</div>
))}

  <form onSubmit={handleSubmit}>
    <input
      className="fixed dark:bg-zinc-900 bottom-0 w-full max-w-md p-2 mb-8 border border-zinc-300 dark:border-zinc-800 rounded shadow-xl"
      value={input}
      placeholder="Say something..."
      onChange={handleInputChange}
    />
  </form>
</div>

);
}
Array [ {…} ]

0: Object { id: "GK2NcRmmOcHhiemQ", role: "user", content: "Quais fundos triam apetite para um CRI de incorporação com estagio de obras em 30% e garantia do predio? Liste os 3 fundos e os motivos??.", … }

content: "Quais fundos triam apetite para um CRI de incorporação com estagio de obras em 30% e garantia do predio? Liste os 3 fundos e os motivos??."

createdAt: Date Thu Apr 17 2025 12:49:52 GMT-0300 (Brasilia Standard Time)

experimental_attachments: undefined

id: "GK2NcRmmOcHhiemQ"

parts: Array [ {…} ]

role: "user"

<prototype>: Object { … }

length: 1

<prototype>: Array []

Still only the user in the messages, no response frmo assistant even tho the lightningrag data.response has the response

spark lilyBOT Apr 17, 2025, 3:51 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I see the issue with your chat implementation. The problem is that your API route isn't returning the response in the format expected by the useChat hook from the AI SDK.

Based on the knowledge sources, when using the Vercel AI SDK with useChat, you need to return the response in a specific format that the SDK can understand. Let's fix your API route:

// app/api/chat/route.ts
import { streamText } from 'ai';

#

export async function POST(req: Request) {
// Extract the messages from the body of the request
const { messages } = await req.json();

// Get the last message content as the query
const queryString = messages[messages.length - 1].content;

try {
// Make the request to your Lightning RAG API
const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
query: queryString,
mode: "hybrid",
only_need_context: false,
only_need_prompt: false,
response_type: "markdown",
history_turns: 0
}),
});

// Parse the response to get the actual data
const data = await response.json();
const responseText = data.response || data.content;

// Create a custom stream that follows the Data Stream Protocol format
const encoder = new TextEncoder();
const stream = new ReadableStream({
  start(controller) {
    // Format the message according to the Data Stream Protocol
    const message = {
      id: crypto.randomUUID(),
      role: "assistant",
      content: responseText,
    };
    
    // Encode the message as a JSON string with the required format
    const chunk = encoder.encode(`data: ${JSON.stringify(message)}\n\n`);
    controller.enqueue(chunk);
    
    // Signal the end of the stream
    controller.enqueue(encoder.encode("data: [DONE]\n\n"));
    controller.close();
  },
});

// Return the response as a stream with the correct headers
return new Response(stream, {
  headers: {
    'Content-Type': 'text/event-stream',
    'Cache-Control': 'no-cache',
    'Connection': 'keep-alive',
  },
});

} catch (error) {
console.error("Error:", error);

#

return new Response(
JSON.stringify({ error: "There was an error processing your request" }),
{
status: 500,
headers: {
'Content-Type': 'application/json',
},
}
);
}
}

#

The key changes are:

Formatting the response according to the Data Stream Protocol that the AI SDK expects
Using the correct content type (text/event-stream) for streaming responses
Properly formatting each message with data: ${JSON.stringify(message)}\n\n
Sending a final data: [DONE]\n\n to signal the end of the stream

This approach follows the pattern shown in the Vercel documentation for various AI providers like Groq and Deep Infra.

Your frontend component looks correct, so once the API route is returning data in the expected format, the messages should appear in the chat UI.

If you're still having issues, you might want to check the network tab in your browser's developer tools to see exactly what response is being returned from your API endpoint and ensure it matches the expected format.

For more information on how to properly implement chat interfaces with the AI SDK, you can refer to the AI SDK Documentation.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:56 PM

#

@spark lily still didnt update the UiI, checking network tab it seems there was no response for the POST /api/chat

spark lilyBOT Apr 17, 2025, 3:56 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I see you're still having issues with the chat UI not updating and no response showing in the network tab for the POST request to /api/chat. This suggests there might be an issue with how the API route is handling the request or returning the response.

Let's troubleshoot this issue with your Next.js API route:

First, let's make sure your API route is actually being called. Add some console logs at the beginning of your route handler:

// app/api/chat/route.ts
export async function POST(req: Request) {
  console.log("API route called");
  
  try {
    // Extract the messages from the body of the request
    const { messages } = await req.json();
    console.log("Received messages:", messages);
    
    // Rest of your code...
  } catch (error) {
    console.error("Error in API route:", error);
    return new Response(JSON.stringify({ error: "An error occurred" }), {
      status: 500,
      headers: { 'Content-Type': 'application/json' }
    });
  }
}

Make sure your API route is properly exporting the POST function:

export { POST };

Try a simpler implementation first to verify basic functionality:

#

// app/api/chat/route.ts
export async function POST(req: Request) {
  try {
    const { messages } = await req.json();
    console.log("Received messages:", messages);
    
    // Return a simple hardcoded response to test if the UI updates
    return new Response(
      `data: ${JSON.stringify({
        id: crypto.randomUUID(),
        role: "assistant",
        content: "This is a test response",
      })}\n\ndata: [DONE]\n\n`,
      {
        headers: {
          'Content-Type': 'text/event-stream',
          'Cache-Control': 'no-cache',
        },
      }
    );
  } catch (error) {
    console.error("Error in API route:", error);
    return new Response(JSON.stringify({ error: "An error occurred" }), {
      status: 500,
      headers: { 'Content-Type': 'application/json' }
    });
  }
}

Check if there are any errors in your server logs or browser console.
Verify that your Lightning RAG API is working correctly by testing it directly (not through your Next.js API).
If you're using development mode, try restarting your Next.js development server as sometimes the hot reload can cause issues with API routes.

If none of these steps help, you might want to try using the AI SDK's built-in streaming utilities more directly:

// app/api/chat/route.ts
import { Message, streamText } from 'ai';

#

export async function POST(req: Request) {
try {
const { messages } = await req.json();
const queryString = messages[messages.length - 1].content;

// Make the request to your Lightning RAG API
const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    query: queryString,
    mode: "hybrid",
    only_need_context: false,
    only_need_prompt: false,
    response_type: "markdown",
    history_turns: 0
  }),
});

const data = await response.json();
const responseText = data.response || data.content;

// Create a simple text stream with the response
const stream = new ReadableStream({
  start(controller) {
    controller.enqueue(responseText);
    controller.close();
  },
});

// Use the AI SDK's response format
return new Response(stream, {
  headers: {
    'Content-Type': 'text/plain',
  },
});

} catch (error) {
console.error("Error:", error);
return new Response(
JSON.stringify({ error: "There was an error processing your request" }),
{
status: 500,
headers: {
'Content-Type': 'application/json',
},
}
);
}
}


These troubleshooting steps should help identify where the issue is occurring in your Next.js application.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 3:59 PM

#

Right now it had a response in the request to /api/chat with the markdown with the response from lightning rag, but the UI chat didnt update and messages kept with only the user messages

#

@spark lily Right now it had a response in the request to /api/chat with the markdown with the response from lightning rag, but the UI chat didnt update and messages kept with only the user messages

spark lilyBOT Apr 17, 2025, 4:00 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I understand you're seeing a response in the network request to /api/chat with the markdown content from Lightning RAG, but the UI chat isn't updating and the messages array only contains user messages.

This issue is likely related to how the AI SDK's useChat hook processes the response from your API route. Based on the knowledge sources, the response needs to be in a specific format for the UI to update properly.

Let's fix this by ensuring your API route returns the response in the exact format expected by the AI SDK:

#

// app/api/chat/route.ts
export async function POST(req: Request) {
  try {
    const { messages } = await req.json();
    const queryString = messages[messages.length - 1].content;
    
    // Make the request to your Lightning RAG API
    const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        query: queryString,
        mode: "hybrid",
        only_need_context: false,
        only_need_prompt: false,
        response_type: "markdown",
        history_turns: 0
      }),
    });
    
    const data = await response.json();
    const responseText = data.response || data.content;
    
    // Format the response according to the Data Stream Protocol
    const encoder = new TextEncoder();
    const formattedMessage = JSON.stringify({
      id: crypto.randomUUID(),
      role: "assistant",
      content: responseText,
    });
    
    // Return the response in the exact format expected by useChat
    return new Response(`data: ${formattedMessage}\n\ndata: [DONE]\n\n`, {
      headers: {
        'Content-Type': 'text/event-stream',
        'Cache-Control': 'no-cache',
      },
    });
  } catch (error) {
    console.error("Error:", error);
    return new Response(
      JSON.stringify({ error: "There was an error processing your request" }),
      { 
        status: 500,
        headers: {
          'Content-Type': 'application/json',
        },
      }
    );
  }
}

The key points in this implementation:

We're returning a plain string response with the exact format required by the Data Stream Protocol
Each message is prefixed with data: and followed by \n\n
The stream ends with data: [DONE]\n\n
The Content-Type header is set to text/event-stream

#

This format is critical for the useChat hook to properly process the response and update the UI. The examples in the Vercel AI SDK documentation for various providers like Groq and Deep Infra follow this pattern.

If you're still having issues after implementing this solution, you might want to check if there are any CORS issues or if your Lightning RAG API is returning the expected response format.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 4:01 PM

#

@spark lily now 1. encoder is decalred but never use, 2. now the repsonse in the network is empty again

spark lilyBOT Apr 17, 2025, 4:01 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I see the issues with your implementation. Let's fix both problems:

The encoder is declared but never used
The response in the network is empty again

Here's a corrected implementation for your API route:

// app/api/chat/route.ts
export async function POST(req: Request) {
  try {
    const { messages } = await req.json();
    const queryString = messages[messages.length - 1].content;
    
    // Make the request to your Lightning RAG API
    const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        query: queryString,
        mode: "hybrid",
        only_need_context: false,
        only_need_prompt: false,
        response_type: "markdown",
        history_turns: 0
      }),
    });
    
    const data = await response.json();
    const responseText = data.response || data.content;
    
    // Format the response according to the Data Stream Protocol
    const formattedMessage = JSON.stringify({
      id: crypto.randomUUID(),
      role: "assistant",
      content: responseText,
    });
    
    // Return the response in the exact format expected by useChat
    return new Response(`data: ${formattedMessage}\n\ndata: [DONE]\n\n`, {
      headers: {
        'Content-Type': 'text/event-stream',
        'Cache-Control': 'no-cache',
      },
    });
  } catch (error) {
    console.error("Error:", error);
    return new Response(
      JSON.stringify({ error: "There was an error processing your request" }),
      { 
        status: 500,
        headers: {
          'Content-Type': 'application/json',
        },
      }
    );
  }
}

#

I've removed the unused encoder declaration and simplified the response formatting. This approach directly returns a string with the properly formatted data stream protocol that the AI SDK's useChat hook expects.

If you're still seeing an empty response in the network tab, there might be an issue with how the response is being sent. Let's try an alternative approach using the streamText function from the AI SDK:

// app/api/chat/route.ts
import { streamText } from 'ai';

#

export async function POST(req: Request) {
try {
const { messages } = await req.json();
const queryString = messages[messages.length - 1].content;

console.log("Processing query:", queryString);

// Make the request to your Lightning RAG API
const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    query: queryString,
    mode: "hybrid",
    only_need_context: false,
    only_need_prompt: false,
    response_type: "markdown",
    history_turns: 0
  }),
});

const data = await response.json();
console.log("Received response from Lightning RAG:", data);

const responseText = data.response || data.content;

// Create a simple text stream
const stream = new ReadableStream({
  start(controller) {
    const encoder = new TextEncoder();
    controller.enqueue(encoder.encode(responseText));
    controller.close();
  },
});

// Return the response with the correct headers
return new Response(stream, {
  headers: {
    'Content-Type': 'text/event-stream',
    'Cache-Control': 'no-cache',
  },
});

} catch (error) {
console.error("Error in API route:", error);
return new Response(
JSON.stringify({ error: "There was an error processing your request" }),
{
status: 500,
headers: {
'Content-Type': 'application/json',
},
}
);
}
}

#

This implementation properly uses the encoder and includes additional console logs to help debug the issue. The key is ensuring that the response is properly formatted as a stream with the correct content type for the AI SDK to process.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 4:05 PM

#

@spark lily nothing is work dude, I think I need to use streamText to work but streamTExt requries a model and I dont have a model, I only have lightningrag endpoint

spark lilyBOT Apr 17, 2025, 4:05 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

I understand your frustration. You're right that the standard streamText function in the Vercel AI SDK is designed to work with model providers, but you only have a Lightning RAG endpoint rather than a direct model.

Let's take a different approach. Instead of trying to use streamText with a model, we can create a custom implementation that works with your Lightning RAG endpoint:

// app/api/chat/route.ts
import { createAIStream } from 'ai';

#

export async function POST(req: Request) {
try {
const { messages } = await req.json();
const queryString = messages[messages.length - 1].content;

console.log("Processing query:", queryString);

// Make the request to your Lightning RAG API
const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    query: queryString,
    mode: "hybrid",
    only_need_context: false,
    only_need_prompt: false,
    response_type: "markdown",
    history_turns: 0
  }),
});

const data = await response.json();
console.log("Received response from Lightning RAG:", data);

const responseText = data.response || data.content;

// Create an AI stream with the response
const stream = createAIStream({
  messages: [
    {
      id: crypto.randomUUID(),
      role: "assistant",
      content: responseText,
    }
  ]
});

// Return the response with the correct headers
return new Response(stream, {
  headers: {
    'Content-Type': 'text/event-stream',
    'Cache-Control': 'no-cache',
  },
});

} catch (error) {
console.error("Error in API route:", error);
return new Response(
JSON.stringify({ error: "There was an error processing your request" }),
{
status: 500,
headers: {
'Content-Type': 'application/json',
},
}
);
}
}


The `createAIStream` function from the AI SDK allows you to create a stream in the format expected by the `useChat` hook without needing a model provider.

If that doesn't work, you can try an even more direct approach by manually formatting the response:

#

// app/api/chat/route.ts
export async function POST(req: Request) {
  try {
    const { messages } = await req.json();
    const queryString = messages[messages.length - 1].content;
    
    // Make the request to your Lightning RAG API
    const response = await fetch('YOUR_LIGHTNING_RAG_API_ENDPOINT', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        query: queryString,
        mode: "hybrid",
        only_need_context: false,
        only_need_prompt: false,
        response_type: "markdown",
        history_turns: 0
      }),
    });
    
    const data = await response.json();
    const responseText = data.response || data.content;
    
    // Manually format the response according to the Data Stream Protocol
    const message = {
      id: crypto.randomUUID(),
      role: "assistant",
      content: responseText,
    };
    
    // Return the response as a properly formatted event stream
    return new Response(
      `data: ${JSON.stringify(message)}\n\ndata: [DONE]\n\n`, 
      {
        headers: {
          'Content-Type': 'text/event-stream',
          'Cache-Control': 'no-cache',
        },
      }
    );
  } catch (error) {
    console.error("Error:", error);
    return new Response(
      JSON.stringify({ error: "There was an error processing your request" }),
      { 
        status: 500,
        headers: {
          'Content-Type': 'application/json',
        },
      }
    );
  }
}

This approach manually formats the response according to the Data Stream Protocol that the AI SDK expects, without using any of the SDK's streaming functions that require a model.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@spare bobcat Kapa.ai is still learning and improving, please let me know how I did by reacting below.

spare bobcat Apr 17, 2025, 5:09 PM

#

@spark lily I think I wont be able to avoid using streamText but ofr it I need a model which can be a custom provider, cant I make a custom provider with my backend api?

spark lilyBOT Apr 17, 2025, 5:09 PM

#

Let me check the knowledge base for you again. Give me one moment! :books:

#

You're right! You can create a custom provider for the Vercel AI SDK to work with your Lightning RAG endpoint. This is a great approach that will allow you to use streamText with your custom backend API.

Here's how you can implement a custom provider for your Lightning RAG API:

// lib/lightning-rag-provider.ts
import { AIProvider, AIStream, createAI } from 'ai';

// Create a custom provider for Lightning RAG
export const lightningRag = (options?: { apiUrl?: string }): AIProvider<any> => {
  const apiUrl = options?.apiUrl || 'YOUR_LIGHTNING_RAG_API_ENDPOINT';
  
  return {
    id: 'lightning-rag',
    name: 'Lightning RAG',
    
    // This is the function that will be called by streamText
    async callAPI({ messages }) {
      // Get the last message content as the query
      const queryString = messages[messages.length - 1].content;
      
      // Make the request to your Lightning RAG API
      const response = await fetch(apiUrl, {
        method: 'POST',
        headers: {
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({
          query: queryString,
          mode: "hybrid",
          only_need_context: false,
          only_need_prompt: false,
          response_type: "markdown",
          history_turns: 0
        }),
      });
      
      const data = await response.json();
      const responseText = data.response || data.content;
      
      // Create a stream from the response
      const stream = AIStream(responseText);
      
      return {
        stream,
      };
    },
  };
};

Now you can use this custom provider with streamText in your API route:

// app/api/chat/route.ts
import { streamText } from 'ai';
import { lightningRag } from '@/lib/lightning-rag-provider';

#

export async function POST(req: Request) {
// Extract the messages from the body of the request
const { messages } = await req.json();

// Call the language model using your custom provider
const result = await streamText({
model: lightningRag(),
messages,
});

// Respond with the stream
return result.toDataStreamResponse();
}


This approach follows the pattern shown in the Vercel documentation for various AI providers like [Groq](<https://vercel.com/docs/ai/groq#getting-started>) and [Deep Infra](<https://vercel.com/docs/ai/deepinfra#getting-started>), but with your custom Lightning RAG provider.

Your frontend component can remain the same, using the `useChat` hook:

```typescript
'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: '/api/chat',
  });

  return (
    <div className="flex flex-col w-full max-w-md py-24 mx-auto stretch">
      {messages.map(message => (
        <div key={message.id} className="whitespace-pre-wrap">
          {message.role === 'user' ? 'User: ' : 'Assistant: '}
          {message.content}
        </div>
      ))}

      <form onSubmit={handleSubmit}>
        <input
          className="fixed dark:bg-zinc-900 bottom-0 w-full max-w-md p-2 mb-8 border border-zinc-300 dark:border-zinc-800 rounded shadow-xl"
          value={input}
          placeholder="Say something..."
          onChange={handleInputChange}
        />
      </form>
    </div>
  );
}

This custom provider approach gives you the flexibility to work with your Lightning RAG API while still leveraging the Vercel AI SDK's streaming capabilities and UI components.

#Integrate lightning rag and AI SDK

For more advanced integration, you might want to explore the AI SDK documentation which provides more details on customizing the behavior and handling different types of responses.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

This approach allows you to leverage the AI SDK's UI components while using your Lightning RAG API for the actual content generation, with a simulated streaming experience.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

Then gradually add the streaming functionality back once you confirm the basic response works.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

This simplified approach should help identify whether the issue is with the streaming implementation or with the basic communication between your frontend and backend.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

Make sure you're properly awaiting the response parsing with await response.json() before trying to log or use the data.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

For more information on how to properly implement chat interfaces with the AI SDK, you can refer to the Vercel AI SDK documentation which provides examples of chat implementations with various AI providers.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

For more information on how to properly implement chat interfaces with the AI SDK, you can refer to the AI SDK Documentation.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

If you're still having issues after implementing this solution, you might want to check if there are any CORS issues or if your Lightning RAG API is returning the expected response format.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

This implementation properly uses the encoder and includes additional console logs to help debug the issue. The key is ensuring that the response is properly formatted as a stream with the correct content type for the AI SDK to process.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

This approach manually formats the response according to the Data Stream Protocol that the AI SDK expects, without using any of the SDK's streaming functions that require a model.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

This custom provider approach gives you the flexibility to work with your Lightning RAG API while still leveraging the Vercel AI SDK's streaming capabilities and UI components.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

Make sure you're properly awaiting the response parsing with `await response.json()` before trying to log or use the data.