Building an AI Customer Support Agent with AIThreads

In this tutorial, we'll build a complete AI-powered customer support agent that can handle email inquiries autonomously, using AIThreads for email infrastructure and OpenAI for response generation.

What We're Building

Our support agent will:

• Receive customer emails via webhooks
• Search a knowledge base for relevant documentation
• Generate contextual responses using GPT-4
• Send replies maintaining proper threading

Prerequisites

You'll need an AIThreads account (free tier works), an OpenAI API key, and a server to receive webhooks. We'll use Node.js for this example.

Step 1: Set Up Your Inbox

const inbox = await aithreads.inboxes.create({
  name: 'support',
  display_name: 'Customer Support',
  webhook_url: 'https://your-server.com/webhook'
});

Step 2: Upload Your Knowledge Base

await aithreads.documents.upload({
  inbox_id: inbox.id,
  file: fs.createReadStream('./product-docs.pdf'),
  name: 'Product Documentation'
});

Step 3: Handle Incoming Emails

app.post('/webhook', async (req, res) => {
  const { thread_id, from, subject, text_body } = req.body;
  
  // Search knowledge base for context
  const context = await aithreads.documents.search({
    inbox_id: inbox.id,
    query: text_body,
    limit: 3
  });
  
  // Generate response with OpenAI
  const response = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [
      { role: 'system', content: `You are a helpful support agent. Use this context: ${context}` },
      { role: 'user', content: text_body }
    ]
  });
  
  // Send reply
  await aithreads.messages.send({
    inbox_id: inbox.id,
    thread_id: thread_id,
    to: [{ email: from }],
    subject: `Re: ${subject}`,
    text_body: response.choices[0].message.content
  });
  
  res.sendStatus(200);
});

Next Steps

This is a basic implementation. For production, you'll want to add error handling, escalation to human agents for complex issues, and rate limiting. Check out our documentation for advanced patterns.