How do I add a chatbot to my app with an AI API?

To add a chatbot to your app with an AI API, you usually need 4 pieces:

1. A UI for chatting
2. A backend that calls the AI API
3. Conversation state so the bot remembers context
4. Safety + limits so your app stays reliable and secure

Basic architecture

Frontend (web/mobile app)

• User types a message
• Your app sends it to your backend

Backend

• Receives the message
• Adds conversation history if needed
• Calls the AI API
• Returns the assistant’s reply

AI API

• Generates the response

---

Typical flow

1. User sends: “Can you help me reset my password?”
2. Your frontend POSTs that message to your server
3. Your server sends it to the AI API with:
   • a system instruction like “You are a helpful support bot”
   • recent chat history
   • the user’s latest message
4. API returns a response
5. Your server sends the response back to the app
6. UI displays it in the chat window

---

Minimal implementation steps

1) Create a chat UI

A simple interface needs:

• message list
• text input
• send button
• loading state

2) Set up a backend endpoint

Example endpoint:

• POST /api/chat

Request body:

{
  "conversationId": "abc123",
  "message": "Hello"
}

3) Call the AI API from your backend

Keep your API key on the server, not in the frontend.

4) Store conversation history

You can store messages in:

• your database
• Redis
• session storage

This helps the bot maintain context.

5) Return the reply

Your backend sends back:

{
  "reply": "Hi! How can I help you today?"
}

---

Example backend idea in Node.js

import express from "express";

const app = express();
app.use(express.json());

app.post("/api/chat", async (req, res) => {
  const { message } = req.body;

  const response = await fetch("https://api.example-ai.com/chat", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${process.env.AI_API_KEY}`
    },
    body: JSON.stringify({
      model: "chat-model",
      messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: message }
      ]
    })
  });

  const data = await response.json();
  res.json({ reply: data.output });
});

app.listen(3000);

---

Important best practices

Keep the API key secret

• Never put it in client-side code
• Use environment variables

Limit context size

• Don’t send the entire chat forever
• Send the most recent messages or summarize older ones

Handle errors

• API timeouts
• rate limits
• invalid requests
• network issues

Add moderation/safety

Depending on your app, you may want:

• content filtering
• spam detection
• user reporting
• human handoff for support bots

Stream responses for better UX

Instead of waiting for the full answer, stream tokens as they’re generated for a faster feel.

---

If you want memory

You have two common options:

Short-term memory

Send the last N messages to the AI on each request.

Long-term memory

Store user profile info or key facts in a database, then inject only relevant details into the prompt.

---

If you want the bot to use your app data

Use tool/function calling or RAG:

• Tool calling: bot can request actions like “look up order status”
• RAG: retrieve relevant documents from your knowledge base and include them in the prompt

---

Recommended simple starter stack

• Frontend: React / Next.js / Flutter
• Backend: Node.js / Python / Go
• Storage: PostgreSQL / Redis
• AI API: any chat/completion API with server-side calls

---

If you want, I can give you:

1. a full working example in JavaScript/Node, Python, or React, or
2. a version tailored to web, iOS, Android, or Flutter.