Step by Step Guide to Building a Landbot API Integration in Python

Introduction

Hey there, fellow developer! Ready to supercharge your chatbot game with Landbot's API? You're in the right place. We're going to walk through building a Landbot API integration in Python, and trust me, it's going to be a breeze.

Prerequisites

Before we dive in, make sure you've got:

• A Python environment (3.6+ recommended)
• The requests library (pip install requests)
• Your Landbot API credentials (if you don't have them, grab them from your Landbot dashboard)

Got all that? Great! Let's get our hands dirty.

Setting up the API Connection

First things first, let's import our modules and set up our API client:

import requests
import json

API_KEY = 'your_api_key_here'
BASE_URL = 'https://api.landbot.io/v1/'

headers = {
    'Authorization': f'Token {API_KEY}',
    'Content-Type': 'application/json'
}

Easy peasy, right? Now we're ready to make some API calls!

Basic API Operations

Let's start with some basic operations. Here's how you can get info about your bot:

def get_bot_info(bot_id):
    response = requests.get(f'{BASE_URL}bots/{bot_id}/', headers=headers)
    return response.json()

Creating a new bot? No sweat:

def create_bot(name, template_id):
    data = {
        'name': name,
        'template': template_id
    }
    response = requests.post(f'{BASE_URL}bots/', headers=headers, data=json.dumps(data))
    return response.json()

Working with Conversations

Now, let's get chatty! Here's how you can fetch conversation data:

def get_conversation(conversation_id):
    response = requests.get(f'{BASE_URL}conversations/{conversation_id}/', headers=headers)
    return response.json()

Sending a message is just as simple:

def send_message(conversation_id, message):
    data = {
        'message': message
    }
    response = requests.post(f'{BASE_URL}conversations/{conversation_id}/messages/', headers=headers, data=json.dumps(data))
    return response.json()

Advanced Features

Ready to level up? Let's play with custom fields:

def set_custom_field(customer_id, field_name, value):
    data = {
        'customer': customer_id,
        'key': field_name,
        'value': value
    }
    response = requests.post(f'{BASE_URL}customers/fields/', headers=headers, data=json.dumps(data))
    return response.json()

Error Handling and Best Practices

Always be prepared! Here's a simple way to handle rate limits:

import time

def api_call_with_retry(func, *args, max_retries=3, **kwargs):
    for attempt in range(max_retries):
        try:
            return func(*args, **kwargs)
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 429 and attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                raise

Testing and Debugging

Don't forget to test! Here's a quick unit test example:

import unittest
from unittest.mock import patch

class TestLandbotAPI(unittest.TestCase):
    @patch('requests.get')
    def test_get_bot_info(self, mock_get):
        mock_get.return_value.json.return_value = {'id': '123', 'name': 'Test Bot'}
        result = get_bot_info('123')
        self.assertEqual(result['name'], 'Test Bot')

if __name__ == '__main__':
    unittest.main()

Deployment Considerations

When you're ready to deploy, remember to:

• Use environment variables for API keys
• Implement proper logging
• Consider using async operations for better performance

Conclusion

And there you have it! You're now equipped to build some seriously cool stuff with the Landbot API. Remember, this is just scratching the surface - there's so much more you can do. Don't be afraid to experiment and push the boundaries.

Happy coding, and may your bots be ever chatty!