Building Your First AI Chatbot with Python: Step-by-Step Tutorial

pip install openai python-dotenv flask

OPENAI_API_KEY=your_api_key_here

import openai
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Set up OpenAI API key
openai.api_key = os.getenv('OPENAI_API_KEY')

class SimpleChatbot:
    def __init__(self):
        self.conversation_history = []

    def get_response(self, user_input):
        """Get response from OpenAI GPT model"""
        try:
            # Add user input to conversation history
            self.conversation_history.append({"role": "user", "content": user_input})

            # Get response from OpenAI
            response = openai.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=self.conversation_history,
                max_tokens=150,
                temperature=0.7
            )

            # Extract the assistant's response
            assistant_response = response.choices[0].message.content

            # Add assistant response to conversation history
            self.conversation_history.append({"role": "assistant", "content": assistant_response})

            return assistant_response

        except Exception as e:
            return f"Sorry, I encountered an error: {str(e)}"

# Test the chatbot
if __name__ == "__main__":
    chatbot = SimpleChatbot()

    print("Chatbot: Hello! I'm your AI assistant. Type 'quit' to exit.")

    while True:
        user_input = input("You: ")

        if user_input.lower() == 'quit':
            print("Chatbot: Goodbye!")
            break

        response = chatbot.get_response(user_input)
        print(f"Chatbot: {response}")

class BusinessChatbot:
    def __init__(self, business_name, business_info):
        self.business_name = business_name
        self.business_info = business_info
        self.conversation_history = [
            {
                "role": "system",
                "content": f"""You are a helpful customer service assistant for {business_name}.

Business Information:
{business_info}

Guidelines:
- Be friendly and professional
- Provide accurate information about the business
- If you don't know something, offer to connect them with a human
- Keep responses concise but helpful
- Always try to be solution-oriented"""
            }
        ]

    def get_response(self, user_input):
        """Get contextual response for business inquiries"""
        try:
            self.conversation_history.append({"role": "user", "content": user_input})

            response = openai.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=self.conversation_history,
                max_tokens=200,
                temperature=0.7
            )

            assistant_response = response.choices[0].message.content
            self.conversation_history.append({"role": "assistant", "content": assistant_response})

            return assistant_response

        except Exception as e:
            return "I'm sorry, I'm having technical difficulties. Please try again or contact our support team."

python chatbot.py

def manage_conversation_memory(self, max_messages=10):
    """Keep conversation history within limits"""
    if len(self.conversation_history) > max_messages:
        # Keep system message and recent messages
        system_msg = self.conversation_history[0]
        recent_messages = self.conversation_history[-(max_messages-1):]
        self.conversation_history = [system_msg] + recent_messages

def detect_intent(self, user_input):
    """Basic intent detection"""
    user_input = user_input.lower()

    if any(word in user_input for word in ['price', 'cost', 'how much']):
        return 'pricing'
    elif any(word in user_input for word in ['contact', 'phone', 'email']):
        return 'contact'
    elif any(word in user_input for word in ['service', 'what do you do']):
        return 'services'
    else:
        return 'general'