Building a Custom Chatbot with OpenAI’s Language Model

Welcome to a step-by-step guide on leveraging the power of OpenAI’s ChatGPT to craft your very own chatbot. Whether you desire an AI customer service agent or a pizza order taker, we’ve got you covered!

1. Introduction

ChatGPT, powered by OpenAI’s expansive language model, allows seamless interaction and conversation. Its multi-turn conversation feature is not just designed for chats but is equally potent for single-turn tasks.

2. Understanding the OpenAI Chat Completions Format

• Basic Principle: ChatGPT is trained to intake a series of messages and output a model-generated message.
• Roles in a Chat:
  • User Messages: The queries or statements we present to ChatGPT.
  • Assistant Messages: Responses from ChatGPT.
  • System Messages: These define the chatbot’s persona and behavior. It’s like whispering instructions to the chatbot without the user knowing.

3. Crafting a Conversation

To make your chatbot interactive:

1. Define a system message to set the tone and behavior.
2. Alternate between user and assistant messages to keep the conversation flowing.

Example:

messages = [
                           {“role”: “system”, “content”: “You are an assistant that speaks like Shakespeare.”},
                           {“role”: “user”, “content”: “Tell me a joke.”}
                       ]

First, ensure you have the OpenAI Python package installed.

pip install openai

4. Building ‘OrderBot’ – Your Pizza Ordering Chatbot

Crafting Helper Functions

A. collect_and_get_response Function

This function manages the interaction between the user and the chatbot. It captures user inputs, appends them to a context list, and fetches model-generated responses.

def collect_and_get_response(context, user_message):
context.append({“role”: “user”, “content”: user_message})

# Generate a model response here
# For simplicity, let’s assume there’s a function named get_model_response
assistant_response = get_model_response(context)

context.append({“role”: “assistant”, “content”: assistant_response})
return assistant_response

B. Initializing and Running the Chat Interface

Kick things off by setting a system message to define the bot’s role, followed by subsequent user-assistant interactions.

Define your initial system message and interact using the collect_and_get_response function.

Example:

context = [
{“role”: “system”, “content”: “You are OrderBot, a pizza ordering assistant. The menu includes…”}
]

user_message = input(“You: “)
response = collect_and_get_response(context, user_message)
print(f”OrderBot: {response}”)

C. Generating an Order Summary

After collecting order details, use a system message to guide the bot to summarize the order in a structured format, like JSON.

def summarize_order(context):
summary_instruction = {
“role”: “system”,
“content”: “Create a JSON summary of the previous food order. Itemize the price for each item…”
}
context.append(summary_instruction)

# Generate a model summary here using a function like get_model_response
order_summary = get_model_response(context)

return order_summary

5. Fine-tuning Your Chatbot

Temperature Settings:

• Higher temperature (e.g., 0.8): Makes the output more random. Ideal for casual interactions.
• Lower temperature (e.g., 0.2): Makes the output predictable. Best for specific tasks or formal interactions.

6. Conclusion

There you have it! A custom chatbot tailored for your pizza restaurant. The possibilities are endless. Adjust the system message, play around with different roles, and customize it to fit any scenario.

Notes:

• You would require an OpenAI API key and potentially a function like get_model_response that interacts with the API. For the sake of brevity, this wasn’t covered in this overview.
• Remember to handle exceptions and edge cases for a robust implementation.