I Built an LLM-Powered AI Chatbot in 10 Minutes — Here’s How You Can Too!

Introduction

I recently built my first LLM-powered chatbot using the OpenAI API, and it was a truly rewarding experience! In this blog, I’ll walk you through how I set it up, wrote the code, and made the chatbot interactive.

Getting Started

To begin, I set up Python and installed the OpenAI library:

1. Install Python and OpenAI Library:

pip install openai

2. Get an API Key: I signed up on OpenAI’s platform and generated my API key.

Building the ChatbotThe basic idea was to create a chatbot that could hold a conversation with context. Here’s the essential code I used:

import openai
import os

# Set the API key
openai.api_key = os.getenv("OPENAI_API_KEY")

conversation_history = [
    {"role": "system", "content": "You are a helpful assistant."}
]

def chat_with_openai(prompt):
    conversation_history.append({"role": "user", "content": prompt})
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=conversation_history
    )
    answer = response.choices[0].message['content'].strip()
    conversation_history.append({"role": "assistant", "content": answer})
    return answer

print("Chatbot ready! Type 'exit' to end.")
while True:
    user_input = input("You: ")
    if user_input.lower() in ["exit", "quit"]:
        print("Goodbye!")
        break
    print(f"Assistant: {chat_with_openai(user_input)}")

For the full code and more details, check out the GitHub repository.

Here’s a quick example of how the chatbot can assist with a simple task, such as providing a recipe for making a cup of tea:

1. Conversation Handling: It stores the conversation history to maintain context, allowing for meaningful replies.

2. Chat Loop: The chatbot runs in a loop, responding to user input until “exit” is typed.

   
   

Enhancements

I made a few improvements:

• Conversation Context: The chatbot remembers previous messages to maintain a natural flow.

• Custom Behavior: By tweaking the initial system message, I experimented with different chatbot personalities.

  
  

Lessons Learned

• Ease of Integration: The OpenAI API is straightforward and powerful, making it ideal for quick AI projects.

• Context Matters: Maintaining conversation history is crucial for coherent responses.

• Error Handling: Adding basic error checks helps ensure a smooth experience.

  
  

Future Plans

I’m planning to:

• Add a GUI: Develop a user-friendly interface using Streamlit to make the chatbot more accessible and visually appealing.

• Prompt System Role Changes: Experiment with different system roles (e.g., “math tutor” or “travel guide”) for more specialized and flexible interactions.

  
  

Conclusion

Building this LLM powered chatbot was a fantastic introduction to AI development. It’s an accessible project for beginners, and I’m excited to keep improving it!

Call to Action

Give it a try and share your experiences. If you’re new to AI, this project is a great way to start!