Simple Implementation of an AI Robot

You can easily implement an AI robot through the following steps. Here, we take the example of creating a simple Q&A AI robot based on Python and the OpenAI API.

1. Preparation

• Register an OpenAI account: Visit the OpenAI official website (https://openai.com/) to register an account and obtain an API key, which is the credential for calling the OpenAI model.
• Install necessary libraries: Use Python’s <span>openai</span> library, which can be installed using the following command:

pip install openai

2. Write the Code

Below is a simple example of Python code:

import openai

# Set the API key
openai.api_key = "your_api_key"

def ask_ai(question):
    try:
        # Call OpenAI's ChatCompletion API
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are a friendly AI assistant capable of answering various questions."},
                {"role": "user", "content": question}
            ]
        )
        # Extract the model's reply
        answer = response.choices[0].message.content
        return answer
    except Exception as e:
        print(f"An error occurred: {e}")
        return None

# Main program
while True:
    question = input("Please enter your question (type 'exit' to end the conversation): ")
    if question == "exit":
        break
    answer = ask_ai(question)
    if answer:
        print("AI response:", answer)

3. Code Explanation

• Set the API key: Replace <span>your_api_key</span> with the actual API key you obtained from OpenAI.
• Define the <span>ask_ai</span> function: This function takes the user’s question as input and calls OpenAI’s <span>ChatCompletion.create</span> method to send a request to the model. The <span>model</span> specifies which model to use, here we use <span>gpt-3.5-turbo</span>. The <span>messages</span> list contains system messages and user messages, where the system message sets the AI’s role and behavior, and the user message is the question posed by the user.
• Main program: An infinite loop continuously receives the user’s questions. When the user types “exit”, the program ends. Otherwise, it calls the <span>ask_ai</span> function to get the AI’s answer and print it.

Other Implementation Methods

If you do not want to use the OpenAI API, you can also use some open-source large models, such as those available on Hugging Face. However, using open-source models usually requires more technical knowledge, including downloading, deploying, and fine-tuning the models. Below is a simple example using the <span>transformers</span> library from Hugging Face:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer
model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

while True:
    question = input("Please enter your question (type 'exit' to end the conversation): ")
    if question == "exit":
        break
    # Encode the input
    input_ids = tokenizer.encode(question, return_tensors="pt")
    # Generate the answer
    output = model.generate(input_ids, max_length=100, num_return_sequences=1)
    # Decode the output
    answer = tokenizer.decode(output[0], skip_special_tokens=True)
    print("AI response:", answer)

This example uses the GPT-2 model, loaded and used via the <span>transformers</span> library. However, the performance of open-source models may not match that of commercial models and may require certain computational resources.

For example: Just like building a simple robot assistant, the preparation work is like gathering materials, and the API key is like the key to open the material warehouse. Writing the code is like following certain steps to assemble the materials, ultimately forming a small assistant that can communicate with you. When you input a question, it is like giving a task to the assistant, which will process it based on the materials and steps provided and give an answer.

^^Source: The official documentation of OpenAI (https://platform.openai.com/docs/) and the official documentation of Hugging Face (https://huggingface.co/docs/transformers/index) provide detailed instructions on using the API and loading models.^^