ChatGPT Python model training guide: adding new common sense to chatbots

ChatGPT Python model training guide: adding new common sense to the chatbot requires specific code examples

Introduction: With the rapid development of artificial intelligence technology, chatbots have Become an integral part of our lives. However, existing chatbots often lack common sense and logic, and cannot understand some basic common sense and common scenarios. This article will introduce how to add new common sense to the chatbot by using the ChatGPT Python model, and give specific code examples.

1. Environment configuration
   Before we begin, we need to configure the appropriate development environment. Here are some necessary steps:
2. Installing Python: Make sure Python is installed on your machine. It is recommended to use Python 3.x version.

3. Install ChatGPT: Use the pip command to install OpenAI’s ChatGPT library. Open a command line window and run the following command:

   pip install openai

4. Configure API key: Create an account on the official OpenAI website and obtain the API key. Set the API key as an environment variable, or specify it directly in code.

5. Create a ChatGPT instance
   Next, we will create a ChatGPT instance that will be used to interact with our chatbot. The code example is as follows:

   import openai
   
   openai.api_key = 'YOUR_API_KEY'
   
   response = openai.Completion.create(
     engine="text-davinci-003",
     prompt="你好，我是你的聊天机器人。请问有什么可以帮助您的吗？",
     max_tokens=50,
     temperature=0.7,
     n=1,
     stop=None
   )
   
   print(response.choices[0].text.strip())

   In the code, we first authenticate using the API key. Then, we call the Completion.create() method to interact with the ChatGPT model. We pass the prompt text to the model as the prompt parameter to specify the chatbot’s initial question. max_tokensThe parameter is used to control the maximum output length generated by the model. The temperature parameter adjusts the diversity of generated text.

6. Add common sense
   In order to add common sense to the chatbot, we can train the model by providing some examples of common questions and answers. The following is a simple example:

   import openai
   
   openai.api_key = 'YOUR_API_KEY'
   
   examples = [
    ["你知道今天是星期几吗？", "是的，今天是星期三。"],
    ["请问北京是中国的首都吗？", "是的，北京是中国的首都。"],
    ["世界上最高的山是什么？", "珠穆朗玛峰是世界上最高的山。"]
   ]
   
   completion = openai.Completion.create(
    engine="text-davinci-003",
    prompt_examples=examples,
    temperature=0.7,
    max_tokens=50
   )
   
   print(completion.choices[0].text.strip())

   In this example, we provide several common questions and corresponding answers as training samples. The model will learn some basic common sense based on these examples. We then call the Completion.create() method to interact with the model, passing the training examples to the model via the prompt_examples parameter.

7. Further optimization
   In order to further improve the common sense level of the chatbot, we can use the following methods:
8. Provide more training samples to cover a wider range of common questions and answers .
9. Adjust the temperature parameters of the model to control the diversity of generated text.
10. Iterative training, repeatedly adjusting the model, and continuously improving the performance of the model based on feedback.

Summary: This article introduces how to use the ChatGPT Python model to add new common sense to the chatbot, and provides specific code examples. By providing training samples to the model, we can make the chatbot better understand and answer some basic common sense questions. Readers can adjust and optimize the model according to their own needs and scenarios.

Reference link:

• OpenAI official documentation: https://openai.com/docs/
• OpenAI ChatGPT GitHub library: https://github.com /openai/openai-python

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