ChatGPT Python Plug-in Development Guide: Improving Chat Interaction Functions

ChatGPT Python plug-in development guide: To improve the function of chat interaction, specific code examples are required

Introduction:
ChatGPT is a powerful chat robot launched by OpenAI Model can realize human-computer dialogue interaction. In order to further enhance the functionality of ChatGPT, the OpenAI team allows developers to customize plug-ins to enhance the interactive capabilities of the chatbot. This article will introduce how to develop a Python plug-in for ChatGPT and provide some specific code examples.

1. Plug-in development preparations

1. Install ChatGPT: First, make sure you have installed OpenAI’s ChatGPT library. You can install the latest version by using the pip command:

   pip install openai

2. OpenAI account and API key: Before using the ChatGPT API, you need to register an account on the OpenAI official website and obtain an API key for Certification.

2. Create the ChatGPT plug-in

1. Import the required modules:
   First, import the necessary modules to develop the ChatGPT plug-in.

   import openai
   import json

2. Initialize ChatGPT:
   Next, initialize the ChatGPT model using the API key.

   openai.api_key = 'YOUR_API_KEY'

3. Define plug-in function:
   Create a function to extend the functionality of ChatGPT. This function receives the text entered by the user, calls the ChatGPT model, and returns the bot's reply.

   def chat_with_plugin(input_text):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=input_text,
        max_tokens=100,
        temperature=0.7
    )
    return response.choices[0].text.strip()

3. Test the ChatGPT plug-in
You can now use the plug-in function defined above for testing. The following is a simple example:

user_input = "你好，我是一个开发者"
bot_response = chat_with_plugin(user_input)
print(bot_response)

4. Further development of the plug-in - using context

1. Update the plug-in function:
   In order to enhance the conversation coherence of ChatGPT, you can Use contextual information as input. Here is an example of a modified plug-in function:

   def chat_with_plugin(input_text, context=None):
    if context:
        prompt = f"{context}
   User: {input_text}"
    else:
        prompt = input_text
   
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100,
        temperature=0.7
    )
   
    if context:
        response_text = response.choices[0].text.strip()
        bot_response = response_text[len(context):].strip()
    else:
        bot_response = response.choices[0].text.strip()
   
    return bot_response

2. Testing the plug-in function with context:
   Now, you can use the context information for testing. The following is an example:

   context = "早上打了一场激烈的篮球比赛"
   user_input = "我感觉累得不行"
   bot_response = chat_with_plugin(user_input, context)
   print(bot_response)

Conclusion:
Through custom plug-ins, we can further extend the functionality of ChatGPT and provide more intelligent and personalized chatbot interactions. This article describes how to develop a Python plug-in for ChatGPT and provides some specific code examples for reference. Developers can further try different plug-in functions and optimizations on this basis. I wish you develop more excellent ChatGPT plug-ins!

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