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

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Release: 2023-10-26 09:03:32
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ChatGPT Python插件开发指南:提升聊天交互的功能

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

    openai.api_key = 'YOUR_API_KEY'
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  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()
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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)
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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
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  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)
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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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