ChatGPT Python plug-in development guide: adding natural language understanding capabilities

ChatGPT Python plug-in development guide: to increase the function of natural language understanding, specific code examples are required

Introduction:
ChatGPT is a powerful natural language generation model. But it has a shortcoming, that is, it lacks the ability to understand natural language. In this article, we will share a guide to developing a Python plugin for ChatGPT to add natural language understanding capabilities. We'll explore how to achieve this using code examples.

Step One: Install the ChatGPT Python Library
First, we need to install OpenAI’s ChatGPT Python library in order to use it in our project. You can use the following command to install:

pip install openai

Step 2: Prepare training data
In order for ChatGPT to have the ability to understand natural language, we need to provide it with sufficient training data. This training data should be annotated so that our models can learn how to understand and answer different types of questions.

An example might look like this:

[
  {
    "input": "天气预报",
    "output": "今天的天气晴朗，温度在25°C左右。"
  },
  {
    "input": "最近有什么好电影推荐吗",
    "output": "《触不可及》是一部非常好的法国电影。"
  },
  ...
]

Step 3: Train the natural language understanding model
Now that we have prepared the training data, next we need to train a natural language understanding model . We can use machine learning algorithms, such as text classification or sequence annotation, to train this model.

The following is a sample code using scikit-learn for text classification:

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

# 加载训练数据
data = [
  {
    "input": "天气预报",
    "output": "今天的天气晴朗，温度在25°C左右。"
  },
  {
    "input": "最近有什么好电影推荐吗",
    "output": "《触不可及》是一部非常好的法国电影。"
  },
  ...
]

# 准备文本和标签
texts = [item['input'] for item in data]
labels = [item['output'] for item in data]

# 特征提取
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)

# 训练模型
clf = MultinomialNB()
clf.fit(X, labels)

Step 4: Use the natural language understanding model
After completing the training of the natural language understanding model, we can Use this in the ChatGPT plugin to enable ChatGPT to understand input from the user.

The following is a sample code using a natural language understanding model:

import openai

# 设置OpenAI的API密钥
openai.api_key = "YOUR_API_KEY"

# 设置ChatGPT插件的配置
configuration = {
    "model": "gpt-3.5-turbo",
    "temperature": 0.7,
    "max_tokens": 100,
    "n": 1,
    "stop": None,
    "logprobs": 0   
}

# 自然语言理解函数
def understand_input(user_input):
    # 使用自然语言理解模型预测输入的语义标签
    label = clf.predict(vectorizer.transform([user_input]))[0]
    
    # 构建ChatGPT格式的输入
    input_text = f"{label}: {user_input}"
    
    # 调用ChatGPT生成理解后的回答
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=input_text,
        **configuration
    )
    
    # 提取ChatGPT生成的回答
    reply = response.choices[0].text.strip().split(':')[1].strip()
    
    return reply

# 用户输入示例
user_input = "天气预报"

# 使用自然语言理解函数获取回答
reply = understand_input(user_input)

# 输出回答
print(reply)

This code example shows how to use a natural language understanding model to predict the semantic tags of the input and build it into the ChatGPT plug-in Input format. Then, we use ChatGPT to extract the answer part from the answer generated and output it.

Conclusion:
In this article, we shared a guide to developing a ChatGPT Python plugin to add natural language understanding capabilities. We discuss ways to achieve this goal using code examples and provide example code for training a natural language understanding model using scikit-learn. Additionally, we demonstrate how to integrate a natural language understanding model with the ChatGPT plug-in to extract semantic tags from user input and generate answers. Hopefully this guide will help you develop smarter ChatGPT plugins.

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