How to support multiple rounds of conversation in ChatGPT's API

1. Problems

ChatGPT's API supports multiple rounds of dialogue. You can use the API to send the user's input into the ChatGPT model, and then return the response generated by the model to the user, thereby achieving multiple rounds of dialogue. The user's previous input and model-generated responses can be retained in each round so that they can be passed on to the next round of dialogue. This approach allows for a more natural conversation flow and provides a better user experience.

2. Realization

When using ChatGPT's API, multiple rounds of conversations can be achieved by passing context or conversation_id in the request. The context or conversation_id can be obtained in the first round of conversation, and then carried in subsequent requests, so that ChatGPT can recognize that this is the same conversation.

Here is an example showing how to send a request and get a response through HttpURLConnection in Java:

import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.io.OutputStreamWriter;
import java.net.HttpURLConnection;
import java.net.URL;

public class Chatbot {
    private static final String API_ENDPOINT = "https://api.openai.com/v1/engines/davinci-codex/completions";

    private String context = null;

    public String sendMessage(String message) throws Exception {
        URL url = new URL(API_ENDPOINT);
        HttpURLConnection connection = (HttpURLConnection) url.openConnection();

        // 设置请求头
        connection.setRequestMethod("POST");
        connection.setRequestProperty("Content-Type", "application/json");
        connection.setRequestProperty("Authorization", "Bearer <your_api_key>");

        // 构造请求体
        String requestBody;
        if (context == null) {
            requestBody = String.format("{\"prompt\": \"%s\"}", message);
        } else {
            requestBody = String.format("{\"prompt\": \"%s\", \"context\": \"%s\"}", message, context);
        }

        // 发送请求
        connection.setDoOutput(true);
        OutputStreamWriter writer = new OutputStreamWriter(connection.getOutputStream());
        writer.write(requestBody);
        writer.flush();
        writer.close();

        // 读取响应
        BufferedReader reader = new BufferedReader(new InputStreamReader(connection.getInputStream()));
        StringBuilder responseBuilder = new StringBuilder();
        String line;
        while ((line = reader.readLine()) != null) {
            responseBuilder.append(line);
        }
        reader.close();

        // 解析响应
        String response = responseBuilder.toString();
        context = extractContext(response);

        return extractResponse(response);
    }

    private String extractResponse(String response) {
        // 从响应中提取出 ChatGPT 返回的文本
        // 这里需要根据具体的 API 返回格式来进行解析
        return "";
    }

    private String extractContext(String response) {
        // 从响应中提取出下一轮对话所需要的 context
        // 这里需要根据具体的 API 返回格式来进行解析
        return "";
    }
}

In the above code, the sendMessage method is used to send a request and get a reply from ChatGPT. If this is the first round of dialogue, you only need to put the message as a prompt in the request body. If this is not the first round of dialogue, you also need to put the context in the request body so that ChatGPT can know which dialogue it is. When we get a reply from ChatGPT, we need to extract the response text and the context needed for the next round of dialogue.

Note that since ChatGPT is a dialogue system based on AI technology, its replies may be incomprehensible or contain inappropriate remarks. Therefore, you need to be cautious when using ChatGPT to avoid unnecessary problems.

3. Problems that need attention

ChatGPT's api seems to use a relatively low model version, which is not as smart as the ChatGPT that everyone uses.

ChatGPT's API uses the pre-trained model released by OpenAI. The version is limited and the latest model will not be used. But the quality of the pre-trained model is very high and can achieve very good natural language processing capabilities, especially in terms of dialogue generation. Of course, you can also improve the quality of dialogue generation by training the model yourself, but this requires a lot of computing resources and time.

In addition, the performance of intelligence does not only depend on the model itself, but also includes the quality of data sets, preprocessing methods, algorithm optimization and other aspects. If you have doubts about the performance of ChatGPT, you can try to adjust the input method, format, content, etc., or improve the results through other algorithm optimizations.

4. How to train a model yourself

Training a language model is a very complex task that requires a lot of data and computing resources. Here are some basic steps:

1. Collect data: To train a language model, you first need a lot of text data. These data can be articles, news, blogs, forums, etc. from the Internet, or text data in some specific fields.

2. Data cleaning and preprocessing: Collected data usually needs to be cleaned and preprocessed, such as removing HTML tags, punctuation marks, and stop words, etc., and word segmentation and part-of-speech tagging are also required for the data.

3. Build a model: After collecting and preprocessing the data, you need to build a language model. Language models typically use deep learning techniques such as Recurrent Neural Networks (RNN), Long Short Term Memory Networks (LSTM), Transformers, and more.

4. Training model: After the model is constructed, data needs to be sent to the model for training. Training models requires massive computing resources, such as GPUs.

5. Evaluate the model: After training the model, the model needs to be evaluated. Evaluating models usually uses metrics such as perplexity and BLEU.

6. Tune the model and parameters: After evaluating the model, the model and parameters can be tuned to improve the performance of the model.

7. Deploying the model: After the model training is complete, the model needs to be deployed to the production environment. Deployment models usually require some software engineering skills, such as using Docker containerization models, using Flask or Django frameworks to build APIs, and so on.

The above are the basic steps of training a language model, each of which is very complicated and requires in-depth study and practice. If you want to train your own language model, it is recommended to start by learning the basics of deep learning, and then gradually go deep into the training and deployment of the language model.

5. Can the ChatGPT API be trained?

ChatGPT is a pre-trained language model developed by OpenAI. It can only be trained on the OpenAI platform. Currently, the training interface is not open to the public. However, you can use the API interface provided by OpenAI to use the trained model to realize functions such as dialogue generation. At the same time, OpenAI also provides some pre-trained models that can be adjusted, and you can choose the appropriate model to meet your needs.