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How to use ChatGPT and Java to develop an intelligent question and answer community
How to use ChatGPT and Java to develop an intelligent question and answer community
How to use ChatGPT and Java to develop an intelligent question and answer community
The intelligent question and answer community has received more and more attention and attention in today's Internet social platforms. It provides users with Provides a convenient way to meet their needs by asking questions and getting answers. With the continuous development of artificial intelligence, it is becoming easier and easier to develop an intelligent question and answer community using ChatGPT and Java. This article will introduce how to use ChatGPT and Java to build a simple intelligent question and answer community, and provide some specific code examples.
Step 1: Set up ChatGPT
First, we need to set up the ChatGPT model to provide question and answer functionality. We can use the GPT model provided by OpenAI or a pre-trained model based on the Hugging Face Transformers library. The following sample code shows an example of using the Hugging Face Transformers library:
import org.apache.commons.lang3.StringUtils;
import org.huggingface.models.GPTModel;
import org.huggingface.tokenizers.GPTTokenizer;
public class ChatGPT {
private GPTModel model;
private GPTTokenizer tokenizer;
public ChatGPT(String modelPath, String tokenizerPath) {
model = GPTModel.fromPretrained(modelPath);
tokenizer = GPTTokenizer.fromPretrained(tokenizerPath);
}
public String generateAnswer(String question) {
String input = "Q: " + question + "
A:";
float[] scores = model.generateScore(input).getScores();
String output = tokenizer.decode(scores);
return StringUtils.substringBetween(output, "A: ", "
");
}
} This code uses the GPT model and GPTTokenizer in the Hugging Face Transformers library, where modelPath and tokenizerPath is the path of the pre-trained model and tokenizer. The generateAnswer method receives a question as input and returns a generated answer.
Step 2: Build a Q&A community
In Java, you can use various development frameworks to build the backend of the Q&A community. Here we use Spring Boot as the development framework and use the REST API to handle the interaction between the front end and the back end. Here is a simple sample code:
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
@SpringBootApplication
@RestController
public class QASystemApp {
private ChatGPT chatGPT;
public QASystemApp() {
chatGPT = new ChatGPT("path/to/model", "path/to/tokenizer");
}
@GetMapping("/answer")
public String getAnswer(@RequestParam String question) {
return chatGPT.generateAnswer(question);
}
public static void main(String[] args) {
SpringApplication.run(QASystemApp.class, args);
}
} In this code, the QASystemApp class is marked as a Spring Boot application using the @SpringBootApplication annotation and ## The #@RestController annotation marks it as a REST API controller. The getAnswer method receives a request parameter named question and calls the chatGPT.generateAnswer method to generate an answer.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>智能问答社区</title>
</head>
<body>
<h1 id="智能问答社区">智能问答社区</h1>
<form id="questionForm">
<label for="question">问题：</label>
<input type="text" id="question" name="question" required>
<button type="submit">提交</button>
</form>
<div id="answer"></div>
<script>
document.getElementById("questionForm").addEventListener("submit", function(event) {
event.preventDefault();
var question = document.getElementById("question").value;
fetch("/answer?question=" + encodeURIComponent(question))
.then(function(response) {
return response.text();
})
.then(function(answer) {
document.getElementById("answer").innerText = answer;
document.getElementById("question").value = "";
});
});
</script>
</body>
</html> element for displaying the answer. When the user submits a question, obtain the value of the question through JavaScript code, and use JavaScript's Fetch API to send a GET request to /answerAPI, and display the generated answer in element. In this way, the development of an intelligent question and answer community using ChatGPT and Java is completed. When a user submits a question through the front-end interface, the back-end will use the ChatGPT model to generate an answer and return the answer to the front-end for display to the user. Of course, this is just a simple example, you can develop and optimize it in depth according to your own needs. I hope this article can help you better understand how to use ChatGPT and Java to develop an intelligent Q&A community.
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