Open ai 开发指南：gpt接口的第一个问答机器人demo

目录

内容

Python代码

C++ 代码

workspace 文件

BUILD文件

Java 代码

maven文件

执行效果

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内容

基于openai接口实现循环gpt问答，并使用一个文件将问答内容进行记录。

Python代码

# -*- coding: utf-8 -*-
import openai
import time
from pathlib import Path

# Set the OpenAI API key
openai.api_key = ""

class ChatBot:
    def __init__(self, model):
        self.user = "\nYou: "
        self.bot = "GPT-3.5-turbo: "
        self.model = model
        self.question_list = []
        self.answer_list = []
        self.text = ''
        self.turns = []
        self.last_result = ''

    def save_dialogue(self):
        # Generate a timestamp and create a filename for saving the dialogue
        timestamp = time.strftime("%Y%m%d-%H%M-%S", time.localtime())  # Timestamp
        file_name = 'output/Chat_' + timestamp + '.md'  # Filename
        f = Path(file_name)
        f.parent.mkdir(parents=True, exist_ok=True)
        
        # Save the dialogue to the file
        with open(file_name, "w", encoding="utf-8") as f:
            for q, a in zip(self.question_list, self.answer_list):
                f.write(f"You: {q}\nGPT-3.5-turbo: {a}\n\n")
        print("Dialogue content saved to file: " + file_name)

    def generate(self):
        print('\nStart your conversation, type "exit" to end.')
        while True:
            question = input(self.user)
            self.question_list.append(question)
            prompt = self.bot + self.text + self.user + question
            
            if question == 'exit':
                break
            else:
                try:
                    response = openai.ChatCompletion.create(
                        model=self.model,
                        messages=[
                            {"role": "user", "content": prompt},
                        ],
                    )

                    result = response["choices"][0]["message"]["content"].strip()
                    print(result)
                    self.answer_list.append(result)

                    self.last_result = result
                    self.turns += [question] + [result]
                    
                    if len(self.turns) <= 10:
                        self.text = " ".join(self.turns)
                    else:
                        self.text = " ".join(self.turns[-10:])
                except Exception as exc:
                    print(exc)
        self.save_dialogue()

if __name__ == '__main__':
    bot = ChatBot('gpt-3.5-turbo')
    bot.generate()

在这个代码中，我们实现了一个与 GPT-3.5-turbo 模型进行对话的聊天机器人。以下是代码的执行流程：

1. 首先，我们导入需要用到的库，包括 openai、time 和 pathlib。
2. 我们设置 OpenAI API 密钥，以便能够调用 GPT-3.5-turbo 模型。
3. 定义一个名为 ChatBot 的类，该类用于与 GPT-3.5-turbo 模型进行交互。
4. 类的 __init__ 方法初始化聊天机器人，包括设定用户和机器人的提示符（self.user 和 self.bot），以及其他一些变量，如问题列表、回答列表等。
5. save_dialogue 方法用于将聊天记录保存到文件中。这个方法首先创建一个带有时间戳的文件名，然后将问题和回答列表中的内容写入该文件。
6. generate 方法用于与 GPT-3.5-turbo 模型进行交互。在这个方法中，我们使用一个循环来获取用户输入的问题，并将其添加到问题列表中。然后，我们构建提示并使用 OpenAI API 调用 GPT-3.5-turbo 模型。我们获取模型的回答，并将其添加到回答列表中。循环会一直进行，直到用户输入 "exit"，此时循环结束，调用 save_dialogue 方法将对话内容保存到文件中。
7. 在主函数（if __name__ == '__main__'）中，我们创建一个 ChatBot 类的实例，并调用 generate 方法开始与 GPT-3.5-turbo 模型的交互。

C++ 代码

// main.cpp
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

using namespace utility;
using namespace web;
using namespace web::http;
using namespace web::http::client;
using namespace concurrency::streams;
namespace {
	const std::string api_key = "";
}
class ChatBot {
public:
/**
* @brief Construct a new ChatBot object
*
* @param model The OpenAI GPT model to use (e.g., "gpt-3.5-turbo")
*/
explicit ChatBot(std::string model) : model_(model) {}

/**
* @brief Save the dialogue content to a Markdown file
*
* @note This function saves the generated dialogue to a Markdown file named
* "Chat_timestamp.md" in the "output" directory.
*/
void SaveDialogue() {
    auto timestamp = std::time(nullptr);
    std::stringstream ss;
    ss << "output/Chat_" << std::put_time(std::localtime(×tamp), "%Y%m%d-%H%M-%S") << ".md";
    std::string file_name = ss.str();

    std::ofstream out(file_name);
    if (!out) {
        std::cerr << "Could not create the output file.\n";
        return;
    }

    for (size_t i = 0; i < question_list_.size(); ++i) {
        out << "You: " << question_list_[i] << "\nGPT-3.5-turbo: " << answer_list_[i] << "\n\n";
    }
    out.close();

    std::cout << "Dialogue content saved to file: " << file_name << std::endl;
}

/**
* @brief Generate answers to the user's questions using the GPT model
*
* @note This function prompts the user for their questions, generates answers
* using the GPT model, and saves the dialogue to a file when the user exits.
*/
void Generate() {
    std::string question;
    std::cout << "\nStart your conversation, type \"exit\" to end." << std::endl;

    while (true) {
        std::cout << "\nYou: ";
        std::getline(std::cin, question);

        if (question == "exit") {
            break;
        }

        question_list_.push_back(question);

        auto answer = GetAnswer(question);
        if (!answer.empty()) {
            std::cout << "GPT-3.5-turbo: " << answer << std::endl;
            answer_list_.push_back(answer);
        }
    }

    SaveDialogue();
}

private:
/**
* @brief Get the GPT model's answer to the given question
*
* @param question The user's question
* @return The GPT model's answer as a string
*
* @note This function sends the user's question to the GPT model and
* retrieves the model's answer as a string. If an error occurs, it
* returns an empty string.
*/
std::string GetAnswer(const std::string& question) {
http_client client(U("https://api.openai.com/v1/engines/gpt-3.5-turbo/completions"));
http_request request(methods::POST);
request.headers().add("Authorization", "Bearer "+api_key);
request.headers().add("Content-Type", "application/json");

json::value body;
body[U("prompt")] = json::value::string(U("GPT-3.5-turbo: " + question));
body[U("max_tokens")] = 50;
body[U("n")] = 1;
body[U("stop")] = json::value::array({json::value::string(U("\n"))});
request.set_body(body);

try {
auto response = client.request(request).get();
auto response_body = response.extract_json().get();
auto result = response_body.at(U("choices")).as_array()[0].at(U("text")).as_string();
return utility::conversions::to_utf8string(result);
} catch (const std::exception& e) {
std::cerr << "Error: " << e.what() << std::endl;
return "";
}
}

std::string model_;
std::vector question_list_;
std::vector answer_list_;
};

int main(){
ChatBot bot("gpt-3.5-turbo");
bot.Generate();
return 0;
}

因为我的最近经常在使用bazel，所以给出一个基于bazel 的C++编译方法

workspace 文件

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

# Add cpprestsdk as a dependency
http_archive(
    name = "cpprestsdk",
    build_file = "@//:cpprestsdk.BUILD",
    sha256 = "106e8a5a4f9667f329b8f277f8f25e1f2d31f1a7c7f9e1f366c5b1e3af2f8c4c",
    strip_prefix = "cpprestsdk-2.10.18",
    urls = ["https://github.com/microsoft/cpprestsdk/archive/v2.10.18.zip"],
)

BUILD文件

cc_binary(
    name = "chatbot",
    srcs = ["main.cpp"],
    deps = [
        "@cpprestsdk//:cpprest",
    ],
    linkopts = [
        # Required for cpprestsdk
        "-lboost_system",
        "-lssl",
        "-lcrypto",
    ],
)

# cpprestsdk.BUILD file content (create this file in the same directory as WORKSPACE)
'''
load("@rules_cc//cc:defs.bzl", "cc_library")

cc_library(
    name = "cpprest",
    hdrs = glob(["cpprestsdk/cpprestsdk/Release/include/**/*.h"]),
    srcs = glob([
        "cpprestsdk/cpprestsdk/Release/src/http/client/*.cpp",
        "cpprestsdk/cpprestsdk/Release/src/http/common/*.cpp",
        "cpprestsdk/cpprestsdk/Release/src/http/listener/*.cpp",
        "cpprestsdk/cpprestsdk/Release/src/json/*.cpp",
        "cpprestsdk/cpprestsdk/Release/src/pplx/*.cpp",
        "cpprestsdk/cpprestsdk/Release/src/uri/*.cpp",
        "cpprestsdk/cpprestsdk/Release/src/utilities/*.cpp",
        "cpprestsdk/cpprestsdk/Release/src/websockets/client/*.cpp",
    ]),
    includes = ["cpprestsdk/cpprestsdk/Release/include"],
    copts = ["-std=c++11"],
    linkopts = [
        "-lboost_system",
        "-lssl",
        "-lcrypto",
    ],
    visibility = ["//visibility:public"],
)
'''

Java 代码

// ChatBot.java
import okhttp3.*;
import com.google.gson.*;
import java.io.*;
import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;
import java.util.ArrayList;

public class ChatBot {
    private static final String API_KEY = "your_openai_api_key_here";

    private String model;
    private ArrayList questionList;
    private ArrayList answerList;

    public ChatBot(String model) {
        this.model = model;
        this.questionList = new ArrayList<>();
        this.answerList = new ArrayList<>();
    }

    public void saveDialogue() {
        LocalDateTime timestamp = LocalDateTime.now();
        DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyyMMdd-HHmm-ss");
        String fileName = "output/Chat_" + timestamp.format(formatter) + ".md";

        try (BufferedWriter writer = new BufferedWriter(new FileWriter(fileName))) {
            for (int i = 0; i < questionList.size(); ++i) {
                writer.write("You: " + questionList.get(i) + "\nGPT-3.5-turbo: " + answerList.get(i) + "\n\n");
            }
            System.out.println("Dialogue content saved to file: " + fileName);
        } catch (IOException e) {
            System.err.println("Could not create the output file.");
            e.printStackTrace();
        }
    }

    public void generate() {
        System.out.println("\nStart your conversation, type \"exit\" to end.");

        BufferedReader reader = new BufferedReader(new InputStreamReader(System.in));
        String question;

        while (true) {
            System.out.print("\nYou: ");
            try {
                question = reader.readLine();
            } catch (IOException e) {
                System.err.println("Error reading input.");
                e.printStackTrace();
                break;
            }

            if ("exit".equalsIgnoreCase(question)) {
                break;
            }

            questionList.add(question);
            String answer = getAnswer(question);
            if (!answer.isEmpty()) {
                System.out.println("GPT-3.5-turbo: " + answer);
                answerList.add(answer);
            }
        }

        saveDialogue();
    }

    private String getAnswer(String question) {
        OkHttpClient client = new OkHttpClient();
        Gson gson = new Gson();

        String requestBodyJson = gson.toJson(new RequestBody("GPT-3.5-turbo: " + question, 50, 1, "\n"));
        RequestBody requestBody = RequestBody.create(requestBodyJson, MediaType.parse("application/json"));

        Request request = new Request.Builder()
                .url("https://api.openai.com/v1/engines/gpt-3.5-turbo/completions")
                .addHeader("Authorization", "Bearer " + API_KEY)
                .addHeader("Content-Type", "application/json")
                .post(requestBody)
                .build();

        try (Response response = client.newCall(request).execute()) {
            if (!response.isSuccessful()) {
                throw new IOException("Unexpected code " + response);
            }

            String responseBodyJson = response.body().string();
            ResponseBody responseBody = gson.fromJson(responseBodyJson, ResponseBody.class);
            return responseBody.choices.get(0).text.trim();
        } catch (IOException e) {
            System.err.println("Error: " + e.getMessage());
            e.printStackTrace();
            return "";
        }
    }

    public static void main(String[] args) {
        ChatBot bot = new ChatBot("gpt-3.5-turbo");
        bot.generate();
    }

    private static class RequestBody {
        String prompt;
        int max_tokens;
        int n;
        String stop;

        RequestBody(String prompt, int max_tokens, int n, String stop) {
            this.prompt = prompt;
            this.max_tokens = max_tokens;
            this.n = n;
            this.stop = stop;
        }
    }

    private static class ResponseBody {
        ArrayList choices;

        private static class Choice {
            String text;
        }
    }
}

maven文件

    4.0.0

    com.example
    gpt-chatbot
    1.0-SNAPSHOT

    
        UTF-8
        1.8
        1.8
    

    
        
            com.squareup.okhttp3
            okhttp
            4.9.3
        
        
            com.google.code.gson
            gson
            2.8.9
        
    

    
        
            
                org.apache.maven.plugins
                maven-compiler-plugin
                3.8.0
            
            
                org.apache.maven.plugins
                maven-jar-plugin
                3.1.0
                
                    
                        
                            true
                            lib/
                            com.example.ChatBot
                        
                    
                
            
            
                org.apache.maven.plugins
                maven-dependency-plugin
                3.1.1
                
                    
                        copy-dependencies
                        package
                        
                            copy-dependencies
                        
                        
                            ${project.build.directory}/lib
                        
                    
                
            
        
    

执行效果