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#include <curl/curl.h>
#include <iostream>
#include <string>

int main() {
CURL *curl;
CURLcode res;
std::string readBuffer;

curl_global_init(CURL_GLOBAL_ALL);
curl = curl_easy_init();
if(curl) {
std::string url = "https://api.explinks.com/v2/scd202407082255150d8801/medgpt-ai-doctor";
// 設置API端點
curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
// 設置請求頭，例如API密鑰
struct curl_slist *headers = NULL;
headers = curl_slist_append(headers, "Content-Type: application/json");
std::string apiKey = "Your-API-Key-Here";
headers = curl_slist_append(headers, ("X-API-KEY: " + apiKey).c_str());
curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
// 假設是POST請求，設置POST字段
std::string postData = "{\"patientInfo\": \"...\"}";
curl_easy_setopt(curl, CURLOPT_POSTFIELDS, postData.c_str());
// 設置回調函數處理響應數據
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);
// 執行請求
res = curl_easy_perform(curl);
// 檢查錯誤
if(res != CURLE_OK) {
fprintf(stderr, "curl_easy_perform() failed: %s\n", curl_easy_strerror(res));
}
// 輸出結果
std::cout << "API Response: " << readBuffer << std::endl;
// 清理
curl_easy_cleanup(curl);
curl_slist_free_all(headers);
}
curl_global_cleanup();
return 0;
}

size_t WriteCallback(void *contents, size_t size, size_t nmemb, std::string *s) {
size_t newLength = size * nmemb;
try {
s->append((char*)contents, newLength);
} catch(std::bad_alloc &e) {
// handle memory problem
return 0;
}
return newLength;
}

PHP集成API案例

// PHP代碼示例，展示如何調用MedGPT AI醫生API

<?php

$url = "https://api.explinks.com/v2/scd202407082255150d8801/medgpt-ai-doctor";

$apiKey = "Your-API-Key-Here";

$postData = array('patientInfo' => '...');

$jsonData = json_encode($postData);



$options = array(

    CURLOPT_URL => $url,

    CURLOPT_POST => true,

    CURLOPT_POSTFIELDS => $jsonData,

    CURLOPT_HTTPHEADER => array(

        'Content-Type: application/json',

        'X-API-KEY: ' . $apiKey

    ),

);



$ch = curl_init();

curl_setopt_array($ch, $options);

$response = curl_exec($ch);

if (!$response) {

    echo "cURL error: " . curl_error($ch);

} else {

    echo "API Response: " . $response;

}

curl_close($ch);

?>

GO集成API案例

// Go代碼示例，展示如何調用MedGPT AI醫生API

package main



import (

    "bytes"

    "encoding/json"

    "fmt"

    "net/http"

)



func main() {

    url := "https://api.explinks.com/v2/scd202407082255150d8801/medgpt-ai-doctor"

    apiKey := "Your-API-Key-Here"

    patientInfo := map[string]string{"patientInfo": "..."}

    jsonData, _ := json.Marshal(patientInfo)



    request, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))

    request.Header.Set("Content-Type", "application/json")

    request.Header.Set("X-API-KEY", apiKey)



    client := &http.Client{}

    response, err := client.Do(request)

    if err != nil {

        fmt.Println("API request error:", err)

        return

    }

    defer response.Body.Close()



    responseBody, _ := ioutil.ReadAll(response.Body)

    fmt.Println("API Response:", string(responseBody))

}

MedGPT AI醫生API是否有替換方案？

在開源社區中，有幾個項目提供了醫療AI API的解決方案，其中包括：

DoctorGLM – 一個基于ChatGLM-6B的中文問診模型，可以在DoctorGLM GitHub倉庫找到。它通過微調提供了多輪對話能力和模型可靠性的提升。
Med-ChatGLM – 一個基于中文醫學知識的ChatGLM模型微調項目，位于Med-ChatGLM GitHub倉庫。該項目通過醫學知識圖譜和GPT3.5 API構建了中文醫學指令數據集，對ChatGLM-6B進行了指令微調。
仲景 (Zhongjing) – 首個實現從預訓練到RLHF（Reinforcement Learning from Human Feedback）全流程訓練的中文醫療大模型，項目可以在Zhongjing GitHub倉庫找到。它展現了很好的泛化能力，并且在某些對話場景中接近專業醫生的專業水平。

下面給出DoctorGLM的Python集成示例：

# 導入所需的庫

from transformers import AutoTokenizer, AutoModelForCausalLM

import torch



# 加載模型和分詞器

model_name = "xionghonglin/DoctorGLM"  # 模型名，根據DoctorGLM的官方模型名填寫

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(model_name)



# 定義一個函數來獲取模型的回答

def get_doctorglm_response(query):

    # 編碼問題

    inputs = tokenizer.encode(query, return_tensors="pt")

    # 生成回答

    response = model.generate(inputs, max_length=512, num_return_sequences=1)

    # 解碼回答

    response_text = tokenizer.decode(response[0], skip_special_tokens=True)

    return response_text



# 用戶輸入的問題

user_question = "我爺爺高血壓可以喝咖啡嗎"



# 獲取并打印AI的回答

ai_response = get_doctorglm_response(user_question)

print(f"AI的回答是: {ai_response}")