如何配置openai的返回Stream数据并转发到h5页面按markdown格式流式输出(最新推荐)

import openai
# 连接 OpenAI API openai.api_key = "YOUR_API_KEY" 
# 设置 OpenAI API 参数 
model_engine = "davinci" 
# 模型引擎 
prompt = "你好，我需要帮助进行自然语言处理任务。" # 请求语句 
temperature = 0.5 # 温度参数 
max_tokens = 100 # 每次最大输出长度 # 获取 OpenAI 返回数据 
completions = openai.Completion.create( engine=model_engine, prompt=prompt, temperature=temperature, max_tokens=max_tokens, n=1, stream=True ) # 分段获取数据 
for i, text in enumerate(iter(completions.choices), start=1): 
print(f"============== 第 {i} 段输出 ==============") 
print(text.text)

import requests 
def process_stream(stream): 
for line in stream.iter_lines(): 
	if line: # 处理返回的数据 
		print(line.decode('utf-8')) # 发起 API 请求并处理返回的数据流 
		response = requests.post(url, headers=headers, data=data, stream=True) 
		process_stream(response.iter_lines()) 

<?php $ch = curl_init(); 
curl_setopt($ch, CURLOPT_URL, 'STREAM_URL'); 
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_WRITEFUNCTION, function($curl, $data) 
{ // 处理获得的数据
 echo $data; 
 return strlen($data); }); 
 curl_exec($ch);
  curl_close($ch); ?> 

import java.net.*; 
import java.io.*; 
public class StreamReceiver { 
	public static void main(String[] args) throws Exception 
		{ URL streamURL = new URL("STREAM_URL"); 
		try (
			BufferedReader reader = new BufferedReader( new InputStreamReader(streamURL.openStream()))) 
				{ String line; 
					while ((line = reader.readLine()) != null) 
					{ // 处理获得的数据 System.out.println(line); 
					}
				} 
		 } 
 } 

<?php // 请求OpenAI接口获取stream数据，并转发到H5页面端 
$openAIUrl = 'https://api.openai.com/v1/engines/davinci-codex/completions'; $headers = array( 'Content-Type: application/json', 'Authorization: Bearer <YOUR_API_KEY>' ); 
$data = array( 'prompt' => 'Hello, world!', 'max_tokens' => 5, 'temperature' => 0.7 ); 
$ch = curl_init(); 
curl_setopt($ch, CURLOPT_URL, $openAIUrl); 
curl_setopt($ch, CURLOPT_POST, true); 
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data)); 
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true); 
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers); 
curl_setopt($ch, CURLOPT_FOLLOWLOCATION, true);
$output = fopen('php://output', 'w'); 
curl_setopt($ch, CURLOPT_WRITEFUNCTION, function ($ch, $chunk) use ($output) { fwrite($output, $chunk); 
return strlen($chunk); }); 
curl_exec($ch); 
fclose($output); // 将stream数据转发给H5页面端 
header('Content-Type: text/event-stream'); 
header('Cache-Control: no-cache'); 
header('Connection: keep-alive'); 
header('Access-Control-Allow-Origin: *'); 
fpassthru($output); 

import java.io.IOException; 
import java.io.InputStream; 
import java.io.OutputStream; 
import java.net.HttpURLConnection; 
import java.net.URL; 
import java.util.concurrent.ExecutorService; 
import java.util.concurrent.Executors; 
import javax.servlet.AsyncContext; 
import javax.servlet.ServletException; 
import javax.servlet.annotation.WebServlet; 
import javax.servlet.http.HttpServlet; 
import javax.servlet.http.HttpServletRequest; 
import javax.servlet.http.HttpServletResponse; 
@WebServlet(asyncSupported = true, urlPatterns = {"/stream"}) 
public class StreamServlet extends HttpServlet 
{ 
private static final ExecutorService executor = Executors.newCachedThreadPool(); 
@Override 
protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { 
// 创建异步上下文 
AsyncContext asyncContext = request.startAsync(); // 提交任务到线程池
 executor.submit(() -> { try { 
// 请求OpenAI接口获取stream数据 
URL url = new URL("https://api.openai.com/v1/engines/davinci-codex/completions"); 
HttpURLConnection conn = (HttpURLConnection) url.openConnection(); 
conn.setRequestMethod("POST"); 
conn.setRequestProperty("Content-Type", "application/json"); 
conn.setRequestProperty("Authorization", "Bearer <YOUR_API_KEY>"); conn.setDoOutput(true); 
OutputStream out = conn.getOutputStream(); 
out.write("{\"prompt\": \"Hello, world!\", \"max_tokens\": 5, \"temperature\": 0.7}".getBytes());
 out.flush(); 
out.close(); 
InputStream in = conn.getInputStream(); 
response.setContentType("text/event-stream"); 
response.setHeader("Cache-Control", "no-cache"); 
response.setHeader("Connection", "keep-alive"); 
response.setHeader("Access-Control-Allow-Origin", "*"); 
byte[] buffer = new byte[1024];
int count;
while ((count = in.read(buffer)) != -1) { 
// 将stream数据转发给H5页面端 
OutputStream outputStream = asyncContext.getResponse().getOutputStream(); 
String data = new String(buffer, 0, count); 
outputStream.write(("data: " + data + "\n\n").getBytes()); 
outputStream.flush(); } in.close(); } 
catch (IOException e) { e.printStackTrace(); } 
finally { asyncContext.complete(); } }); } } 

import requests from flask 
import Flask, Response app = Flask(__name__) @app.route('/stream') 
def stream(): # 请求OpenAI接口获取stream数据 
url = 'https://api.openai.com/v1/engines/davinci-codex/completions' 
headers = { 'Content-Type': 'application/json', 'Authorization': 'Bearer <YOUR_API_KEY>' } 
data = { 'prompt': 'Hello, world!', 'max_tokens': 5, 'temperature': 0.7 } res = requests.post(url, headers=headers, json=data, stream=True) 
# 将stream数据转发给H5页面端 
def generate(): 
for chunk in res.iter_content(chunk_size=1024): 
if chunk: yield 'data: ' + chunk.decode() + '\n\n' 
return Response(generate(), mimetype='text/event-stream', headers={ 'Cache-Control': 'no-cache', 'Connection': 'keep-alive', 'Access-Control-Allow-Origin': '*' }) 
if __name__ == '__main__': 
app.run() 

  <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script> 

<textarea id="mdInput"></textarea> <div id="htmlOutput"></div> 

var markdown = document.getElementById('mdInput').value; 
var html = marked(markdown); 
document.getElementById('htmlOutput').innerHTML = html; 

const markdown = require('markdown').markdown; 
 const stream = require('stream'); 
 const outputStream = new stream.Writable({ 
 	write(chunk, encoding, callback) { 
		 const text = chunk.toString(); 
		 const markdownText = markdown.toHTML(text); 
		 process.stdout.write(markdownText); 
		 callback(); }, 
		 }); 
 // Stream output to markdown formatter 
 originalOutputStream.pipe(outputStream);