我写了一个叫 fetch-stream-parser 的库,直接npm i fetch-stream-parser引入即可,用法如下: import fetchParser from '@async-util/fetch'; const openAiKey = process.env.OPENAI_KEY; (async function () { // 两个参数跟fetch的参数一毛一样 const fp = await fetchParser('https://api.openai.com/v...
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import{OpenAI}from"openai-streams";exportdefaultasyncfunctionhandler(){conststream=awaitOpenAI("completions",{model:"text-davinci-003",prompt:"Write a happy sentence.\n\n",max_tokens:100,});returnnewResponse(stream);}exportconstconfig={runtime:"edge",}; ...
constargs:CreateCompletionRequest={prompt:"What is the greatest toilet in the world?",model:"text-davinci-003",max_tokens:2500,temperature:0.9,};consthandleText:OnTextCallback=async(text)=>{if(!text)return;console.log(text);};consttext=awaitstreamCompletion({args,apiKey,onText:handleText})...
Describe the bug According to How_to_stream_completions response = openai.ChatCompletion.create( model='gpt-3.5-turbo', messages=[ {'role': 'user', 'content': "What's 1+1? Answer in one word."} ], temperature=0, stream=True ) for chunk i...
async def async_ollama(): response = await litellm.acompletion( model="ollama/llama2", messages=[{ "content": "what's the weather" ,"role": "user"}], api_base="http://localhost:11434", stream=True ) async for chunk in response: ...
可以使用OpenAI API的stream参数来实现流式输出,并且可以使用max_tokens参数控制每次返回数据的长度。 以下是一个示例代码,演示如何使用OpenAI API来实现流式输出并分段加载: AI检测代码解析 python import openai # 连接 OpenAI API openai.api_key = "YOUR_API_KEY" ...
Začněte požadavek na dokončení a získejte objekt, který může streamovat data odpovědí, jakmile budou k dispozici. C# Kopírovat public virtual System.Threading.Tasks.Task<Azure.Response<Azure.AI.OpenAI.StreamingCompletions>> GetCompletionsStreamingAsync (string deploymentOrModel...
// Function to generate text using GPT-3 modelletGPT3 =async(prompt) => {constresponse =awaitopenai.createCompletion({model:"text-davinci-003",prompt,max_tokens:500,});returnresponse.data.choices[0].text;}; letGPT35Turbo =async(message) ...
= async () => { setOutputValue(""); const url = "http://xxx.xxx.xxx.xxx:xxx/v1/chat/completions"; const data = { model: "chatglm2-6b", messages: [ { role: "user", content: "请实现一个登陆功能", }, ], temperature: 0.75, stream: true, }; testDataString = ""; const ...