2.2chat completion chunk object 表示模型根据提供的输入返回的聊天响应的chunk流。与chat completion object唯一的不同是choices字段包含内容: delta:模型流式响应生成的聊天增量消息,比如第一个chunk流的消息内容是“Hel”,第二个chunk流的消息内容是“Hello ”,第三个chunk流的消息内容是“Hello wo”,第四个chunk...
在OpenAI Chat Completion API里,通过设置stream为true来实现Stream流式传输。 1. 没有设置stream或stream为false 我们来看一个典型的chat completion的request和response长什么样 Request: curlhttps://api.openai.com/v1/chat/completions\ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPE...
OpenAI的ChatCompletion和Completion都是自然语言生成模型的接口,但它们的用途和应用场景略有不同。 总之: ChatCompletion适用于生成对话和聊天场景的文本, Completion则适用于更为广泛的自然语言生成场景。 使用例子 由于两个的接口参数基本一致,我们这里就只通过例子介绍不一样的,通用部分请看:OpenAI.Completion.create 接...
response = chat_gpt(user_input) print("Bot:", response) And this is the full error: 这是所有的错误: … You tried to access openai.ChatCompletion, but this is no longer supported in openai>=1.0.0 – see the README at GitHub – openai/openai-python: The official Python library for th...
现在,Completion api 的提示返回的答案几乎与 ChatCompletion 相同: prompt ="""<|im_start|>system <|im_end|> <|im_start|>user Give me something intresting: <|im_end|> <|im_start|>assistant """regular_response = openai.Completion.create( engine="my_model",# gpt-35-turboprompt=prompt, ...
如果你想使用聊天gpt模型,你需要使用/chat/completionsAPI,但你的请求必须调整。 prompt = f"Rate the following review as an integer from 1 to 5, where 1 is the worst and 5 is the best: \"{review}\"" response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ {"...
OpenAI的聊天补全（Chat completion）是一种基于机器学习的自然语言处理技术，可以根据用户输入的聊天内容，自动补全下一句话或下几句话，以达到更加自然流畅的聊天体验。OpenAI的聊天补全技术采用了深度学习模型，如GPT-3，它可以从大量的语料库中学习和理解语言的规律和结构，从而生成高质量、连贯的聊天内容。OpenAI的...
openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who won the world series in 2020?"}, {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series ...
completion = client.chat.completions.create( model="gpt-3.5-turbo", response_format={ "type": "json_object" }, messages=[ {"role": "system", "content": "You are a helpful assistant designed to output JSON.."}, {"role": "user", "content": "Generate me 3 Jargons that I can use...
response = openai.ChatCompletion.create( model=model_engine, temperature=0.7, max_tokens=1024, n=1, stop=None, messages=messages, ) Print the chatbot response print(messages) @app.route(“/”) def index(): return render_template(“index.html”) ...