response = client.chat.completions.create( model="gpt-3.5-turbo-0613", messages = message, temperature=0, functions= functions, function_call="auto", ) message = response.choices[0].message.content print(message) completion = client.chat.completions.create( model="gpt-3.5-turbo", messages=mess...
client = openai.Client(base_url=base_url, api_key="EMPTY") resp = client.chat.completions.create(**data) result_str = "" for r in resp: result_str += r.choices[0].delta.content print("result_str---\n", result_str) 3.2 flask调用方式 把如下文件内容写入infer_flask.py即可进行测试验...
file_content = json.loads(client.files.content(file_id=file_object.id).content)["content"] # 生成请求消息 message_content = f"请对\n{file_content}\n的内容进行分析,并撰写一份摘要。" response = client.chat.completions.create( model="glm-4-long", messages=[ {"role": "user", "content"...
response = client.chat.completions.create(model="gpt-4o", messages=messages) result = response.choices[0].message.content 这段代码很好理解,包含一个OpenAI类的实例client以及一个函数调用,其余都是标准的python代码。 那如果用LangChain写呢? fromlangchain_openaiimportChatOpenAI fromlangchain_core.output_p...
由于最新的LangChain 0.1.7集成的ChatZhipuAI类和最新zhipuai SDK版本兼容性方面有问题,需要重新包装一个类。代码如下: """ZHIPU AI chat models wrapper."""from __future__ import annotations import asyncio import logging from functools import partial ...
from openai import OpenAIclient = OpenAI(api_key='your key',base_url='https://open.bigmodel.cn/api/paas/v4/')completion = client.chat.completions.create(model="glm-4",messages=[{"role": "user", "content": "Hi, who are you?"}])print(completion.choices[0].message.content) ...
# 初始化OpenAI客户端client = openai.Client(api_key=openai_api_key)def get_completion(prompt, model="gpt-3.5-turbo"):# 使用传入的prompt构建消息列表messages = [{"role": "user", "content": prompt}]try:# 调用API并传入参数response = client.chat.completions.create(model=model,messages=messages...
{"role": "system", "content": "You are an expert translator"},9 {"role": "user", "content": f"Translate the following from English into {language}"},10 {"role": "user", "content": f"{text}"},11]1213response = client.chat.completions.create(model="gpt-4o", messages=messages...
Completions API 主要用于补全问题,用户输入一段提示文字,模型按照文字的提示给出对应的输出。 Chat模型升级的核心功能是对话, 它基于大量高质量对话文本进行微调,能够更好的理解用户对话意图,所以它能更顺利的完成与用户的对话(大语言模型本质上都是概率模型,根据前文提示进行补全是⼤语⾔模型的原始功能,而对话类的...
/Chat/chat 与llm模型对话(通过LLMChain) ,想要使用langchain 0.2 这个接口的功能,应该怎么调用 njzfw1024 commented on Jul 26, 2024 njzfw1024 on Jul 26, 2024 { "model": "qw72Blora", "messages": [ { "role": "system", "content": "你是一个人工智能助手." }, { "role": "user", "...