OpenAI ChatGpt Gpt-3.5-turbo

Q&A 第一个Sample

import openai
openai.api_key = "sk-XXXX-key需要在https://openai.com/注册后获取"completion = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "2023年在上海哪里赏樱花？"}]
)print(completion["choices"][0]["message"]["content"].strip())

返回结果

FAQ第一个Sample

首先安装 OpenAI、GPT Index 和 Gradio 库

pip install openai
pip install gpt_index
pip install gradio

复制以下代码，取名chatgptsample.py

from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import gradio as gr
import sys
import os#设置全局变量保存OPENAI_API_KEY
os.environ["OPENAI_API_KEY"] = "sk-XXXX-key需要在https://openai.com/注册后获取"#定义聊天函数
#参数directory_path：需要训练的文件路径
def construct_index(directory_path):max_input_size = 4096num_outputs = 512max_chunk_overlap = 20chunk_size_limit = 600prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)# 模型加载，这里选用"text-davinci-003"，也可以是最新的"gpt-3.5-turbo"llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7, model_name="text-davinci-003", max_tokens=num_outputs))documents = SimpleDirectoryReader(directory_path).load_data()index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)#使用OpenAI LLM模型分析文档并建立索引信息    index.save_to_disk('index.json')return index#定义聊天函数
#参数input_text：用户输入的问题
def myfirstrobot(input_text):#加载检索用的索引文件index = GPTSimpleVectorIndex.load_from_disk('index.json')response = index.query(input_text, response_mode="compact")return response.response#回调函数chatbot
#通过inputs向函数chatbot传入用户问题
#Input的Placeholder设置为"Enter your text"
#网页标题设置为"Custom-trained AI Chatbot"
iface = gr.Interface(fn=myfirstrobot,inputs=gr.inputs.Textbox(lines=7, label="Enter your text"),outputs="text",title="Custom-trained AI Chatbot")
#指定训练文件的路径，并生成索引文件
index = construct_index("docs")
#启动网页
iface.launch(share=True)

需要训练的问卷放在指定文件夹下