yshs 1.1.0

The open-source yshs library.

Here is the open library of Yuan Shang Han Shan (YSHS) co. ltd.

Install

pip install yshs

Update logs

• 20240617, v1.1.0 支持OpenAI格式

Usage

List Models

列出所有可用的模型

import os, sys
import yshs
yshs.api_key = os.getenv('YSHS_API_KEY')

response = yshs.Models.list(refresh=True, return_all_info=True)
print(response)

Reuqest Model via OpenAI Client

from openai import OpenAI

base_url = 'https://www.yshs.vip/v1'
api_key = os.getenv("YSHS_API_KEY")
model = "openai/gpt-3.5-turbo"
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "hello"},  
]

clent = OpenAI(
    base_url='https://www.yshs.vip/v1',
    api_key=os.getenv("YSHS_API_KEY")
)

rst = client.chat.completions.create(
    model=model,
    messages=messages,
    stream=True)

full_response = ''
for chunk in res:
    x = chunk.choices[0].delta.content
    if x:
        full_response += x
        print(x, end='', flush=True)
print()  

Request AI Model

import os, sys
import yshs
yshs.api_key = os.getenv('YSHS_API_KEY')

def request_model():
    responese = yshs.LLM.chat(
        model="openai/gpt-3.5-turbo",  # 选择模型
        messages=[
          {"role": "system", "content": "You are a helpful assistant."},  # 系统提示
          {"role": "user", "content": "Who won the world series in 2020?"},  # 第一个问题
          # {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},  # 第一个的答案
          # {"role": "user", "content": "Where was it played?"}  # 第二个问题
      ]
    )

    full_response = ""
    for x in responese:
        sys.stdout.write(x)  # 逐token输出
        sys.stdout.flush()
        full_response += x
    print()
    return full_response

answer = request_model()
# print(answer)

Continuous Conversation

Create thread to continue the conversation automatically.

from yshs import Client

client = Client()  # 注；一个Client可包含多个ChatThead, 一个ChatThead对应一个Conversation（根据chat_id区分），一个Conversation有多个轮次（turns）

prompt = "hello"  # user prompt
for chunk in client.send_prompt(prompt, chat_id=None):  # send_prompt()时自动创建ChatThead
    print(chunk['response'], end='', flush=True)
print()
chat_id = chunk['chat_id']
print(f'chat_id: {chat_id}')

prompt = 'who are you?'
for chunk in client.send_prompt(prompt, chat_id=chat_id):  # send_prompt()时自动匹配ChatThead，若不存在则创建
    print(chunk['response'], end='', flush=True)
print()
print(f'chat_id: {chat_id}')