通过Promptulate低代码解决方案轻松创建高效LLM Agent应用

import promptulate as pne

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"},
]
response: str = pne.chat(messages=messages)
print(response)

I am a helpful assistant designed to assist you with any questions or tasks you may have. How can I assist you today?

import promptulate as pne

response = pne.chat(
    messages="When is your knowledge up to?",
    model="gpt-4-1106-preview"
)
print(response)

My knowledge is up to date as of March 2021. Any events or developments occurring after that date would not be included in my responses. If you're asking for any recent information or updates, I recommend checking the latest sources as my information might not be current.

import os
import promptulate as pne

os.environ["ANTHROPIC_API_KEY"] = "你的API密钥"

response = pne.chat(messages=messages, model="claude-2")
print(response)

from typing import List
import promptulate as pne
from pydantic import BaseModel, Field

class LLMResponse(BaseModel):
    provinces: List[str] = Field(description="中国所有省份")

response: LLMResponse = pne.chat(
    messages="请告诉我中国所有的省份。",
    output_schema=LLMResponse
)
print(response.provinces)

I am an AI assistant here to help you with any questions or tasks you may have. How can I assist you today?
{'id': 'chatcmpl-8UK0tfwlkixWyaxKJ2XWNGMVGFPo0', 'choices': [{'finish_reason': 'stop', 'index': 0, 'message': {'content': 'I am an AI assistant here to help you with any questions or tasks you may have. How can I assist you today?', 'role': 'assistant'}}], 'created': 1702237461, 'model': 'gpt-3.5-turbo-0613', 'object': 'chat.completion', 'system_fingerprint': None, 'usage': {'completion_tokens': 25, 'prompt_tokens': 20, 'total_tokens': 45}, '_response_ms': 2492.372}

import promptulate as pne

response = pne.chat("你是谁？", stream=True)

for chunk in response:
    print(chunk)