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使用python中的异步生成器构建chatgpt应用的惯用方法

关于

CHATGPT API使用称为ChatMl的新输入格式。在Openai的Python客户端中，该格式被使用类似：

 messages = [
    { "role" : "system" , "content" : "Greet the user!" },
    { "role" : "user" , "content" : "Hello world!" },
]

这里的想法是使用异步生成器逐步构建消息，然后使用它来生成完成。异步发电机对于做这种事情的多功能和简单抽象。它们也可以很容易地组成。

 # Equivalent turbo-chat generator
async def example ():
    yield System ( content = "Greet the user!" )
    yield User ( content = "Hello World!" )

    # To run generation, just yield Generate(),
    # the lib will take care of correctly running the app, and
    # return the value back here.
    output = yield Generate ()
    print ( output . content )

请参阅下面的更多详细示例。

安装

pip install turbo-chat

例子

 from typing import AsyncGenerator , Union

from turbo_chat import (
    turbo ,
    System ,
    User ,
    Assistant ,
    GetInput ,
    Generate ,
    run ,
)

# Get user
async def get_user ( id ):
    return { "zodiac" : "pisces" }

# Set user zodiac mixin
# Notice that no `@turbo()` decorator used here
async def set_user_zodiac ( user_id : int ):

    user_data : dict = await get_user ( user_id )
    zodiac : str = user_data [ "zodiac" ]

    yield User ( content = f"My zodiac sign is { zodiac } " )


# Horoscope app
@ turbo ( temperature = 0.0 )
async def horoscope ( user_id : int ):

    yield System ( content = "You are a fortune teller" )

    # Yield from mixin
    async for output in set_user_zodiac ( user_id ):
        yield output

    # Prompt runner to ask for user input
    input = yield GetInput ( message = "What do you want to know?" )

    # Yield the input
    yield User ( content = input )

    # Generate (overriding the temperature)
    value = yield Generate ( temperature = 0.9 )

# Let's run this
app : AsyncGenerator [ Union [ Assistant , GetInput ], str ] = horoscope ({ "user_id" : 1 })

_input = None
while not ( result := await ( app . run ( _input )). done :
    if result . needs_input :
        # Prompt user with the input message
        _input = input ( result . content )
        continue

    print ( result . content )

# Output
# >>> What do you want to know? Tell me my fortune
# >>> As an AI language model, I cannot predict the future or provide supernatural fortune-telling. However, I can offer guidance and advice based on your current situation and past experiences. Is there anything specific you would like me to help you with?
#

自定义内存

您还可以在执行之间自定义如何在执行之间持续存在。

 from turbo_chat import turbo , BaseMemory

class RedisMemory ( BaseMemory ):
    """Implement BaseMemory methods here"""

    async def setup ( self , ** kwargs ) -> None :
        ...

    async def append ( self , item ) -> None :
        ...

    async def clear ( self ) -> None :
        ...


# Now use the memory in a turbo_chat app
@ turbo ( memory_class = RedisMemory )
async def app ():
    ...

直接获取对内存对象的访问（只需声明附加参数）

 @ turbo ()
async def app ( some_param : Any , memory : BaseMemory ):

    messages = await memory . get ()
    ...

产生对内部使用的响应，但不要屈服于下游

 @ turbo ()
async def example ():
    yield System ( content = "You are a good guy named John" )
    yield User ( content = "What is your name?" )
    result = yield Generate ( forward = False )

    yield User ( content = "How are you doing?" )
    result = yield Generate ()

b = example ()
results = [ output async for output in b ]

assert len ( results ) == 1

添加一个简单的内存中缓存

您还可以子类BaseCache类以创建自定义缓存。

 cache = SimpleCache ()

@ turbo ( cache = cache )
async def example ():
    yield System ( content = "You are a good guy named John" )
    yield User ( content = "What is your name?" )
    result = yield Generate ()

b = example ()
results = [ output async for output in b ]

assert len ( cache . cache ) == 1