turbo ai
0.3.11
Cara idiomatik untuk membangun aplikasi chatgpt menggunakan generator async di python
API ChatGPT menggunakan format input baru yang disebut ChatML. Di Openai's Python Client, format ini digunakan sesuatu seperti ini:
messages = [
{ "role" : "system" , "content" : "Greet the user!" },
{ "role" : "user" , "content" : "Hello world!" },
]Idenya di sini adalah untuk membangun pesan secara bertahap menggunakan generator async dan kemudian menggunakannya untuk menghasilkan penyelesaian. Generator async adalah abstraksi yang sangat fleksibel dan sederhana untuk melakukan hal -hal semacam ini. Mereka juga dapat disusun bersama dengan sangat mudah.
# 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 )Lihat contoh lebih rinci di bawah ini.
pip install turbo-chat 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?
#Anda juga dapat menyesuaikan bagaimana pesan tersebut bertahan di antara eksekusi.
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 ():
... @ turbo ()
async def app ( some_param : Any , memory : BaseMemory ):
messages = await memory . get ()
... @ 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 Anda juga dapat menagih kelas BaseCache untuk membuat cache khusus.
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
