Idiomatic way to build chatgpt apps using async generators in python
The ChatGPT API uses a new input format called chatml. In openai's python client, the format is used something like this:
messages = [
{"role": "system", "content": "Greet the user!"},
{"role": "user", "content": "Hello world!"},
]
The idea here is to incrementally build the messages using an async generator and then use that to generate completions. Async generators are incredibly versatile and simple abstraction for doing this kind of stuff. They can also be composed together very easily.
# 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)
See more detailed example below.
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?
#
You can also customize how the messages are persisted in-between the executions.
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
You can also subclass the BaseCache class to create a custom cache.
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
FAQs
Idiomatic way to build chatgpt apps using async generators in python
We found that turbo-chat demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
Malicious Go packages are impersonating popular libraries to install hidden loader malware on Linux and macOS, targeting developers with obfuscated payloads.
Security News
Bybit's $1.46B hack by North Korea's Lazarus Group pushes 2025 crypto losses to $1.6B in just two months, already surpassing all of 2024's $1.49B total.
Security News
OpenSSF has published OSPS Baseline, an initiative designed to establish a minimum set of security-related best practices for open source software projects.