text: It should be for {audience} audience.    (Max 15 words)    """    passawait write_me_short_post(topic="old movies")Simplified streamingIf we want to leverage streaming:we need to define prompt as async function turn on the streaming on the decorator, or we can define PromptType with streaming oncapture the stream using StreamingContextThis way we just mark which prompt should be streamed, not needing to tinker with what LLM should we use, passing around the creating and distribute streaming handler into particular part of our chain... just turn the streaming on/off on prompt/prompt type...The streaming will happen only if we call it in streaming context ... there we can define a simple function to handle the stream# this code example is complete and should run as it isfrom langchain_decorators import StreamingContext, llm_prompt# this will mark the prompt for streaming (useful if we want stream just some prompts in our app... but don't want to pass distribute the callback handlers)# note that only async functions can be streamed (will get an error if it's not)@llm_prompt(capture_stream=True) async def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers"):    """    Write me a short header for my post about {topic} for {platform} platform.     It should be for {audience} audience.    (Max 15 words)    """    pass# just an arbitrary  function to demonstrate the streaming... will be some websockets code in the real worldtokens=[]def capture_stream_func(new_token:str):    tokens.append(new_token)# if we want to capture the stream, we need to wrap the execution into StreamingContext... # this will allow us to capture the stream even if the prompt call is hidden inside higher level method# only the prompts marked with