@llm_prompt
def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers")->str:
    """
    Write me a short header for my post about {topic} for {platform} platform. 
    It should be for {audience} audience.
    (Max 15 words)
    """
    return

# run it naturally
write_me_short_post(topic="starwars")
# or
write_me_short_post(topic="starwars", platform="redit")

# define global settings for all prompty (if not set - chatGPT is the current default)
from langchain_decorators import GlobalSettings

GlobalSettings.define_settings(
    default_llm=ChatOpenAI(temperature=0.0), this is default... can change it here globally
    default_streaming_llm=ChatOpenAI(temperature=0.0,streaming=True), this is default... can change it here for all ... will be used for streaming
)

#You can change the default prompt types
from langchain_decorators import PromptTypes, PromptTypeSettings

PromptTypes.AGENT_REASONING.llm = ChatOpenAI()

# Or you can just define your own ones:
class MyCustomPromptTypes(PromptTypes):
    GPT4=PromptTypeSettings(llm=ChatOpenAI(model="gpt-4"))

@llm_prompt(prompt_type=MyCustomPromptTypes.GPT4) 
def write_a_complicated_code(app_idea:str)->str:
    ...

from langchain_openai import OpenAI

@llm_prompt(
    llm=OpenAI(temperature=0.7),
    stop_tokens=["\nObservation"],
    ...
    )
def creative_writer(book_title:str)->str:
    ...

@llm_prompt()
async def write_me_short_post(topic:str, platform:str="twitter", memory:SimpleMemory = None):
    """
    {history_key}
    Write me a short header for my post about {topic} for {platform} platform. 
    It should be for {audience} audience.
    (Max 15 words)
    """
    pass

await write_me_short_post(topic="old movies")

# this code example is complete and should run as it is

from 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 world
tokens=[]
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 capture_stream will be captured here
with StreamingContext(stream_to_stdout=True, callback=capture_stream_func):
    result = await run_prompt()
    print("Stream finished ... we can distinguish tokens thanks to alternating colors")


print("\nWe've captured",len(tokens),"tokens🎉\n")
print("Here is the result:")
print(result)

@llm_prompt
def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers"):
    """
    Here is a good way to write a prompt as part of a function docstring, with additional documentation for devs.

    It needs to be a code block, marked as a `<prompt>` language
    ```<prompt>
    Write me a short header for my post about {topic} for {platform} platform. 
    It should be for {audience} audience.
    (Max 15 words)

# this code example is complete and should run as it is

from langchain_decorators import llm_prompt

@llm_prompt
def write_name_suggestions(company_business:str, count:int)->list:
    """ Write me {count} good name suggestions for company that {company_business}
    """
    pass

write_name_suggestions(company_business="sells cookies", count=5)

from langchain_decorators import llm_prompt
from pydantic import BaseModel, Field


class TheOutputStructureWeExpect(BaseModel):
    name:str = Field (description="The name of the company")
    headline:str = Field( description="The description of the company (for landing page)")
    employees:list[str] = Field(description="5-8 fake employee names with their positions")

@llm_prompt()
def fake_company_generator(company_business:str)->TheOutputStructureWeExpect:
    """ Generate a fake company that {company_business}
    {FORMAT_INSTRUCTIONS}
    """
    return

company = fake_company_generator(company_business="sells cookies")

# print the result nicely formatted
print("Company name: ",company.name)
print("company headline: ",company.headline)
print("company employees: ",company.employees)

from pydantic import BaseModel
from langchain_decorators import llm_prompt

class AssistantPersonality(BaseModel):
    assistant_name:str
    assistant_role:str
    field:str

    @property
    def a_property(self):
        return "whatever"

    def hello_world(self, function_kwarg:str=None):
        """
        We can reference any {field} or {a_property} inside our prompt... and combine it with {function_kwarg} in the method
        """

    
    @llm_prompt
    def introduce_your_self(self)->str:
        """
        ``` <prompt:system>
        You are an assistant named {assistant_name}. 
        Your role is to act as {assistant_role}