it as markdown, and thus not showing new lines properly))    """    passthe roles here are model native roles (assistant, user, system for chatGPT)Optional sectionsyou can define a whole sections of your prompt that should be optionalif any input in the section is missing, the whole section won't be renderedthe syntax for this is as follows:@llm_promptdef prompt_with_optional_partials():    """    this text will be rendered always, but    {? anything inside this block will be rendered only if all the {value}s parameters are not empty (None | "")   ?}    you can also place it in between the words    this too will be rendered{? , but        this  block will be rendered only if {this_value} and {this_value}        is not empty?} !    """Output parsersllm_prompt decorator natively tries to detect the best output parser based on the output type. (if not set, it returns the raw string)list, dict and pydantic outputs are also supported natively (automatically)# this code example is complete and should run as it isfrom langchain_decorators import llm_prompt@llm_promptdef write_name_suggestions(company_business:str, count:int)->list:    """ Write me {count} good name suggestions for company that {company_business}    """    passwrite_name_suggestions(company_business="sells cookies", count=5)More complex structures‚Äãfor dict / pydantic you need to specify the formatting instructions...
lanchchain decorators is a layer on the top of LangChain that provides syntactic sugar üç≠ for writing custom langchain prompts and chains
lanchchain decorators is a layer on the top of LangChain that provides syntactic sugar üç≠ for writing custom langchain prompts and chains ->: it as markdown, and thus not showing new lines properly))    """    passthe roles here are model native roles (assistant, user, system for chatGPT)Optional sectionsyou can define a whole sections of your prompt that should be optionalif any input in the section is missing, the whole section won't be renderedthe syntax for this is as follows:@llm_promptdef prompt_with_optional_partials():    """    this text will be rendered always, but    {? anything inside this block will be rendered only if all the {value}s parameters are not empty (None | "")   ?}    you can also place it in between the words    this too will be rendered{? , but        this  block will be rendered only if {this_value} and {this_value}        is not empty?} !    """Output parsersllm_prompt decorator natively tries to detect the best output parser based on the output type. (if not set, it returns the raw string)list, dict and pydantic outputs are also supported natively (automatically)# this code example is complete and should run as it isfrom langchain_decorators import llm_prompt@llm_promptdef write_name_suggestions(company_business:str, count:int)->list:    """ Write me {count} good name suggestions for company that {company_business}    """    passwrite_name_suggestions(company_business="sells cookies", count=5)More complex structures‚Äãfor dict / pydantic you need to specify the formatting instructions...