ju-bezdek/langchain-decoratorsMain principles and benefits:more pythonic way of writing codewrite multiline prompts that won't break your code flow with indentationmaking use of IDE in-built support for hinting, type checking and popup with docs to quickly peek in the function to see the prompt, parameters it consumes etc.leverage all the power of ü¶úüîó LangChain ecosystemadding support for optional parameterseasily share parameters between the prompts by binding them to one classHere is a simple example of a code written with LangChain Decorators ‚ú®@llm_promptdef 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 naturallywrite_me_short_post(topic="starwars")# orwrite_me_short_post(topic="starwars", platform="redit")Quick startInstallation‚Äãpip install langchain_decoratorsExamples‚ÄãGood idea on how to start is to review the examples here:jupyter notebookcolab notebookDefining other parametersHere we are just marking a function as a prompt with llm_prompt decorator, turning it effectively into a LLMChain. Instead of running it Standard LLMchain takes much more init parameter than just inputs_variables and prompt... here is this implementation detail hidden in the decorator.
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 ->: ju-bezdek/langchain-decoratorsMain principles and benefits:more pythonic way of writing codewrite multiline prompts that won't break your code flow with indentationmaking use of IDE in-built support for hinting, type checking and popup with docs to quickly peek in the function to see the prompt, parameters it consumes etc.leverage all the power of ü¶úüîó LangChain ecosystemadding support for optional parameterseasily share parameters between the prompts by binding them to one classHere is a simple example of a code written with LangChain Decorators ‚ú®@llm_promptdef 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 naturallywrite_me_short_post(topic="starwars")# orwrite_me_short_post(topic="starwars", platform="redit")Quick startInstallation‚Äãpip install langchain_decoratorsExamples‚ÄãGood idea on how to start is to review the examples here:jupyter notebookcolab notebookDefining other parametersHere we are just marking a function as a prompt with llm_prompt decorator, turning it effectively into a LLMChain. Instead of running it Standard LLMchain takes much more init parameter than just inputs_variables and prompt... here is this implementation detail hidden in the decorator.