attribute that exposes what input variables the prompt template expects.It defines a format method that takes in keyword arguments corresponding to the expected input_variables and returns the formatted prompt.We will create a custom prompt template that takes in the function name as input and formats the prompt to provide the source code of the function. To achieve this, let's first create a function that will return the source code of a function given its name.import inspectdef get_source_code(function_name):    # Get the source code of the function    return inspect.getsource(function_name)Next, we'll create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.from langchain.prompts import StringPromptTemplatefrom pydantic import BaseModel, validatorPROMPT = """\Given the function name and source code, generate an English language explanation of the function.Function Name: {function_name}Source Code:{source_code}Explanation:"""class FunctionExplainerPromptTemplate(StringPromptTemplate, BaseModel):    """A custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function."""    @validator("input_variables")    def validate_input_variables(cls, v):        """Validate that the input variables are correct."""        if len(v) != 1 or "function_name" not in v:            raise ValueError("function_name must be the only input_variable.")        return v    def format(self, **kwargs) -> str:        # Get the source code of the function        source_code = get_source_code(kwargs["function_name"])        # Generate the prompt to be sent to the language model        prompt = PROMPT.format(            function_name=kwargs["function_name"].__name__, source_code=source_code        )        return prompt    def _prompt_type(self):        return "function-explainer"Use the custom prompt template‚ÄãNow that we have
Let's suppose we want the LLM to generate English language explanations of a function given its name. To achieve this task, we will create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.
Let's suppose we want the LLM to generate English language explanations of a function given its name. To achieve this task, we will create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function. ->: attribute that exposes what input variables the prompt template expects.It defines a format method that takes in keyword arguments corresponding to the expected input_variables and returns the formatted prompt.We will create a custom prompt template that takes in the function name as input and formats the prompt to provide the source code of the function. To achieve this, let's first create a function that will return the source code of a function given its name.import inspectdef get_source_code(function_name):    # Get the source code of the function    return inspect.getsource(function_name)Next, we'll create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.from langchain.prompts import StringPromptTemplatefrom pydantic import BaseModel, validatorPROMPT = """\Given the function name and source code, generate an English language explanation of the function.Function Name: {function_name}Source Code:{source_code}Explanation:"""class FunctionExplainerPromptTemplate(StringPromptTemplate, BaseModel):    """A custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function."""    @validator("input_variables")    def validate_input_variables(cls, v):        """Validate that the input variables are correct."""        if len(v) != 1 or "function_name" not in v:            raise ValueError("function_name must be the only input_variable.")        return v    def format(self, **kwargs) -> str:        # Get the source code of the function        source_code = get_source_code(kwargs["function_name"])        # Generate the prompt to be sent to the language model        prompt = PROMPT.format(            function_name=kwargs["function_name"].__name__, source_code=source_code        )        return prompt    def _prompt_type(self):        return "function-explainer"Use the custom prompt template‚ÄãNow that we have