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mellea.stdlib.genslot

A method to generate outputs based on python functions and a Generative Slot function.

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Functions

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create_response_format

create_response_format(func: Callable[..., R]) -> type[FunctionResponse[R]]
Create a Pydantic response format class for a given function. Args:
  • func: A function with exactly one argument
Returns:
  • A Pydantic model class that inherits from FunctionResponse[T]

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describe_function

describe_function(func: Callable) -> FunctionDict
Generates a FunctionDict given a function. Args:
  • func : Callable function that needs to be passed to generative slot.
Returns:
  • Function dict of the passed function.

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get_annotation

get_annotation(func: Callable, key: str, val: Any) -> str
Returns a annotated list of arguments for a given function and list of arguments. Args:
  • func : Callable Function
  • key : Arg keys
  • val : Arg Values
Returns:
  • An annotated string for a given func.

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bind_function_arguments

bind_function_arguments(func: Callable[P, R], *args: P.args, **kwargs: P.kwargs) -> dict[str, Any]
Bind arguments to function parameters and return as dictionary. Args:
  • func: The function to bind arguments for.
  • *args: Positional arguments to bind.
  • **kwargs: Keyword arguments to bind.
Returns:
  • Dictionary mapping parameter names to bound values with defaults applied.

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generative

generative(func: Callable[P, R]) -> GenerativeSlot[P, R]
Convert a function into an AI-powered function. This decorator transforms a regular Python function into one that uses an LLM to generate outputs. The function’s entire signature - including its name, parameters, docstring, and type hints - is used to instruct the LLM to imitate that function’s behavior. The output is guaranteed to match the return type annotation using structured outputs and automatic validation. Tip: Write the function and docstring in the most Pythonic way possible, not like a prompt. This ensures the function is well-documented, easily understood, and familiar to any Python developer. The more natural and conventional your function definition, the better the AI will understand and imitate it. Args:
  • func: Function with docstring and type hints. Implementation can be empty (…).
Returns:
  • An AI-powered function that generates responses using an LLM based on the
  • original function’s signature and docstring.
Examples: 
>>> from mellea import generative, start_session
>>> session = start_session()
>>> @generative
... def summarize_text(text: str, max_words: int = 50) -> str:
...     '''Generate a concise summary of the input text.'''
...     ...
>>>
>>> summary = summarize_text(session, "Long text...", max_words=30)
>>> from typing import List
>>> from dataclasses import dataclass
>>>
>>> @dataclass
... class Task:
...     title: str
...     priority: str
...     estimated_hours: float
>>>
>>> @generative
... def create_project_tasks(project_desc: str, count: int) -> List[Task]:
...     '''Generate a list of realistic tasks for a project.
...
...     Args:
...         project_desc: Description of the project
...         count: Number of tasks to generate
...
...     Returns:
...         List of tasks with titles, priorities, and time estimates
...     '''
...     ...
>>>
>>> tasks = create_project_tasks(session, "Build a web app", 5)
>>> @generative
... def analyze_code_quality(code: str) -> Dict[str, Any]:
...     '''Analyze code quality and provide recommendations.
...
...     Args:
...         code: Source code to analyze
...
...     Returns:
...         Dictionary containing:
...         - score: Overall quality score (0-100)
...         - issues: List of identified problems
...         - suggestions: List of improvement recommendations
...         - complexity: Estimated complexity level
...     '''
...     ...
>>>
>>> analysis = analyze_code_quality(
...     session,
...     "def factorial(n): return n * factorial(n-1)",
...     model_options={"temperature": 0.3}
... )
>>> @dataclass
... class Thought:
...     title: str
...     body: str
>>>
>>> @generative
... def generate_chain_of_thought(problem: str, steps: int = 5) -> List[Thought]:
...     '''Generate a step-by-step chain of thought for solving a problem.
...
...     Args:
...         problem: The problem to solve or question to answer
...         steps: Maximum number of reasoning steps
...
...     Returns:
...         List of reasoning steps, each with a title and detailed body
...     '''
...     ...
>>>
>>> reasoning = generate_chain_of_thought(session, "How to optimize a slow database query?")

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Classes

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FunctionResponse

Generic base class for function response formats.

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FunctionDict

Return Type for a Function Component.

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ArgumentDict

Return Type for a Argument Component.

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Argument

An Argument Component.

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Function

A Function Component.

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GenerativeSlot

A generative slot component. Methods:

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parts

parts(self)
Not implemented.

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format_for_llm

format_for_llm(self) -> TemplateRepresentation
Formats the instruction for Formatter use.