summarize

marvin.fns.summarize

Functions

summarize

def summarize(data: Any, instructions: str | None = None, agent: Agent | None = None, thread: Thread | str | None = None, context: dict[str, Any] | None = None, handlers: list[Handler | AsyncHandler] | None = None, prompt: str | None = None) -> str

Creates a summary of the input data using a language model.

This function uses a language model to analyze the input data and create a concise summary that captures the key information and semantic meaning.

Args: data: The input data to summarize. Can be any type. instructions: Optional additional instructions to guide the summarization. Used to provide specific guidance about what aspects to focus on or how to structure the summary. agent: Optional custom agent to use for summarization. If not provided, the default agent will be used. thread: Optional thread for maintaining conversation context. Can be either a Thread object or a string thread ID. context: Optional dictionary of additional context to include in the task. handlers: Optional list of handlers to use for the task. prompt: Optional prompt to use for the task. If not provided, the default prompt will be used.

Returns: A string containing the generated summary.

Examples:

>>> # Basic summarization
>>> summarize("Long article about climate change...")
'Key findings on climate impacts...'

>>> # Summarize with specific instructions
>>> summarize("Technical documentation...", instructions="Focus on API changes")
'Major API updates include...'

summarize_async

async def summarize_async(data: Any, instructions: str | None = None, agent: Agent | None = None, thread: Thread | str | None = None, context: dict[str, Any] | None = None, handlers: list[Handler | AsyncHandler] | None = None, prompt: str | None = None) -> str

Asynchronously creates a summary of the input data using a language model.

This function uses a language model to analyze the input data and create a concise summary that captures the key information and semantic meaning.

Args: data: The input data to summarize. Can be any type. instructions: Optional additional instructions to guide the summarization. Used to provide specific guidance about what aspects to focus on or how to structure the summary. agent: Optional custom agent to use for summarization. If not provided, the default agent will be used. thread: Optional thread for maintaining conversation context. Can be either a Thread object or a string thread ID. context: Optional dictionary of additional context to include in the task. handlers: Optional list of handlers to use for the task. prompt: Optional prompt to use for the task. If not provided, the default prompt will be used.

Returns: A string containing the generated summary.

Examples:

>>> # Basic summarization
>>> await summarize_async("Long article about climate change...")
'Key findings on climate impacts...'

>>> # Summarize with specific instructions
>>> await summarize_async("Technical documentation...", instructions="Focus on API changes")
'Major API updates include...'

---

Parent Module: fns