aw.preparing

Preparation agent for transforming data to meet target requirements.

Implements ReAct pattern with functional validation (try-the-purpose approach).

class aw.preparing.PreparationAgent(config: StepConfig = None, target: str = 'generic', target_validator: Callable = None)

Agent that prepares data to meet target requirements.

Uses ReAct loop with functional validation: 1. Thought: Analyze current data state vs. requirements 2. Action: Generate transformation code 3. Observe: Execute code and capture result 4. Validate: Try to use data for its purpose (e.g., visualization) 5. Repeat or finish

Example

>>> agent = PreparationAgent(target='cosmo-ready')
>>> context = Context({'loading': {'df': df}})
>>> prepared_df, metadata = agent.execute(df, context)

execute(input_df: Any, context: MutableMapping[str, Any]) → tuple[Any, dict[str, Any]]

Execute preparation agent to transform data.

Parameters:

• input_df – Input DataFrame to prepare

• context – Context for storing intermediate results

Returns:

Tuple of (prepared_dataframe, metadata)

aw.preparing.create_preparation_agent(target: str = 'generic', validator: Callable = None, llm: str = None, max_retries: int = 3) → PreparationAgent

Factory function to create a preparation agent.

Parameters:

• target – Target format/purpose

• validator – Custom validator function

• llm – LLM model name or callable

• max_retries – Maximum retry attempts

Returns:

Configured PreparationAgent

Example

>>> agent = create_preparation_agent(
...     target='cosmo-ready',
...     validator=cosmo_validator,
...     max_retries=5
... )