logging
Structured metadata logging.
kitaru.log() attaches structured key-value metadata to the current
checkpoint or execution. It is context-sensitive: inside a checkpoint
it attaches to that checkpoint; inside a flow but outside a checkpoint
it attaches to the execution.
Example
from kitaru import checkpoint
@checkpoint
def call_model(prompt: str) -> str:
response = model.generate(prompt)
kitaru.log(
tokens=response.usage.total_tokens,
cost=response.usage.cost,
model=response.model,
)
return response.textfunclog(**kwargs) -> NoneAttach structured metadata to the current checkpoint or execution.
Standard keys include cost, tokens, latency, but arbitrary
user-defined keys are accepted.
Notes
Values should be JSON-serializable. Metadata is persisted through ZenML's run-metadata APIs. Multiple calls in the same scope append metadata entries; repeated keys with dictionary values are merged on hydration, while repeated non-dictionary keys resolve to latest value.
paramkwargsAny= {}Returns
None