langchain_community.callbacks.mlflow_callback.MlflowLogger

class langchain_community.callbacks.mlflow_callback.MlflowLogger(**kwargs: Any)

Callback Handler that logs metrics and artifacts to mlflow server.

Parameters

• name (str) – Name of the run.

• experiment (str) – Name of the experiment.

• tags (dict) – Tags to be attached for the run.

• tracking_uri (str) – MLflow tracking server uri.

This handler implements the helper functions to initialize, log metrics and artifacts to the mlflow server.

Methods

__init__(**kwargs)
artifact(path)	To upload the file from given path as artifact.
finish_run()	To finish the run.
html(html, filename)	To log the input html string as html file artifact.
jsonf(data, filename)	To log the input data as json file artifact.
langchain_artifact(chain)
metric(key, value)	To log metric to mlflow server.
metrics(data[, step])	To log all metrics in the input dict.
start_run(name, tags)	To start a new run, auto generates the random suffix for name
table(name, dataframe)	To log the input pandas dataframe as a html table
text(text, filename)	To log the input text as text file artifact.

__init__(**kwargs: Any)

artifact(path: str) → None

To upload the file from given path as artifact.

finish_run() → None

To finish the run.

html(html: str, filename: str) → None

To log the input html string as html file artifact.

jsonf(data: Dict[str, Any], filename: str) → None

To log the input data as json file artifact.

langchain_artifact(chain: Any) → None

metric(key: str, value: float) → None

To log metric to mlflow server.

metrics(data: Union[Dict[str, float], Dict[str, int]], step: Optional[int] = 0) → None

To log all metrics in the input dict.

start_run(name: str, tags: Dict[str, str]) → None

To start a new run, auto generates the random suffix for name

table(name: str, dataframe) → None

To log the input pandas dataframe as a html table

text(text: str, filename: str) → None

To log the input text as text file artifact.