"""

MLflow integration for Feast Feature Store.

This module provides seamless integration between Feast and MLflow. When enabled

in feature_store.yaml, feature metadata is logged to MLflow

during get_historical_features and get_online_features calls.

Usage:

Configure MLflow in your feature_store.yaml:

project: my_project

# ... other config ...

mlflow:

enabled: true

tracking_uri: https://mlflow.example.com # or set MLFLOW_TRACKING_URI

auto_log: true

When ``tracking_uri`` is omitted, the ``MLFLOW_TRACKING_URI`` environment

variable is used. If neither is set, MLflow falls back to its own default.

All functionality is accessed through ``store.mlflow``:

- ``store.mlflow.start_run()`` — start an MLflow run pre-tagged with Feast metadata

- ``store.mlflow.log_model()`` — log a model with ``feast_features.json``

- ``store.mlflow.resolve_features()`` — map an MLflow model to its feature service

- ``store.mlflow.get_training_entity_df()`` — reproduce training by pulling entity

data from a previous MLflow run's artifacts

"""

from feast.mlflow_integration.config import MlflowConfig

from feast.mlflow_integration.entity_df_builder import FeastMlflowEntityDfError

from feast.mlflow_integration.model_resolver import FeastMlflowModelResolutionError

__all__ = [

"MlflowConfig",

"FeastMlflowModelResolutionError",

"FeastMlflowEntityDfError",

]