added from_dict() method to LTRModelConfig

[aarondelgiudice]

from_dict() method adds support for instantiating a LTRModelConfig object from a dictionary. Allows users to easily export/import LTRModelConfig objects as json objects.

Examples
--------
>>> from eland.ml.ltr import LTRModelConfig, QueryFeatureExtractor

>>> ltr_model_config = LTRModelConfig(
...     feature_extractors=[
...        QueryFeatureExtractor(
...            feature_name='title_bm25',
...            query={ "match": { "title": "{{query}}" } }
...        ),
...        QueryFeatureExtractor(
...            feature_name='descritption_bm25',
...            query={ "match": { "description": "{{query}}" } }
...        )
...     ]
... )
... # Optional, export 'ltr_model_config.to_dict()' to JSON for version control
...
... config_dict = < load config_dict from JSON>
... 
... # instantiate a new LTRModelConfig object
... ltr_model_config2 = LTRModelConfig.from_dict(config_dict)

Closes issues 807

[prodsecmachine]

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[Copilot]

Pull Request Overview

This PR adds a from_dict() class method to the LTRModelConfig class to enable instantiation from dictionary representations, supporting JSON import/export functionality for LTR model configurations.

• Added from_dict() class method with comprehensive docstring and examples
• Implemented parsing logic for query extractors from dictionary format
• Added error handling for invalid configurations and unknown extractor types

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[Copilot]

There's a spelling error in the example - 'description_bm25' should match the variable name 'descritption_bm25' used in the PR description, or the PR description should be corrected to use 'description_bm25'.

[Copilot]

Missing validation for the 'feature_extractors' key. If the dictionary doesn't contain this key, a KeyError will be raised. Add a check to ensure 'feature_extractors' exists in the nested dictionary.

Suggested change

	feature_extractors = []
	for feature_extractor in d[TYPE_LEARNING_TO_RANK]["feature_extractors"]:
	if "query_extractor" in feature_extractor:
	if "feature_extractors" not in d[TYPE_LEARNING_TO_RANK]:
	raise ValueError(
	f"Invalid LTR model config, missing 'feature_extractors' key in '{TYPE_LEARNING_TO_RANK}'"
	)
	feature_extractors = []
	for feature_extractor in d[TYPE_LEARNING_TO_RANK]["feature_extractors"]: