Enhance documentation and attributes for reporting-related models

[jfrench9]

Summary

This PR enhances documentation and attribute definitions across four core models used in the reporting pipeline: AttributionFilter, ClassificationLite, InitialEntityData, and LineItemMetadataPredicate. These changes improve code clarity, maintainability, and correctness of the model layer.

Key Accomplishments

• Attribution Filter: Refined attribute definitions and documentation, improving clarity around filter behavior and usage (~25 lines updated with structural improvements)
• Classification Lite: Minor documentation/attribute updates to align with broader model conventions
• Initial Entity Data: Significant expansion with 45 new lines of code, adding new attributes and comprehensive documentation to support richer entity initialization workflows
• Line Item Metadata Predicate: Restructured and improved attribute definitions and documentation for better readability and accuracy (~45 lines refactored)

Changes Breakdown

Model	Type of Change
attribution_filter.py	Attribute refinement, doc improvements
classification_lite.py	Minor doc/attribute alignment
initial_entity_data.py	Major expansion — new attributes and documentation
line_item_metadata_predicate.py	Restructuring and doc improvements

Breaking Changes

• Potential breaking changes if downstream consumers rely on specific attribute names or structures in AttributionFilter or LineItemMetadataPredicate that were refactored. Consumers of InitialEntityData may need to account for newly added attributes.
• Review serialization/deserialization contracts to ensure API compatibility is maintained.

Testing Notes

• Verify that all four models serialize and deserialize correctly against the expected API contract
• Validate that existing reporting workflows (attribution filtering, classification, entity initialization, line item metadata predicates) function as expected with the updated models
• Pay special attention to InitialEntityData as it received the most substantial additions — ensure new attributes have proper default handling and validation
• Confirm backward compatibility with any persisted data or cached model instances

Infrastructure Considerations

• No infrastructure changes required
• These are model-layer changes only; however, if any code generation tooling is used to produce these models, ensure the generator templates are also updated to stay in sync
• Dependent services consuming these models should be tested for compatibility before deployment to production

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🤖 Generated with Claude Code

Branch Info:

• Source: feature/reporting-styles
• Target: main
• Type: feature

Co-Authored-By: Claude noreply@anthropic.com