Fix #7203: schema_to_pydantic_model loses $defs type information (array -> object)

[cobyfrombrooklyn-bot]

Problem

schema_to_pydantic_model incorrectly converts non-object $defs entries (like arrays) into empty object models. When a $defs entry has type: "array" with items: {"type": "string"}, the function creates a Pydantic BaseModel with no properties instead of preserving the array type.

This was reported in #7203 with a clear repro case using Pydantic v2's type TaskData = list[str].

Root Cause

_process_definitions() calls json_schema_to_pydantic() for every $defs entry, which ultimately calls _json_schema_to_model(). That method always creates a BaseModel from properties, but non-object definitions (arrays, primitives) have no properties, resulting in an empty model.

Fix

Added _resolve_non_object_def() that checks if a $defs entry has a non-object type. If so, it resolves it to the correct Python type using the existing _extract_field_type() method (which already handles arrays, constrained integers, etc.). Only object-type definitions are processed as Pydantic models.

Tests

Added 3 test cases in test_json_to_pydantic.py:

1. test_defs_with_array_type_preserved - Reproduces the exact bug from schema_to_pydantic_model loses $defs type information (array → object) #7203. Verifies that a $defs entry with type: array, items: {type: string} is correctly preserved as List[str].
2. test_defs_with_integer_type_preserved - Tests primitive type definitions (type: integer with constraints) in $defs.
3. test_defs_with_mixed_object_and_array_types - Tests schemas with both object and non-object $defs entries to ensure they coexist correctly.

All 78 tests pass (75 existing + 3 new). Tested on macOS ARM (Apple Silicon).

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