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* feat: add Pydantic BaseModel and dataclass support for Python type inference - Add AST-based detection of Pydantic BaseModel inheritance patterns - Add AST-based detection of @dataclass decorator (all variants) - Implement recursive field schema extraction with type inference - Add thread-safe stack-based module storage for nested parsing - Add RAII cleanup guard to ensure memory safety on all code paths - Add security limits: 200 fields max, 10 recursion levels max - Add comprehensive test coverage: 3 new tests for Pydantic/dataclass - Maintain 100% backward compatibility with existing type system This enables ML/AI practitioners to use Pydantic models as function parameters with automatic UI generation from model schemas. Implementation highlights: - Zero code execution: Pure AST analysis for safety - Thread-safe: Stack-based storage prevents race conditions - Memory-safe: RAII pattern guarantees cleanup - Security-hardened: Field count and recursion depth limits - Performance-optimized: Depth-limited recursion, lazy parsing Test results: All 12 tests passing (9 existing + 3 new) Closes #4700 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> * fix: improve Pydantic/dataclass parser robustness and error handling This commit addresses critical bugs and improves error handling in the Python parser for Pydantic BaseModel and dataclass support. ## Critical Fixes 1. **Thread-local storage RAII pattern**: Fixed bug where parse failures could leave the module stack in an inconsistent state. Now uses proper functional composition with .ok().map() to ensure cleanup always happens. 2. **Recursion depth warnings**: Added explicit warning messages when the recursion depth limit (10 levels) is reached during type extraction. Made the limit a named constant for clarity. 3. **Unsupported type warnings**: Added informative warning messages for unsupported type annotations (Union types and forward references) to help users understand why their types aren't being inferred. ## Improvements - Added 10 comprehensive test cases covering: - Empty Pydantic models - List[T] and Optional[T] types - Dataclass with decorator arguments - Dict types - Regular classes (non-model types) - Invalid syntax handling - Datetime fields - Multiple model definitions - Nested models - All 21 tests pass successfully ## Testing Verified that: - Parser handles malformed code gracefully - RAII cleanup works correctly with early returns - Warning messages are clear and actionable - No memory leaks or panics Closes #4700 * refactor: Separate Pydantic/dataclass code into dedicated module. Created src/pydantic_parser.rs with thread-local storage, model detection, and type extraction logic. Moved 12 Pydantic tests to tests/pydantic_tests.rs and removed duplicate code from lib.rs. All 21 tests passing. * opti and publish --------- Co-authored-by: Devdatta Talele <devtalele0@gmail.com> Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>