⚡ Bolt: Optimize JSON parsing in Pydantic validators

[google-labs-jules]

💡 What: Implemented @functools.lru_cache on a private helper function _parse_types_json that parses JSON strings into immutable tuples, and updated the public parse_pokemon_types validator to return a fresh list copy.
🎯 Why: Pydantic v2 validators (mode="before") for Pokemon and PokemonSummary call parse_pokemon_types to parse JSON strings from the database. Parsing the same JSON strings (e.g., ["grass", "poison"]) repeatedly is a performance bottleneck, particularly for list endpoints fetching many records.
📊 Impact: Reduces repetitive JSON parsing overhead by caching the results for identical type strings, which significantly improves the performance of generating Pydantic model lists from database queries.
🔬 Measurement: Run PYTHONPATH=src python3 -m pytest tests/test_pokemon_schemas.py to verify functionality is preserved, and test the /api/v1/pokemon endpoint performance to observe the speedup.

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PR created automatically by Jules for task 9817129356697477254 started by @edsonesf

[google-labs-jules]

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