qodo-merge-pro[bot]
commented
1 month ago PR Reviewer Guide 🔍
Here are some key observations to aid the review process:
| ⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪ |
| 🧪 No relevant tests |
| 🔒 No security concerns identified |
⚡ Recommended focus areas for reviewPotential Recursion Issue The transformation logic handles recursive types but might still encounter issues with deeply nested or complex type structures. Consider adding a maximum recursion depth limit as a safeguard.```python def _transform_type_recursive(tp: Any, local_cache: dict[Any, Any]) -> Any: """ Does the actual recursion: - If `tp` is in `GLOBAL_REPLACEMENTS`, use that replacement. - If it's a Pydantic model, build a new model with replaced field types. - If it's a container (list, tuple, Union, etc.), transform each argument. - Otherwise, return as-is. """ if tp in local_cache: # Already transformed (or in process); return it return local_cache[tp] # If exactly in the global replacements, do a direct swap if tp in GLOBAL_REPLACEMENTS: replaced = GLOBAL_REPLACEMENTS[tp] local_cache[tp] = replaced return replaced ```Cache Management The global cache (_GLOBAL_TYPE_TRANSFORM_CACHE) grows unbounded without any mechanism to clear it. Consider adding a cache size limit or a way to invalidate entries.```python _GLOBAL_TYPE_TRANSFORM_CACHE: dict[Any, Any] = {} ```Type Preservation When transforming Pydantic models, field validators, config settings, and other model metadata might be lost. Verify if this is acceptable for the use case.```python def _transform_model( model_cls: type[BaseModel], local_cache: dict[Any, Any] ) -> type[BaseModel]: """ Build a new Pydantic model that mirrors `model_cls` but with replaced field types. Prevent infinite recursion with local_cache placeholders. """ # If we've started building this model in the recursion, return immediately if model_cls in local_cache and local_cache[model_cls] is None: return model_cls # Mark as 'in progress' with None local_cache[model_cls] = None # Build new fields field_defs = {} for field_name, field_info in model_cls.model_fields.items(): old_annotation = field_info.annotation new_annotation = _transform_type_recursive(old_annotation, local_cache) if field_info.is_required(): default_val = ... else: default_val = field_info.default field_defs[field_name] = (new_annotation, default_val) new_name = f"{model_cls.__name__}Transformed" NewModel = create_model( new_name, __base__=BaseModel, **field_defs, ) local_cache[model_cls] = NewModel return NewModel ``` |
netlify[bot]
deepsource-io[bot]
JavaScript
Python
AutoGPT-Agent
Although returning a Prisma object on an RPC is a bad practice, we have instances where we do so and the type contains a
prisma.Jsonfield. This Json field can't be seamlessly serialized and then converted back into the Prisma object.Changes 🏗️
Replacing prisma object as return type on notification service with a plain pydantic object as DTO.
Checklist 📋
For code changes: