dingo / rules

---
description: Python coding conventions for the Dingo SDK
globs:
  - dingo/**/*.py
  - mcp_server.py
  - test/**/*.py
---

# Python Conventions

## Code Style

- Follow PEP 8 (enforced by pre-commit hooks)
- Use type hints for function signatures
- Use `@classmethod` for evaluator `eval()` methods
- Prefer `getattr(obj, 'field', default)` over direct attribute access for optional Data fields

## Import Rules

- **Core deps** (numpy, pydantic, requests, etc.): top-level imports OK
- **Optional deps** (pyarrow, transformers, boto3, sqlalchemy, cv2, fasttext, etc.): **must** use lazy imports inside methods with clear `ImportError` messages:

```python
# Correct — lazy import with helpful error
def load_data(self):
    try:
        import pyarrow.parquet as pq
    except ImportError:
        raise ImportError("pyarrow is required for Parquet support. Install: pip install dingo-python[parquet]")

# Wrong — top-level import of optional dep
import pyarrow.parquet as pq
```

## Evaluator Patterns

### Rule Evaluator

```python
@Model.rule_register('QUALITY_BAD_CATEGORY', ['default', 'pretrain'])
class RuleMyCheck(BaseRule):
    _required_fields = [RequiredField.CONTENT]

    @classmethod
    def eval(cls, input_data: Data) -> EvalDetail:
        res = EvalDetail(metric=cls.__name__)
        # evaluation logic
        if problem_found:
            res.status = True
            res.label = [f"{cls.metric_type}.{cls.__name__}"]
            res.reason = ["Description of the issue"]
        else:
            res.label = [QualityLabel.QUALITY_GOOD]
        return res
```

### LLM Evaluator

```python
@Model.llm_register('LLMMyEvaluator')
class LLMMyEvaluator(BaseOpenAI):
    prompt = """Your evaluation prompt here..."""

    @classmethod
    def build_messages(cls, input_data: Data) -> List:
        return [
            {'role': 'system', 'content': cls.prompt},
            {'role': 'user', 'content': input_data.content}
        ]
```

## Error Handling

- Evaluators should not raise exceptions for bad input data; return `EvalDetail` with appropriate error label instead
- Use `log.warning()` / `log.error()` from `dingo.utils` for logging
- External API calls: always handle timeouts and connection errors

## Testing

- Test files: `test/scripts/` mirroring `dingo/` structure
- Use pytest fixtures and parametrize for multiple test cases
- Test data in `test/data/`