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Merge pull request #3805 from pipecat-ai/mb/dataclass-basemodel

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Add dataclass vs Pydantic BaseModel convention to CLAUDE.md
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Mark Backman
2026-02-23 19:32:31 -05:00
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parent cdd65b6c0a c527e1f30f
commit 9c2ac661a3
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CLAUDE.md
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@@ -107,6 +107,9 @@ All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
- **Type hints**: Required for complex async code.
- **Dataclass vs Pydantic**: Use `@dataclass` for frames and internal pipeline data (high-frequency, no validation needed). Use Pydantic `BaseModel` for configuration, parameters, metrics, and external API data (benefits from validation and serialization). Specifically:
- `@dataclass`: Frame types, context aggregator pairs, internal data containers
- `BaseModel`: Service `InputParams`, transport/VAD/turn params, metrics data, API request/response models, serializer params
### Docstring Example