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| def _construct_from_dict(self, data: dict[str, Any]) -> Optional[Config]: | ||
| filtered: dict[str, Any] = {} | ||
| allowed_top = {field.name for field in fields(Config)} | ||
| for key, value in data.items(): | ||
| if key in allowed_top: | ||
| filtered[key] = value | ||
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| for section, schema in ( | ||
| ("llm", LLMConfig), | ||
| ("prompt", PromptConfig), | ||
| ("database", DatabaseConfig), | ||
| ("evaluator", EvaluatorConfig), | ||
| ): | ||
| section_data = filtered.get(section) | ||
| if isinstance(section_data, dict): | ||
| allowed_fields = {field.name for field in fields(schema)} | ||
| filtered[section] = { | ||
| name: section_data[name] | ||
| for name in allowed_fields | ||
| if name in section_data |
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The new _construct_from_dict builds allowed_top and section field sets via dataclasses.fields(Config) and the same for LLMConfig, PromptConfig, etc. These types are pydantic BaseModels (the surrounding code already relies on Config.model_validate), so dataclasses.fields() raises TypeError: must be called with a dataclass type or instance. As a result the fallback loader now crashes whenever load_config() fails—which is exactly when this path is exercised—so heavy hitter workflows without an API key cannot load at all. Use pydantic’s model_fields (or another safe method) to enumerate allowed keys instead of dataclasses.fields.
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Summary
Testing
https://chatgpt.com/codex/tasks/task_e_6900a73460c8833085587f41b9dacefa