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af9ad48c9b
- config_from_env_py_infra: dataclass + field metadata, tipos str/int/float/bool/list - dotenv_load_py_infra: parser .env con semantica de no-sobreescritura - 15 tests unitarios Python, todos PASS - registry.db actualizado con fn index (685 funciones, 109 tipos) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
"""config_from_env — popula dataclasses desde variables de entorno usando field metadata."""
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import os
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from dataclasses import fields as dc_fields
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def config_from_env(target_class: type) -> object:
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"""Crea una instancia de un dataclass poblada desde variables de entorno.
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Cada campo del dataclass puede tener metadata que guia el mapeo:
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- ``env``: nombre de la variable de entorno (obligatorio para usar este sistema)
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- ``required``: bool, si True y la var no esta seteada lanza ValueError
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- ``default``: valor string usado cuando la var no esta seteada (opcional)
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- ``secret``: bool, documentacion solamente, sin efecto en runtime
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La conversion de tipo es automatica segun el tipo anotado del campo:
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- ``str``: sin conversion
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- ``int``: int(value)
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- ``float``: float(value)
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- ``bool``: True para "1", "true", "yes" (case-insensitive); False para el resto
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- ``list[str]`` o ``list``: split por coma, stripping espacios
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Args:
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target_class: clase dataclass con campo metadata en sus fields.
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Returns:
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instancia de target_class con todos los campos poblados.
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Raises:
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ValueError: si un campo requerido no esta seteado, o si la conversion de tipo falla.
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TypeError: si target_class no es un dataclass.
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"""
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try:
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all_fields = dc_fields(target_class) # raises TypeError if not dataclass
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except TypeError:
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raise TypeError(f"config_from_env: {target_class.__name__} is not a dataclass")
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kwargs: dict = {}
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errors: list[str] = []
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for f in all_fields:
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meta = f.metadata
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env_key: str | None = meta.get("env")
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if env_key is None:
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# no env tag → leave at dataclass default (will be provided via default_factory)
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continue
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required: bool = meta.get("required", False)
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default_str: str | None = meta.get("default")
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raw = os.environ.get(env_key, "")
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if not raw:
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if default_str is not None:
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raw = default_str
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elif required:
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errors.append(f"field '{f.name}': env var '{env_key}' is required but not set")
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continue
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else:
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continue # optional, unset — use dataclass default
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# type conversion
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try:
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kwargs[f.name] = _coerce(raw, f.type, f.name)
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except (ValueError, TypeError) as exc:
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errors.append(str(exc))
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if errors:
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raise ValueError("config_from_env: " + "; ".join(errors))
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return target_class(**kwargs)
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def _coerce(raw: str, annotation: type | str, field_name: str) -> object:
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"""Coerce raw string to the annotated type."""
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# handle string annotations (from __future__ import annotations or forward refs)
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if isinstance(annotation, str):
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ann_str = annotation.lower().strip()
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else:
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# Get the type name for comparison
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ann_str = getattr(annotation, "__name__", str(annotation)).lower()
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if ann_str in ("str", "string"):
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return raw
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if ann_str == "int":
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try:
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return int(raw)
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except ValueError:
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raise ValueError(f"field '{field_name}': cannot convert {raw!r} to int")
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if ann_str == "float":
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try:
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return float(raw)
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except ValueError:
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raise ValueError(f"field '{field_name}': cannot convert {raw!r} to float")
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if ann_str == "bool":
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return raw.strip().lower() in ("1", "true", "yes")
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# list / list[str]
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if "list" in ann_str:
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parts = [p.strip() for p in raw.split(",") if p.strip()]
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return parts
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# fallback: return as string
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return raw
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