Agnostic config parser and validator for Python applications.
Project description
fig-jam
Single-call configuration loader that discovers, parses, validates, and documents project settings with minimal boilerplate.
Quick start
from fig_jam import get_config
config = get_config(
path="./config.yaml", # file or directory; defaults to ~
section="database", # optional top-level key to extract
validator={"host": str, "port": int}, # or list[str], dataclass, Pydantic
)
print(config) # {"host": "127.0.0.1", "port": 8050}
get_config returns validated data when exactly one candidate succeeds, otherwise
raises ConfigError with actionable guidance and a suggested config template.
What it does
- Discovers config files when given a directory; chooses the sole candidate that passes validation, or asks you to disambiguate.
- Parses common formats: JSON; INI/CFG; TOML via
tomllib/tomli; YAML viapyyaml. Files are decoded with BOM-aware UTF-8 fallback to ASCII/Latin-1/CP1252. - Applies environment overrides declared on dataclass or Pydantic validators via
__env_overrides__ = {"field": "ENV_VAR"}before validation. - Validates payloads using:
None: return raw mapping.list[str]: assert keys exist;strict=True(default) filters to those keys.dict[str, type]: assert keys exist, coerce types, return filtered mapping.- Dataclass: coerce field types (including nested dataclasses/models) and instantiate.
- Pydantic model: let Pydantic validate and instantiate.
strictmust stayTruefor dict/dataclass/Pydantic validators; may be set toFalsewith a list validator to keep extra keys.
Why it’s helpful
- Zero per-project parser setup—formats are auto-detected and optional dependencies are only required when their formats are present.
- Clear
ConfigErrormessages capture discovery, parsing, overrides, and validation diagnostics so you know what to fix next. - Built-in template rendering shows the expected shape (and env vars) for the current validator and file format.
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