bootstrap_fabric_env

Public callable

Bootstrap 00_env_config environment readiness for FabricOps notebooks.

This is a one-call bootstrap helper used at the start of a FabricOps run. It validates/loads configuration, resolves required environment targets, gathers runtime and AI availability metadata, and optionally executes smoke checks before quality/governance/lineage workflows continue.

Parameters:

Name	Type	Description	Default
env	str	Environment key to bootstrap from path_config.	"Sandbox"
required_targets	list[str] | None	Target names that must resolve for the selected environment. Defaults to ["Source", "Unified"].	None
check_ai	bool	Whether to include Fabric AI availability checks.	True
smoke_test	bool	Whether to execute :func:run_config_smoke_tests.	True
config	FrameworkConfig | dict[str, Any] | None	Framework configuration object or compatible mapping.	None
notebook_name	str | None	Notebook name used in runtime metadata and naming checks.	None

Returns:

Type	Description
ConfigBootstrapResult	Structured bootstrap result containing resolved paths, runtime metadata, AI status, smoke-check results, and overall readiness status.

Raises:

Type	Description
ValueError	Raised when config is missing or fails configuration/path validation.

Notes

Side effects are limited to runtime/import checks. The helper does not create Fabric assets or write persistent state.

Examples:

>>> result = bootstrap_fabric_env(config=my_config, env="Sandbox", notebook_name="00_env_config")
>>> result.readiness_status in {"ready", "not_ready"}
True

Source code in src/fabricops_kit/config.py

def bootstrap_fabric_env(
    env: str = "Sandbox",
    required_targets: list[str] | None = None,
    check_ai: bool = True,
    smoke_test: bool = True,
    config: FrameworkConfig | dict[str, Any] | None = None,
    notebook_name: str | None = None,
) -> ConfigBootstrapResult:
    """Bootstrap 00_env_config environment readiness for FabricOps notebooks.

    This is a one-call bootstrap helper used at the start of a FabricOps run.
    It validates/loads configuration, resolves required environment targets,
    gathers runtime and AI availability metadata, and optionally executes smoke
    checks before quality/governance/lineage workflows continue.

    Parameters
    ----------
    env : str, default="Sandbox"
        Environment key to bootstrap from ``path_config``.
    required_targets : list[str] | None, optional
        Target names that must resolve for the selected environment. Defaults
        to ``["Source", "Unified"]``.
    check_ai : bool, default=True
        Whether to include Fabric AI availability checks.
    smoke_test : bool, default=True
        Whether to execute :func:`run_config_smoke_tests`.
    config : FrameworkConfig | dict[str, Any] | None, optional
        Framework configuration object or compatible mapping.
    notebook_name : str | None, optional
        Notebook name used in runtime metadata and naming checks.

    Returns
    -------
    ConfigBootstrapResult
        Structured bootstrap result containing resolved paths, runtime metadata,
        AI status, smoke-check results, and overall readiness status.

    Raises
    ------
    ValueError
        Raised when ``config`` is missing or fails configuration/path
        validation.

    Notes
    -----
    Side effects are limited to runtime/import checks. The helper does not
    create Fabric assets or write persistent state.

    Examples
    --------
    >>> result = bootstrap_fabric_env(config=my_config, env="Sandbox", notebook_name="00_env_config")
    >>> result.readiness_status in {"ready", "not_ready"}
    True
    """
    normalized = load_fabric_config(config) if config is not None else None
    if normalized is None:
        raise ValueError("config is required for bootstrap_fabric_env.")
    required_targets = required_targets or ["Source", "Unified"]
    resolved_paths = {target: get_path(env=env, target=target, config=normalized) for target in required_targets}
    ai_result = check_fabric_ai_functions_available() if check_ai else {"available": None, "message": "AI check disabled."}
    runtime_meta = _get_fabric_runtime_metadata(notebook_name=notebook_name)
    smoke = run_config_smoke_tests(
        normalized,
        env=env,
        required_targets=required_targets,
        check_ai=check_ai,
        check_io_import=False,
        notebook_name=notebook_name,
        ai_result=ai_result,
    ) if smoke_test else []
    status = "ready" if all(r.status in {"pass", "skipped", "warn"} for r in smoke) else "not_ready"
    return ConfigBootstrapResult(
        environment=env,
        paths=resolved_paths,
        runtime_metadata=runtime_meta,
        ai_availability=ai_result,
        smoke_test_results=smoke,
        readiness_status=status,
    )