abstract_essentials

The lean, dependency-free core carved out of abstract_utilities.

Every symbol here depends only on the Python standard library — no third-party imports, no from .imports import * chains, no submodule fan-out. It imports fast and installs anywhere (desktop, server, Termux/Android phone), which makes it a safe foundation for the rest of the abstract_* tree to build on without dragging in the monolith.

Why this exists

abstract_utilities is imported by nearly every abstract_* package, so its size and entanglement (star-exports across a dozen submodules) ripple everywhere. abstract_essentials is the ~50-function subset that is actually used in practice, extracted as a clean, explicit API.

Install

pip install abstract_essentials

Use

from abstract_essentials import make_list, get_any_value, safe_read_from_json, get_logFile

The public API is whatever is listed in abstract_essentials.__all__ (53 symbols: json/path/file/string/list/type/log helpers).

Migrating off abstract_utilities

abstract_utilities can become a thin compatibility shim that re-exports from here (see abstract_utilities_compat_shim.py shipped alongside this scaffold), so existing from abstract_utilities import X keeps working while new code imports from abstract_essentials.

ML/model MIME hints

MIME_TYPES['machine_learning'] and MEDIA_TYPES['machine_learning'] cover GGUF, safetensors, ONNX, PyTorch, TFLite, Keras and Core ML files, plus shared checkpoint, array and serialization extensions. The application/x-* model labels are local package conventions, not asserted IANA registrations.

from abstract_essentials import get_mime_type, derive_media_type, derive_media_types

get_mime_type('weights.SAFETENSORS')  # 'application/x-safetensors'
derive_media_type('weights.gguf')    # 'machine_learning'
derive_media_types('weights.bin')   # ('executable', 'machine_learning')
derive_media_types('weights.h5')    # ('data_science', 'pandas_data', 'machine_learning')

These functions inspect the filename only and accept paths or dotted extensions. Both category helpers are case-insensitive. The singular helper preserves the first-match order of existing categories; the plural helper retains ambiguity. Generic .bin, .ckpt, .pb, .model, and pickle files keep application/octet-stream: an extension does not establish model contents. JSON configs remain documents; a model directory's supporting files are not automatically models. Directory bundles are not detected by these helpers.

For discovery, use these mappings to shortlist candidates, then inspect content and retain detected format, container MIME, and extension agreement separately. For example, PyTorch checkpoints can use ZIP containers, while safetensors uses a length-prefixed JSON tensor header. A filename MIME hint is not proof that either parser will accept the file, nor permission to unpickle it.

The existing find_utils defaults derive their extension sets from this registry, so new ML extensions also enter its default exclusion set. Pass explicit scan filters when collecting model files.

License

MIT — putkoff / Abstract Endeavors.