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Components catalog

Maturity labels

  • Now: Stable and supported in current releases.
  • Preview: Usable today, but behavior and APIs may evolve.
  • Planned: Not yet implemented.

Note

Status: Now

This page lists the bundled Omnipy component families and what practical problem each one solves.

General

General components provide foundational typed utilities used across workflows, including chain-style model composition and generic helper tasks. Use this family when you want reusable type-safe building blocks without committing to a domain-specific format.

Problem it solves: Shared typed primitives for cross-cutting workflow logic.

JSON

JSON components provide models, datasets, tasks, and flows for structured and semi-structured JSON, including nested variants. They are useful when incoming API payloads are irregular and you still want repeatable parsing, coercion, and transformation behavior.

Problem it solves: Turning messy JSON payloads into predictable typed structures.

Nested

Nested components focus on hierarchical content where nested dictionaries/lists are the native representation. They keep deep structures explicit so you can validate and transform without losing shape information too early.

Problem it solves: Working safely with hierarchical data before flattening/table conversion.

Raw

Raw components handle plain bytes/text and line/item/column splitting and joining operations. They are a good fit for pre-parse cleanup steps, lightweight parsing, and conversion between raw and structured representations.

Problem it solves: Deterministic text/byte normalization before typed parsing.

Remote

Remote components provide typed URL/query models and dataset/task patterns for loading data from HTTP endpoints. This family is aimed at API ingestion where you need typed request inputs and structured response handling.

Problem it solves: Reliable API boundary handling with typed request/response shapes.

Tables

Tables components provide row-wise and column-wise tabular models plus CSV/TSV parsing and conversion utilities. They are useful when moving between analysis-friendly records and broader pipeline data structures.

Problem it solves: Typed table parsing and conversion between record-style and column-style data.

Quick sanity check examples

>>> import omnipy as om
>>> NotIterableExceptStrOrBytesModel = om.NotIterableExceptStrOrBytesModel
>>> JsonListOfDictsModel = om.JsonListOfDictsModel
>>> NestedDataset = om.NestedDataset
>>> SplitToLinesModel = om.SplitToLinesModel
>>> HttpUrlModel = om.HttpUrlModel
>>> TsvTableModel = om.TsvTableModel
>>> NotIterableExceptStrOrBytesModel('abc')
>>> JsonListOfDictsModel([{'id': 's1'}])
>>> NestedDataset({'group': {'value': 1}}).json()
>>> SplitToLinesModel('a\nb')
>>> HttpUrlModel('https://example.org/api')
>>> TsvTableModel('a\tb\n1\t2\n')