AshMatics Core DataModels

Version: 0.3.1

Canonical Pydantic data models for AshMatics healthcare applications.

Changelog

v0.3.1 (2026-01-25) — ASHKBAPP-66

  • Added PROCESS_DOCUMENTATION to GovernanceCategory enum
  • This is a core CAI framework category required for MCP service compatibility

Overview

This library provides the single source of truth for data contracts across the AshMatics ecosystem:

  • Knowledge Base (KB)
  • CoreApp
  • ashmatics-tools SDK
  • AI Watch applications

Features

  • FDA Vocabulary: OpenFDA-aligned schemas for manufacturers, clearances, classifications, recalls, adverse events
  • MongoDB Document Schemas: Three-tier structure for all kb_* collections (evidence, regulatory, model cards, products, manufacturers, use cases)
  • Governance Document Models: Clinical AI Governance Framework artifacts (policies, SOPs, work products, process documentation)
  • Use Case Taxonomy: Clinical AI use case categorization
  • Rich Validation: Built-in validators for regulatory identifiers (K numbers, product codes)
  • Database Agnostic: Pure Pydantic models, no ORM coupling
  • Type Safe: Full type hints with mypy support

Installation

# From git (recommended for now)
pip install git+https://github.com/AsherInformatics/ashmatics-core-datamodels.git

# Or add to pyproject.toml
# dependencies = [
#     "ashmatics-datamodels @ git+https://github.com/AsherInformatics/ashmatics-core-datamodels.git",
# ]

Quick Start

from ashmatics_datamodels.fda import (
    FDA_ManufacturerBase,
    FDA_510kClearance,
    FDA_DeviceClass,
    ClearanceType,
)

# Create a manufacturer
manufacturer = FDA_ManufacturerBase(
    manufacturer_name="Medical AI Corp",
    applicant="Medical AI Corp",
)

# Create a 510(k) clearance with validation
clearance = FDA_510kClearance(
    k_number="K240001",  # Validated format
    clearance_date="2024-08-15",
    device_name="AI-Chest Scanner",
    device_class=FDA_DeviceClass.CLASS_2,
)

Package Structure

ashmatics_datamodels/
├── common/          # Base models, validators, regulators, frameworks
├── fda/             # FDA vocabulary (manufacturers, clearances, classifications, recalls, adverse events)
├── documents/       # MongoDB document schemas (three-tier structure)
├── use_cases/       # Clinical AI use case taxonomy
└── utils/           # Parsing and normalization utilities

Documentation

📚 Full Documentation (when published)

Or build locally:

uv pip install -e ".[docs]"
uv run mkdocs serve

Design Documents

License

Apache 2.0 - See LICENSE for details.

Contributing

This is an internal Asher Informatics library. For questions, contact info@asherinformatics.com.

Project links

Data verified by PyPI on Jul 11, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.8.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for JFK-Ashmatics from gravatar.com JFK-Ashmatics

Credits

Author: Asher Informatics PBC

GitHub Statistics

Data verified by PyPI on Jul 11, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache-2.0)

Requires

Python >=3.11

Provides Extra

dev docs

Tags

510k clinical-ai fda healthcare medical-devices openfda pydantic regulatory

Classifiers

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