AshMatics Core DataModels
Version: 0.8.1
Canonical Pydantic data models for AshMatics healthcare applications.
Changelog
v0.8.1 (2026-08-13) — ASHFORGE-536
RegistryDeploymentgains three values for admin/operational systems:ehr,enterprise_system,agentic_platform— inserted beforeplatformin the device→cloud order (SRS-REG-03). Same PRODUCT binding tier as the rest of the enum (deliberately unbound to ash:).
v0.8.0 (2026-08-03) — ASHFORGE-412
- Added the
registrymodule: the AI System Registry rule vocabularies (ADR-036 §2.5) —RegistryCategory,RegistryAIType,RegistrySourcing,RegistryDeployment— the canonical home that coreapp's DjangoTextChoicesand the frontend'saiRegistryTaxonomies.tspin to by parity test. Every value is a rule-engine key; changing one is a vocabulary decision, not a rename (SRS-REG-AC-6). - AC-2 resolved: SRS-REG-03's
clinical_useboolean is DERIVED (is_clinical_use), never stored — the stored value is the three-way category. The decision test lives in theRegistryCategorydocstring: does the system's output assert something about an individual patient's condition or care? - AC-3 resolved: the org-level typed questions retire;
PortfolioSizeBucketandOrgSourcingMixsurvive as derived vocabularies (portfolio_size_bucketfrom the active-entry count,org_sourcing_mixrolling per-system sourcing up with hybrid counting half). - Ontology anchoring declared in a
SchemeBindingtable (registry/bindings.py) with a three-state status (BOUND / PENDING / PRODUCT) and enforced by an rdflib guard:RegistryAITypeis BOUND toash:AIParadigmSchemebyskos:notation(othercarved out as a deliberate UI collapse of the finer paradigms — the guard also fails if a carved-out value later gains a concept);RegistryCategoryis PENDING the clinical/operational/administrative scope-zone triad +ash:OperationalPurposeSchemebeing authored in ashmatics-ontology (skips loudly in CI until it lands); sourcing, deployment, and the derived vocabularies are recorded PRODUCT so "unanchored" is always a decision, never an oversight. - Interop note: this module binds by
skos:notation(predictive) while themethodsmodule binds by concept local name (ap-predictive). The mapping is mechanical (ap-+ notation) but rule authors bridging registry systems to CHAR method applicability must map, not string-compare. - Ontology v2.3.0 / ADR-007 landed mid-story (ASHFORGE-436) and this release consumes it (guards require
ashmatics-ontology >= 2.3.0):RegistryCategoryis now BOUND toash:ScopeZoneScheme(the ontology allows multi-zone systems; the registry stores one primary zone — additive to widen later). NewDeploymentStatusenum BOUND toash:DeploymentStatusScheme(per-deployment lifecycle on theforge:deploysApplicationedge; distinct fromRegistryDeploymenttopology, and from coreapp's minimal active/retired entry status). Neworg.ProviderOrgTypeenum BOUND toash:SemanticType_T093, tighteningOrganizationModel.organization_typefrom barestr— coreapp emitsNonethere today, so nothing breaks; mapping coreapp's legacy org-type values is the unblocked follow-up. (NamedProviderOrgType, not the handover'sOrganizationType, because coreapp already has an unrelatedOrganizationTypeTextChoices — a platform-actor/access-control axis with zero value overlap.) - Sourcing resolved (decision 2026-08-03): the ontology's
ash:SourcingScheme(commercial / ehr_embedded / homegrown / research) is an acquisition-channel axis, not the obligation triad. The channel is what the customer answers, so newSourcingChannelis the STORED per-entry field, BOUND to the scheme; the SRS-REG-15a obligation triad (RegistrySourcing) becomes DERIVED viasourcing_obligation()(commercial/ehr_embedded → vendor, homegrown/research → in_house,locally_adapted=Trueon a vendor channel → hybrid). Same stored-fact/derived-judgment philosophy asclinical_use. Downstream: coreapp's sourcing picker and SRS-REG-15a wording must move to channel values (dev-only data so far). - NOT bound, deliberately:
ash:OperationalPurposeScheme— explicitly a seed vocabulary, nothing consumes purpose yet (note itsprior_authorization_opsnotation is distinct from the clinical scheme'sprior_authorization).
v0.7.0 (2026-07-12) — ASHKBAPP-99
- Added the
artifactsmodule: the aigov-framework ADR-006 artifact plane, ported field-for-field from that repo's legacymodels_pydantic/pydantic_models.py(now a deprecation shim over this module). KB base-content side:ToolRef,PracticeView,BaseArtifact,CompiledView; coreapp tenant-instantiation side:DecisionRecord,ExportRecord,InstanceArtifact,InstanceIndex. InstanceArtifactcarries the one clean ontology binding (x_ontology_class: ashcai:WorkProduct), guard-checked against the TTL. Fuller bindings (e.g.BaseArtifact.typevsash:DocumentKindScheme) deferred to the ADR-002 promotion gate — different stored values make that a redesign, not a port.- Behavior tightening vs the legacy plain
BaseModel: extra fields forbidden, assignment validation (houseAshMaticsBaseModel). A field-parity test pins the port against the legacy shape.
v0.6.0 (2026-07-12) — ASHKBAPP-99
- Added the
methodsmodule: CHAR governance-method contracts per aigov-framework ADR-011 §5.MethodDefinition/ApplicabilityProfile/EvidenceRef/DefaultRulemodelmethod_registry.yamlfield-for-field;MethodSet/ApprovedMethodSetcarry the shared-set and Blueprint-resolved shapes;MethodRegistryround-trips the whole registry document without loss (acceptance-tested against the live file). ApplicabilityProfileaxes arex_ontology_scheme-bound enums against the five ash facet schemes plus the newash:ModelClassScheme(ontology ADR-006), each also carrying itsashcai:methodAppliesTosubproperty viax_ontology_property. Values use concept local names (ap-predictive), the canonical CHAR concept IDs.- CLF v0.6.0 rule-grammar types shared with coreapp MethodRoute (ADR-031):
ConditionScope,EvaluationTime,MethodControlAction,SystemAttribute. - New ADR-002 rdflib binding guard (
tests/methods/test_ontology_binding.py): accepts concept local names in addition to notations/prefLabels (the CHAR ID convention), and adds a reverse-completeness check so facet-scheme concepts without enum members also fail CI. This supersedes the static SKOS snapshot in the aigov-framework'svalidate_method_registry.py. - ID grammars exported as constants (
METHOD_ID_PATTERN,JUNCTION_REF_PATTERN, ...) so framework validators import one truth. - Requires
ashmatics-ontology >= 2.2.0(GovernanceMethodScheme,ModelClassScheme). - Deliberately NOT included: the aigov-framework's legacy
models_pydantic/pydantic_models.pyreconciliation — a separate PR per the Phase 2 handoff (no new contracts land in that file). - Committed but never separately released to PyPI; first shipped in 0.7.0.
v0.5.0 (2026-07-11) — ASHKBAPP-91
DocumentType(thekb_documentsdocument_typediscriminator) is now the KIND axis, single-sourced from the ontologyash:DocumentKindScheme(ADR-002 Decision 5). AddedGENERAL(kb_general) fallback;USE_CASEkept but deprecated (ADR-005 — the Mongo use-case path is retired to the Postgreskb_use_casesspine).- New
RegulatoryRegion(8) andRegulatoryPathway(7) enums, ontology-bound toash:RegulatoryRegionScheme/ash:RegulatoryPathwayScheme; added optionalregulatory_region/regulatory_pathwayfields toRegulatoryMetadataContent(regulator scoping carried as sibling fields — the "split" model). - Extended the ADR-002 rdflib binding guard to the document models (
tests/documents/test_ontology_binding.py): every scheme-bound enum value must be a real concept in its scheme, so vocabulary drift fails tests. - Requires
ashmatics-ontology >= 2.1.0(DocumentKindScheme).
v0.4.0 (2026-06-02) — JAC-27
- Added the
orgmodule: FORGE-aligned organization-instance shape, withx_ontology_schemebindings to theforge:ontology and the initial ADR-002 rdflib binding guard (tests/org/test_ontology_binding.py). Committed but never separately released to PyPI; first shipped in 0.5.0.
v0.3.1 (2026-01-25) — ASHKBAPP-66
- Added
PROCESS_DOCUMENTATIONtoGovernanceCategoryenum - 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.
Metadata
Release files for ashmatics-datamodels 0.8.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ashmatics_datamodels-0.8.2.tar.gz | 143.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ashmatics_datamodels-0.8.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 261.3 kB
Release files / ashmatics_datamodels-0.8.2.tar.gz
| Download URL | ashmatics_datamodels-0.8.2.tar.gz |
|---|---|
| Size | 143.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
af61be0a4812093eed31ac1c9cc9fdc50e3d2d899bd578806cff8af142e99601
|
|
BLAKE2b-256 checksum How to use checksums |
e62aa30ddea714f5d9bdb8ea107c694851497a9991f2ec56f47e2d389b2a4427
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 26, 2026.
Transparency logRelease files / ashmatics_datamodels-0.8.2-py3-none-any.whl
| Download URL | ashmatics_datamodels-0.8.2-py3-none-any.whl |
|---|---|
| Size | 117.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
38f3652f8aed9860d010ba22116f1f4450e09da55576a0b7035d696bad0dfd26
|
|
BLAKE2b-256 checksum How to use checksums |
2a8abf1d52e491bb567d7a72ae7b72e9de4d222a2a5a6607d763cda6527ce618
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 26, 2026.
Transparency log