railiance-fabric/workplans/RAIL-FAB-WP-0016-canon-aligned-graph-model-reset-reingest.md

id, type, title, domain, repo, status, owner, topic_slug, planning_priority, planning_order, created, updated, depends_on_workplans, state_hub_workstream_id

id	type	title	domain	repo	status	owner	topic_slug	planning_priority	planning_order	created	updated	depends_on_workplans	state_hub_workstream_id
RAIL-FAB-WP-0016	workplan	Canon-Aligned Graph Model Reset And Reingest	railiance	railiance-fabric	active	codex	railiance	high	16	2026-05-23	2026-05-23	RAIL-FAB-WP-0015	4f776f2c-9a54-4658-8e30-eb0d3fc00b32

RAIL-FAB-WP-0016 - Canon-Aligned Graph Model Reset And Reingest

Goal

Use InfoTechCanon to refactor Railiance Fabric's nodes and edges model, then archive/drop prior registry graph data and reingest all repositories to build a renewed model aligned with canon semantics.

Background

The current Fabric graph grew through iterative discovery and projection work. That helped make the registry practically useful, but the node and edge model now needs a deliberate canon alignment pass before broad adoption. The canon now provides:

• a reusable consumer alignment review kit,
• railiance-fabric conformance support for entity and edge capture,
• PURPOSES / INTENT / SCOPE guidance,
• graph-oriented canonical entity categories,
• CARING and Kubernetes benchmark pressure around roles, scope, evidence, and derived relationships.

This workplan should use those canon surfaces to make Fabric's graph cleaner, more semantically stable, and easier to visualize without confusing canonical relationships with display-only graph edges.

Safety Boundary

This workplan intentionally includes a destructive reset phase, but the reset MUST NOT be executed casually. Implementation must first produce an export, backup, rollback note, and explicit operator command. Prior registry data may be dropped only after the replacement model, scanner changes, and validation path are ready.

Canon Inputs

• info-tech-canon review kit: review-kit/alignment
• Railiance conformance pack: infospace/evaluations/railiance-fabric/conformance-pack.yaml
• Entity and edge capture criteria: infospace/evaluations/railiance-fabric/entity-edge-capture-criteria.yaml
• Mapping expectations: infospace/evaluations/railiance-fabric/mapping-expectations.yaml
• Visualization examples: infospace/evaluations/railiance-fabric/visualization-examples.yaml
• PURPOSES / INTENT / SCOPE pattern and model extension

Tasks

T01 - Canon alignment review and target model

id: RAIL-FAB-WP-0016-T01
status: done
priority: high
state_hub_task_id: "865c048b-fddc-43ee-a379-b61ca31df85b"

• Run the canon consumer alignment review workflow against railiance-fabric.
• Select relevant canon surfaces for graph capture, governance, purpose, evidence, tasks, landscape, DevSecOps, network, observability, and tagging.
• Produce a target node and edge taxonomy with direct, partial, conflicting, and missing mappings.

T02 - Refactor nodes, edges, schemas, and validation

id: RAIL-FAB-WP-0016-T02
status: in_progress
priority: high
state_hub_task_id: "26fbc0d5-3b82-45d2-8307-97dffb9de500"

• Refactor Fabric graph nodes toward canon-aligned categories such as source repository, software system, service, endpoint, deployment, runtime resource, datastore, flow, policy, control, evidence, task, consumer purpose, and telemetry signal.
• Refactor canonical edges toward relationships such as built_from, implements, exposes, depends_on, deploys, flows_to, governed_by, evidenced_by, observed_by, part_of, reads_or_writes, and creates_task.
• Keep display-only visualization edges separate from canonical edges.
• Update schemas, validators, scanner output, registry projection, docs, and graph explorer mapping as needed.

T03 - Export, archive, and controlled reset

id: RAIL-FAB-WP-0016-T03
status: todo
priority: high
state_hub_task_id: "f9ce7cd7-48c1-4aa0-9760-b2bcf38feedd"

• Export the current registry graph, discovery snapshots, accepted projections, and reingest metadata before any destructive action.
• Add a guarded reset path that requires an explicit operator command and records what was dropped.
• Document rollback limits and the intended post-reset source of truth.
• Drop prior graph data only after backup and validation gates are satisfied.

T04 - Full repository reingest

id: RAIL-FAB-WP-0016-T04
status: todo
priority: high
state_hub_task_id: "1d3efc3b-029e-4db5-9a83-b658f5ccdebd"

• Reingest all registered/local repositories using the new canon-aligned model.
• Start with deterministic scanner output and ingest-only mode.
• Review changed, conflicted, and review-required repos before acceptance.
• Project accepted graph state only after model validation and sample review.

T05 - Validation, visualization, and State Hub readiness

id: RAIL-FAB-WP-0016-T05
status: todo
priority: high
state_hub_task_id: "420336e1-3450-4bbc-8c0f-d091098ee990"

• Add regression tests for canonical node categories, edge categories, duplicate identity, destructive reset guardrails, and reingest idempotency.
• Verify graph explorer displays the renewed model clearly.
• Produce before/after counts and representative examples.
• Confirm State Hub can ingest the renewed graph as a read model.

Acceptance

• The target Fabric node and edge model is explicitly mapped to InfoTechCanon.
• Display-only graph relationships are separated from canonical relationships.
• Prior graph data is exported or archived before any destructive reset.
• The reset path is explicit, guarded, documented, and test-covered.
• All registered/local repos are reingested into a renewed canon-aligned graph.
• Validation and graph explorer review show cleaner entity and edge capture.
• Any canon gaps or pressure discovered during the work are recorded as canon feedback, not silently folded into Fabric-specific semantics.