vibe12-brick-data-model

name	vibe12-brick-data-model
description	Use when building or validating BRICK graphs, canonical data models, SparkQL prep, brick_timeseries_ref, entity_id, or data-model import/TTL APIs. Triggers on: BRICK, SparkQL, data-model, brick graph, canonical model, timeseries ref, external_ref.

Vibe12 BRICK data modeling

Data layers

Layer	Storage	API
Telemetry	DynamoDB device_id = series_id	/api/series
Point registry	meta row ts_ms=-11	/api/points/{site}/{building}
BRICK graph	meta row ts_ms=-10	GET/PUT /api/brick/{site}/{building}
Canonical model	meta row ts_ms=-13	/api/data-model/...
TTL (SparkQL)	meta row ts_ms=-12	/api/data-model/.../ttl

Auto refs (ingest)

Every MQTT sample gets brick_timeseries_ref with:

• external_ref = series_id (Dynamo key)
• entity_id = brick:{site}/{building}/point/{series_id slashes}

Fetch all: GET /api/brick/timeseries-ref/{site}/{building}

Agent workflow

1. Confirm telemetry via vibe12-ai-commissioning-api
2. GET /api/points/... — registry matches human CSV
3. GET /api/brick/... — bootstrap graph from registry if empty
4. Human validates semantics in SparkQL against TTL
5. POST /api/data-model/.../import if importing TTL patches

Code

• aws_cloud_pipeline/web_lambda/brick_model.py
• aws_cloud_pipeline/ingest_lambda/brick_timeseries.py

Human sign-off

Agent proposes graph/TTL; human confirms HVAC archetype and point meanings before FDD go-live.