fabric-iq

Name: fabric-iq
Author: PatrickGallucci

16stars1forks

How to install fabric-iq?

patrickgallucci/fabric-skills/fabric-iq

$npx -y skills add PatrickGallucci/fabric-skills --skill fabric-iq

Installs into the current project.

Summary

Guide for working with Microsoft Fabric IQ (preview), the semantic intelligence workload for unified data and business vocabulary. Use when creating or managing ontology items, defining entity types, binding data to ontologies, creating relationship types, configuring data agents

Files · 1

View on GitHub

SKILL.md

1	# Microsoft Fabric IQ
2
3	Fabric IQ (preview) is a Fabric workload for unifying data across OneLake and organizing it according to your business vocabulary. It exposes data to analytics, AI agents, and applications with consistent semantic meaning and context.
4
5	## When to Use This Skill
6
7	- Creating or managing ontology items in Fabric IQ
8	- Defining entity types, properties, and relationship types
9	- Binding data from lakehouses, eventhouses, or semantic models to ontologies
10	- Generating ontologies from Power BI semantic models
11	- Configuring Fabric data agents with ontology as a source
12	- Working with Graph in Microsoft Fabric for traversals and graph queries
13	- Enabling Fabric IQ tenant settings in the admin portal
14	- Querying ontology graphs using the preview experience
15	- Building operations agents that reason across business concepts
16	- remediate ontology creation, data binding, or agent integration issues
17	- Automating Fabric IQ items via REST API or PowerShell
18
19	## Prerequisites
20
21	1. A Fabric workspace with a Microsoft Fabric-enabled capacity (F2+ or P1+)
22	2. Required tenant settings enabled (see [tenant-settings.md](./references/tenant-settings.md))
23	3. Data in OneLake (lakehouse tables), an eventhouse, or Power BI semantic models
24
25	## Fabric IQ Items Overview
26
27	Fabric IQ contains five items that work together:
28
29	\| Item \| Purpose \| Shared With \|
30	\|------\|---------\|-------------\|
31	\| Ontology (preview) \| Enterprise vocabulary and semantic layer — entity types, relationships, properties, data bindings \| IQ only \|
32	\| Graph in Microsoft Fabric (preview) \| Native graph storage/compute for nodes, edges, traversals, path finding \| Real-Time Intelligence \|
33	\| Fabric data agent (preview) \| Conversational Q&A using generative AI, grounded in ontology \| Data Science \|
34	\| Operations agent (preview) \| AI agent to monitor real-time data and recommend actions \| Real-Time Intelligence \|
35	\| Power BI semantic model \| Curated analytics model for reporting and DAX \| Power BI \|
36
37	### Choosing the Right Item
38
39	\| Scenario \| Use \|
40	\|----------\|-----\|
41	\| Cross-domain consistency, governance, AI agent grounding \| Ontology \|
42	\| Relationship-heavy questions (impact chains, shortest paths) \| Graph \|
43	\| Trusted KPIs and fast visuals with dimensional modeling \| Power BI semantic model \|
44	\| Operational context, stateful twins, what-if simulation \| Digital twin builder (Real-Time Intelligence) \|
45
46	## Step-by-Step Workflows
47
48	### Workflow 1: Create an Ontology from OneLake
49
50	For the complete walkthrough with all field mappings, see [ontology-workflows.md](./references/ontology-workflows.md#from-onelake).
51
52	1. Navigate to your Fabric workspace and select + New item > Ontology (preview)
53	2. Name the ontology (letters, numbers, underscores only — no spaces or dashes)
54	3. Add entity types from the ribbon or canvas
55	4. Bind static or time series data from OneLake sources
56	5. Set entity type keys (unique identifier properties)
57	6. Create relationship types between entity types and bind them to source data
58	7. Use the preview experience to explore entity instances and the ontology graph
59
60	### Workflow 2: Generate an Ontology from a Semantic Model
61
62	For the complete walkthrough, see [ontology-workflows.md](./references/ontology-workflows.md#from-semantic-model).
63
64	1. Navigate to your Power BI semantic model in Fabric
65	2. Select Generate Ontology from the ribbon
66	3. Choose workspace and name the ontology
67	4. Verify generated entity types, bindings, and relationship types
68	5. Configure any incomplete relationship bindings manually
69
70	### Workflow 3: Connect an Ontology to a Data Agent
71
72	For the complete walkthrough, see [ontology-workflows.md](./references/ontology-workflows.md#data-agent).
73
74	1. Create a Data agent item in your workspace
75	2. Add the ontology as a knowledge source
76	3. Add agent instructions (e.g., `Support group by in GQL`)
77	4. Test queries in the agent chat to validate semantic grounding
78
79	### Workflow 4: Validate Tenant Prerequisites
80
81	Run the [prereq validation script](./scripts/Validate-FabricIQPrereqs.ps1) to check your environment:
82
83	```powershell
84	./scripts/Validate-FabricIQPrereqs.ps1 -TenantId "your-tenant-id"
85	```
86
87	## Key Concepts
88
89	### Ontology Core Concepts
90
91	\| Concept \| Description \|
92	\|---------\|-------------\|
93	\| Entity type

People who liked this also viewed

6 picks

Type

fabric-iq

16stars1forks

View on GitHub

How to install fabric-iq?

patrickgallucci/fabric-skills/fabric-iq

$npx -y skills add PatrickGallucci/fabric-skills --skill fabric-iq

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Microsoft Fabric IQ
2
3	Fabric IQ (preview) is a Fabric workload for unifying data across OneLake and organizing it according to your business vocabulary. It exposes data to analytics, AI agents, and applications with consistent semantic meaning and context.
4
5	## When to Use This Skill
6
7	- Creating or managing ontology items in Fabric IQ
8	- Defining entity types, properties, and relationship types
9	- Binding data from lakehouses, eventhouses, or semantic models to ontologies
10	- Generating ontologies from Power BI semantic models
11	- Configuring Fabric data agents with ontology as a source
12	- Working with Graph in Microsoft Fabric for traversals and graph queries
13	- Enabling Fabric IQ tenant settings in the admin portal
14	- Querying ontology graphs using the preview experience
15	- Building operations agents that reason across business concepts
16	- remediate ontology creation, data binding, or agent integration issues
17	- Automating Fabric IQ items via REST API or PowerShell
18
19	## Prerequisites
20
21	1. A Fabric workspace with a Microsoft Fabric-enabled capacity (F2+ or P1+)
22	2. Required tenant settings enabled (see [tenant-settings.md](./references/tenant-settings.md))
23	3. Data in OneLake (lakehouse tables), an eventhouse, or Power BI semantic models
24
25	## Fabric IQ Items Overview
26
27	Fabric IQ contains five items that work together:
28
29	\| Item \| Purpose \| Shared With \|
30	\|------\|---------\|-------------\|
31	\| Ontology (preview) \| Enterprise vocabulary and semantic layer — entity types, relationships, properties, data bindings \| IQ only \|
32	\| Graph in Microsoft Fabric (preview) \| Native graph storage/compute for nodes, edges, traversals, path finding \| Real-Time Intelligence \|
33	\| Fabric data agent (preview) \| Conversational Q&A using generative AI, grounded in ontology \| Data Science \|
34	\| Operations agent (preview) \| AI agent to monitor real-time data and recommend actions \| Real-Time Intelligence \|
35	\| Power BI semantic model \| Curated analytics model for reporting and DAX \| Power BI \|
36
37	### Choosing the Right Item
38
39	\| Scenario \| Use \|
40	\|----------\|-----\|
41	\| Cross-domain consistency, governance, AI agent grounding \| Ontology \|
42	\| Relationship-heavy questions (impact chains, shortest paths) \| Graph \|
43	\| Trusted KPIs and fast visuals with dimensional modeling \| Power BI semantic model \|
44	\| Operational context, stateful twins, what-if simulation \| Digital twin builder (Real-Time Intelligence) \|
45
46	## Step-by-Step Workflows
47
48	### Workflow 1: Create an Ontology from OneLake
49
50	For the complete walkthrough with all field mappings, see [ontology-workflows.md](./references/ontology-workflows.md#from-onelake).
51
52	1. Navigate to your Fabric workspace and select + New item > Ontology (preview)
53	2. Name the ontology (letters, numbers, underscores only — no spaces or dashes)
54	3. Add entity types from the ribbon or canvas
55	4. Bind static or time series data from OneLake sources
56	5. Set entity type keys (unique identifier properties)
57	6. Create relationship types between entity types and bind them to source data
58	7. Use the preview experience to explore entity instances and the ontology graph
59
60	### Workflow 2: Generate an Ontology from a Semantic Model
61
62	For the complete walkthrough, see [ontology-workflows.md](./references/ontology-workflows.md#from-semantic-model).
63
64	1. Navigate to your Power BI semantic model in Fabric
65	2. Select Generate Ontology from the ribbon
66	3. Choose workspace and name the ontology
67	4. Verify generated entity types, bindings, and relationship types
68	5. Configure any incomplete relationship bindings manually
69
70	### Workflow 3: Connect an Ontology to a Data Agent
71
72	For the complete walkthrough, see [ontology-workflows.md](./references/ontology-workflows.md#data-agent).
73
74	1. Create a Data agent item in your workspace
75	2. Add the ontology as a knowledge source
76	3. Add agent instructions (e.g., `Support group by in GQL`)
77	4. Test queries in the agent chat to validate semantic grounding
78
79	### Workflow 4: Validate Tenant Prerequisites
80
81	Run the [prereq validation script](./scripts/Validate-FabricIQPrereqs.ps1) to check your environment:
82
83	```powershell
84	./scripts/Validate-FabricIQPrereqs.ps1 -TenantId "your-tenant-id"
85	```
86
87	## Key Concepts
88
89	### Ontology Core Concepts
90
91	\| Concept \| Description \|
92	\|---------\|-------------\|
93	\| Entity type

People who liked this also viewed

6 picks

Type