Thanks to visit codestin.com
Credit goes to github.com

Skip to content

SoomroFarhanH/SemanticModelBPforAI

Repository files navigation

SemanticModelBPforAI

image

📋 Prerequisites

  • Run inside a Microsoft Fabric workspace notebook
  • The semantic model must be published to the same (or an accessible) Fabric workspace
  • You need Build (or higher) permissions on the semantic model
  • semantic-link-labs will be installed automatically in the next cell

A scored Scorecard (0–100) with a rating (AI Ready / Mostly Ready / Needs Improvement / Not Ready) and a prioritised action list (Critical → Important → Recommended).

How to use

Upload to a Microsoft Fabric workspace notebook

Set dataset and workspace parameters in cell 5

Run all cells

Limitation:

It can not able to access Prep Data AI setup

It is checking the best practices documented at https://learn.microsoft.com/en-us/fabric/data-science/semantic-model-best-practices#prep-for-ai-make-semantic-model-ai-ready

11 Checks + 1 Bonus

Check Max Score

1 Star Schema — M:M relationships, bidirectional cross-filter, isolated tables 15

2 Business-friendly naming — detects DIM_, FACT_, _AMT, all-caps, abbreviations 10

3 Object descriptions — coverage % across tables, columns, measures 15

4 Synonyms — inspects TOM for synonyms on tables/columns/measures 5

5 Implicit measures — numeric columns with Summarize By ≠ None 10

6 Duplicate/overlapping measures — semantic groups + near-identical names 5

7 Ambiguous date fields — multiple date columns without guidance 5

8 Hidden objects risk — hidden columns that break Verified Answers 5

9 Model complexity/bloat — visible helper measures, column/measure counts 5

10 Prep for AI — annotation scan for AI Schema, Instructions, Verified Answers + full manual checklist 15

11 Best Practice Analyzer — runs the full 60+ rule BPA 10

  • Bonus: Measure Dependencies — shows what each measure depends on for AI Schema config —

About

AI Readiness Analyzer for Power BI Semantic Models. Runs 11+ automated checks on star schema, naming, descriptions, and AI configurations. Provides 0-100 scorecard with prioritized actions to optimize semantic models for AI agents in Microsoft Fabric.

Topics

Resources

Stars

Watchers

Forks

Packages

 
 
 

Contributors