---
id: semantic-link-labs
version: "0.17.0"
license: MIT License
license_treatment: permissive
maintenance: active
---

# semantic-link-labs — Semantic Link Labs for Microsoft Fabric

License: permissive · Maintenance: active · Downloads: 2.1M/mo

## What it is and what it does

Semantic Link Labs is a Python library built on top of Microsoft Fabric's Semantic Link, designed to run in Jupyter notebooks within the Fabric environment. It provides utilities for programmatically managing Power BI semantic models—including migration to Direct Lake, backup and restore, refresh operations, and DAX query analysis—as well as report metadata inspection and capacity management. The library aims to automate repetitive tasks and reduce manual intervention in Fabric workflows.

The package depends on semantic-link-sempy as its primary runtime dependency, along with anytree, polib, jsonpath_ng, anywidget, and sqlglot. It is classified as Alpha and supports Python 3.10 and later. The library is actively maintained by Microsoft with recent releases and a public GitHub repository accepting bug reports and feature requests.

Use it for:

- Migrate Power BI semantic models from import or DirectQuery to Direct Lake mode.
- Run best practice analysis on semantic models and reports to identify issues.
- Back up, restore, and deploy semantic models across Fabric workspaces.
- Analyze DAX query dependencies and column usage in semantic models.
- Manage Fabric capacity provisioning and migration between SKU types.
- Auto-generate measure descriptions and manage incremental refresh policies.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Extends Microsoft Fabric's Semantic Link with utilities for managing Power BI semantic models, reports, and capacities programmatically within Jupyter notebooks.

Yes, if you work within Microsoft Fabric notebooks and need to automate semantic model or report management. The library is actively maintained, has no known vulnerabilities, uses a permissive MIT License, and installs with low friction. However, its utility is narrowly scoped to the Fabric environment; it is not a general-purpose Power BI tool and requires Python 3.10 or later.

## Install

pip install semantic-link-labs
uv add semantic-link-labs
poetry add semantic-link-labs

## Installing semantic-link-labs

Before you install: Low friction install with a pure Python wheel. Active maintenance with a release 17 days old and recent commits. Depends on semantic-link-sempy plus six other libraries with no compiled dependencies.

License in practice: MIT License permits commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.

Quickstart:

pip install semantic-link-labs

import semantic_link_labs
# Use within Microsoft Fabric notebooks for semantic model operations

Requires Python 3.10 or later and is designed for use within Microsoft Fabric notebooks; functionality outside that environment is not documented.

Verify before relying:

- Exact scope of functionality available outside Microsoft Fabric notebooks is unclear.
- Whether semantic-link-sempy is a separate package or a bundled dependency is not specified.
- Performance characteristics and scalability limits for large semantic models are not documented.
- Specific API signatures and usage patterns beyond the featured scenarios listed.

## Package facts

- License: MIT License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 2.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

power bi semantic model automation, microsoft fabric notebook utilities, direct lake migration tools, power bi report metadata analysis, fabric capacity management, dax query analysis, semantic model backup restore, power-bi, microsoft-fabric, automation

[View on SkillFed](https://skillfed.io/packages/semantic-link-labs) · [View on PyPI](https://pypi.org/project/semantic-link-labs/)