Skip to content

Repo sync for protected branch #2886

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
learn-build-service-prod merged 4 commits into from
May 7, 2026
Merged

Repo sync for protected branch #2886

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
b9a99ee
Update SLM embeddings Python image prerequisite
adieldar May 7, 2026
3b5c1a7
Merge pull request #7461 from adieldar/bugfix/slm-embeddings-python-i…
prmerger-automator[bot] May 7, 2026
c21b9ee
Merge pull request #7462 from MicrosoftDocs/main
learn-build-service-prod[bot] May 7, 2026
baeda57
Merging changes synced from https://github.com/MicrosoftDocs/dataexpl…
May 7, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 4 additions & 2 deletions data-explorer/kusto/includes/slm-embeddings-fl-adx.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,12 +1,14 @@
---
ms.topic: include
ms.date: 04/27/2026
ms.date: 05/07/2026
---

The function `slm_embeddings_fl()` is a [UDF (user-defined function)](../query/functions/user-defined-functions.md) that generates text embeddings using local Small Language Models (SLM). This function converts text into numerical vector representations that can be used for semantic search, similarity analysis, and other natural language processing tasks.
Currently the function supports [harrier-v1-270m](https://huggingface.co/microsoft/harrier-oss-v1-270m), [jina-v2-small](https://huggingface.co/jinaai/jina-embeddings-v2-small-en), and [e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) models.

[!INCLUDE [python-zone-pivot-fabric](../includes/python-zone-pivot-fabric.md)]
## Prerequisites

* The `python()` plugin must be [enabled on the cluster](/azure/data-explorer/language-extensions#enable-language-extensions-on-your-cluster) with either the `3.11.7` or `3.11.7 DL` Python sandbox image. This is required for the inline Python used in the function. To review the package contents for these images, see [Python package reference](../query/python-package-reference.md).
* Alter the cluster's [callout policy](../management/callout-policy.md) to allow access to the external artifacts (which are referenced in the KQL code below):

```kusto
Expand Down
6 changes: 4 additions & 2 deletions data-explorer/kusto/includes/slm-embeddings-fl-fabric.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,12 +1,14 @@
---
ms.topic: include
ms.date: 04/27/2026
ms.date: 05/07/2026
---

The function `slm_embeddings_fl()` is a [UDF (user-defined function)](../query/functions/user-defined-functions.md) that generates text embeddings using local Small Language Models (SLM). This function converts text into numerical vector representations that can be used for semantic search, similarity analysis, and other natural language processing tasks.
Currently the function supports [harrier-v1-270m](https://huggingface.co/microsoft/harrier-oss-v1-270m), [jina-v2-small](https://huggingface.co/jinaai/jina-embeddings-v2-small-en), and [e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) models.

[!INCLUDE [python-zone-pivot-fabric](../includes/python-zone-pivot-fabric.md)]
## Prerequisites

* The `python()` plugin must be [enabled on the database](/fabric/real-time-analytics/python-plugin) with either the `3.11.7` or `3.11.7 DL` Python sandbox image. This is required for the inline Python used in the function. To review the package contents for these images, see [Python package reference](../query/python-package-reference.md).
* Create a lakehouse to host the external artifacts (which are referenced in the KQL code below), preferably in the same workspace as your eventhouse.

## Syntax
Expand Down