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Thanks for the feedback! Sorry you got tripped up in some places, I'll work harder to make sure the flow is clean and doesn't get interrupted in the future.

Possibly! It depends on the complexity you need. I'd suggest trying to prototype the functionality you need to see how it can fit. Regarding cost, when using copilot studio, it's billed based on the number of messages that flow through the copilot app you create, so I don't think you would see per user charges for Teams users. Take a look at: www.microsoft.com/en-us/microsoft-copilot/microsoft-copilot-studio#Pricing

If you use the default pool (I almost always do) a cluster will start processing Fabric jobs in 4-5 seconds since Fabric has cluster nodes already running and waiting to be assigned.

@@robkerrai I never had a chance to with Fabric but by default pool, do you mean that a cluster is always up and running in your environment and your job simply takes the resources from there. Or do you mean that default pool is Serverless and it will only charge me for the time I will use it and also, it will spin up within 4-5 seconds? That would be extremely helpful if true. We are working with HD Insight and need to have our nodes running all the time only because we can't afford a time of 10-15 minutes for a node/cluster to spin up when a new user starts his Notebook.

Yes to both questions. Fabric is SAAS (not PAAS), so it works differently in that you pay for a "capacity" that you choose (your peak usage/second), and is a relatively fixed cost rather than clusters you manage yourself billed as a variable cost. The Default pool is a large group of always running nodes waiting to be assigned to your work as needed--you don't pay for compute per-se, you pay for the maximum nodes you can use at any given time. Default pool probably would solve the low latency of starting clusters issue you have. It is possible in Fabric to design your own cluster configuration, which would probably have the same startup delay in a PAAS environment, but needing to do that is the exception not the rule. The default pool meets most general computing needs.

Perfect, thank you! That clarified a lot of things. Thanks for your time, really appreciate it

Thanks for watching!

My pleasure!

If I'm not mistaken .whl files aren't encrypted. Try changing the extension to .zip and using unzip to get at teh code within the .whl file.

You can use Azure AI to train a custom model for your own forms. I'd like to make a video about this in the future so stay tuned!

Sorry Dylan I don't understand what yo mean by "fetch". Could you clarify?

I don't have a complete series. I'd recomed Will's series on his @LearnMicrosoftFabric channel www.youtube.com/@LearnMicrosoftFabric

So glad it was helpful!

It's a good question. I see the benefit with this feature is to view Power BI output within a notebook during development. For example, if the data in a Direct Lake model is being updated in the notebook, the visual is available within the notebook context.

Microsoft's vision as presented at FabCon 24 was that different branches in a CI/CD environment are connected to different workspaces. The git features as I write this is pretty basic, but the design set out at FabCon is moving toward a more automated approach where creating a branch creates a workspace with all artifacts. In that scenario you probably would have a lakehouse in the Dev workspace, another lakehouse (with the same name) in a Prod lakehouse, etc.

Thanks

This is a pipeline (not mirroring). You are correct when using mirroring the integration runs behind the scenes.

Not sure I understand. Which portion of the video are you referring to?

Glad it was helpful!

You can use Azure AI to train a custom model for your own forms. I'd like to make a video about this in the future so stay tuned!

Glad it was helpful!

I think you could accomplish this using Fabric environments. Each environment can have different .whl files attached to it, so when you choose an environment to run your code, that code will use whatever .whl files are pre-installed in the environment.

Glad it helped!

This would be difficult to share as it has some content I don't own.

My pleasure!

Thanks for subbing!

Good point!

Glad it was helpful!

The GUI-based tools are definitely geared around one model per endpoint. Multiple models are possible but require more configuration work. I can't paste URLs here, but if you search the learn site for "multiple local models deployment" it should turn up some doc pages how to get it working.

@@robkerraiI see a solution inside a container istance. But I'll search again. thank you.

These models are most commonly part of enterprise deployments, and don't get a lot of PR coverage. Microsoft published a case study where Volvo uses Doc Intel to speed processing of supply chain documents you can probably find via a web search. These types of models can make a lot of impact on cost savings.

Image attachments are supported by the API when formulating a prompt, but I don't see support for documents like PDFs and spreadsheets int he docs.

To use linked documents, search the learn documentation for "Use content on SharePoint or OneDrive for Business for generative answers". I think this should point you in the right direction. Good luck with your project!

If you don't want old content in the vector DB, the best approach is probably not to add it to the vector DB (or remove old entries as new ones are added, or truncate/reload the vector DB so it only contains relevant docs). The alternate approach is when you receive the results of a vector query, you will have the JSON metadata for each entry, so you can filter out only the most recent results that way. HTH.

Hard to say from the information you posted. Make sure to obtain a Gemini key from Google--that would be the most likely reason for a request not succeeding.

Glad you liked it!

Thanks, will do!

Glad the videos are helpful! The feedback is encouraging. In your scenario I tend to agree, you probably don't have a good way to give AzureAI access to your data via a URL since it's the content of an email. You're right that the examples usually give a URL that's publicly available on the Internet (not very likely in an enterprise scenario). However the passed URL approach could work in an enterprise solution if, for example, you had a file in Azure Blob store and generated a short-lived SAS token to append to the URL passed to AI Services. I prefer Base64 for "small" input payloads, but I would go for a SAS token if the input file was large to reduce the size of the request to Azure.

Thanks!

Genius! Can you please do a video of how the openAI model in Azure was configured in order to give you these results? Thanks

The code is at the blog post link (see link in the description). Thanks!

@@robkerrai thank you!

I have a post on my blog for how to deploy a Streamlit app to a docker container. This technique would apply to this scenario as well. robkerr.ai/publish-streamlit-app-docker-azure-container/ Thanks!

Glad it was helpful!

You're welcome! Glad it was helpful.

Possibly. There are a lot of connectors available. At some point Microsoft's PromptFlow becomes potentially a better approach since it's open to any custom code you want to incorporate in the solution. Thanks!

According to the MSFT docs, Semantic Link is only supported within Fabric, so while SemPy could be used outside a Pandas notebook, it would still need to be within the context of Fabric.

Thanks! Glad you enjoyed it.