Microsoft office home & Business 2021 and Microsoft office LTSC stand 2021 happening intermittent error.  after finishing typing a reply email and clicking the reply button, an error prompt appears. The user needs to close the email, reopen it, and retype the message.

i have did window update, update office, repair office, disable add-in, re-install office, create new outlook profile. 

​Microsoft office home & Business 2021 and Microsoft office LTSC stand 2021 happening intermittent error.  after finishing typing a reply email and clicking the reply button, an error prompt appears. The user needs to close the email, reopen it, and retype the message.i have did window update, update office, repair office, disable add-in, re-install office, create new outlook profile.    Read More

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Hi,

I hope you guys are not tired solving someone’s problem.

I currently have SP list and manage to use CALCULATED Column with other columns.
This is my formula for that calculated column.
=IF([Months to Pay]>=3,DATE(YEAR([First Payment]),MONTH([First Payment]),DAY([First Payment])+30),” “)

But my problem is that it wont be called in POWER AUTOMATE Filter Query.
Now I hope you can help turn this into Power Automate.

I have 3 columns, No. of Months will Pay (number), First Payment (date), Second Payment(date)

I tried it using init variable and update items, but it wont work.

How can I able to formulate it? Its like First Payment * No of Months to Pay = Second Payment

Thank you.

​Hi, I hope you guys are not tired solving someone’s problem.I currently have SP list and manage to use CALCULATED Column with other columns.This is my formula for that calculated column.=IF([Months to Pay]>=3,DATE(YEAR([First Payment]),MONTH([First Payment]),DAY([First Payment])+30),” “) But my problem is that it wont be called in POWER AUTOMATE Filter Query.Now I hope you can help turn this into Power Automate.I have 3 columns, No. of Months will Pay (number), First Payment (date), Second Payment(date) I tried it using init variable and update items, but it wont work. How can I able to formulate it? Its like First Payment * No of Months to Pay = Second Payment Thank you.    Read More

​

Just upgraded system to Window 11. OneDrive is running well. It’s strange that the right click of file or folder is missing “View online” function, but it has “Always keep on this device” function. How to get this solved and show “View online” on right click?

When using Window 10, “View online” function was there in right click.

​Just upgraded system to Window 11. OneDrive is running well. It’s strange that the right click of file or folder is missing “View online” function, but it has “Always keep on this device” function. How to get this solved and show “View online” on right click? When using Window 10, “View online” function was there in right click.    Read More

​

Ok. So you want to supplement your LLM chat application with your own knowledge base but you could not be bothered about the cumbersome code development that might be involved. Or you are more visual-oriented and would like to make sense of your application workflow.

LangFlow is a drag-and-drop framework that helps you build fully customizable GenAI applications. You can assemble several components with a few clicks to create the exact Retrieval Augmented Generation application you envision, powered by your data source. This means you can access a more reliable, easy-to-build, GenAI model built to address your unique business needs.

In this tutorial, we will utilize some of the available building components in LangFlow to make an application that provides food recommendations based on the US dietary guidelines. This project builds upon our previous work where we integrated Azure Openai and Document Intelligence to scan food products and get more insights to guide our nutrition.

This project requires that you have:

Access to Azure Openai
Python 3.10

Creating a LangFlow account

To start LangFlow, ensure you are working in an environment with python 3.10.  Then go to your terminal and run the following (I promise, you will not have to write any code beyond this):

python -m pip install langflow -U

python -m langflow run

If everything runs successfully, you should get something similar to the image below. Click on the endpoint. This should direct you to your LangFlow account. Proceed to create a new project.

Creating the workflow

Deploying an Azure Openai model

Now, open another page and log onto your Azure portal, and create an Openai resource (fill out this registration form if you don’t already have access):

Subscription: Select your active subscription
Resource group: Select an existing resource group or create a new one
Name: Name your resource. 
Region: Randomly select any region from the available lists of regions.
Pricing: Select Standard S0

After deploying the resource, click on “Go to Azure Openai studio” on the top pane

 Scroll down on the left pane and click on the “Deployments” page
 Click on “create new deployment” next 
 Select GPT4o in the list of models
 Assign a name to the deployment (note the deployment name)
 Reduce the token rate limit to 7K and then “Create”

Once that has been successfully deployed, go back to the Azure portal. Click on the Openai resource you just deployed and copy the endpoint and keys.

Building your application pipeline

Go back to your LangFlow page. Note the panes on the right; this is where you will find your necessary building components. Click on the inputs dropdown menu and drag the chat input pane onto the canvas. Do the same with the outputs menu and select ‘chat output’. Go to the models pane, find and select ‘Azure Openai’.

Once that is done, connect the components as seen in the image above:

Paste the following to the template field in the ‘Prompt’ pane:You are an AI assistant that helps users resolve their question

Question:{question}
Connect the input node in ‘Chat Input’ to the ‘Prompt’ question node
Connect the prompt message in ‘Chat Input’ to the text node in ‘Azure OpenAI’.
Fill in the copied credentials.

Now let’s test it. Click on the play button in the top-right corner on the Chat Output pane. Click on ‘playground’ in the bottom right of the screen and ask any question. It should return a response successfully:

Creating a vector database and populating it with vector embeddings

Keeping to the objective of our application, we need a database to store, retrieve, and query our data on dietary guidelines. We need to be able to search this database and retrieve information closely related to our query. To do this we would be using Azure OpenAI embeddings to create the vectors that represent this relationship.

First sign up on Astra db. Once that is done, you should get an interface like the image below. If not, toggle the drop-down in the red box and select Astra db. Then select ‘create database’ on the right in the yellow box.  Name the database and select Azure as the provider (please note, this costs a minimal fee to use), select us-east-2 as the region and proceed to create the database. Once created, note the database details on the right of the create database page. 

Now go back to your LangFlow page. Save the current flow and then create a new project. On this page populate the screen with the following components:

‘File’

‘Recursive Character Text splitting’

‘Azure OpenAI Embeddings’ 

‘Astra DB’

You should have something similar to the image below:

Return to OpenAI studio and follow the steps in creating and deploying a vector embedding model (select text-embedding-002 as the model type) as stated earlier. Now go back to the LangFlow pane and do the following:

File component:

Attach the US dietary guideline PDF.
Connect the output to the input node in the recursive character text split pane

Recursive split:

Leave as default
Connect to Astra DB ‘Ingest Data’ node

Azure Openai Embeddings:

Fill the endpoint and API keys as you did with the GPT4o model
Fill in the name of the deployed model
Connect the embeddings model to Astra DB

Astra DB:

Return to the Astra DB page and copy the API and token from the database detail
Name the collection
Fill the parameters
Press the play button on the top right and then give it a few minutes to complete

The process above will create a collection of the vector embeddings in the database and might take a few minutes. Return to the Astra DB database page and you should see the collection has been created and proceed to inspect it.

Creating the final workflow

Next, open the previously saved flow and connect the Astra DB pane to the existing flow:

Fill the details of the Astra db as same as before
Create a new thread that connects chat input data to Astra DB
Drag ‘Parse Data’ onto the pane and connect Astra DB ‘search result’ node to ‘data’ 

Copy the template below and paste into the template field of the ‘prompt’ pane:

You are an AI assistant that helps that recommends healthy meals based on the {guidelines} provided

question: {question}
context: {guidelines}

Connect the text node in ‘Parse Data’ to the ‘guidelines’ node in templates. If all instructions are followed correctly, it should look something like this:

Now we have completed the pipeline and are ready to test.

Click on  ‘playground’ and test out your app!

Bonus:

You can export this flow and integrate it into your app. LangFlow provides several API codes. Below, we generate a chat widget by simply copying the API code:

Click on API in the bottom left of the screen next to ‘Playground’ in LangFlow. Click on the Chat widget HTML and copy the code.
Create an  HTML file in VS code. Paste the code. Right-click and ‘ open with live server’
This will direct you to a page with the chat widget.

References and further reading:
1. AstraDB On Azure 

2. Vector database 

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Ciao Readers,

Just a week no writing . The moment of another blog post arrived .

In this post, I am going to show you how to set up alerts for disconnected Arc agents using Azure Monitor. If you are not familiar with Azure Arc, it is a service that lets you manage and govern your hybrid cloud resources from a single pane of glass. More about it in the Azure Arc overview public documentation page.

One of the benefits of using Arc is that it allows you to collect data from your hybrid resources, so you monitor the health and performance of them. It is ‘a prerequisite’ for enabling Azure Monitor. With that in mind, why it is important to get the alert when a hybrid virtual machine gets disconnected, or the Arc agent status is reported as Offline? Ouch, you did not know they were offline !!!

There are several reasons that spread from management to compliance including monitoring why you need to be aware if your resources are communicating properly or not . Let me give you a few of them:

When a hybrid virtual machine is onboarded, every connection is authenticated using a Managed Identity created automatically during the onboarding process. This System Assigned Managed Identity is renewed automatically and can be set as expired if the system does not communicate for more than 60 days. Should this be the case, there is no way to reset the identity. You have to offboard and re-onboard the machine together with all the installed extensions and configurations
When the hybrid machine is disconnected, no monitoring data can be sent. This can lead up to something really bad like:

Customers go blind about infrastructure health
Machine will maintain the unsent monitoring data in the local cache on the C drive using up to 10 GB of disk space
Old, cached data will be deleted so monitoring data loss is expected
Machines with small disks can quickly and easily run out of disk space. Can you imagine that on a Domain Controller?

I just gave you two reasons and given them, I do not think you need any additional one, right? I think you have got the importance of being alerted when an Arc agent gets disconnected as soon as possible by now. Yes, the sooner, the better.

Therefore, you will agree with me that it is necessary to create an alarm. To achieve the goal of creating the alert, you can take advantage of the ability to Create alerts with Azure Resource Graph and Log Analytics.

Let us have a look at the query to be used. The query should give you back one line per monitored server (any alert should give you actionable information and the affected resource is the first in the list) where the last status is reported as Disconnected.

A good query should return records for hybrid machine not connected since a given amount of time. The value in this case is your choice, but I would recommend something not that wide (15 minutes could be a good compromise).

Once you have a good record set, you should configure the alert rule to use the Table rows as Measure and the Count as aggregation type. The Aggregation granularity, which is driving the data range the query will consider, could be set at 1 day

The alert rule logic will be then configured to measure the number of rows returned by the query. The alert will fire if records (even a single one) are returned.

Assuming that your preference will be to get an alert where resources have not been connecting for the last 15 minutes, you create an alert that uses a query similar to the following one:

arg(“”).resources
| where type == “microsoft.hybridcompute/machines”
| where tostring(properties.status) == “Disconnected”
| extend lastContactedDate = todatetime(properties.lastStatusChange)
| where lastContactedDate <= ago(15m)
| extend status = tostring(properties.status)
| project id, Computer=name, status, lastContactedDate

Running the suggested query, will return something similar to the following image, which will fire the alert in line with the Alert logic condition provided as sample:

I trust you all will be more than able to continue with alert creation; hence I am to stop here avoid consuming your eyes anymore .

Thanks for reading through !!!

Disclaimer

The sample scripts are not supported under any Microsoft standard support program or service. The sample scripts are provided AS IS without a warranty of any kind. Microsoft further disclaims all implied warranties including, without limitation, any implied warranties of merchantability or of fitness for a particular purpose. The entire risk arising out of the use or performance of the sample scripts and documentation remains with you. In no event shall Microsoft, its authors, or anyone else involved in the creation, production, or delivery of the scripts be liable for any damages whatsoever (including, without limitation, damages for loss of business profits, business interruption, loss of business information, or other pecuniary loss) arising out of the use of or inability to use the sample scripts or documentation, even if Microsoft has been advised of the possibility of such damages.

​Microsoft Tech Community – Latest Blogs –Read More