October 23, 2024 is our special day for Small Basic.  The newest version of my program is SFF730-12.  Have fun and Happy 16th Birthday, Small Basic!!

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Building Generative AI applications can feel daunting for traditional app developers. What does the end-to-end application development cycle look like? What models should I use, and where do I find them? What tools should I be using for build, test, and deploy, my AI application? This blog post gives you a sneak peek at a week-long series of posts that were just published, that give you a hands-on journey through the process. Let’s learn more!

Kicking Off Azure AI Week!

This week we published a 5-part blog on the Build Intelligent Apps initiative’s #30DaysOfIA series. Our focus was application developers who wanted to build a custom copilot code-first on Azure AI, allowing them to have more control over various decisions made in the end-to-end workflow for generative AI applications. We did this by walking through two core samples (Contoso Chat and Contoso Creative Writer) from prompt to production. Along the way, we shared insights into key tasks and the developer tools to simplify them.

In this blog post, we’ll briefly introduce the two applications and give you an overview of what the series covers, with links to each post for deeper dives. Ready? Let’s Go!

1. What are we building?

Our first application is Contoso Chat, a customer service chatbot that answers user questions about a retailer’s products,  using the Retrieval Augmented Generation pattern (RAG) to ground responses in both the product catalog and customer purchase history.
 

Our second application is Contoso Creative Writer, a content publishing assistant that uses the Multi-Agent Conversation pattern to coordinate and execute multiple tasks autonomously, on behalf of the user.

2. How are we building it?

The figure below shows the AI Application Architecture for the Contoso Chat retail copilot. User requests are received through an endpoint hosted in Azure Container Apps, then processed using a RAG-based workflow that uses Azure AI Search (product index) and Azure Cosmos DB (customer database) with Azure OpenAI Services (model deployment) to process user requests and return the response back to the UI.

The next figure shows the AI Application Architecture for the Contoso Creative Writer multi-agent copilot which follows a similar user interaction flow – except that processing now requires coordination across multiple agentic AI tasks before final output is generated.

3. What does the developer workflow look like?

We’re glad you asked! If you’ve explored generative AI application development before, you’re probably familiar with this GenAIOps application lifecycle which breaks down the developer workflow into 3 stages: ideation (build and validate a prototype), augmentation (iterate & evaluate with larger input datasets), operationalization (deploy to production).

In this blog series, we map this lifecycle to a very clear developer workflow as shown below, giving you an intuitive sense for the task to perform, and the tool to use to accomplish it, at each stage.

Get started reading the posts, in this order:

Kicking Off Azure AI Week – Learn about the app scenarios, architecture & lifecycle.
Provision with AZD – Provision Azure infrastructure & setup your dev environment.
Ideate with Prompty – Build an app prototype using Prompty assets and tooling.
Evaluate with AI – Build custom evaluators and use AI-assisted evaluation flows.
Deploy with ACA – Create a FastAPI server & deploy with Azure Container Apps.

Here’s a visual summary of what you’ll learn:

If you found this series valuable, please star the repos to help others discover them!

Contoso Chat – custom retail copilot with Retrieval Augmented Generation
Contoso Creative Writer – custom content copilot with Multi-Agent Collaboration

5. Next Steps

Want to get hands-on experience building these copilots? Take these actions today!

Register for Microsoft AI Tour – join an instructor-led workshop session.
Register for Microsoft Ignite – look for related lab & breakout sessions on Azure AI.
Browse the AI Templates Collection – explore samples for more scenarios.

Have a scenario you want to build a custom copilot for? Have questions about Prompty, Azure AI Studio, or the GenAI Ops workflow? Want to provide feedback on the samples? Leave us a comment here and let us know! Happy learning!

​Microsoft Tech Community – Latest Blogs –Read More 

Is there any possible way to achieve Auditing and encryption in Dataverse for teams?

​Is there any possible way to achieve Auditing and encryption in Dataverse for teams?   Read More

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Hello,

I am trying to put together a spreadsheet that allows me to compare members of our team. We work in different “Sub” teams as you will. Just because you work on one team, does not mean your will not also be a part of another team. We encourage people to work on three different sub teams to allow them to work with others and allow for more free thinking ideas and strategies. I would like to be able to compare our team and see how well others work on different teams and try to objectively identify who really excels working with certain people and who does not. 

So my problem is when I try and use AVERAGEIFS or STDEV.S it only pulls the first available value. As the team and year changes I would Expect the average and the standard deviation to change as well. I have attached a spread sheet with my problem. Any clues with how to fix it would be greatly appreciated. 

Thank You,

Tripp

​Hello, I am trying to put together a spreadsheet that allows me to compare members of our team. We work in different “Sub” teams as you will. Just because you work on one team, does not mean your will not also be a part of another team. We encourage people to work on three different sub teams to allow them to work with others and allow for more free thinking ideas and strategies. I would like to be able to compare our team and see how well others work on different teams and try to objectively identify who really excels working with certain people and who does not.  So my problem is when I try and use AVERAGEIFS or STDEV.S it only pulls the first available value. As the team and year changes I would Expect the average and the standard deviation to change as well. I have attached a spread sheet with my problem. Any clues with how to fix it would be greatly appreciated.  Thank You,Tripp  Read More

​

Overcoming Asymmetrical Routing in Azure Virtual WAN: A Collaborative Journey

In the rapidly evolving landscape of cloud networking, professionals often encounter complex challenges that demand innovative solutions. This blog post delves into a recent scenario involving Azure Virtual WAN (VWAN), where a team embarked on a collaborative journey to address asymmetrical routing issues. This case study not only highlights the technical intricacies but also underscores the importance of collaboration and knowledge sharing in the tech community.

The Challenge:

The core issue revolved around asymmetrical routing within an Azure VWAN architecture, which included two hubs located in different regions. The primary goal was to ensure seamless connectivity between a Palo Alto NGFW in one hub and Panorama in another, without disrupting the existing VPN default routes. The asymmetry in routing was particularly problematic for traffic intended to reach Panorama from the NGFW, as the return traffic defaulted through the VPN, deviating from the desired path.

The configuration issue with the firewall is acknowledged as a known issue. The only mitigation provided is documented in Microsoft’s official documentation. https://learn.microsoft.com/en-us/azure/virtual-wan/whats-new#known-issues

Possible solutions:

Exposing the Panorama to the public IP and creating a relay subnet for routing / advertising summary route from NCUS to SCUS for NCUS subnet (10.193.0.0/16)

​Microsoft Tech Community – Latest Blogs –Read More 

So I recenly took an udemy course part of which reinforcement learning was included as part of a broad AI course. So based on what I learnt about Q Learning(a type of reinforcement learning, under stereotypical Artificial Intelligence) , even though the implementation was not discussed in any full details, I had to go through a provided book to understand how it’s implemented in practice. And YouTube was not helping matters.

I need any additional opinions I can get from you, thanks.

Now in order for me to apply Q Learning to TicTacToe, I have to make the AI (Agent)always play X (makes the first move) for simplicity in my AI software development.

Q Learning algorithm is based on Bellmans Equation. Literally it’s about rewarding Favourable actions taken at different states and punishing unfavorable actions taken at different states also. Rewarding and punishing are one and the same number(variable) in the Bellmans equation. There is also the learning rate and discount factors, which are both variables too in the Bellmans equation.

For every action the agent takes at every state it always get a tiny reward (positive or negative), then after winning or losing it gets a much larger reward (positive or negative).

So how does the agent remembers all it has learnt, by looking at the q table,and checking for the action with the highest Q value. These Q values are updated by Bellmans equation whether positively or negatively.

Now the first challenge is I have to pair all the possible valid board configurations (states space) to all the possible valid actions that can be taken for each states in a Q table dictionary, and map all the pairs to Q values of 0 (for initialization). I will write a Python code that will generate this mapping and remove all impossible states (where X is greater than 0 by more than 1 is definitely invalid). Also make Q values for actions where ever is occupied by either X or O as – 1.0 to prevent agent from making such moves.

I will make 4 different players in different classes of the game software who would play with the agent at different stages of it’s learning automatically to update the Q values of every actions taken by agent in each game state, instead of waiting for the final result before updating Q value(my initial mistake when I was still learning about Q Learning) .

Below is for any of the 5 agents (Balanced, Quick Myopic, Quick Overplanner, Slow Myopic and Slow Overplanner) selected at the start of the training games. These agents have different combinations of hyperparameters (learning rate and discount factor)

To train the agent I will make it play against 4 heuristics players (players programed to play only in a certain way) all using different playing strategies.

For starting stage, agent will play with random player 1 for 2000 iterations of games and update it’s Q values for all the state action pairs it encountered.

Then for the next 2000 iterations, agent will play with a player 2 that always favor the center if available for its first move otherwise plays at any corner piece, otherwise plays any random available space.

Then for the next 2000 iters, agent will play against a player 3 that plays randomly until Agent is about to make a winning move, then blocks it. Not really trying to move, just blocking agent winning moves.

Then for the next 2000 iters, agent will play with a player 4 that tries to complete a line as soon as possible, by playing in corners that are impossible for agent to block, that is playing in triangular corners that leads to a definite win if agent doesn’t win on time.

Now create separate classes (.NET MAUI) for these four players that would train a selected agent chosen with options to pick the desired iterations of games for the training of the agent with that player.

For the reward system, +1 reward on completing a line. – 1 for allowing opponent win and not blocking it. +0. 5 for playing a position that can lead to a win in its next move. – 0.5 by playing in a position that cannot lead to a win in its next move (that is a move that doesn’t form a straight line of three with an empty cell anywhere in the line) and – 0.5 for playing along a line that is played by an opponent. This is the reward system rules.

So bells formula would be used for updating the Q value for every action taken for every state in the Q Table already defined in their respective Q Table json file for the particular agent being trained. We would use both learning rate, discount factor and reward from reward system for every action taken at every state.

Below is the link to the Q table (the brain of the AI);

https://www.kaggle.com/datasets/adedapoadeniran/tictactoe-q-learning-table

And below is the link to the code that generated the Q table.

https://www.kaggle.com/code/adedapoadeniran/reinforcement-learning-for-tictactoe-ai/

Thanks for your attention.

This was really mentally tasking to come up with and figure out.

​So I recenly took an udemy course part of which reinforcement learning was included as part of a broad AI course. So based on what I learnt about Q Learning(a type of reinforcement learning, under stereotypical Artificial Intelligence) , even though the implementation was not discussed in any full details, I had to go through a provided book to understand how it’s implemented in practice. And YouTube was not helping matters. I need any additional opinions I can get from you, thanks. Now in order for me to apply Q Learning to TicTacToe, I have to make the AI (Agent)always play X (makes the first move) for simplicity in my AI software development.Q Learning algorithm is based on Bellmans Equation. Literally it’s about rewarding Favourable actions taken at different states and punishing unfavorable actions taken at different states also. Rewarding and punishing are one and the same number(variable) in the Bellmans equation. There is also the learning rate and discount factors, which are both variables too in the Bellmans equation.For every action the agent takes at every state it always get a tiny reward (positive or negative), then after winning or losing it gets a much larger reward (positive or negative).So how does the agent remembers all it has learnt, by looking at the q table,and checking for the action with the highest Q value. These Q values are updated by Bellmans equation whether positively or negatively. Now the first challenge is I have to pair all the possible valid board configurations (states space) to all the possible valid actions that can be taken for each states in a Q table dictionary, and map all the pairs to Q values of 0 (for initialization). I will write a Python code that will generate this mapping and remove all impossible states (where X is greater than 0 by more than 1 is definitely invalid). Also make Q values for actions where ever is occupied by either X or O as – 1.0 to prevent agent from making such moves. I will make 4 different players in different classes of the game software who would play with the agent at different stages of it’s learning automatically to update the Q values of every actions taken by agent in each game state, instead of waiting for the final result before updating Q value(my initial mistake when I was still learning about Q Learning) .  Below is for any of the 5 agents (Balanced, Quick Myopic, Quick Overplanner, Slow Myopic and Slow Overplanner) selected at the start of the training games. These agents have different combinations of hyperparameters (learning rate and discount factor)To train the agent I will make it play against 4 heuristics players (players programed to play only in a certain way) all using different playing strategies.For starting stage, agent will play with random player 1 for 2000 iterations of games and update it’s Q values for all the state action pairs it encountered.Then for the next 2000 iterations, agent will play with a player 2 that always favor the center if available for its first move otherwise plays at any corner piece, otherwise plays any random available space.Then for the next 2000 iters, agent will play against a player 3 that plays randomly until Agent is about to make a winning move, then blocks it. Not really trying to move, just blocking agent winning moves.Then for the next 2000 iters, agent will play with a player 4 that tries to complete a line as soon as possible, by playing in corners that are impossible for agent to block, that is playing in triangular corners that leads to a definite win if agent doesn’t win on time. Now create separate classes (.NET MAUI) for these four players that would train a selected agent chosen with options to pick the desired iterations of games for the training of the agent with that player.For the reward system, +1 reward on completing a line. – 1 for allowing opponent win and not blocking it. +0. 5 for playing a position that can lead to a win in its next move. – 0.5 by playing in a position that cannot lead to a win in its next move (that is a move that doesn’t form a straight line of three with an empty cell anywhere in the line) and – 0.5 for playing along a line that is played by an opponent. This is the reward system rules. So bells formula would be used for updating the Q value for every action taken for every state in the Q Table already defined in their respective Q Table json file for the particular agent being trained. We would use both learning rate, discount factor and reward from reward system for every action taken at every state. Below is the link to the Q table (the brain of the AI);https://www.kaggle.com/datasets/adedapoadeniran/tictactoe-q-learning-tableAnd below is the link to the code that generated the Q table.https://www.kaggle.com/code/adedapoadeniran/reinforcement-learning-for-tictactoe-ai/Thanks for your attention.This was really mentally tasking to come up with and figure out.  Read More

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Credit- GO – Loan app- CUsTomER -care HELPLINE – number+91))* 7047363916 ++7019785677 now

​Credit- GO – Loan app- CUsTomER -care HELPLINE – number+91))* 7047363916 ++7019785677 now  Read More

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Effectively managing large document volumes is essential for modern applications, particularly to maintain fast and reliable querying. With Azure Logic Apps, you can now automate document indexing into Azure Cosmos DB, in addition to the existing capability of indexing in AI Search, offering the flexibility to use either service as a vector store.

In this post, we’ll walk through a scenario where Logic Apps automates the ingestion and indexing of documents, such as PDFs, into Azure Cosmos DB. This approach not only reduces operational overhead but also ensures that your data remains highly accessible and queryable.

Why use Logic Apps for document indexing in Cosmos DB?

Automated Workflows: By automating document indexing, you eliminate manual tasks and ensure that documents are indexed as soon as they are uploaded.
Scalability: As your document volume grows, Azure Cosmos DB’s global distribution ensures your data remains scalable and highly available.
Seamless Integration: Logic App enables you to easily integrate with other Azure services, such as Blob Storage and AI models, enhancing your document indexing with intelligence and automation.

Scenario Overview

In this scenario, we automate the ingestion of document content from Azure Blob Storage, parsing it, and indexing it into Azure Cosmos DB. When a blob (such as a PDF or text document) is uploaded, a Logic App workflow is triggered to process the document and store its data in a Cosmos DB container, making it easily retrievable and queryable. Here is what the workflow will look like: 

Key steps in the workflow:

Blob Upload Detection: The Logic App starts by detecting when a new blob (document) is added or updated in Azure Blob Storage using the event-based trigger.
Read Blob Content: The workflow reads the content of the uploaded blob and prepares it for further processing.
Document Parsing: Logic Apps parses the document, extracting the relevant content, such as text or metadata. This can include PDF extraction or text chunking for larger documents.
Chunk Text: For larger documents, the content is split into manageable chunks to ensure smooth processing and indexing.
Generate Embeddings Using AI: Using Azure AI, the Logic App generates embeddings from the document content. These embeddings allow for enhanced data processing, categorization, and structure mapping within Cosmos DB.
Map to Schema: The extracted data and embeddings are mapped to a predefined schema to ensure consistency in how documents are indexed within Cosmos DB.
Bulk Update in Cosmos DB: Finally, the processed document is stored and indexed in Cosmos DB. The “Create or update many items in bulk” action ensures that multiple items are processed efficiently for fast querying.

Here is a GitHub sample logic app that has the ingestion workflow to index data in Azure Cosmos DB.

Conclusion

By leveraging Azure Logic Apps to automate document indexing into Azure Cosmos DB, you can streamline data workflows, reduce manual intervention, and ensure your data is organized for optimal performance. This powerful integration simplifies the process, making it easier for teams to manage large volumes of documents and scale as needed. 

We are also planning on adding support to allow retrieval of the indexed content soon. Stay tuned for more updates and please let us know your thoughts and feedback.

​Microsoft Tech Community – Latest Blogs –Read More 

I am running Excel 365 64 Bit v16.0.17328.20550 on Windows 11 Surface Pro 9 machine.  When I try to change the windows size in Excel by dragging the bottom right corner and resizing diagonally Excel is painfully slow to resize and redraw the window, noticeably lagging behind the mouse movement.  The odd thing is if I resize the same window diagonally using any of the other 3 corners it works fine.  If I resize just horziontally or just vertically by dragging one of the edges it is also ok…..only the bottom right corner causes issues which per Murphy’s Law is the one I always use.

Seems very odd but has anyone else seen this issue?  

​I am running Excel 365 64 Bit v16.0.17328.20550 on Windows 11 Surface Pro 9 machine.  When I try to change the windows size in Excel by dragging the bottom right corner and resizing diagonally Excel is painfully slow to resize and redraw the window, noticeably lagging behind the mouse movement.  The odd thing is if I resize the same window diagonally using any of the other 3 corners it works fine.  If I resize just horziontally or just vertically by dragging one of the edges it is also ok…..only the bottom right corner causes issues which per Murphy’s Law is the one I always use. Seems very odd but has anyone else seen this issue?    Read More

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Thanks in advance for any help.

​I am working on a client tenant, and they have installed this Teams Apps inside their tenant:-https://github.com/OfficeDev/microsoft-teams-emergency-operations-centernow when i open my Teams i can see this tab been added automatically:-  And it is added inside the Apps list:- Now they asked to do some changes, so i clone the code from GitHub >> modify the code using visual studio.. now i want to reupload the updated Teams app to the tenant .. but when i went to the SharePoint app catalog , i only found this app:- so my first question is where i need to upload the apps? and can i run those commands to generate .sppkg file:-gulp clean gulp build gulp bundle –ship gulp package-solution –ship  Second question, inside the deployment steps for the Teams app @ https://github.com/OfficeDev/microsoft-teams-emergency-operations-center/wiki/Deployment-Guide#7-create-the-teams-app-packages  they mentioned to define the <<appDomain>> & <<clientId>> .. So this means i need to get those from the current Teams apps that is already installed? i do not have to generate new ones? am I correct? If this is the case, then is there a way to get those from any where inside our tenant ? i am global office365 admin.Thanks in advance for any help.  Read More

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