Create a generative AI chat app

Create a generative AI chat app

https://microsoftlearning.github.io/mslearn-ai-studio/Instructions/02a-AI-foundry-sdk.html

1. Go to https://ai.azure.com/

2. Create a new Project and deploy 'gpt-4o' model

C# Code:

using System;

using Azure;

using System.IO;

using System.Text;

using System.Collections.Generic;

using Microsoft.Extensions.Configuration;

using Azure.Identity;

using Azure.AI.Projects;

using Azure.AI.Inference;

namespace chat_app

{

    class Program

    {

        static void Main(string[] args)

        {

            Console.Clear();

            try

            {

                IConfigurationBuilder builder =

                    new ConfigurationBuilder().AddJsonFile("appsettings.json");

                IConfigurationRoot configuration = builder.Build();

                string project_connection = "https://sree-2937-resource.services.ai.

                    azure.com/api/projects/sree-2937";

                string model_deployment = "my-gpt-4o-2";

                DefaultAzureCredentialOptions options = new()

                {

                    ExcludeEnvironmentCredential = true,

                    ExcludeManagedIdentityCredential = true

                };

                var projectClient = new AIProjectClient(

                     new Uri(project_connection),

                     new DefaultAzureCredential(options));

                ChatCompletionsClient chat = projectClient.GetChatCompletionsClient();

                var prompt = new List<ChatRequestMessage>(){

                 new ChatRequestSystemMessage("You are a helpful AI assistant that

                    answers questions.")

             };

                string input_text = "";

                while (input_text.ToLower() != "quit")

                {

                    Console.WriteLine("Enter the prompt (or type 'quit' to exit):");

                    input_text = Console.ReadLine();

                    if (input_text.ToLower() != "quit")

                    {

                        prompt.Add(new ChatRequestUserMessage(input_text));

                        var requestOptions = new ChatCompletionsOptions()

                        {

                            Model = model_deployment,

                            Messages = prompt

                        };

                        Response<ChatCompletions> response =

                            chat.Complete(requestOptions);

                        var completion = response.Value.Content;

                        Console.WriteLine(completion);

                        prompt.Add(new ChatRequestAssistantMessage(completion));

                    }

                }

            }

            catch (Exception ex)

            {

                Console.WriteLine(ex.Message);

            }

        }

    }

}

OutPut:
1.  dotnet add package Azure.Identity

     dotnet add package Azure.AI.Projects --version 1.0.0-beta.9

     dotnet add package Azure.AI.Inference --version 1.0.0-beta.5

2. AZ LOGIN

3. dotnet run

4. 

Python Code:

import os

from dotenv import load_dotenv

from azure.identity import DefaultAzureCredential

from azure.ai.projects import AIProjectClient

from azure.ai.inference.models import SystemMessage, UserMessage, AssistantMessage

def main():

    os.system('cls' if os.name=='nt' else 'clear')

       

    try:

   

        load_dotenv()

        project_connection = "https://sree-2937-resource.services.ai.azure.com/api

            /projects/sree-2937"

        model_deployment =  "my-gpt-4o-2"

       

        projectClient = AIProjectClient(            

         credential=DefaultAzureCredential(

             exclude_environment_credential=True,

             exclude_managed_identity_credential=True

         ),

         endpoint=project_connection,

        )

       

        chat = projectClient.inference.get_chat_completions_client()

        prompt=[

         SystemMessage("You are a helpful AI assistant that answers questions.")

        ]

        while True:

            input_text = input("Enter the prompt (or type 'quit' to exit): ")

            if input_text.lower() == "quit":

                break

            if len(input_text) == 0:

                print("Please enter a prompt.")

                continue

           

            prompt.append(UserMessage(input_text))

            response = chat.complete(

                model=model_deployment,

                messages=prompt)

            completion = response.choices[0].message.content

            print(completion)

            prompt.append(AssistantMessage(completion))

    except Exception as ex:

        print(ex)

if __name__ == '__main__':

    main()

OutPut:
1.  python -m venv labenv

    ./labenv/bin/Activate.ps1

    pip install python-dotenv azure-identity azure-ai-projects azure-ai-inference

2. AZ LOGIN

3. python chat-app.py

4. 

Hybrid Connections WebSockets in Azure Relay

Hybrid Connections WebSockets in Azure Relay

Source: https://learn.microsoft.com/en-us/azure/azure-relay/relay-hybrid-connections-dotnet-get-started

1. Create a Namespace

Get Managed credentials 

Create Hybrid Connection 

2. Create a server application (listener)

using Microsoft.Azure.Relay;

using System;

using System.Collections.Generic;

using System.IO;

using System.Linq;

using System.Text;

using System.Threading;

using System.Threading.Tasks;

namespace myserver

{

    public class Program

    {

        private const string RelayNamespace = "sreerelay.servicebus.windows.net";

        private const string ConnectionName = "sreehybridconn";

        private const string KeyName = "RootManageSharedAccessKey";

        private const string Key = "Wtcg6qDIGI4aFts+qYH+zmHwCL1Q=";

        public static void Main(string[] args)

        {

            RunAsync().GetAwaiter().GetResult();

        }

        private static async Task RunAsync()

        {

            var cts = new CancellationTokenSource();

            var tokenProvider =

            TokenProvider.CreateSharedAccessSignatureTokenProvider(KeyName, Key);

            var listener = new HybridConnectionListener(new Uri(string.Format(

            "sb://{0}/{1}", RelayNamespace, ConnectionName)), tokenProvider);

            listener.Connecting += (o, e) => { Console.WriteLine("Connecting"); };

            listener.Offline += (o, e) => { Console.WriteLine("Offline"); };

            listener.Online += (o, e) => { Console.WriteLine("Online"); };

            await listener.OpenAsync(cts.Token);

            Console.WriteLine("Server listening");

            cts.Token.Register(() => listener.CloseAsync(CancellationToken.None));

            new Task(() => Console.In.ReadLineAsync().ContinueWith((s) => {

            cts.Cancel(); })).Start();

            while (true)

            {

                var relayConnection = await listener.AcceptConnectionAsync();

                if (relayConnection == null)

                {

                    break;

                }

                ProcessMessagesOnConnection(relayConnection, cts);

            }

            await listener.CloseAsync(cts.Token);

        }

        private static async void ProcessMessagesOnConnection(HybridConnectionStream

        relayConnection, CancellationTokenSource cts)

        {

            Console.WriteLine("New session");

            var reader = new StreamReader(relayConnection);

            var writer = new StreamWriter(relayConnection) { AutoFlush = true };

            while (!cts.IsCancellationRequested)

            {

                try

                {

                    var line = await reader.ReadLineAsync();

                    if (string.IsNullOrEmpty(line))

                    {

                        await relayConnection.ShutdownAsync(cts.Token);

                        break;

                    }

                    Console.WriteLine(line);

                    await writer.WriteLineAsync($"Echo: {line}");

                }

                catch (IOException)

                {

                    Console.WriteLine("Client closed connection");

                    break;

                }

            }

            Console.WriteLine("End session");

            await relayConnection.CloseAsync(cts.Token);

        }

    }

}

3. Create a client application (sender)

using Microsoft.Azure.Relay;

using System;

using System.Collections.Generic;

using System.IO;

using System.Linq;

using System.Text;

using System.Threading;

using System.Threading.Tasks;

namespace myclient

{

    class Program

    {

        private const string RelayNamespace = "sreerelay.servicebus.windows.net";

        private const string ConnectionName = "sreehybridconn";

        private const string KeyName = "RootManageSharedAccessKey";

        private const string Key = "Wtcg6qDIGI4aFts+qYH+zmHwCL1Q=";

        static void Main(string[] args)

        {

            RunAsync().GetAwaiter().GetResult();

        }

        private static async Task RunAsync()

        {

            Console.WriteLine("Enter lines of text to send to the server with ENTER");

            var tokenProvider =

            TokenProvider.CreateSharedAccessSignatureTokenProvider(KeyName, Key);

            var client = new HybridConnectionClient(new Uri(String.Format(

            "sb://{0}/{1}", RelayNamespace, ConnectionName)), tokenProvider);

            var relayConnection = await client.CreateConnectionAsync();

            var reads = Task.Run(async () =>

            {

                var reader = new StreamReader(relayConnection);

                var writer = Console.Out;

                do

                {

                    string line = await reader.ReadLineAsync();

                    if (String.IsNullOrEmpty(line))

                        break;

                    await writer.WriteLineAsync(line);

                }

                while (true);

            });

            var writes = Task.Run(async () =>

            {

                var reader = Console.In;

                var writer = new StreamWriter(relayConnection) { AutoFlush = true };

                do

                {

                    string line = await reader.ReadLineAsync();

                    await writer.WriteLineAsync(line);

                    if (String.IsNullOrEmpty(line))

                        break;

                }

                while (true);

            });

           

            await Task.WhenAll(reads, writes);

            await relayConnection.CloseAsync(CancellationToken.None);

        }

    }

}

OutPut:

Actionable Email Developer Dashboard

 Actionable Email Developer Dashboard:

Source:

Get started with actionable messages in Office 365: https://learn.microsoft.com/en-us/outlook/actionable-messages/get-started

Register your service with the actionable email developer dashboard: https://learn.microsoft.com/en-us/outlook/actionable-messages/email-dev-dashboard

1. Create Provider in ''Actionable Email Developer Dashboard - https://outlook.office.com/connectors/oam/publish "

2. Save "Provider Id (originator)" in notepad.

3. Approve submitted provider here: https://outlook.office.com/connectors/oam/admin

4. Prepare Json as below: 

<html>

<head>

  <meta http-equiv="Content-Type" content="text/html; charset=utf-8">

  <script type="application/adaptivecard+json">{

    "type": "AdaptiveCard",

    "version": "1.0",

    "hideOriginalBody": true,

    "originator": "Provider Id (originator) : GUID from Previous step",

    "body": [

      {

        "type": "TextBlock",

        "text": "Visit the Outlook Dev Portal",

        "size": "large"

      },

      {

        "type": "TextBlock",

        "text": "Click **Learn More** to learn more about Actionable Messages!"

      },

      {

        "type": "Input.Text",

        "id": "feedbackText",

        "placeholder": "Let us know what you think about Actionable Messages"

      }

    ],

    "actions": [

      {

        "type": "Action.Http",

        "title": "Send Feedback",

        "method": "POST",

        "url": "https://...",

        "body": "{{feedbackText.value}}"

      },

      {

        "type": "Action.OpenUrl",

        "title": "Learn More",

        "url": "https://learn.microsoft.com/outlook/actionable-messages"

      }

    ]

  }

  </script>

</head>

<body>

Visit the <a href="https://learn.microsoft.com/outlook/actionable-messages">Outlook Dev Portal</a> to learn more about Actionable Messages.

</body>

</html>

C# Code:

using Azure.Identity;

using Microsoft.Graph;

using Microsoft.Graph.Models;

using Microsoft.Graph.Users.Item.SendMail;

public class Program

{

    public static async Task Main(string[] args)

    {

        var clientId = "6e4110e7-e5b0d411db60";

        var tenantId = "bb55f134-82bf54373c6d";

        var clientSecret = "imx8Q~Q~AHcGN";

        var userFromEmail = "user1@test.onmicrosoft.com";

        var userToEmails = "user2@test.onmicrosoft.com,user3@test.onmicrosoft.com";

        var credential = new ClientSecretCredential(tenantId, clientId, clientSecret);

        var graphClient = new GraphServiceClient(credential);

        string adaptiveCardJson = @"<html>

        <head>

          <meta http-equiv='Content-Type' content='text/html; charset=utf-8'>

          <script type='application/adaptivecard+json'>{

            'type': 'AdaptiveCard',

            'version': '1.0',

            'hideOriginalBody': true,

            'originator': 'a115aabe-03994fbaf1d',

            'body': [

              {

                'type': 'TextBlock',

                'text': 'Visit the Outlook Dev Portal',

                'size': 'large'

              },

              {

                'type': 'TextBlock',

                'text': 'Click **Learn More** to learn more about Actionable Messages!'

              },

              {

                'type': 'Input.Text',

                'id': 'feedbackText',

                'placeholder': 'Let us know what you think about Actionable Messages'

              }

            ],

            'actions': [

              {

                'type': 'Action.Http',

                'title': 'Send Feedback',

                'method': 'POST',

                'url': 'https://...',

                'body': '{{feedbackText.value}}'

              },

              {

                'type': 'Action.OpenUrl',

                'title': 'Learn More',

                'url': 'https://learn.microsoft.com/outlook/actionable-messages'

              }

            ]

          }

          </script>

        </head>

        <body>

        Visit the <a href='https://learn.microsoft.com/outlook/actionable-messages'>Outlook Dev

        Portal</a> to learn more about Actionable Messages.

        </body>

        </html>";

        List<string> userToEmailList = new List<string>(userToEmails.Split(','));

        var message = new Message

        {

            Subject = "Test Subject",

            Body = new ItemBody

            {

                ContentType = BodyType.Html,

                Content = $"{adaptiveCardJson}"

            },

            ToRecipients = userToEmailList.Select(email => new Recipient { EmailAddress =

            new EmailAddress { Address = email } }).ToList(),

        };

        var sendMailRequest = new SendMailPostRequestBody { Message = message };

        await graphClient.Users[userFromEmail].SendMail.PostAsync(sendMailRequest);

        Console.ReadKey();

    }

}

OutPut:

Connect SharePoint using Azure App Application/Delegate Authentication in C#

1. Connect SharePoint using Azure App Application Authentication in C#
Create Azure app and give below Application permissions. 

using Azure.Identity;

using Microsoft.Graph;

public class Program

{

    public static async Task Main(string[] args)

    {

        string tenantId = "";

        string clientId = "";

        string clientSecret = "";

        string siteId = "283f598a-6b0f-4ba5-af06-c72a0cef8f42";

        string listId = "e9609d64-1f36-45a2-8260-743998ea2cd4";

        var credential = new ClientSecretCredential(tenantId, clientId, clientSecret);

        var graphClient = new GraphServiceClient(credential);

        var items = await graphClient.Sites[siteId].Lists[listId].Items.GetAsync();

        foreach (var item in items.Value)

        {

            Console.WriteLine($"Item ID: {item.Id}, Created By: {item.CreatedBy?.User?.DisplayName}");

        }

        Console.ReadKey();

    }

}

2. Connect SharePoint using Azure App Delegate Authentication in C#

Azure AI services | Language service | Azure Cognitive Search | Sentiment analysis and opinion mining

Azure AI services | Language service | Azure Cognitive Search | Sentiment analysis and opinion mining

1. Create a Language resource in Azure and copy languagekey.

using Azure;

using System;

using Azure.AI.TextAnalytics;

using System.Collections.Generic;

namespace Example

{

    class Program

    {

        // This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"

        // Environment.GetEnvironmentVariable("LANGUAGE_KEY");

        static string languageKey = "9BuOuIbUCOGGygz";

       

        //Environment.GetEnvironmentVariable("LANGUAGE_ENDPOINT");

        static string languageEndpoint = "https://sreelanguage.cognitiveservices.azure.com/";

        private static readonly AzureKeyCredential credentials = new AzureKeyCredential(languageKey);

        private static readonly Uri endpoint = new Uri(languageEndpoint);

        // Example method for detecting opinions text.

        static void SentimentAnalysisWithOpinionMiningExample(TextAnalyticsClient client)

        {

            var documents = new List<string>

            {

                "The food and service were unacceptable. The concierge was nice, however."

            };

            AnalyzeSentimentResultCollection reviews = client.AnalyzeSentimentBatch(documents,

            options: new AnalyzeSentimentOptions()

            {

                IncludeOpinionMining = true

            });

            foreach (AnalyzeSentimentResult review in reviews)

            {

                Console.WriteLine($"Document sentiment: {review.DocumentSentiment.Sentiment}\n");

                Console.WriteLine($"\tPositive score:

                {review.DocumentSentiment.ConfidenceScores.Positive:0.00}");

                Console.WriteLine($"\tNegative score:

                {review.DocumentSentiment.ConfidenceScores.Negative:0.00}");

                Console.WriteLine($"\tNeutral score:

                {review.DocumentSentiment.ConfidenceScores.Neutral:0.00}\n");

                foreach (SentenceSentiment sentence in review.DocumentSentiment.Sentences)

                {

                    Console.WriteLine($"\tText: \"{sentence.Text}\"");

                    Console.WriteLine($"\tSentence sentiment: {sentence.Sentiment}");

                    Console.WriteLine($"\tSentence positive score:

                    {sentence.ConfidenceScores.Positive:0.00}");

                    Console.WriteLine($"\tSentence negative score:

                    {sentence.ConfidenceScores.Negative:0.00}");

                    Console.WriteLine($"\tSentence neutral score:

                    {sentence.ConfidenceScores.Neutral:0.00}\n");

                    foreach (SentenceOpinion sentenceOpinion in sentence.Opinions)

                    {

                        Console.WriteLine($"\tTarget: {sentenceOpinion.Target.Text}, Value:

                        {sentenceOpinion.Target.Sentiment}");

                        Console.WriteLine($"\tTarget positive score:

                        {sentenceOpinion.Target.ConfidenceScores.Positive:0.00}");

                        Console.WriteLine($"\tTarget negative score:

                        {sentenceOpinion.Target.ConfidenceScores.Negative:0.00}");

                        foreach (AssessmentSentiment assessment in sentenceOpinion.Assessments)

                        {

                            Console.WriteLine($"\t\tRelated Assessment: {assessment.Text}, Value:

                            {assessment.Sentiment}");

                            Console.WriteLine($"\t\tRelated Assessment positive score:

                            {assessment.ConfidenceScores.Positive:0.00}");

                            Console.WriteLine($"\t\tRelated Assessment negative score:

                            {assessment.ConfidenceScores.Negative:0.00}");

                        }

                    }

                }

                Console.WriteLine($"\n");

            }

        }

        static void Main(string[] args)

        {

            var client = new TextAnalyticsClient(endpoint, credentials);

            SentimentAnalysisWithOpinionMiningExample(client);

            Console.Write("Press any key to exit.");

            Console.ReadKey();

        }

    }

}

OutPut:

Azure AI services | Document intelligence | Use prebuilt Document Intelligence models

Azure AI services | Document intelligence | Use prebuilt Document Intelligence models

Source: https://github.com/MicrosoftLearning/mslearn-ai-document-intelligence

https://documentintelligence.ai.azure.com/studio

C# Code:

using Azure;

using Azure.AI.FormRecognizer.DocumentAnalysis;

// dotnet add package Azure.AI.FormRecognizer --version 4.1.0

// Store connection information

string endpoint = "https://sreedocumentintelligence.cognitiveservices.azure.com/";

string apiKey = "BxcKE20FOGiN8b";

Uri fileUri = new Uri("https://github.com/MicrosoftLearning/mslearn-ai-document-intelligence/blob

/main/Labfiles/01-prebuild-models/sample-invoice/sample-invoice.pdf?raw=true");

Console.WriteLine("\nConnecting to Forms Recognizer at: {0}", endpoint);

Console.WriteLine("Analyzing invoice at: {0}\n", fileUri.ToString());

// Create the client

var cred = new AzureKeyCredential(apiKey);

var client = new DocumentAnalysisClient(new Uri(endpoint), cred);

// Analyze the invoice

AnalyzeDocumentOperation operation = await client.AnalyzeDocumentFromUriAsync(WaitUntil.Completed,

"prebuilt-invoice", fileUri);

// Display invoice information to the user

AnalyzeResult result = operation.Value;

foreach (AnalyzedDocument invoice in result.Documents)

{

    if (invoice.Fields.TryGetValue("VendorName", out DocumentField? vendorNameField))

    {

        if (vendorNameField.FieldType == DocumentFieldType.String)

        {

            string vendorName = vendorNameField.Value.AsString();

            Console.WriteLine($"Vendor Name: '{vendorName}', with confidence

            {vendorNameField.Confidence}.");

        }

    }

    if (invoice.Fields.TryGetValue("CustomerName", out DocumentField? customerNameField))

    {

        if (customerNameField.FieldType == DocumentFieldType.String)

        {

            string customerName = customerNameField.Value.AsString();

            Console.WriteLine($"Customer Name: '{customerName}', with confidence

            {customerNameField.Confidence}.");

        }

    }

    if (invoice.Fields.TryGetValue("InvoiceTotal", out DocumentField? invoiceTotalField))

    {

        if (invoiceTotalField.FieldType == DocumentFieldType.Currency)

        {

            CurrencyValue invoiceTotal = invoiceTotalField.Value.AsCurrency();

            Console.WriteLine($"Invoice Total: '{invoiceTotal.Symbol}{invoiceTotal.Amount}',

            with confidence {invoiceTotalField.Confidence}.");

        }

    }

}

Console.WriteLine("\nAnalysis complete.\n");

OutPut:

Python Code:

from azure.core.credentials import AzureKeyCredential

from azure.ai.formrecognizer import DocumentAnalysisClient

# pip install azure-ai-formrecognizer==3.3.3

# Store connection information

endpoint = "https://sreedocumentintelligence.cognitiveservices.azure.com/"

key = "BxcKE20FOGiN8b"

fileUri = "https://github.com/MicrosoftLearning/mslearn-ai-document-intelligence/blob/main/Labfiles

/01-prebuild-models/sample-invoice/sample-invoice.pdf?raw=true"

fileLocale = "en-US"

fileModelId = "prebuilt-invoice"

print(f"\nConnecting to Forms Recognizer at: {endpoint}")

print(f"Analyzing invoice at: {fileUri}")

# Create the client

document_analysis_client = DocumentAnalysisClient(

     endpoint=endpoint, credential=AzureKeyCredential(key)

)

# Analyse the invoice

poller = document_analysis_client.begin_analyze_document_from_url(

     fileModelId, fileUri, locale=fileLocale

)

# Display invoice information to the user

receipts = poller.result()

   

for idx, receipt in enumerate(receipts.documents):

    vendor_name = receipt.fields.get("VendorName")

    if vendor_name:

        print(f"\nVendor Name: {vendor_name.value}, with confidence {vendor_name.confidence}.")

    customer_name = receipt.fields.get("CustomerName")

    if customer_name:

        print(f"Customer Name: '{customer_name.value}, with confidence {customer_name.confidence}.")

    invoice_total = receipt.fields.get("InvoiceTotal")

    if invoice_total:

        print(f"Invoice Total: '{invoice_total.value.symbol}{invoice_total.value.amount},

        with confidence {invoice_total.confidence}.")

print("\nAnalysis complete.\n")

OutPut: