Azure WebJob with Timer Trigger and Application Insights Logging in .NET 8

 Azure WebJob with Timer Trigger and Application Insights Logging in .NET 8

// dotnet add package Microsoft.Azure.WebJobs.Extensions

// dotnet add package Microsoft.Azure.WebJobs.Extensions.Timers

// dotnet add package Microsoft.Extensions.Hosting

// dotnet add package Microsoft.Extensions.Logging.ApplicationInsights

using Microsoft.Extensions.Hosting;

using Microsoft.Extensions.DependencyInjection;

using Microsoft.Azure.WebJobs;

using Microsoft.Extensions.Logging;

using Microsoft.ApplicationInsights.Extensibility;

var host = new HostBuilder()

    .ConfigureWebJobs(b =>

    {

        b.AddTimers();

    })

    .ConfigureLogging((context, b) =>

    {

        b.AddConsole();

        // Add Application Insights logging

        b.AddApplicationInsights(

            configureTelemetryConfiguration: (config) =>

            {

                // Optionally configure Telemetry here

            },

            configureApplicationInsightsLoggerOptions: (options) => { }

        );

    })

    .ConfigureServices((context, services) =>

    {

        services.AddSingleton<MyFunctions>();

        // Set Application Insights connection string or instrumentation key

        services.Configure<TelemetryConfiguration>((config) =>

        {

            //config.ConnectionString =

context.Configuration["APPLICATIONINSIGHTS_CONNECTION_STRING"];

            config.ConnectionString =

"InstrumentationKey=5f3e190f-999a-46f6-aea1-ddc46b8a9de9;IngestionEndpoint=

https://westus2-4.in.applicationinsights.azure.com/;

LiveEndpoint=https://westus2.livediagnostics.monitor.azure.com";

            // "InstrumentationKey=5f3e190f-999a-46f6-aea1-ddc46b8a9de9;

IngestionEndpoint=https://westus2-4.in.applicationinsights.azure.com/;

LiveEndpoint=https://westus2.livediagnostics.monitor.azure.com;

ApplicationId=528f3612-f614-4768-a707-1aeb6d61c551

        });

    })

    .Build();

host.Run();

public class MyFunctions

{

    private readonly ILogger<MyFunctions> _logger;

    public MyFunctions(ILogger<MyFunctions> logger)

    {

        _logger = logger;

    }

    public void ProcessTimer([TimerTrigger("0 */1 * * * *")] TimerInfo timerInfo)

    {

        _logger.LogInformation($"WebJob executed at: {DateTime.Now}");

    }

}

output:

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.