Create a generative AI app that uses your own data

Create a generative AI app that uses your own data
https://microsoftlearning.github.io/mslearn-ai-studio/Instructions/04-Use-own-data.html

C#:

// rm -r mslearn-ai-foundry -f

// git clone https://github.com/microsoftlearning/mslearn-ai-studio mslearn-ai-foundry

// cd mslearn-ai-foundry/labfiles/rag-app/c-sharp

// dotnet add package Azure.AI.OpenAI

// dotnet run

using System;

using Azure;

using System.IO;

using System.Text;

using System.Collections.Generic;

using Microsoft.Extensions.Configuration;

using Azure.AI.OpenAI;

using System.ClientModel;

using Azure.AI.OpenAI.Chat;

using OpenAI.Chat;

namespace rag_app

{

    class Program

    {

        static void Main(string[] args)

        {

            // Clear the console

            Console.Clear();

            try

            {

// {

//     "OPEN_AI_ENDPOINT": "https://ai-myhub1588559212155.openai.azure.com/",

//     "OPEN_AI_KEY": "31IQTmnEDSqTsGIrEighShdn3VJrFdVF78JD9fgBiPHrcjVy0aG2JQQJ99BF

        ACHYHv6XJ3w3AAAAACOGXGSQ",

//     "CHAT_MODEL": "gpt-4o",

//     "EMBEDDING_MODEL": "text-embedding-ada-002",

//     "SEARCH_ENDPOINT": "https://rg1aisearchservice1.search.windows.net",

//     "SEARCH_KEY": "3JfXmon3dnBbi9UDymp3kmO5fa1cPdGiQDiNG9xjcTAzSeBX8Wkm",

//     "INDEX_NAME": "brochures-index"

// }

                // Get config settings

                IConfigurationBuilder builder = new ConfigurationBuilder()

                .AddJsonFile("appsettings.json");

                IConfigurationRoot configuration = builder.Build();

                string open_ai_endpoint = configuration["OPEN_AI_ENDPOINT"];

                string open_ai_key = configuration["OPEN_AI_KEY"];

                string chat_model = configuration["CHAT_MODEL"];

                string embedding_model = configuration["EMBEDDING_MODEL"];

                string search_url = configuration["SEARCH_ENDPOINT"];

                string search_key = configuration["SEARCH_KEY"];

                string index_name = configuration["INDEX_NAME"];

                // Get an Azure OpenAI chat client

                AzureOpenAIClient azureClient = new(

                    new Uri(open_ai_endpoint),

                    new AzureKeyCredential(open_ai_key));

                ChatClient chatClient = azureClient.GetChatClient(chat_model);

                // Initialize prompt with system message

                var prompt = new List<ChatMessage>()

                {

                    new SystemChatMessage("You are a travel assistant that provides

                    information on travel services available from Margie's Travel.")

                };

                // Loop until the user types 'quit'

                string input_text = "";

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

                {

                    // Get user input

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

                    input_text = Console.ReadLine();

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

                    {

                        // Add the user input message to the prompt

                        prompt.Add(new UserChatMessage(input_text));

                        // (DataSource is in preview and subject to breaking changes)

                        #pragma warning disable AOAI001

                        // Additional parameters to apply RAG pattern using the

                        AI Search index

                        ChatCompletionOptions options = new();

                        options.AddDataSource(new AzureSearchChatDataSource()

                        {

                            // The following params are used to search the index

                            Endpoint = new Uri(search_url),

                            IndexName = index_name,

                            Authentication = DataSourceAuthentication

                            .FromApiKey(search_key),

                            // The following params are used to vectorize the query

                            QueryType = "vector",

                            VectorizationSource = DataSourceVectorizer

                            .FromDeploymentName(embedding_model),

                        });

                        // Submit the prompt with the data source options and display

                        the response

                        ChatCompletion completion = chatClient.CompleteChat(prompt,

                        options);

                        var completionText = completion.Content[0].Text;

                        Console.WriteLine(completionText);

                        // Add the response to the chat history

                        prompt.Add(new AssistantChatMessage(completionText));

                        #pragma warning restore AOAI001

                    }

                }

            }

            catch (Exception ex)

            {

                Console.WriteLine(ex.Message);

            }

        }

    }

}

Output:

Python:

# rm -r mslearn-ai-foundry -f

# git clone https://github.com/microsoftlearning/mslearn-ai-studio mslearn-ai-foundry

# cd mslearn-ai-foundry/labfiles/rag-app/python

# python -m venv labenv

# ./labenv/bin/Activate.ps1

# pip install -r requirements.txt openai

# code rag-app.py

# python rag-app.py

import os

from dotenv import load_dotenv

from openai import AzureOpenAI

def main():

    # Clear the console

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

# OPEN_AI_ENDPOINT="https://ai-myhub1588559212155.openai.azure.com/"

# OPEN_AI_KEY="31IQTmnEDSqTsGIrEighShdn3VJrFdVF78JD9fgBiPHrcjVy0aG2

    JQQJ99BFACHYHv6XJ3w3AAAAACOGXGSQ"

# CHAT_MODEL="gpt-4o"

# EMBEDDING_MODEL="text-embedding-ada-002"

# SEARCH_ENDPOINT="https://rg1aisearchservice1.search.windows.net"

# SEARCH_KEY="3JfXmon3dnBbi9UDymp3kmO5fa1cPdGiQDiNG9xjcTAzSeBX8Wkm"

# INDEX_NAME="brochures-index"

    try:

        # Get configuration settings

        load_dotenv()

        open_ai_endpoint = os.getenv("OPEN_AI_ENDPOINT")

        open_ai_key = os.getenv("OPEN_AI_KEY")

        chat_model = os.getenv("CHAT_MODEL")

        embedding_model = os.getenv("EMBEDDING_MODEL")

        search_url = os.getenv("SEARCH_ENDPOINT")

        search_key = os.getenv("SEARCH_KEY")

        index_name = os.getenv("INDEX_NAME")

        # Get an Azure OpenAI chat client

        chat_client = AzureOpenAI(

            api_version = "2024-12-01-preview",

            azure_endpoint = open_ai_endpoint,

            api_key = open_ai_key

        )

        # Initialize prompt with system message

        prompt = [

            {"role": "system", "content": "You are a travel assistant that provides

            information on travel services available from Margie's Travel."}

        ]

        # Loop until the user types 'quit'

        while True:

            # Get input text

            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

            # Add the user input message to the prompt

            prompt.append({"role": "user", "content": input_text})

            # Additional parameters to apply RAG pattern using the AI Search index

            rag_params = {

                "data_sources": [

                    {

                        # he following params are used to search the index

                        "type": "azure_search",

                        "parameters": {

                            "endpoint": search_url,

                            "index_name": index_name,

                            "authentication": {

                                "type": "api_key",

                                "key": search_key,

                            },

                            # The following params are used to vectorize the query

                            "query_type": "vector",

                            "embedding_dependency": {

                                "type": "deployment_name",

                                "deployment_name": embedding_model,

                            },

                        }

                    }

                ],

            }

            # Submit the prompt with the data source options and display the response

            response = chat_client.chat.completions.create(

                model=chat_model,

                messages=prompt,

                extra_body=rag_params

            )

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

            print(completion)

            # Add the response to the chat history

            prompt.append({"role": "assistant", "content": completion})

    except Exception as ex:

        print(ex)

if __name__ == '__main__':

    main()

output:

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.