Merge pull request #36 from Azure-Samples/gk/26-aggregate-miyagi-skil…

…ls-into-a-notebook

services/frontend/src/layouts/sidebar/left-menu.tsx

@@ -68,7 +68,7 @@ export default function Sidebar({ className }: { className?: string }) {
             </h2>
             <button className="h-12 rounded-lg bg-brand text-white"
                     onClick={() => openInNewTab(I_APP_URL)}>
-              Lab Guide {' '}
+              Workshop Guide {' '}
             </button>
           </div>
         </div>

services/recommendation-service/Controllers/RecommendationsController.cs

@@ -1,33 +1,71 @@
+using System.Collections.Generic;
+using System.Threading.Tasks;
+using GBB.Miyagi.RecommendationService.Utils;
 using Microsoft.AspNetCore.Mvc;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Connectors.Memory.Qdrant;
+using Microsoft.SemanticKernel.Skills.Web;
 
 namespace GBB.Miyagi.RecommendationService.Controllers
 {
     [ApiController]
     [Route("[controller]")]
     public class RecommendationsController : ControllerBase
     {
-        private static readonly string[] Questions = new[]
-        {
-            "Portfolio allocation", "Expenses reduction"
-        };
-
-        private readonly ILogger<RecommendationsController> _logger;
+        private readonly IKernel _kernel;
+        private readonly QdrantMemoryStore _memoryStore;
+        private readonly WebSearchEngineSkill _webSearchEngineSkill;
 
-        public RecommendationsController(ILogger<RecommendationsController> logger)
+        public RecommendationsController(IKernel kernel, QdrantMemoryStore memoryStore, WebSearchEngineSkill webSearchEngineSkill)
         {
-            _logger = logger;
+            _kernel = kernel;
+            _memoryStore = memoryStore;
+            _webSearchEngineSkill = webSearchEngineSkill;
         }
 
-        [HttpPost(Name = "GetRecommendations")]
-        public IEnumerable<Response> Post()
+        [HttpGet("GetRecommendations")]
+        public async Task<IActionResult> GetRecommendations(string user, string portfolio)
         {
-            return Enumerable.Range(1, 5).Select(index => new Response
-                {
-                    Date = DateTime.Now.AddDays(index),
-                    InteractionNum = Random.Shared.Next(-20, 55),
-                    Summary = "Todo: SK to LLM flow"
-                })
-                .ToArray();
+
+            Console.WriteLine("== Printing Collections in DB ==");
+            var collections = _memoryStore.GetCollectionsAsync();
+            await foreach (var collection in collections)
+            {
+                Console.WriteLine(collection);
+            }
+
+            string memoryCollectionName = Env.Var("QDRANT_MEMORY_COLLECTION"); // Replace with your desired memory collection name
+            string prompt = $"How should {user} allocate {portfolio}?";
+
+            // Get embeddings for the prompt
+
+            // var embeddings = await _kernel.RunAsync()
+
+            // Search Qdrant vector store
+            var searchResults = _kernel.Memory.SearchAsync(memoryCollectionName, embeddings, limit: 3, minRelevanceScore: 0.8);
+            var similarTexts = new List<string>();
+
+            await foreach (var item in searchResults)
+            {
+                similarTexts.Add(item.Metadata.Text);
+            }
+
+            // Get latest finance news from Bing
+            var newsResults = await _webSearchEngineSkill.SearchAsync($"latest finance news {portfolio}", count: 3);
+            var newsTexts = new List<string>();
+
+            foreach (var newsItem in newsResults)
+            {
+                newsTexts.Add(newsItem.Title);
+            }
+
+            // Build the new prompt with similar texts and news
+            string newPrompt = $"{prompt}\n\nSimilar recommendations:\n{string.Join("\n", similarTexts)}\n\nLatest news:\n{string.Join("\n", newsTexts)}\n\n";
+
+            // Call text completion API
+            var completionResult = await _kernel.RunAsync(newPrompt, _kernel.Skills.Get("text-davinci-003"));
+
+            return Ok(new { Recommendation = completionResult });
         }
     }
-}
+}

services/recommendation-service/GBB.Miyagi.RecommendationService.csproj

@@ -11,6 +11,8 @@
   <ItemGroup>
     <PackageReference Include="Confluent.Kafka" Version="2.1.0" />
     <PackageReference Include="Microsoft.SemanticKernel" Version="0.12.207.1-preview" />
+    <PackageReference Include="Microsoft.SemanticKernel.Connectors.Memory.Qdrant" Version="0.12.207.1-preview" />
+    <PackageReference Include="Microsoft.SemanticKernel.Skills.Web" Version="0.12.207.1-preview" />
     <PackageReference Include="Microsoft.VisualStudio.Azure.Containers.Tools.Targets" Version="1.18.1" />
     <PackageReference Include="Swashbuckle.AspNetCore" Version="6.5.0" />
   </ItemGroup>

services/recommendation-service/Models/Question.cs

@@ -0,0 +1,6 @@
+namespace GBB.Miyagi.RecommendationService.Models;
+
+public class Question
+{
+    public string input { get; set; }
+}

services/recommendation-service/Response.cs → services/recommendation-service/Models/Response.cs

@@ -1,4 +1,4 @@
-namespace GBB.Miyagi.RecommendationService
+namespace GBB.Miyagi.RecommendationService.Models
 {
     public class Response
     {

services/recommendation-service/Program.cs

@@ -1,3 +1,10 @@
+using System.Globalization;
+using GBB.Miyagi.RecommendationService.Utils;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Connectors.Memory.Qdrant;
+using Microsoft.SemanticKernel.Skills.Web;
+using Microsoft.SemanticKernel.Skills.Web.Bing;
+
 var builder = WebApplication.CreateBuilder(args);
 
 // Add services to the container.
@@ -7,6 +14,53 @@
 builder.Services.AddEndpointsApiExplorer();
 builder.Services.AddSwaggerGen();
 
+// Register the required services
+builder.Services.AddSingleton<IKernel>(provider =>
+{
+    int qdrantPort = int.Parse(Env.Var("QDRANT_PORT"), CultureInfo.InvariantCulture);
+    QdrantMemoryStore memoryStore = new QdrantMemoryStore(Env.Var("QDRANT_ENDPOINT"), qdrantPort, vectorSize: 1536, ConsoleLogger.Log);
+    
+    IKernel kernel = Kernel.Builder
+        .WithLogger(ConsoleLogger.Log)
+        .Configure(c =>
+        {
+            c.AddAzureTextCompletionService(
+                Env.Var("AZURE_OPENAI_SERVICE_ID"),
+                Env.Var("AZURE_OPENAI_DEPLOYMENT_NAME"),
+                Env.Var("AZURE_OPENAI_ENDPOINT"),
+                Env.Var("AZURE_OPENAI_KEY"));
+            c.AddAzureTextEmbeddingGenerationService(
+                Env.Var("AZURE_OPENAI_EMBEDDINGS_SERVICE_ID"),
+                Env.Var("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME"),
+                Env.Var("AZURE_OPENAI_EMBEDDINGS_ENDPOINT"),
+                Env.Var("AZURE_OPENAI_EMBEDDINGS_KEY"));
+        })
+        .WithMemoryStorage(memoryStore)
+        .Build();
+
+    return kernel;
+});
+
+builder.Services.AddSingleton<QdrantMemoryStore>(provider =>
+{
+    int qdrantPort = int.Parse(Env.Var("QDRANT_PORT"), CultureInfo.InvariantCulture);
+    QdrantMemoryStore memoryStore = new QdrantMemoryStore(Env.Var("QDRANT_ENDPOINT"), qdrantPort, vectorSize: 1536, ConsoleLogger.Log);
+    return memoryStore;
+});
+
+builder.Services.AddSingleton<BingConnector>(provider =>
+{
+    var bingConnector = new BingConnector(Env.Var("BING_API_KEY"));
+    return bingConnector;
+});
+
+builder.Services.AddSingleton<WebSearchEngineSkill>(provider =>
+{
+    var bingConnector = provider.GetRequiredService<BingConnector>();
+    var bing = new WebSearchEngineSkill(bingConnector);
+    return bing;
+});
+
 var app = builder.Build();
 
 // Configure the HTTP request pipeline.

services/recommendation-service/README.MD

@@ -5,4 +5,22 @@ Microservice that leverages Semantic Kernel to orchestrate the recommendation fl
 
 ![Recommendation Service](../../ancillary/images/sk-memory-orchestration.png)
 
+## Getting Started
 
+Set the following environment variables:
+
+```bash
+dotnet user-secrets set "BING_API_KEY" ""
+dotnet user-secrets set "AZURE_OPENAI_SERVICE_ID" ""
+dotnet user-secrets set "AZURE_OPENAI_DEPLOYMENT_NAME" ""
+dotnet user-secrets set "AZURE_OPENAI_ENDPOINT" "https://{}.openai.azure.com/"
+dotnet user-secrets set "AZURE_OPENAI_KEY" ""
+dotnet user-secrets set "QDRANT_ENDPOINT" "http://localhost"
+dotnet user-secrets set "QDRANT_PORT" "6333"
+dotnet user-secrets set "QDRANT_MEMORY_COLLECTION" ""
+dotnet user-secrets set "AZURE_OPENAI_EMBEDDINGS_SERVICE_ID" ""
+dotnet user-secrets set "AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME" ""
+dotnet user-secrets set "AZURE_OPENAI_EMBEDDINGS_ENDPOINT" "https://{}.openai.azure.com/"
+dotnet user-secrets set "AZURE_OPENAI_EMBEDDINGS_KEY" ""
+
+```

services/recommendation-service/Utils/ConsoleLogger.cs

@@ -0,0 +1,35 @@
+using System;
+using Microsoft.Extensions.Logging;
+
+namespace GBB.Miyagi.RecommendationService.Utils;
+
+/// <summary>
+/// Basic logger printing to console
+/// </summary>
+internal static class ConsoleLogger
+{
+    internal static ILogger Log => LogFactory.CreateLogger<object>();
+
+    private static ILoggerFactory LogFactory => s_loggerFactory.Value;
+
+    private static readonly Lazy<ILoggerFactory> s_loggerFactory = new(LogBuilder);
+
+    private static ILoggerFactory LogBuilder()
+    {
+        return LoggerFactory.Create(builder =>
+        {
+            builder.SetMinimumLevel(LogLevel.Warning);
+
+            // builder.AddFilter("Microsoft", LogLevel.Trace);
+            // builder.AddFilter("Microsoft", LogLevel.Debug);
+            // builder.AddFilter("Microsoft", LogLevel.Information);
+            // builder.AddFilter("Microsoft", LogLevel.Warning);
+            // builder.AddFilter("Microsoft", LogLevel.Error);
+
+            builder.AddFilter("Microsoft", LogLevel.Warning);
+            builder.AddFilter("System", LogLevel.Warning);
+
+            builder.AddConsole();
+        });
+    }
+}

services/recommendation-service/Utils/Env.cs

@@ -0,0 +1,36 @@
+using System;
+using Microsoft.Extensions.Configuration;
+
+namespace GBB.Miyagi.RecommendationService.Utils;
+
+#pragma warning disable CA1812 // instantiated by AddUserSecrets
+internal sealed class Env
+#pragma warning restore CA1812
+{
+    /// <summary>
+    /// Simple helper used to load env vars and secrets like credentials,
+    /// to avoid hard coding them in the sample code
+    /// </summary>
+    /// <param name="name">Secret name / Env var name</param>
+    /// <returns>Value found in Secret Manager or Environment Variable</returns>
+    internal static string Var(string name)
+    {
+        var configuration = new ConfigurationBuilder()
+            .AddUserSecrets<Env>()
+            .Build();
+
+        var value = configuration[name];
+        if (!string.IsNullOrEmpty(value))
+        {
+            return value;
+        }
+
+        value = Environment.GetEnvironmentVariable(name);
+        if (string.IsNullOrEmpty(value))
+        {
+            throw new MiyagiException($"Secret / Env var not set: {name}");
+        }
+
+        return value;
+    }
+}

services/recommendation-service/Utils/MiyagiException.cs

@@ -0,0 +1,16 @@
+namespace GBB.Miyagi.RecommendationService.Utils;
+
+public class MiyagiException : Exception
+{
+    public MiyagiException() : base()
+    {
+    }
+
+    public MiyagiException(string message) : base(message)
+    {
+    }
+
+    public MiyagiException(string message, Exception innerException) : base(message, innerException)
+    {
+    }
+}

services/user-service/src/main/java/com/microsoft/gbb/miyagi/userservice/util/SemanticKernelFacade.java

@@ -17,10 +17,13 @@ public class SemanticKernelFacade {
     @Value("${endpoints.sk-ping}")
     private String pingEndpoint;
 
+    @Value("${endpoints.sk-base-url}")
+    private String SK_BASE_URL;
+
     private final WebClient webClient;
 
     public SemanticKernelFacade(WebClient.Builder webClientBuilder) {
-        this.webClient = webClientBuilder.baseUrl("http://localhost:7071").build();
+        this.webClient = webClientBuilder.baseUrl(SK_BASE_URL).build();
     }
 
     public Mono<String> executePlan(Integer maxSteps) {

services/user-service/src/main/resources/application.yaml

@@ -47,6 +47,7 @@ springdoc:
   api-docs:
     path: /api-docs
 endpoints:
+  sk-base-url: ${SK_BASE_URL:http://localhost:7071}
   execute-plan: ${SK_BASE_URL:http://localhost:7071}/api/planner/execute/{maxSteps?}
   invoke-function: ${SK_BASE_URL:http://localhost:7071}/api/skills/{skillName}/invoke/{functionName}
   sk-ping: ${SK_BASE_URL:http://localhost:7071}/api/ping