Refactor vector embedding in bit Boilerplate (#11448)

src/Templates/Boilerplate/Bit.Boilerplate/.template.config/template.json

@@ -526,7 +526,7 @@
                     ]
                 },
                 {
-                    "condition": "(signalR != true && database != PostgreSQL && database != SqlServer)",
+                    "condition": "(database != PostgreSQL && database != SqlServer)",
                     "exclude": [
                         "src/Server/Boilerplate.Server.Api/Services/ProductEmbeddingService.cs"
                     ]

src/Templates/Boilerplate/Bit.Boilerplate/src/Client/Boilerplate.Client.Core/Components/Pages/Products/ProductsPage.razor.cs

@@ -80,7 +80,7 @@ private void PrepareGridDataProvider()
                 var queriedRequest = productController.WithQuery(query.ToString());
                 var data = await (string.IsNullOrWhiteSpace(searchQuery)
                             ? queriedRequest.GetProducts(req.CancellationToken)
-                            : queriedRequest.GetProductsBySearchQuery(searchQuery, req.CancellationToken));
+                            : queriedRequest.SearchProducts(searchQuery, req.CancellationToken));
 
                 return BitDataGridItemsProviderResult.From(data!.Items!, (int)data!.TotalCount);
             }

src/Templates/Boilerplate/Bit.Boilerplate/src/Directory.Packages.props

@@ -81,6 +81,8 @@
         <PackageVersion Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="Microsoft.Extensions.AI" Version="9.9.0" />
         <PackageVersion Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="Microsoft.Extensions.AI.OpenAI" Version="9.9.0-preview.1.25458.4" />
         <PackageVersion Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="Microsoft.Extensions.AI.AzureAIInference" Version="9.9.0-preview.1.25458.4" />
+        <PackageVersion Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="SmartComponents.LocalEmbeddings.SemanticKernel" Version="0.1.0-preview10148" />
+        <PackageVersion Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="Microsoft.SemanticKernel.Core" Version="1.65.0" />
         <PackageVersion Condition=" ('$(database)' == 'PostgreSQL' OR '$(database)' == '') " Include="Pgvector.EntityFrameworkCore" Version="0.2.2" />
         <PackageVersion Condition="'$(module)' == 'Admin' OR '$(module)' == ''" Include="Newtonsoft.Json" Version="13.0.4" />
         <PackageVersion Condition=" '$(appInsights)' == 'true' OR '$(appInsights)' == '' " Include="Microsoft.Extensions.Logging.ApplicationInsights" Version="2.23.0" />

src/Templates/Boilerplate/Bit.Boilerplate/src/Server/Boilerplate.Server.Api/Boilerplate.Server.Api.csproj

@@ -63,8 +63,9 @@
         <PackageReference Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="Microsoft.Extensions.AI" />
         <PackageReference Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="Microsoft.Extensions.AI.AzureAIInference" />
         <PackageReference Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="Microsoft.Extensions.AI.OpenAI" />
+        <PackageReference Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="Microsoft.SemanticKernel.Core" />
+        <PackageReference Condition=" ('$(signalR)' == 'true' OR '$(signalR)' == '') OR ('$(database)' == 'PostgreSQL' OR '$(database)' == '') OR ('$(database)' == 'SqlServer' OR '$(database)' == '') " Include="SmartComponents.LocalEmbeddings.SemanticKernel" />
         <PackageReference Condition=" ('$(database)' == 'PostgreSQL' OR '$(database)' == '') " Include="Pgvector.EntityFrameworkCore" />
-
         <Using Include="Microsoft.EntityFrameworkCore.Migrations" />
         <Using Include="Microsoft.EntityFrameworkCore.Metadata.Builders" />
         <Using Include="Microsoft.AspNetCore.Identity.EntityFrameworkCore" />

src/Templates/Boilerplate/Bit.Boilerplate/src/Server/Boilerplate.Server.Api/Controllers/Products/ProductController.cs

@@ -21,7 +21,7 @@ public partial class ProductController : AppControllerBase, IProductController
     //#if (signalR == true)
     [AutoInject] private IHubContext<AppHub> appHubContext = default!;
     //#endif
-    //#if (signalR == true || database == "PostgreSQL" || database == "SqlServer")
+    //#if (database == "PostgreSQL" || database == "SqlServer")
     [AutoInject] private ProductEmbeddingService productEmbeddingService = default!;
     //#endif
     [AutoInject] private ResponseCacheService responseCacheService = default!;
@@ -47,10 +47,10 @@ public async Task<PagedResult<ProductDto>> GetProducts(ODataQueryOptions<Product
     }
 
     [HttpGet("{searchQuery}")]
-    public async Task<PagedResult<ProductDto>> GetProductsBySearchQuery(string searchQuery, ODataQueryOptions<ProductDto> odataQuery, CancellationToken cancellationToken)
+    public async Task<PagedResult<ProductDto>> SearchProducts(string searchQuery, ODataQueryOptions<ProductDto> odataQuery, CancellationToken cancellationToken)
     {
         //#if (database == "PostgreSQL" || database == "SqlServer")
-        var query = (IQueryable<ProductDto>)odataQuery.ApplyTo((await (productEmbeddingService.GetProductsBySearchQuery(searchQuery, cancellationToken))).Project(),
+        var query = (IQueryable<ProductDto>)odataQuery.ApplyTo((await (productEmbeddingService.SearchProducts(searchQuery, cancellationToken))).Project(),
             ignoreQueryOptions: AllowedQueryOptions.Top | AllowedQueryOptions.Skip | AllowedQueryOptions.OrderBy /* Ordering can disrupt the results of the embedding service. */);
         var totalCount = await query.LongCountAsync(cancellationToken);
 
@@ -59,9 +59,7 @@ public async Task<PagedResult<ProductDto>> GetProductsBySearchQuery(string searc
 
         return new PagedResult<ProductDto>(await query.ToArrayAsync(cancellationToken), totalCount);
         //#else
-        // Embedding based search is only implemented for PostgreSQL.
-        // Simply return whole products list.
-        return await GetProducts(odataQuery, cancellationToken);
+        throw new NotImplementedException(); // Embedding based search is only implemented for PostgreSQL and SQL Server only.
         //#endif
     }
 

src/Templates/Boilerplate/Bit.Boilerplate/src/Server/Boilerplate.Server.Api/Data/Configurations/Chatbot/SystemPromptConfiguration.cs

@@ -174,7 +174,8 @@ These are the primary functional areas of the application beyond account managem
     - If the user asks multiple questions, list them back to the user to confirm understanding, then address each one separately with clear headings. If needed, ask them to prioritize: ""I see you have multiple questions. Which issue would you like me to address first?""
 
     - Never request sensitive information (e.g., passwords, PINs). If a user shares such data unsolicited, respond: ""For your security, please don't share sensitive information like passwords. Rest assured, your data is safe with us."" " +
-//#if (module == 'Sales')
+//#if (module == "Sales")
+//#if (database == "PostgreSQL" || database == "SqlServer")
         @"### Handling Car Recommendation Requests:
 **[[[CAR_RECOMMENDATION_RULES_BEGIN]]]**
 *   **If a user asks for help choosing a car, for recommendations, or expresses purchase intent (e.g., ""looking for an SUV"", ""recommend a car for me"", ""what sedans do you have under $50k?""):**
@@ -195,6 +196,7 @@ These are the primary functional areas of the application beyond account managem
 **[[[CAR_RECOMMENDATION_RULES_END]]]**
 " +
 //#endif
+//#endif
 //#if (ads == true)
         @"### Handling advertisement trouble requests:
 **[[[ADS_TROUBLE_RULES_BEGIN]]]""

src/Templates/Boilerplate/Bit.Boilerplate/src/Server/Boilerplate.Server.Api/Data/Configurations/Product/ProductConfiguration.cs

@@ -19,7 +19,12 @@ public void Configure(EntityTypeBuilder<Product> builder)
         //#if (database == "PostgreSQL" || database == "SqlServer")
         if (AppDbContext.IsEmbeddingEnabled)
         {
-            builder.Property(p => p.Embedding).HasColumnType("vector(1536)"); // 1536 for text-embedding-3-small
+            builder.Property(p => p.Embedding).HasColumnType("vector(384)"); // Checkout appsettings.json's AI:EmbeddingOptions:Dimensions
+            //#if (database == "PostgreSQL")
+            builder.HasIndex(m => m.Embedding)
+                .HasMethod("hnsw") // ivfflat
+                .HasOperators("vector_cosine_ops");
+            //#endif
         }
         else
         {

src/Templates/Boilerplate/Bit.Boilerplate/src/Server/Boilerplate.Server.Api/Program.Services.cs

@@ -4,6 +4,8 @@
 using System.IO.Compression;
 //#if (signalR == true || database == "PostgreSQL" || database == "SqlServer")
 using System.ClientModel.Primitives;
+using Microsoft.SemanticKernel.Embeddings;
+using SmartComponents.LocalEmbeddings.SemanticKernel;
 //#endif
 //#if (database == "Sqlite")
 using Microsoft.Data.Sqlite;
@@ -67,7 +69,7 @@ public static void AddServerApiProjectServices(this WebApplicationBuilder builde
         services.AddScoped<PhoneService>();
         services.AddScoped<PhoneServiceJobsRunner>();
         //#if (module == "Sales" || module == "Admin")
-        //#if (signalR == true || database == "PostgreSQL" || database == "SqlServer")
+        //#if (database == "PostgreSQL" || database == "SqlServer")
         services.AddScoped<ProductEmbeddingService>();
         //#endif
         //#endif
@@ -434,6 +436,10 @@ void AddDbContext(DbContextOptionsBuilder options)
                 Endpoint = appSettings.AI.OpenAI.EmbeddingEndpoint,
                 Transport = new HttpClientPipelineTransport(sp.GetRequiredService<IHttpClientFactory>().CreateClient("AI"))
             }).AsIEmbeddingGenerator())
+            .ConfigureOptions(options =>
+            {
+                configuration.GetRequiredSection("AI:EmbeddingOptions").Bind(options);
+            })
             .UseLogging()
             .UseOpenTelemetry();
             // .UseDistributedCache()
@@ -446,10 +452,26 @@ void AddDbContext(DbContextOptionsBuilder options)
                 {
                     Transport = new Azure.Core.Pipeline.HttpClientTransport(sp.GetRequiredService<IHttpClientFactory>().CreateClient("AI"))
                 }).AsIEmbeddingGenerator(appSettings.AI.AzureOpenAI.EmbeddingModel))
+            .ConfigureOptions(options =>
+            {
+                configuration.GetRequiredSection("AI:EmbeddingOptions").Bind(options);
+            })
             .UseLogging()
             .UseOpenTelemetry();
             // .UseDistributedCache()
         }
+        else
+        {
+            services.AddEmbeddingGenerator(sp => new LocalTextEmbeddingGenerationService()
+                .AsEmbeddingGenerator())
+                .ConfigureOptions(options =>
+                {
+                    configuration.GetRequiredSection("AI:EmbeddingOptions").Bind(options);
+                })
+                .UseLogging()
+                .UseOpenTelemetry();
+                // .UseDistributedCache()
+        }
         //#endif
 
         builder.Services.AddHangfire(configuration =>

src/Templates/Boilerplate/Bit.Boilerplate/src/Server/Boilerplate.Server.Api/Services/ProductEmbeddingService.cs

@@ -11,98 +11,106 @@ namespace Boilerplate.Server.Api.Services;
 /// 1- Simple string matching (e.g., `Contains` method).
 /// 2- Full-text search using database capabilities (e.g., PostgreSQL's full-text search).
 /// 3- Vector-based search using embeddings (e.g., using OpenAI's embeddings).
-/// This service implements vector-based search using embeddings that has the following advantages:
-///     - More accurate search results based on semantic meaning rather than just similarity matching.
-///     - Multi-language support, as embeddings can capture the meaning of words across different languages.
-/// And has the following disadvantages:
-///     - Requires additional processing to generate embeddings for the text.
-///     - Require more storage space for embeddings compared to simple text search.
-/// The simple full-text search would be enough for product search case, but we have implemented the vector-based search to demonstrate how to use embeddings in the project.
+/// 4- Hybrid approach combining full-text search and vector-based search.
+/// The vector-based search is overkill for products search, but we implemented it here so you can see how to implement it in case you need it for other scenarios.
 /// </summary>
 public partial class ProductEmbeddingService
 {
-    private const float SIMILARITY_THRESHOLD = 0.85f;
+    private const float DISTANCE_THRESHOLD = 0.65f;
 
     [AutoInject] private AppDbContext dbContext = default!;
-    [AutoInject] private IWebHostEnvironment env = default!;
-    [AutoInject] private IServiceProvider serviceProvider = default!;
+    [AutoInject] private IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator = default!;
 
-    public async Task<IQueryable<Product>> GetProductsBySearchQuery(string searchQuery, CancellationToken cancellationToken)
+    public async Task<IQueryable<Product>> SearchProducts(string searchQuery, CancellationToken cancellationToken)
     {
-        //#if (database != "PostgreSQL" && database != "SqlServer")
-        // The RAG has been implemented for PostgreSQL / SQL Server only. Check out https://github.com/bitfoundation/bitplatform/blob/develop/src/Templates/Boilerplate/Bit.Boilerplate/src/Server/Boilerplate.Server.Api/Services/ProductEmbeddingService.cs
-        return dbContext.Products.Where(p => p.Name!.Contains(searchQuery) || p.Category!.Name!.Contains(searchQuery));
-        //#else
-        var embeddedUserQuery = await EmbedText(searchQuery, cancellationToken);
-        if (embeddedUserQuery is null)
-            return dbContext.Products.Where(p => p.Name!.Contains(searchQuery) || p.Category!.Name!.Contains(searchQuery));
+        if (AppDbContext.IsEmbeddingEnabled is false)
+            throw new InvalidOperationException("Embeddings are not enabled. Please enable them to use this feature.");
+
+        // It would be a good idea to try finding products using full-text search first, and if not enough results are found, then use the vector-based search.
+        // Note that test products data that have been seeded do not have embeddings, so searching for them will not return any results.
+
+        var embeddedSearchQuery = await embeddingGenerator.GenerateAsync(searchQuery, cancellationToken: cancellationToken);
+
         //#if (database == "PostgreSQL")
-        var value = new Pgvector.Vector(embeddedUserQuery.Value);
+        var value = new Pgvector.Vector(embeddedSearchQuery.Vector);
         //#else
         //#if (IsInsideProjectTemplate == true)
         /*
         //#endif
-        var value = new Microsoft.Data.SqlTypes.SqlVector<float>(embeddedUserQuery.Value);
+        var value = new Microsoft.Data.SqlTypes.SqlVector<float>(embeddedSearchQuery.Vector);
         //#if (IsInsideProjectTemplate == true)
         */
         //#endif
         //#endif
         return dbContext.Products
         //#if (database == "PostgreSQL")
-            .Where(p => p.Embedding!.CosineDistance(value!) < SIMILARITY_THRESHOLD).OrderBy(p => p.Embedding!.CosineDistance(value!));
+            .Where(p => p.Embedding!.CosineDistance(value!) < DISTANCE_THRESHOLD).OrderBy(p => p.Embedding!.CosineDistance(value!));
         //#elif (database == "SqlServer")
         //#if (IsInsideProjectTemplate == true)
         /*
         //#endif
-            .Where(p => p.Embedding.HasValue && EF.Functions.VectorDistance("cosine", p.Embedding.Value, value) < SIMILARITY_THRESHOLD).OrderBy(p => EF.Functions.VectorDistance("cosine", p.Embedding!.Value, value!));
+            .Where(p => p.Embedding.HasValue && EF.Functions.VectorDistance("cosine", p.Embedding.Value, value) < DISTANCE_THRESHOLD).OrderBy(p => EF.Functions.VectorDistance("cosine", p.Embedding!.Value, value!));
         //#if (IsInsideProjectTemplate == true)
         */
         //#endif
         //#endif
-        //#endif
     }
 
     public async Task Embed(Product product, CancellationToken cancellationToken)
     {
-        //#if (database != "PostgreSQL" && database != "SqlServer")
-        return; // The RAG has been implemented for PostgreSQL / SQL Server only.
-        //#else
-        await dbContext.Entry(product).Reference(p => p.Category).LoadAsync(cancellationToken);
+        if (AppDbContext.IsEmbeddingEnabled is false)
+            throw new InvalidOperationException("Embeddings are not enabled. Please enable them to use this feature.");
+
+        List<(string text, float weight)> inputs = [];
 
-        // TODO: Needs to be improved.
-        var embedding = await EmbedText($@"
-Name: **{product.Name}**
-Manufacture: **{product.Category!.Name}**
-Description: {product.DescriptionText}
-Appearance: {product.PrimaryImageAltText}", cancellationToken);
+        await dbContext.Entry(product)
+            .Reference(p => p.Category)
+            .LoadAsync(cancellationToken);
 
-        if (embedding.HasValue)
+        inputs.Add(($"Id: {product.ShortId}", 0.9f));
+        inputs.Add(($"Name: {product.Name}", 0.9f));
+        if (string.IsNullOrEmpty(product.DescriptionText) is false)
         {
-            product.Embedding = new(embedding.Value);
+            inputs.Add((product.DescriptionText, 0.7f));
         }
-        //#endif
-    }
+        if (string.IsNullOrEmpty(product.PrimaryImageAltText) is false)
+        {
+            inputs.Add((product.PrimaryImageAltText, 0.5f));
+        }
+        inputs.Add((product.Category!.Name!, 0.9f));
 
-    private async Task<ReadOnlyMemory<float>?> EmbedText(string input, CancellationToken cancellationToken)
-    {
-        //#if (database != "PostgreSQL" && database != "SqlServer")
-        return null; // The RAG has been implemented for PostgreSQL / SQL Server only.
-        //#else
-        if (AppDbContext.IsEmbeddingEnabled is false)
-            return null;
-        var embeddingGenerator = serviceProvider.GetService<IEmbeddingGenerator<string, Embedding<float>>>();
-        if (embeddingGenerator is null)
-            return env.IsDevelopment() ? null : throw new InvalidOperationException("Embedding generator is not registered.");
+        var texts = inputs.Select(i => i.text).ToArray();
 
-        input = $@"
-Name: **{input}**
-Manufacture: **{input}**
-Description: {input}
-Appearance: {input}";
+        var embeddingsResponse = await embeddingGenerator.GenerateAsync(texts, cancellationToken: cancellationToken);
 
+        var vectors = embeddingsResponse.Select(e => e.Vector.ToArray()).ToArray();
+        var weights = inputs.Select(t => t.weight).ToArray();
 
-        var embedding = await embeddingGenerator.GenerateVectorAsync(input, options: new() { }, cancellationToken);
-        return embedding.ToArray();
-        //#endif
+        if (vectors.Any(v => v.Length != vectors[0].Length))
+        {
+            throw new InvalidOperationException("All embedding vectors must have the same length.");
+        }
+
+        var embedding = new float[vectors[0].Length];
+        for (int i = 0; i < embedding.Length; i++)
+        {
+            embedding[i] = 0f;
+            for (int j = 0; j < vectors.Length; j++)
+            {
+                embedding[i] += weights[j] * vectors[j][i];
+            }
+        }
+
+        // L2 normalize the embedding for cosine distance stability
+        float norm = (float)Math.Sqrt(embedding.Sum(v => v * v));
+        if (norm > 0)
+        {
+            for (int i = 0; i < embedding.Length; i++)
+            {
+                embedding[i] /= norm;
+            }
+        }
+
+        product.Embedding = new(embedding);
     }
 }