Feat/litedb connector

dotnet/Directory.Packages.props

@@ -155,6 +155,7 @@
     <PackageVersion Include="DuckDB.NET.Data.Full" Version="1.2.0" />
     <PackageVersion Include="DuckDB.NET.Data" Version="1.1.3" />
     <PackageVersion Include="MongoDB.Driver" Version="2.30.0" />
+    <PackageVersion Include="LiteDB" Version="6.0.0-prerelease.63" />
     <PackageVersion Include="Microsoft.Graph" Version="5.90.0" />
     <PackageVersion Include="Microsoft.OpenApi" Version="1.6.24" />
     <PackageVersion Include="Microsoft.OpenApi.Readers" Version="1.6.24" />

dotnet/SK-dotnet.slnx

@@ -149,6 +149,7 @@
     <Project Path="src/VectorData/InMemory/InMemory.csproj" />
     <Project Path="src/VectorData/Milvus/Milvus.csproj" />
     <Project Path="src/VectorData/MongoDB/MongoDB.csproj" />
+    <Project Path="src/VectorData/LiteDb/LiteDb.csproj" />
     <Project Path="src/VectorData/PgVector/PgVector.csproj" />
     <Project Path="src/VectorData/Pinecone/Pinecone.csproj" />
     <Project Path="src/VectorData/Qdrant/Qdrant.csproj" />
@@ -324,6 +325,7 @@
     <Project Path="test/VectorData/InMemory.UnitTests/InMemory.UnitTests.csproj" />
     <Project Path="test/VectorData/MongoDB.ConformanceTests/MongoDB.ConformanceTests.csproj" />
     <Project Path="test/VectorData/MongoDB.UnitTests/MongoDB.UnitTests.csproj" />
+    <Project Path="test/VectorData/LiteDb.UnitTests/LiteDb.UnitTests.csproj" />
     <Project Path="test/VectorData/PgVector.ConformanceTests/PgVector.ConformanceTests.csproj" />
     <Project Path="test/VectorData/PgVector.UnitTests/PgVector.UnitTests.csproj" />
     <Project Path="test/VectorData/Pinecone.ConformanceTests/Pinecone.ConformanceTests.csproj" />

dotnet/samples/GettingStartedWithVectorStores/GettingStartedWithVectorStores.csproj

@@ -42,6 +42,7 @@
     <ProjectReference Include="..\..\src\Connectors\Connectors.AzureOpenAI\Connectors.AzureOpenAI.csproj" />
     <ProjectReference Include="..\..\src\VectorData\AzureAISearch\AzureAISearch.csproj" />
     <ProjectReference Include="..\..\src\VectorData\InMemory\InMemory.csproj" />
+    <ProjectReference Include="..\..\src\VectorData\LiteDb\LiteDb.csproj" />
     <ProjectReference Include="..\..\src\VectorData\Qdrant\Qdrant.csproj" />
     <ProjectReference Include="..\..\src\VectorData\Redis\Redis.csproj" />
     <ProjectReference Include="..\..\src\SemanticKernel.Abstractions\SemanticKernel.Abstractions.csproj" />

dotnet/samples/GettingStartedWithVectorStores/README.md

@@ -4,6 +4,10 @@ This project contains a step by step guide to get started using Vector Stores wi
 
 The examples can be run as integration tests but their code can also be copied to stand-alone programs.
 
+## Step 5 — LiteDB advanced scenario
+
+`Step5_LiteDb_AdvancedScenario` shows how to combine transactional batch upserts, per-property embedding generators and metrics, and the extended filter surface (`Contains`, `StartsWith`, `EndsWith`, `IN`) when working with the LiteDB connector.
+
 ## Configuring Secrets
 
 Most of the examples will require secrets and credentials, to access OpenAI, Azure OpenAI,

dotnet/samples/GettingStartedWithVectorStores/Step5_LiteDb_AdvancedScenario.cs

@@ -0,0 +1,173 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Threading;
+using System.Threading.Tasks;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.LiteDb;
+
+namespace GettingStartedWithVectorStores;
+
+/// <summary>
+/// Demonstrates advanced LiteDB usage: per-property generators and metrics, transactional batch upserts, and expressive filters.
+/// </summary>
+public sealed class Step5_LiteDb_AdvancedScenario(ITestOutputHelper output) : BaseTest(output)
+{
+    private static readonly string[] FilterCities = new[] { "Seattle", "Portland" };
+
+    [Fact]
+    public async Task RunEndToEndScenarioAsync()
+    {
+        var services = new ServiceCollection();
+        services.AddSingleton<IEmbeddingGenerator<string, Embedding<float>>>(new SampleEmbeddingGenerator("store"));
+        services.AddLiteDbVectorStore(_ => new LiteDbVectorStoreOptions
+        {
+            DisposeDatabase = false,
+            DistanceMetric = LiteDbDistanceMetric.Cosine,
+            AutoCreateVectorIndexes = true,
+            CollectionNamePrefix = "sample_"
+        });
+        services.AddLiteDbCollection<string, SampleHotel>("hotels", _ => new VectorStoreCollectionDefinition
+        {
+            Properties =
+            {
+                new VectorStoreKeyProperty(nameof(SampleHotel.HotelId), typeof(string)),
+                new VectorStoreDataProperty(nameof(SampleHotel.City), typeof(string)),
+                new VectorStoreDataProperty(nameof(SampleHotel.Tags), typeof(List<string>)),
+                new VectorStoreDataProperty(nameof(SampleHotel.Description), typeof(string)),
+                new VectorStoreVectorProperty(nameof(SampleHotel.Overview), typeof(string), 3)
+                {
+                    DistanceFunction = DistanceFunction.DotProductSimilarity,
+                    EmbeddingGenerator = new SampleEmbeddingGenerator("overview")
+                },
+                new VectorStoreVectorProperty(nameof(SampleHotel.Amenities), typeof(string), 3)
+                {
+                    DistanceFunction = DistanceFunction.EuclideanDistance,
+                    EmbeddingGenerator = new SampleEmbeddingGenerator("amenities")
+                }
+            }
+        });
+
+        using var provider = services.BuildServiceProvider();
+        var collection = provider.GetRequiredService<VectorStoreCollection<string, SampleHotel>>();
+        await collection.EnsureCollectionExistsAsync();
+
+        var hotels = new[]
+        {
+            new SampleHotel
+            {
+                HotelId = "alpha",
+                City = "Seattle",
+                Description = "Waterfront spa resort",
+                Tags = new List<string> { "spa", "rooftop" },
+                Overview = "0.9,0.05,0.05",
+                Amenities = "0.2,0.5,0.3"
+            },
+            new SampleHotel
+            {
+                HotelId = "beta",
+                City = "Portland",
+                Description = "Modern downtown escape",
+                Tags = new List<string> { "boutique", "spa" },
+                Overview = "0.7,0.1,0.2",
+                Amenities = "0.6,0.2,0.2"
+            },
+            new SampleHotel
+            {
+                HotelId = "gamma",
+                City = "San Francisco",
+                Description = "Historic harbor view",
+                Tags = new List<string> { "historic", "view" },
+                Overview = "0.1,0.8,0.1",
+                Amenities = "0.3,0.1,0.6"
+            }
+        };
+
+        // Batch upsert executes inside a transaction – either all records are stored or none are.
+        await collection.UpsertAsync(hotels);
+
+#pragma warning disable CA1866 // the literal "t" is clearer for sample filtering.
+        var searchOptions = new VectorSearchOptions<SampleHotel>
+        {
+            VectorProperty = h => h.Overview,
+            Filter = h => (h.Tags.Contains("spa") || h.City!.StartsWith("Sea"))
+                && FilterCities.Contains(h.City!)
+                && h.Description!.EndsWith("t"),
+            IncludeVectors = false
+        };
+#pragma warning restore CA1866
+
+        var results = await collection.SearchAsync("0.8,0.1,0.1", top: 2, searchOptions).ToListAsync();
+
+        foreach (var result in results)
+        {
+            this.WriteLine($"Hotel: {result.Record.HotelId} in {result.Record.City} (score {result.Score:F3})");
+        }
+
+        Assert.Single(results);
+        Assert.Equal("alpha", results[0].Record.HotelId);
+    }
+
+    private sealed class SampleHotel
+    {
+        [VectorStoreKey]
+        public string? HotelId { get; set; }
+
+        [VectorStoreData]
+        public string? City { get; set; }
+
+        [VectorStoreData]
+        public string? Description { get; set; }
+
+        [VectorStoreData]
+        public List<string> Tags { get; set; } = new();
+
+        [VectorStoreVector(Dimensions: 3)]
+        public string? Overview { get; set; }
+
+        [VectorStoreVector(Dimensions: 3)]
+        public string? Amenities { get; set; }
+    }
+
+    private sealed class SampleEmbeddingGenerator(string tag) : IEmbeddingGenerator<string, Embedding<float>>
+    {
+        public Task<GeneratedEmbeddings<Embedding<float>>> GenerateAsync(IEnumerable<string> values, EmbeddingGenerationOptions? options = null, CancellationToken cancellationToken = default)
+        {
+            var embeddings = new GeneratedEmbeddings<Embedding<float>>();
+            foreach (var value in values)
+            {
+                embeddings.Add(new Embedding<float>(ParseVector(value, tag)));
+            }
+
+            return Task.FromResult(embeddings);
+        }
+
+        public Task<Embedding<float>> GenerateAsync(string value, EmbeddingGenerationOptions? options = null, CancellationToken cancellationToken = default)
+            => Task.FromResult(new Embedding<float>(ParseVector(value, tag)));
+
+        public object? GetService(Type serviceType, object? serviceKey = null)
+            => null;
+
+        public void Dispose()
+        {
+        }
+
+        private static float[] ParseVector(string value, string tagPrefix)
+        {
+            var values = value.Split(',', StringSplitOptions.RemoveEmptyEntries | StringSplitOptions.TrimEntries)
+                .Select(float.Parse)
+                .ToArray();
+
+            // Different prefixes allow callers to verify generator precedence in diagnostics.
+            return tagPrefix switch
+            {
+                "overview" => values,
+                _ => values.Select(v => Math.Clamp(v + 0.05f, 0f, 1f)).ToArray()
+            };
+        }
+    }
+}

dotnet/src/VectorData/LiteDb/LiteDb.csproj

@@ -0,0 +1,37 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <AssemblyName>Microsoft.SemanticKernel.Connectors.LiteDb</AssemblyName>
+    <RootNamespace>$(AssemblyName)</RootNamespace>
+    <TargetFrameworks>net8.0;netstandard2.1</TargetFrameworks>
+    <VersionSuffix>preview</VersionSuffix>
+  </PropertyGroup>
+
+  <Import Project="$(RepoRoot)/dotnet/nuget/nuget-package.props" />
+
+  <PropertyGroup>
+    <Title>LiteDB provider for Microsoft.Extensions.VectorData</Title>
+    <Description>LiteDB provider for Microsoft.Extensions.VectorData by Semantic Kernel</Description>
+    <PackageReadmeFile>VECTORDATA-CONNECTORS-NUGET.md</PackageReadmeFile>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <None Include="$(RepoRoot)/dotnet/nuget/VECTORDATA-CONNECTORS-NUGET.md" Link="VECTORDATA-CONNECTORS-NUGET.md" Pack="true" PackagePath="." />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="LiteDB" />
+    <PackageReference Include="Microsoft.Extensions.DependencyInjection.Abstractions" />
+    <PackageReference Include="Microsoft.Extensions.AI.Abstractions" />
+    <PackageReference Include="System.Numerics.Tensors" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\VectorData.Abstractions\VectorData.Abstractions.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <InternalsVisibleTo Include="SemanticKernel.Connectors.LiteDb.UnitTests" />
+  </ItemGroup>
+
+</Project>