Semantic Kernel & Kernel Memory - SQL Connector

This is a connector for the Semantic Kernel.

This package has been deprecated as it is legacy and is no longer maintained.

It provides a connection to a SQL database for the Semantic Kernel for the memories.

About Semantic Kernel

Semantic Kernel (SK) is a lightweight SDK enabling integration of AI Large Language Models (LLMs) with conventional programming languages. The SK extensible programming model combines natural language semantic functions, traditional code native functions, and embeddings-based memory unlocking new potential and adding value to applications with AI.

Please take a look at Semantic Kernel for more information.

About Kernel Memory

Kernel Memory (KM) is a multi-modal AI Service specialized in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support for Retrieval Augmented Generation (RAG), synthetic memory, prompt engineering, and custom semantic memory processing.

Semantic Kernel Plugin

To install this memory store, you need to add the required nuget package to your project:

dotnet add package SemanticKernel.Connectors.Memory.SqlServer

Usage

To add your SQL Server memory connector, add the following statements to your kernel initialization code:

        using SemanticKernel.Connectors.Memory.SqlServer;

        var kernel = Kernel.CreateBuilder()
                        .Build();

        var sqlMemoryStore = await SqlServerMemoryStore.ConnectAsync(connectionString: "Server=.;Database=SK;Trusted_Connection=True;");
        var semanticTextMemory = new SemanticTextMemory(sqlMemoryStore, kernel.GetRequiredService<ITextEmbeddingGenerationService>());

        kernel.ImportPluginFromObject(new TextMemoryPlugin(semanticTextMemory));

The memory store will populate all the needed tables during startup and let you focus on the development of your plugin.

Kernel Memory Plugin

dotnet add package KernelMemory.MemoryStorage.SqlServer

Usage

To add your SQL Server memory connector, add the following statements to your kernel memory initialization code:

using SemanticKernel.Connectors.Memory.SqlServer;
...
var memory = new KernelMemoryBuilder()
    .WithOpenAIDefaults(Env.Var("OPENAI_API_KEY"))
    .WithSqlServerMemoryDb("YouSqlConnectionString")
    .Build<MemoryServerless>();

Then you can use the memory store to import documents and ask questions:

// Import a file
await memory.ImportDocumentAsync("meeting-transcript.docx", tags: new() { { "user", "Blake" } });

// Import multiple files and apply multiple tags
await memory.ImportDocumentAsync(new Document("file001")
    .AddFile("business-plan.docx")
    .AddFile("project-timeline.pdf")
    .AddTag("user", "Blake")
    .AddTag("collection", "business")
    .AddTag("collection", "plans")
    .AddTag("fiscalYear", "2023"));

var answer1 = await memory.AskAsync("How many people attended the meeting?");

var answer2 = await memory.AskAsync("what's the project timeline?", filter: new MemoryFilter().ByTag("user", "Blake"));

The memory store will populate all the needed tables during startup and let you focus on the development of your plugin.

License

This project is licensed under the MIT License.