https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.SqlServer

SQL Server connector for Semantic Kernel plugin and Kernel Memory
https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.SqlServer

ai artificial-intelligence indexing llm memory openai rag sdk semantic-search sql-server

Last synced: about 1 month ago
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SQL Server connector for Semantic Kernel plugin and Kernel Memory

• Host: GitHub
• URL: https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.SqlServer
• Owner: kbeaugrand
• License: mit
• Created: 2023-08-15T09:19:39.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-21T06:52:30.000Z (7 months ago)
• Last Synced: 2024-05-22T06:03:53.598Z (7 months ago)
• Topics: ai, artificial-intelligence, indexing, llm, memory, openai, rag, sdk, semantic-search, sql-server
• Language: C#
• Homepage:
• Size: 193 KB
• Stars: 39
• Watchers: 3
• Forks: 10
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Codeowners: .github/CODEOWNERS

Awesome Lists containing this project

• awesome-semantickernel - SemanticKernel.Connectors.Memory.SqlServer

README

        
# Semantic Kernel & Kernel Memory - SQL Connector

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[![Version](https://img.shields.io/github/v/release/kbeaugrand/SemanticKernel.Connectors.Memory.SqlServer)](https://img.shields.io/github/v/release/kbeaugrand/SemanticKernel.Connectors.Memory.SqlServer)

[![License](https://img.shields.io/github/license/kbeaugrand/SemanticKernel.Connectors.Memory.SqlServer)](https://img.shields.io/github/v/release/kbeaugrand/SemanticKernel.Connectors.Memory.SqlServer)

This is a connector for the [Semantic Kernel](https://aka.ms/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 This package has been deprecated as it is legacy and is no longer maintained.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](https://aka.ms/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:

```dotnetcli

dotnet add package SemanticKernel.Connectors.Memory.SqlServer

```

## Usage

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

```csharp

        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());

        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

```bash

dotnet add package KernelMemory.MemoryStorage.SqlServer

```

## Usage

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

```csharp

using SemanticKernel.Connectors.Memory.SqlServer;

...

var memory = new KernelMemoryBuilder()

    .WithOpenAIDefaults(Env.Var("OPENAI_API_KEY"))

    .WithSqlServerMemoryDb("YouSqlConnectionString")

    .Build();

```

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

```csharp

// 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](LICENSE).