LLamaSharp/docs/Examples/SemanticKernelMemory.md

Semantic kernel memory

using LLama.Common;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Memory;
using LLamaSharp.SemanticKernel.TextEmbedding;
using Microsoft.SemanticKernel.AI.Embeddings;
using Microsoft.SemanticKernel.Plugins.Memory;


public class SemanticKernelMemory
{
    private const string MemoryCollectionName = "SKGitHub";

    public static async Task Run()
    {
        var loggerFactory = ConsoleLogger.LoggerFactory;
        Console.WriteLine("Example from: https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/KernelSyntaxExamples/Example14_SemanticMemory.cs");
        Console.Write("Please input your model path: ");
        var modelPath = Console.ReadLine();

        var seed = 1337u;
        // Load weights into memory
        var parameters = new ModelParams(modelPath)
        {
            Seed = seed,
            EmbeddingMode = true
        };

        using var model = LLamaWeights.LoadFromFile(parameters);
        var embedding = new LLamaEmbedder(model, parameters);

        Console.WriteLine("====================================================");
        Console.WriteLine("======== Semantic Memory (volatile, in RAM) ========");
        Console.WriteLine("====================================================");

        /* You can build your own semantic memory combining an Embedding Generator
            * with a Memory storage that supports search by similarity (ie semantic search).
            *
            * In this example we use a volatile memory, a local simulation of a vector DB.
            *
            * You can replace VolatileMemoryStore with Qdrant (see QdrantMemoryStore connector)
            * or implement your connectors for Pinecone, Vespa, Postgres + pgvector, SQLite VSS, etc.
            */

        var memory = new MemoryBuilder()
            .WithTextEmbeddingGeneration(new LLamaSharpEmbeddingGeneration(embedding))
            .WithMemoryStore(new VolatileMemoryStore())
            .Build();

        await RunExampleAsync(memory);
    }

    private static async Task RunExampleAsync(ISemanticTextMemory memory)
    {
        await StoreMemoryAsync(memory);

        await SearchMemoryAsync(memory, "How do I get started?");

        /*
        Output:

        Query: How do I get started?

        Result 1:
            URL:     : https://github.com/microsoft/semantic-kernel/blob/main/README.md
            Title    : README: Installation, getting started, and how to contribute

        Result 2:
            URL:     : https://github.com/microsoft/semantic-kernel/blob/main/samples/dotnet-jupyter-notebooks/00-getting-started.ipynb
            Title    : Jupyter notebook describing how to get started with the Semantic Kernel

        */

        await SearchMemoryAsync(memory, "Can I build a chat with SK?");

        /*
        Output:

        Query: Can I build a chat with SK?

        Result 1:
            URL:     : https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT
            Title    : Sample demonstrating how to create a chat skill interfacing with ChatGPT

        Result 2:
            URL:     : https://github.com/microsoft/semantic-kernel/blob/main/samples/apps/chat-summary-webapp-react/README.md
            Title    : README: README associated with a sample chat summary react-based webapp

        */

        await SearchMemoryAsync(memory, "Jupyter notebook");

        await SearchMemoryAsync(memory, "README: README associated with a sample chat summary react-based webapp");

        await SearchMemoryAsync(memory, "Jupyter notebook describing how to pass prompts from a file to a semantic skill or function");
    }

    private static async Task SearchMemoryAsync(ISemanticTextMemory memory, string query)
    {
        Console.WriteLine("\nQuery: " + query + "\n");

        var memories = memory.SearchAsync(MemoryCollectionName, query, limit: 10, minRelevanceScore: 0.5);

        int i = 0;
        await foreach (MemoryQueryResult result in memories)
        {
            Console.WriteLine($"Result {++i}:");
            Console.WriteLine("  URL:     : " + result.Metadata.Id);
            Console.WriteLine("  Title    : " + result.Metadata.Description);
            Console.WriteLine("  Relevance: " + result.Relevance);
            Console.WriteLine();
        }

        Console.WriteLine("----------------------");
    }

    private static async Task StoreMemoryAsync(ISemanticTextMemory memory)
    {
        /* Store some data in the semantic memory.
            *
            * When using Azure Cognitive Search the data is automatically indexed on write.
            *
            * When using the combination of VolatileStore and Embedding generation, SK takes
            * care of creating and storing the index
            */

        Console.WriteLine("\nAdding some GitHub file URLs and their descriptions to the semantic memory.");
        var githubFiles = SampleData();
        var i = 0;
        foreach (var entry in githubFiles)
        {
            var result = await memory.SaveReferenceAsync(
                collection: MemoryCollectionName,
                externalSourceName: "GitHub",
                externalId: entry.Key,
                description: entry.Value,
                text: entry.Value);

            Console.WriteLine($"#{++i} saved.");
            Console.WriteLine(result);
        }

        Console.WriteLine("\n----------------------");
    }

    private static Dictionary<string, string> SampleData()
    {
        return new Dictionary<string, string>
        {
            ["https://github.com/microsoft/semantic-kernel/blob/main/README.md"]
                = "README: Installation, getting started, and how to contribute",
            ["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/notebooks/02-running-prompts-from-file.ipynb"]
                = "Jupyter notebook describing how to pass prompts from a file to a semantic skill or function",
            ["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/notebooks//00-getting-started.ipynb"]
                = "Jupyter notebook describing how to get started with the Semantic Kernel",
            ["https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT"]
                = "Sample demonstrating how to create a chat skill interfacing with ChatGPT",
            ["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/src/SemanticKernel/Memory/VolatileMemoryStore.cs"]
                = "C# class that defines a volatile embedding store",
            ["https://github.com/microsoft/semantic-kernel/blob/main/samples/dotnet/KernelHttpServer/README.md"]
                = "README: How to set up a Semantic Kernel Service API using Azure Function Runtime v4",
            ["https://github.com/microsoft/semantic-kernel/blob/main/samples/apps/chat-summary-webapp-react/README.md"]
                = "README: README associated with a sample chat summary react-based webapp",
        };
    }
}