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<h1 id="semantic-kernel-with-kernel-memory">Semantic-kernel - with kernel-memory</h1>
<p>Semantic Memory allows to store your data like traditional DBs, adding the ability to query it using natural language.</p>
<pre><code class="language-cs">using LLama.Common;
using Microsoft.SemanticKernel.Memory;
using LLamaSharp.SemanticKernel.TextEmbedding;

namespace LLama.Examples.Examples
{
    public class SemanticKernelMemory
    {
        private const string MemoryCollectionName = &quot;SKGitHub&quot;;

        public static async Task Run()
        {
            string modelPath = UserSettings.GetModelPath();

            Console.WriteLine(&quot;This example is from: \n&quot; +
                &quot;https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/KernelSyntaxExamples/Example14_SemanticMemory.cs&quot;);

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

            /* 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, &quot;How do I get started?&quot;);

            /*
            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, &quot;Can I build a chat with SK?&quot;);

            /*
            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, &quot;Jupyter notebook&quot;);

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

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

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

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

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

            Console.WriteLine(&quot;----------------------&quot;);
        }

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

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

            Console.WriteLine(&quot;\n----------------------&quot;);
        }

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

</code></pre>


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