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Merge pull request #139 from drasticactions/embeddings-semantickernel
Add TextEmbedding for Semantic Kernel
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‎LLama.Examples/LLama.Examples.csproj‎

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.Extensions.Logging.Console" Version="7.0.0" />
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<PackageReference Include="Microsoft.SemanticKernel" Version="0.21.230828.2-preview" />
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</ItemGroup>
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‎LLama.Examples/NewVersion/SemanticKernelMemory.cs‎

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using Microsoft.SemanticKernel.Memory;
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using Microsoft.SemanticKernel;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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using LLama.Common;
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using LLamaSharp.SemanticKernel.TextEmbedding;
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using Microsoft.SemanticKernel.AI.Embeddings;
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namespace LLama.Examples.NewVersion
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{
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public class SemanticKernelMemory
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{
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private const string MemoryCollectionName = "SKGitHub";
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public static async Task Run()
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{
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var loggerFactory = ConsoleLogger.LoggerFactory;
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Console.WriteLine("Example from: https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/KernelSyntaxExamples/Example14_SemanticMemory.cs");
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Console.Write("Please input your model path: ");
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var modelPath = Console.ReadLine();
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var seed = 1337;
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// Load weights into memory
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var parameters = new ModelParams(modelPath)
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{
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Seed = seed,
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EmbeddingMode = true
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};
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using var model = LLamaWeights.LoadFromFile(parameters);
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var embedding = new LLamaEmbedder(model, parameters);
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Console.WriteLine("====================================================");
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Console.WriteLine("======== Semantic Memory (volatile, in RAM) ========");
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Console.WriteLine("====================================================");
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/* You can build your own semantic memory combining an Embedding Generator
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* with a Memory storage that supports search by similarity (ie semantic search).
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*
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* In this example we use a volatile memory, a local simulation of a vector DB.
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*
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* You can replace VolatileMemoryStore with Qdrant (see QdrantMemoryStore connector)
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* or implement your connectors for Pinecone, Vespa, Postgres + pgvector, SQLite VSS, etc.
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*/
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var kernelWithCustomDb = Kernel.Builder
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.WithLoggerFactory(ConsoleLogger.LoggerFactory)
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.WithAIService<ITextEmbeddingGeneration>("local-llama-embed", new LLamaSharpEmbeddingGeneration(embedding), true)
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.WithMemoryStorage(new VolatileMemoryStore())
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.Build();
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await RunExampleAsync(kernelWithCustomDb);
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}
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private static async Task RunExampleAsync(IKernel kernel)
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{
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await StoreMemoryAsync(kernel);
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await SearchMemoryAsync(kernel, "How do I get started?");
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/*
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Output:
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Query: How do I get started?
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Result 1:
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URL: : https://github.com/microsoft/semantic-kernel/blob/main/README.md
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Title : README: Installation, getting started, and how to contribute
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Result 2:
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URL: : https://github.com/microsoft/semantic-kernel/blob/main/samples/dotnet-jupyter-notebooks/00-getting-started.ipynb
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Title : Jupyter notebook describing how to get started with the Semantic Kernel
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*/
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await SearchMemoryAsync(kernel, "Can I build a chat with SK?");
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/*
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Output:
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Query: Can I build a chat with SK?
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Result 1:
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URL: : https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT
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Title : Sample demonstrating how to create a chat skill interfacing with ChatGPT
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Result 2:
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URL: : https://github.com/microsoft/semantic-kernel/blob/main/samples/apps/chat-summary-webapp-react/README.md
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Title : README: README associated with a sample chat summary react-based webapp
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*/
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await SearchMemoryAsync(kernel, "Jupyter notebook");
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await SearchMemoryAsync(kernel, "README: README associated with a sample chat summary react-based webapp");
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await SearchMemoryAsync(kernel, "Jupyter notebook describing how to pass prompts from a file to a semantic skill or function");
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}
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private static async Task SearchMemoryAsync(IKernel kernel, string query)
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{
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Console.WriteLine("\nQuery: " + query + "\n");
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var memories = kernel.Memory.SearchAsync(MemoryCollectionName, query, limit: 10, minRelevanceScore: 0.5);
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int i = 0;
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await foreach (MemoryQueryResult memory in memories)
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{
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Console.WriteLine($"Result {++i}:");
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Console.WriteLine(" URL: : " + memory.Metadata.Id);
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Console.WriteLine(" Title : " + memory.Metadata.Description);
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Console.WriteLine(" Relevance: " + memory.Relevance);
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Console.WriteLine();
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}
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Console.WriteLine("----------------------");
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}
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private static async Task StoreMemoryAsync(IKernel kernel)
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{
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/* Store some data in the semantic memory.
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*
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* When using Azure Cognitive Search the data is automatically indexed on write.
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*
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* When using the combination of VolatileStore and Embedding generation, SK takes
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* care of creating and storing the index
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*/
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Console.WriteLine("\nAdding some GitHub file URLs and their descriptions to the semantic memory.");
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var githubFiles = SampleData();
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var i = 0;
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foreach (var entry in githubFiles)
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{
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var result = await kernel.Memory.SaveReferenceAsync(
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collection: MemoryCollectionName,
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externalSourceName: "GitHub",
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externalId: entry.Key,
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description: entry.Value,
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text: entry.Value);
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Console.WriteLine($"#{++i} saved.");
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Console.WriteLine(result);
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}
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Console.WriteLine("\n----------------------");
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}
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private static Dictionary<string, string> SampleData()
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{
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return new Dictionary<string, string>
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{
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["https://github.com/microsoft/semantic-kernel/blob/main/README.md"]
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= "README: Installation, getting started, and how to contribute",
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["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/notebooks/02-running-prompts-from-file.ipynb"]
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= "Jupyter notebook describing how to pass prompts from a file to a semantic skill or function",
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["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/notebooks//00-getting-started.ipynb"]
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= "Jupyter notebook describing how to get started with the Semantic Kernel",
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["https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT"]
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= "Sample demonstrating how to create a chat skill interfacing with ChatGPT",
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["https://github.com/microsoft/semantic-kernel/blob/main/dotnet/src/SemanticKernel/Memory/VolatileMemoryStore.cs"]
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= "C# class that defines a volatile embedding store",
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["https://github.com/microsoft/semantic-kernel/blob/main/samples/dotnet/KernelHttpServer/README.md"]
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= "README: How to set up a Semantic Kernel Service API using Azure Function Runtime v4",
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["https://github.com/microsoft/semantic-kernel/blob/main/samples/apps/chat-summary-webapp-react/README.md"]
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= "README: README associated with a sample chat summary react-based webapp",
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};
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}
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}
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}

‎LLama.Examples/NewVersion/TestRunner.cs‎

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Console.WriteLine("10: Constrain response to json format using grammar.");
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Console.WriteLine("11: Semantic Kernel Prompt.");
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Console.WriteLine("12: Semantic Kernel Chat.");
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Console.WriteLine("13: Semantic Kernel Memory.");
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while (true)
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{
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{
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await SemanticKernelChat.Run();
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}
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else if (choice == 13)
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{
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await SemanticKernelMemory.Run();
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}
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else
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{
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Console.WriteLine("Cannot parse your choice. Please select again.");

‎LLama.Examples/RepoUtils.cs‎

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using Microsoft.Extensions.Logging;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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namespace LLama.Examples
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{
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/// <summary>
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/// Basic logger printing to console
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/// </summary>
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internal static class ConsoleLogger
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{
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internal static ILogger Logger => LoggerFactory.CreateLogger<object>();
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internal static ILoggerFactory LoggerFactory => s_loggerFactory.Value;
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private static readonly Lazy<ILoggerFactory> s_loggerFactory = new(LogBuilder);
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private static ILoggerFactory LogBuilder()
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{
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return Microsoft.Extensions.Logging.LoggerFactory.Create(builder =>
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{
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builder.SetMinimumLevel(LogLevel.Warning);
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builder.AddFilter("Microsoft", LogLevel.Trace);
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builder.AddFilter("Microsoft", LogLevel.Debug);
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builder.AddFilter("Microsoft", LogLevel.Information);
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builder.AddFilter("Microsoft", LogLevel.Warning);
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builder.AddFilter("Microsoft", LogLevel.Error);
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builder.AddFilter("Microsoft", LogLevel.Warning);
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builder.AddFilter("System", LogLevel.Warning);
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builder.AddConsole();
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});
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}
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}
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}

‎LLama.SemanticKernel/README.md‎

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- [SemanticKernelChat](../LLama.Examples/NewVersion/SemanticKernelChat.cs)
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- [SemanticKernelPrompt](../LLama.Examples/NewVersion/SemanticKernelPrompt.cs)
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- [SemanticKernelMemory](../LLama.Examples/NewVersion/SemanticKernelMemory.cs)
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## ITextCompletion
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```csharp
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var ex = new InteractiveExecutor(context);
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var chatGPT = new LLamaSharpChatCompletion(ex);
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```
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## ITextEmbeddingGeneration
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```csharp
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using var model = LLamaWeights.LoadFromFile(parameters);
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var embedding = new LLamaEmbedder(model, parameters);
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var kernelWithCustomDb = Kernel.Builder
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.WithLoggerFactory(ConsoleLogger.LoggerFactory)
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.WithAIService<ITextEmbeddingGeneration>("local-llama-embed", new LLamaSharpEmbeddingGeneration(embedding), true)
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.WithMemoryStorage(new VolatileMemoryStore())
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.Build();
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```

‎LLama.SemanticKernel/TextEmbedding/LLamaSharpEmbeddingGeneration.cs‎

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using LLama;
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using Microsoft.SemanticKernel.AI.Embeddings;
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namespace LLamaSharp.SemanticKernel.TextEmbedding;
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public sealed class LLamaSharpEmbeddingGeneration : ITextEmbeddingGeneration
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{
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private LLamaEmbedder _embedder;
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public LLamaSharpEmbeddingGeneration(LLamaEmbedder embedder)
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{
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_embedder = embedder;
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}
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/// <inheritdoc/>
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public async Task<IList<ReadOnlyMemory<float>>> GenerateEmbeddingsAsync(IList<string> data, CancellationToken cancellationToken = default)
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{
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return data.Select(text => new ReadOnlyMemory<float>(_embedder.GetEmbeddings(text))).ToList();
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}
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}

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