wehub-resource-sync
140 lines
4.5 KiB
C#
140 lines
4.5 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
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using System;
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using System.IO;
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using System.Linq;
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using Microsoft.Extensions.AI;
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using Microsoft.Extensions.Configuration;
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using Microsoft.Extensions.VectorData;
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using Microsoft.ML.OnnxRuntimeGenAI;
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using Microsoft.SemanticKernel;
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using Microsoft.SemanticKernel.ChatCompletion;
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using Microsoft.SemanticKernel.Connectors.InMemory;
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using Microsoft.SemanticKernel.Connectors.Onnx;
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using Microsoft.SemanticKernel.Data;
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using Microsoft.SemanticKernel.PromptTemplates.Handlebars;
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Console.OutputEncoding = System.Text.Encoding.UTF8;
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// Ensure you follow the preparation steps provided in the README.md
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var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
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// Path to the folder of your downloaded ONNX PHI-3 model
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var chatModelPath = config["Onnx:ModelPath"]!;
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var chatModelId = config["Onnx:ModelId"] ?? "phi-3";
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// Path to the file of your downloaded ONNX BGE-MICRO-V2 model
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var embeddingModelPath = config["Onnx:EmbeddingModelPath"]!;
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// Path to the vocab file your ONNX BGE-MICRO-V2 model
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var embeddingVocabPath = config["Onnx:EmbeddingVocabPath"]!;
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// If using Onnx GenAI 0.5.0 or later, the OgaHandle class must be used to track
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// resources used by the Onnx services, before using any of the Onnx services.
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using var ogaHandle = new OgaHandle();
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// Load the services
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var builder = Kernel.CreateBuilder()
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.AddOnnxRuntimeGenAIChatCompletion(chatModelId, chatModelPath)
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.AddBertOnnxEmbeddingGenerator(embeddingModelPath, embeddingVocabPath);
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// Build Kernel
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var kernel = builder.Build();
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// Get the instances of the services
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using var chatService = kernel.GetRequiredService<IChatCompletionService>() as OnnxRuntimeGenAIChatCompletionService;
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var embeddingService = kernel.GetRequiredService<IEmbeddingGenerator<string, Embedding<float>>>();
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// Create a vector store and a collection to store information
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var vectorStore = new InMemoryVectorStore(new() { EmbeddingGenerator = embeddingService });
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var collection = vectorStore.GetCollection<string, InformationItem>("ExampleCollection");
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await collection.EnsureCollectionExistsAsync();
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// Save some information to the memory
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var collectionName = "ExampleCollection";
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foreach (var factTextFile in Directory.GetFiles("Facts", "*.txt"))
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{
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var factContent = File.ReadAllText(factTextFile);
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await collection.UpsertAsync(new InformationItem()
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{
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Id = Guid.NewGuid().ToString(),
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Text = factContent
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});
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}
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// Add a plugin to search the database with.
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var vectorStoreTextSearch = new VectorStoreTextSearch<InformationItem>(collection);
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kernel.Plugins.Add(vectorStoreTextSearch.CreateWithSearch("SearchPlugin"));
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// Start the conversation
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while (true)
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{
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// Get user input
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Console.ForegroundColor = ConsoleColor.White;
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Console.Write("User > ");
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var question = Console.ReadLine()!;
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// Clean resources and exit the demo if the user input is null or empty
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if (question is null || string.IsNullOrWhiteSpace(question))
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{
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// To avoid any potential memory leak all disposable
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// services created by the kernel are disposed
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DisposeServices(kernel);
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return;
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}
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// Invoke the kernel with the user input
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var response = kernel.InvokePromptStreamingAsync(
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promptTemplate: @"Question: {{input}}
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Answer the question using the memory content:
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{{#with (SearchPlugin-Search input)}}
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{{#each this}}
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{{this}}
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-----------------
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{{/each}}
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{{/with}}",
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templateFormat: "handlebars",
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promptTemplateFactory: new HandlebarsPromptTemplateFactory(),
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arguments: new KernelArguments()
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{
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{ "input", question },
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{ "collection", collectionName }
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});
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Console.Write("\nAssistant > ");
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await foreach (var message in response)
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{
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Console.Write(message);
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}
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Console.WriteLine();
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}
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static void DisposeServices(Kernel kernel)
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{
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foreach (var target in kernel
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.GetAllServices<IChatCompletionService>()
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.OfType<IDisposable>())
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{
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target.Dispose();
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}
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}
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/// <summary>
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/// Information item to represent the embedding data stored in the memory
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/// </summary>
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internal sealed class InformationItem
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{
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[VectorStoreKey]
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[TextSearchResultName]
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public string Id { get; set; } = string.Empty;
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[VectorStoreData]
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[TextSearchResultValue]
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public string Text { get; set; } = string.Empty;
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[VectorStoreVector(Dimensions: 384)]
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public string Embedding => this.Text;
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}
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