如何使用用于微软语义内核（Microsoft Semantic Kernel）的Elasticsearch矢量存储连接器进行人工智能代理开发

dotnet add package "Elastic.Clients.Elasticsearch" -v 8.16.2
dotnet add package "Elastic.SemanticKernel.Connectors.Elasticsearch" -v 0.1.2
dotnet add package "Microsoft.Extensions.Hosting" -v 9.0.0
dotnet add package "Microsoft.SemanticKernel.Connectors.AzureOpenAI" -v 1.30.0
dotnet add package "Microsoft.SemanticKernel.PromptTemplates.Handlebars" -v 1.30.0

using System;
using System.IO;
using System.Linq;
using System.Threading.Tasks;

using Elastic.Clients.Elasticsearch;
using Elastic.Transport;

using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using Microsoft.Extensions.VectorData;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Data;
using Microsoft.SemanticKernel.Embeddings;
using Microsoft.SemanticKernel.PromptTemplates.Handlebars;

/// <summary>
/// Data model for storing a "hotel" with a name, a description, a  description embedding and an optional reference link.
/// </summary>
public sealed record Hotel
{
	[VectorStoreRecordKey]
	public required string HotelId { get; set; }

	[TextSearchResultName]
	[VectorStoreRecordData(IsFilterable = true)]
	public required string HotelName { get; set; }

	[TextSearchResultValue]
	[VectorStoreRecordData(IsFullTextSearchable = true)]
	public required string Description { get; set; }

	[VectorStoreRecordVector(Dimensions: 1536, DistanceFunction.CosineSimilarity, IndexKind.Hnsw)]
	public ReadOnlyMemory<float>? DescriptionEmbedding { get; set; }

	[TextSearchResultLink]
	[VectorStoreRecordData]
	public string? ReferenceLink { get; set; }
}

var builder = Host.CreateApplicationBuilder(args);

// Register AI services.
var kernelBuilder = builder.Services.AddKernel();

kernelBuilder.AddAzureOpenAIChatCompletion("gpt-4o", "https://my-service.openai.azure.com", "my_token");

kernelBuilder.AddAzureOpenAITextEmbeddingGeneration("ada-002", "https://my-service.openai.azure.com", "my_token");

// Register text search service.
kernelBuilder.AddVectorStoreTextSearch<Hotel>();

// Register Elasticsearch vector store.
var elasticsearchClientSettings = new ElasticsearchClientSettings(new Uri("https://my-elasticsearch-instance.cloud"))
    .Authentication(new BasicAuthentication("elastic", "my_password"));

kernelBuilder.AddElasticsearchVectorStoreRecordCollection<string, Hotel>("skhotels", elasticsearchClientSettings);

// Build the host.
using var host = builder.Build();

// For demo purposes, we access the services directly without using a DI context.

var kernel = host.Services.GetService<Kernel>()!;
var embeddings = host.Services.GetService<ITextEmbeddingGenerationService>()!;
var vectorStoreCollection = host.Services.GetService<IVectorStoreRecordCollection<string, Hotel>>()!;

// Register search plugin.
var textSearch = host.Services.GetService<VectorStoreTextSearch<Hotel>>()!;
kernel.Plugins.Add(textSearch.CreateWithGetTextSearchResults("SearchPlugin"));

await vectorStoreCollection.CreateCollectionIfNotExistsAsync();

// CSV format: ID;Hotel Name;Description;Reference Link
var hotels = (await File.ReadAllLinesAsync("hotels.csv"))
    .Select(x => x.Split(';'));

foreach (var chunk in hotels.Chunk(25))
{
    var descriptionEmbeddings = await embeddings.GenerateEmbeddingsAsync(chunk.Select(x => x[2]).ToArray());
    
    for (var i = 0; i < chunk.Length; ++i)
    {
        var hotel = chunk[i];
        await vectorStoreCollection.UpsertAsync(new Hotel
        {
            HotelId = hotel[0],
            HotelName = hotel[1],
            Description = hotel[2],
            DescriptionEmbedding = descriptionEmbeddings[i],
            ReferenceLink = hotel[3]
        });
    }
}

{
  "mappings": {
    "properties": {
      "descriptionEmbedding": {
        "dims": 1536,
        "index": true,
        "index_options": {
          "type": "hnsw"
        },
        "similarity": "cosine",
        "type": "dense_vector"
      },
      "hotelName": {
        "type": "keyword"
      },
      "description": {
        "type": "text"
      }
    }
  }
}

// Invoke the LLM with a template that uses the search plugin to
// 1. get related information to the user query from the vector store
// 2. add the information to the LLM prompt.
var response = await kernel.InvokePromptAsync(
    promptTemplate: """
                    Please use this information to answer the question:
                    {{#with (SearchPlugin-GetTextSearchResults question)}}
                      {{#each this}}
                        Name: {{Name}}
                        Value: {{Value}}
                        Source: {{Link}}
                        -----------------
                      {{/each}}
                    {{/with}}
                    
                    Include the source of relevant information in the response.

                    Question: {{question}}
                    """,
    arguments: new KernelArguments
    {
        { "question", "Please show me all hotels that have a rooftop bar." },
    },
    templateFormat: "handlebars",
    promptTemplateFactory: new HandlebarsPromptTemplateFactory());

Console.WriteLine(response.ToString());

// > The hotel that has a rooftop bar is Skyline Suites. You can find more information about this hotel [here](https://example.com/yz567).

9;
Skyline Suites;
Offering panoramic city views from every suite, this hotel is perfect for those who love the urban landscape. Enjoy luxurious amenities, a rooftop bar, and close proximity to attractions. Luxurious and contemporary.;
https://example.com/yz567