wehub-resource-sync
90 lines
3.6 KiB
C#
90 lines
3.6 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
|
|
|
|
using Microsoft.Extensions.AI;
|
|
using Microsoft.Extensions.VectorData;
|
|
using Microsoft.SemanticKernel.Connectors.InMemory;
|
|
|
|
namespace GettingStartedWithVectorStores;
|
|
|
|
/// <summary>
|
|
/// Example showing how to do vector searches with an in-memory vector store.
|
|
/// </summary>
|
|
public class Step2_Vector_Search(ITestOutputHelper output, VectorStoresFixture fixture) : BaseTest(output), IClassFixture<VectorStoresFixture>
|
|
{
|
|
/// <summary>
|
|
/// Do a basic vector search where we just want to retrieve the single most relevant result.
|
|
/// </summary>
|
|
[Fact]
|
|
public async Task SearchAnInMemoryVectorStoreAsync()
|
|
{
|
|
var collection = await GetVectorStoreCollectionWithDataAsync();
|
|
|
|
// Search the vector store.
|
|
var searchResultItem = await SearchVectorStoreAsync(
|
|
collection,
|
|
"What is an Application Programming Interface?",
|
|
fixture.EmbeddingGenerator);
|
|
|
|
// Write the search result with its score to the console.
|
|
Console.WriteLine(searchResultItem.Record.Definition);
|
|
Console.WriteLine(searchResultItem.Score);
|
|
}
|
|
|
|
/// <summary>
|
|
/// Search the given collection for the most relevant result to the given search string.
|
|
/// </summary>
|
|
/// <param name="collection">The collection to search.</param>
|
|
/// <param name="searchString">The string to search matches for.</param>
|
|
/// <param name="embeddingGenerator">The service to generate embeddings with.</param>
|
|
/// <returns>The top search result.</returns>
|
|
internal static async Task<VectorSearchResult<Glossary>> SearchVectorStoreAsync(VectorStoreCollection<string, Glossary> collection, string searchString, IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator)
|
|
{
|
|
// Generate an embedding from the search string.
|
|
var searchVector = (await embeddingGenerator.GenerateAsync(searchString)).Vector;
|
|
|
|
// Search the store and get the single most relevant result.
|
|
var searchResultItems = await collection.SearchAsync(
|
|
searchVector,
|
|
top: 1).ToListAsync();
|
|
return searchResultItems.First();
|
|
}
|
|
|
|
/// <summary>
|
|
/// Do a more complex vector search with pre-filtering.
|
|
/// </summary>
|
|
[Fact]
|
|
public async Task SearchAnInMemoryVectorStoreWithFilteringAsync()
|
|
{
|
|
var collection = await GetVectorStoreCollectionWithDataAsync();
|
|
|
|
// Generate an embedding from the search string.
|
|
var searchString = "How do I provide additional context to an LLM?";
|
|
var searchVector = (await fixture.EmbeddingGenerator.GenerateAsync(searchString)).Vector;
|
|
|
|
// Search the store with a filter and get the single most relevant result.
|
|
var searchResultItems = await collection.SearchAsync(
|
|
searchVector,
|
|
top: 1,
|
|
new()
|
|
{
|
|
Filter = g => g.Category == "AI"
|
|
}).ToListAsync();
|
|
|
|
// Write the search result with its score to the console.
|
|
Console.WriteLine(searchResultItems.First().Record.Definition);
|
|
Console.WriteLine(searchResultItems.First().Score);
|
|
}
|
|
|
|
private async Task<VectorStoreCollection<string, Glossary>> GetVectorStoreCollectionWithDataAsync()
|
|
{
|
|
// Construct the vector store and get the collection.
|
|
var vectorStore = new InMemoryVectorStore();
|
|
var collection = vectorStore.GetCollection<string, Glossary>("skglossary");
|
|
|
|
// Ingest data into the collection using the code from step 1.
|
|
await Step1_Ingest_Data.IngestDataIntoVectorStoreAsync(collection, fixture.EmbeddingGenerator);
|
|
|
|
return collection;
|
|
}
|
|
}
|