wehub-resource-sync
75 lines
3.7 KiB
C#
75 lines
3.7 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
|
|
|
|
using Microsoft.Extensions.AI;
|
|
using Microsoft.Extensions.VectorData;
|
|
using Microsoft.SemanticKernel.Connectors.Redis;
|
|
using StackExchange.Redis;
|
|
|
|
namespace GettingStartedWithVectorStores;
|
|
|
|
/// <summary>
|
|
/// Example that shows that you can use the dynamic data modeling to interact with a vector database.
|
|
/// This makes it possible to use the vector store abstractions without having to create your own strongly-typed data model.
|
|
/// </summary>
|
|
public class Step4_Use_DynamicDataModel(ITestOutputHelper output, VectorStoresFixture fixture) : BaseTest(output), IClassFixture<VectorStoresFixture>
|
|
{
|
|
/// <summary>
|
|
/// Example showing how to query a vector store that uses dynamic data modeling.
|
|
///
|
|
/// This example requires a Redis server running on localhost:6379. To run a Redis server in a Docker container, use the following command:
|
|
/// docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
|
|
/// </summary>
|
|
[Fact]
|
|
public async Task SearchAVectorStoreWithDynamicMappingAsync()
|
|
{
|
|
// Construct a redis vector store.
|
|
var vectorStore = new RedisVectorStore(ConnectionMultiplexer.Connect("localhost:6379").GetDatabase());
|
|
|
|
// First, let's use the code from step 1 to ingest data into the vector store
|
|
// using the custom data model, simulating a scenario where someone else ingested
|
|
// the data into the database previously.
|
|
var collection = vectorStore.GetCollection<string, Glossary>("skglossary");
|
|
var customDataModelCollection = vectorStore.GetCollection<string, Glossary>("skglossary");
|
|
await Step1_Ingest_Data.IngestDataIntoVectorStoreAsync(customDataModelCollection, fixture.EmbeddingGenerator);
|
|
|
|
// To use dynamic data modeling, we still have to describe the storage schema to the vector store
|
|
// using a record definition. The benefit over a custom data model is that this definition
|
|
// does not have to be known at compile time.
|
|
// E.g. it can be read from a configuration or retrieved from a service.
|
|
var recordDefinition = new VectorStoreCollectionDefinition
|
|
{
|
|
Properties =
|
|
[
|
|
new VectorStoreKeyProperty("Key", typeof(string)),
|
|
new VectorStoreDataProperty("Category", typeof(string)),
|
|
new VectorStoreDataProperty("Term", typeof(string)),
|
|
new VectorStoreDataProperty("Definition", typeof(string)),
|
|
new VectorStoreVectorProperty("DefinitionEmbedding", typeof(ReadOnlyMemory<float>), 1536),
|
|
]
|
|
};
|
|
|
|
// Now, let's create a collection that uses a dynamic data model.
|
|
var dynamicDataModelCollection = vectorStore.GetDynamicCollection("skglossary", recordDefinition);
|
|
|
|
// 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 generic data model collection and get the single most relevant result.
|
|
var searchResultItems = await dynamicDataModelCollection.SearchAsync(
|
|
searchVector,
|
|
top: 1).ToListAsync();
|
|
|
|
// Write the search result with its score to the console.
|
|
// Note that here we can loop through all the properties
|
|
// without knowing the schema, since the properties are
|
|
// stored as a dictionary of string keys and object values
|
|
// when using the dynamic data model.
|
|
foreach (var property in searchResultItems.First().Record)
|
|
{
|
|
Console.WriteLine($"{property.Key}: {property.Value}");
|
|
}
|
|
Console.WriteLine(searchResultItems.First().Score);
|
|
}
|
|
}
|