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What are Semantic Kernel Vector Store connectors? (Preview)

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Warning

The Semantic Kernel Vector Store functionality is in preview, and improvements that require breaking changes may still occur in limited circumstances before release.

Tip

If you are looking for information about the legacy Memory Store connectors, refer to the Memory Stores page.

Vector databases have many use cases across different domains and applications that involve natural language processing (NLP), computer vision (CV), recommendation systems (RS), and other areas that require semantic understanding and matching of data.

One use case for storing information in a vector database is to enable large language models (LLMs) to generate more relevant and coherent responses. Large language models often face challenges such as generating inaccurate or irrelevant information; lacking factual consistency or common sense; repeating or contradicting themselves; being biased or offensive. To help overcome these challenges, you can use a vector database to store information about different topics, keywords, facts, opinions, and/or sources related to your desired domain or genre. The vector database allows you to efficiently find the subset of information related to a specific question or topic. You can then pass information from the vector database with your prompt to your large language model to generate more accurate and relevant content.

For example, if you want to write a blog post about the latest trends in AI, you can use a vector database to store the latest information about that topic and pass the information along with the ask to a LLM in order to generate a blog post that leverages the latest information.

Semantic Kernel and .net provides an abstraction for interacting with Vector Stores and a list of out-of-the-box connectors that implement these abstractions. Features include creating, listing and deleting collections of records, and uploading, retrieving and deleting records. The abstraction makes it easy to experiment with a free or locally hosted Vector Store and then switch to a service when needing to scale up.

The Vector Store Abstraction

The main interfaces in the Vector Store abstraction are the following.

Microsoft.Extensions.VectorData.IVectorStore

IVectorStore contains operations that spans across all collections in the vector store, e.g. ListCollectionNames. It also provides the ability to get IVectorStoreRecordCollection<TKey, TRecord> instances.

Microsoft.Extensions.VectorData.IVectorStoreRecordCollection<TKey, TRecord>

IVectorStoreRecordCollection<TKey, TRecord> represents a collection. This collection may or may not exist, and the interface provides methods to check if the collection exists, create it or delete it. The interface also provides methods to upsert, get and delete records. Finally, the interface inherits from IVectorizedSearch<TRecord> providing vector search capabilities.

Microsoft.Extensions.VectorData.IVectorizedSearch<TRecord>

IVectorizedSearch<TRecord> contains a method for doing vector searches. IVectorStoreRecordCollection<TKey, TRecord> inherits from IVectorizedSearch<TRecord> making it possible to use IVectorizedSearch<TRecord> on its own in cases where only search is needed and no record or collection management is needed.

IVectorizableTextSearch<TRecord>

IVectorizableTextSearch<TRecord> contains a method for doing vector searches where the vector database has the ability to generate embeddings automatically. E.g. you can call this method with a text string and the database will generate the embedding for you and search against a vector field. This is not supported by all vector databases and is therefore only implemented by select connectors.

Getting started with Vector Store connectors

Import the necessary nuget packages

All the vector store interfaces and any abstraction related classes are available in the Microsoft.Extensions.VectorData.Abstractions nuget package. Each vector store implementation is available in its own nuget package. For a list of known implementations, see the Out-of-the-box connectors page.

The abstractions package can be added like this.

dotnet add package Microsoft.Extensions.VectorData.Abstractions --prerelease

Warning

From version 1.23.0 of Semantic Kernel, the Vector Store abstractions have been removed from Microsoft.SemanticKernel.Abstractions and are available in the new dedicated Microsoft.Extensions.VectorData.Abstractions package.

Note that from version 1.23.0, Microsoft.SemanticKernel.Abstractions has a dependency on Microsoft.Extensions.VectorData.Abstractions, therefore there is no need to reference additional packages. The abstractions will however now be in the new Microsoft.Extensions.VectorData namespace.

When upgrading from 1.22.0 or earlier to 1.23.0 or later, you will need to add an additional using Microsoft.Extensions.VectorData; clause in files where any of the Vector Store abstraction types are used e.g. IVectorStore, IVectorStoreRecordCollection, VectorStoreRecordDataAttribute, VectorStoreRecordKeyProperty, etc.

This change has been made to support vector store providers when creating their own implementations. A provider only has to reference the Microsoft.Extensions.VectorData.Abstractions package. This reduces potential version conflicts and allows Semantic Kernel to continue to evolve fast without impacting vector store providers.

Define your data model

The Semantic Kernel Vector Store connectors use a model first approach to interacting with databases. This means that the first step is to define a data model that maps to the storage schema. To help the connectors create collections of records and map to the storage schema, the model can be annotated to indicate the function of each property.

using Microsoft.Extensions.VectorData;

public class Hotel
{
    [VectorStoreRecordKey]
    public ulong HotelId { get; set; }

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

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

    [VectorStoreRecordVector(Dimensions: 4, DistanceFunction.CosineDistance, IndexKind.Hnsw)]
    public ReadOnlyMemory<float>? DescriptionEmbedding { get; set; }

    [VectorStoreRecordData(IsFilterable = true)]
    public string[] Tags { get; set; }
}

from dataclasses import dataclass, field
from typing import Annotated
from semantic_kernel.data import (
    DistanceFunction,
    IndexKind,
    VectorStoreRecordDataField,
    VectorStoreRecordDefinition,
    VectorStoreRecordKeyField,
    VectorStoreRecordVectorField,
    vectorstoremodel,
)

@vectorstoremodel
@dataclass
class Hotel:
    hotel_id: Annotated[str, VectorStoreRecordKeyField()] = field(default_factory=lambda: str(uuid4()))
    hotel_name: Annotated[str, VectorStoreRecordDataField(is_filterable=True)]
    description: Annotated[str, VectorStoreRecordDataField(is_full_text_searchable=True)]
    description_embedding: Annotated[list[float], VectorStoreRecordVectorField(dimensions=4, distance_function=DistanceFunction.COSINE, index_kind=IndexKind.HNSW)]
    tags: Annotated[list[str], VectorStoreRecordDataField(is_filterable=True)]

Tip

For more information on how to annotate your data model, refer to defining your data model.

Tip

For an alternative to annotating your data model, refer to defining your schema with a record definition.

Connect to your database and select a collection

Once you have defined your data model, the next step is to create a VectorStore instance for the database of your choice and select a collection of records.

In this example, we'll use Qdrant. You will therefore need to import the Qdrant nuget package.

dotnet add package Microsoft.SemanticKernel.Connectors.Qdrant --prerelease

Since databases support many different types of keys and records, we allow you to specify the type of the key and record for your collection using generics. In our case, the type of record will be the Hotel class we already defined, and the type of key will be ulong, since the HotelId property is a ulong and Qdrant only supports Guid or ulong keys.

using Microsoft.SemanticKernel.Connectors.Qdrant;
using Qdrant.Client;

// Create a Qdrant VectorStore object
var vectorStore = new QdrantVectorStore(new QdrantClient("localhost"));

// Choose a collection from the database and specify the type of key and record stored in it via Generic parameters.
var collection = vectorStore.GetCollection<ulong, Hotel>("skhotels");

Since databases support many different types of keys and records, we allow you to specify the type of the key and record for your collection using generics. In our case, the type of record will be the Hotel class we already defined, and the type of key will be str, since the HotelId property is a str and Qdrant only supports str or int keys.

from semantic_kernel.connectors.memory.qdrant import QdrantStore

# Create a Qdrant VectorStore object, this will look in the environment for Qdrant related settings, and will fall back to the default, which is to run in-memory.
vector_store = QdrantStore()

# Choose a collection from the database and specify the type of key and record stored in it via Generic parameters.
collection = vector_store.get_collection(
    collection_name="skhotels", 
    data_model_type=Hotel
)

Tip

For more information on what key and field types each Vector Store connector supports, refer to the documentation for each connector.

Create the collection and add records

// Create the collection if it doesn't exist yet.
await collection.CreateCollectionIfNotExistsAsync();

// Upsert a record.
string descriptionText = "A place where everyone can be happy.";
ulong hotelId = 1;

// Create a record and generate a vector for the description using your chosen embedding generation implementation.
// Just showing a placeholder embedding generation method here for brevity.
await collection.UpsertAsync(new Hotel
{
    HotelId = hotelId,
    HotelName = "Hotel Happy",
    Description = descriptionText,
    DescriptionEmbedding = await GenerateEmbeddingAsync(descriptionText),
    Tags = new[] { "luxury", "pool" }
});

// Retrieve the upserted record.
Hotel? retrievedHotel = await collection.GetAsync(hotelId);

Create the collection and add records

# Create the collection if it doesn't exist yet.
await collection.create_collection_if_not_exists()

# Upsert a record.
description = "A place where everyone can be happy."
hotel_id = "1"

await collection.upsert(Hotel(
    hotel_id = hotel_id,
    hotel_name = "Hotel Happy",
    description = description,
    description_embedding = await GenerateEmbeddingAsync(description),
    tags = ["luxury", "pool"]
))

# Retrieve the upserted record.
retrieved_hotel = await collection.get(hotel_id)

Tip

For more information on how to generate embeddings see embedding generation.

// Generate a vector for your search text, using your chosen embedding generation implementation.
// Just showing a placeholder method here for brevity.
var searchVector = await GenerateEmbeddingAsync("I'm looking for a hotel where customer happiness is the priority.");
// Do the search.
var searchResult = await collection.VectorizedSearchAsync(searchVector, new() { Top = 1 }).Results.ToListAsync()

// Inspect the returned hotels.
Hotel hotel = searchResult.First().Record;
Console.WriteLine("Found hotel description: " + hotel.Description);

Tip

For more information on how to generate embeddings see embedding generation.

Next steps

Learn about the Vector Store data architecture How to ingest data into a Vector Store