What are Semantic Kernel Vector Store connectors? (Preview)
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.
The Vector Store Abstraction
The main interfaces in the Vector Store abstraction are the following.
com.microsoft.semantickernel.data.vectorstorage.VectorStore
VectorStore contains operations that spans across all collections in the vector store, e.g. listCollectionNames.
It also provides the ability to get VectorStoreRecordCollection<Key, Record> instances.
com.microsoft.semantickernel.data.vectorstorage.VectorStoreRecordCollection<Key, Record>
VectorStoreRecordCollection<Key, Record> 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 VectorizedSearch<Record> providing vector search capabilities.
com.microsoft.semantickernel.data.vectorsearch.VectorizedSearch<Record>
VectorizedSearch<Record> contains a method for doing vector searches.
VectorStoreRecordCollection<Key, Record> inherits from VectorizedSearch<Record> making it possible to use
VectorizedSearch<Record> on its own in cases where only search is needed and no record or collection management is needed.
com.microsoft.semantickernel.data.vectorsearch.VectorizableTextSearch<Record>
VectorizableTextSearch<Record> 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)]
import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordData;
import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordKey;
import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordVector;
import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
import java.util.Collections;
import java.util.List;
public class Hotel {
@VectorStoreRecordKey
private String hotelId;
@VectorStoreRecordData(isFilterable = true)
private String name;
@VectorStoreRecordData(isFullTextSearchable = true)
private String description;
@VectorStoreRecordVector(dimensions = 4, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_DISTANCE)
private List<Float> descriptionEmbedding;
@VectorStoreRecordData(isFilterable = true)
private List<String> tags;
public Hotel() { }
public Hotel(String hotelId, String name, String description, List<Float> descriptionEmbedding, List<String> tags) {
this.hotelId = hotelId;
this.name = name;
this.description = description;
this.descriptionEmbedding = Collections.unmodifiableList(descriptionEmbedding);
this.tags = Collections.unmodifiableList(tags);
}
public String getHotelId() { return hotelId; }
public String getName() { return name; }
public String getDescription() { return description; }
public List<Float> getDescriptionEmbedding() { return descriptionEmbedding; }
public List<String> getTags() { return tags; }
}
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
)
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 String, since the hotelId property is a String and JDBC store only supports String keys.
import com.microsoft.semantickernel.data.jdbc.JDBCVectorStore;
import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreOptions;
import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreRecordCollectionOptions;
import com.microsoft.semantickernel.data.jdbc.mysql.MySQLVectorStoreQueryProvider;
import com.mysql.cj.jdbc.MysqlDataSource;
import java.util.List;
public class Main {
public static void main(String[] args) {
// Create a MySQL data source
var dataSource = new MysqlDataSource();
dataSource.setUrl("jdbc:mysql://localhost:3306/sk");
dataSource.setPassword("root");
dataSource.setUser("root");
// Create a JDBC vector store
var vectorStore = JDBCVectorStore.builder()
.withDataSource(dataSource)
.withOptions(
JDBCVectorStoreOptions.builder()
.withQueryProvider(MySQLVectorStoreQueryProvider.builder()
.withDataSource(dataSource)
.build())
.build()
)
.build();
// Get a collection from the vector store
var collection = vectorStore.getCollection("skhotels",
JDBCVectorStoreRecordCollectionOptions.<Hotel>builder()
.withRecordClass(Hotel.class)
.build()
);
}
}
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)
// Create the collection if it doesn't exist yet.
collection.createCollectionAsync().block();
// Upsert a record.
var description = "A place where everyone can be happy";
var hotelId = "1";
var hotel = new Hotel(
hotelId,
"Hotel Happy",
description,
generateEmbeddingsAsync(description).block(),
List.of("luxury", "pool")
);
collection.upsertAsync(hotel, null).block();
// Retrieve the upserted record.
var retrievedHotel = collection.getAsync(hotelId, null).block();
Tip
For more information on how to generate embeddings see embedding generation.
Do a vector search
// 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);
// Generate a vector for your search text, using your chosen embedding generation implementation.
// Just showing a placeholder method here for brevity.
var searchVector = generateEmbeddingsAsync("I'm looking for a hotel where customer happiness is the priority.").block();
// Do the search.
var searchResult = collection.searchAsync(searchVector, VectorSearchOptions.builder()
.withTop(1).build()
).block();
Hotel record = searchResult.getResults().get(0).getRecord();
System.out.printf("Found hotel description: %s\n", record.getDescription());
Tip
For more information on how to generate embeddings see embedding generation.