What are Semantic Kernel Vector Store connectors? (Experimental)

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Warning

The Semantic Kernel Vector Store functionality is experimental, still in development and is subject to change.

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 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.

Getting started with Vector Store connectors

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.SemanticKernel.Data;

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, IndexKind.Hnsw, DistanceFunction.CosineDistance)]
    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.

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() { Limit = 1 }).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