Legacy Semantic Kernel Memory Stores

Article
11/11/2024

Tip

We recommend using the Vector Store abstractions instead of the legacy Memory Stores. For more information on how to use the Vector Store abstractions start here.

Semantic Kernel provides a set of Memory Store abstractions where the primary interface is Microsoft.SemanticKernel.Memory.IMemoryStore.

Memory Store vs Vector Store abstractions

As part of an effort to evolve and expand the vector storage and search capbilities of Semantic Kernel, we have released a new set of abstractions to replace the Memory Store abstractions. We are calling the replacement abstractions Vector Store abstractions. The purpose of both are similar, but their interfaces differ and the Vector Store abstractions provide expanded functionality.

Characteristic	Legacy Memory Stores	Vector Stores
Main Interface	IMemoryStore	IVectorStore
Abstractions nuget package	Microsoft.SemanticKernel.Abstractions	Microsoft.Extensions.VectorData.Abstractions
Naming Convention	{Provider}MemoryStore, e.g. RedisMemoryStore	{Provider}VectorStore, e.g. RedisVectorStore
Supports record upsert, get and delete	Yes	Yes
Supports collection create and delete	Yes	Yes
Supports vector search	Yes	Yes
Supports choosing your preferred vector search index and distance function	No	Yes
Supports multiple vectors per record	No	Yes
Supports custom schemas	No	Yes
Supports multiple vector types	No	Yes
Supports metadata pre-filtering for vector search	No	Yes
Supports vector search on non-vector databases by downloading the entire dataset onto the client and doing a local vector search	Yes	No

Available Memory Store connectors

Semantic Kernel offers several Memory Store connectors to vector databases that you can use to store and retrieve information. These include:

Service	C#	Python
Vector Database in Azure Cosmos DB for NoSQL	C#	Python
Vector Database in vCore-based Azure Cosmos DB for MongoDB	C#	Python
Azure AI Search	C#	Python
Azure PostgreSQL Server	C#
Azure SQL Database	C#
Chroma	C#	Python
DuckDB	C#
Milvus	C#	Python
MongoDB Atlas Vector Search	C#	Python
Pinecone	C#	Python
Postgres	C#	Python
Qdrant	C#	Python
Redis	C#	Python
Sqlite	C#
Weaviate	C#	Python

Migrating from Memory Stores to Vector Stores

If you wanted to migrate from using the Memory Store abstractions to the Vector Store abtractions there are various ways in which you can do this.

Use the existing collection with the Vector Store abstractions

The simplest way in many cases could be to just use the Vector Store abstractions to access a collection that was created using the Memory Store abstractions. In many cases this is possible, since the Vector Store abstraction allows you to choose the schema that you would like to use. The main requirement is to create a data model that matches the schema that the legacy Memory Store implementation used.

E.g. to access a collection created by the Azure AI Search Memory Store, you can use the following Vector Store data model.

using Microsoft.Extensions.VectorData;

class VectorStoreRecord
{
    [VectorStoreRecordKey]
    public string Id { get; set; }

    [VectorStoreRecordData]
    public string Description { get; set; }

    [VectorStoreRecordData]
    public string Text { get; set; }

    [VectorStoreRecordData]
    public bool IsReference { get; set; }

    [VectorStoreRecordData]
    public string ExternalSourceName { get; set; }

    [VectorStoreRecordData]
    public string AdditionalMetadata { get; set; }

    [VectorStoreRecordVector(VectorSize)]
    public ReadOnlyMemory<float> Embedding { get; set; }
}

Tip

For more detailed examples on how to use the Vector Store abstractions to access collections created using a Memory Store, see here.

Create a new collection

In some cases migrating to a new collection may be preferable than using the existing collection directly. The schema that was chosen by the Memory Store may not match your requirements, especially with regards to filtering.

E.g. The Redis Memory store uses a schema with three fields:

string metadata
long timestamp
float[] embedding

All data other than the embedding or timestamp is stored as a serialized json string in the Metadata field. This means that it is not possible to index the individual values and filter on them. E.g. perhaps you may want to filter using the ExternalSourceName, but this is not possible while it is inside a json string.

In this case, it may be better to migrate the data to a new collection with a flat schema. There are two options here. You could create a new collection from your source data or simply map and copy the data from the old to the new. The first option may be more costly as you will need to regenerate the embeddings from the source data.

Tip

For an example using Redis showing how to copy data from a collection created using the Memory Store abstractions to one created using the Vector Store abstractions see here.

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