Object mapping, and more, for Redis and .NET

Redis OM .NET makes it easy to model Redis data in your .NET Applications.

Redis OM .NET | Redis OM Node.js | Redis OM Spring | Redis OM Python

Redis OM provides high-level abstractions for using Redis in .NET, making it easy to model and query your Redis domain objects.

This preview release contains the following features:

• Declarative object mapping for Redis objects
• Declarative secondary-index generation
• Fluent APIs for querying Redis
• Fluent APIs for performing Redis aggregations

Using the dotnet cli, run:

dotnet add package Redis.OM 

Before writing any code you'll need a Redis instance with the appropriate Redis modules! The quickest way to get this is with Docker:

docker run -p 6379:6379 -p 8001:8001 redis/redis-stack

This launches the redis-stack an extension of Redis that adds all manner of modern data structures to Redis. You'll also notice that if you open up http://localhost:8001 you'll have access to the redis-insight GUI, a GUI you can use to visualize and work with your data in Redis.

With Redis OM, you can model your data and declare indexes with minimal code. For example, here's how we might model a customer object:

[Document(StorageType=StorageType.Json)]publicclassCustomer{[Indexed]publicstringFirstName{get;set;}[Indexed]publicstringLastName{get;set;}publicstringEmail{get;set;}[Indexed(Sortable=true)]publicintAge{get;set;}[Indexed]publicstring[]NickNames{get;set;}}

Notice that we've applied the Document attribute to this class. We've also specified that certain fields should be Indexed.

Now we need to create the Redis index. So we'll connect to Redis and then call CreateIndex on an IRedisConnection:

varprovider=newRedisConnectionProvider("redis://localhost:6379");provider.Connection.CreateIndex(typeof(Customer));

Redis OM provides limited support for schema migration at this time. You can check if the index definition in Redis matches your current index definition using the IsIndexCurrent method on the RedisConnection. Then you may use that output to determine when to re-create your indexes when your types change. An example implementation of this would look like:

varprovider=newRedisConnectionProvider("redis://localhost:6379");vardefinition=provider.Connection.GetIndexInfo(typeof(Customer));if(!provider.Connection.IsIndexCurrent(typeof(Customer))){provider.Connection.DropIndex(typeof(Customer));provider.Connection.CreateIndex(typeof(Customer));}

There are two methods for indexing embedded documents with Redis.OM, an embedded document is a complex object, e.g. if our Customer model had an Address property with the following model:

[Document(IndexName="address-idx",StorageType=StorageType.Json)]publicpartialclassAddress{publicstringStreetName{get;set;}publicstringZipCode{get;set;}[Indexed]publicstringCity{get;set;}[Indexed]publicstringState{get;set;}[Indexed(CascadeDepth=1)]publicAddressForwardingAddress{get;set;}[Indexed]publicGeoLocLocation{get;set;}[Indexed]publicintHouseNumber{get;set;}}

You can index fields by JSON path, in the top level model, in this case Customer you can decorate the Address property with an Indexed and/or Searchable attribute, specifying the JSON path to the desired field:

[Document(StorageType=StorageType.Json)]publicclassCustomer{[Indexed]publicstringFirstName{get;set;}[Indexed]publicstringLastName{get;set;}publicstringEmail{get;set;}[Indexed(Sortable=true)]publicintAge{get;set;}[Indexed]publicstring[]NickNames{get;set;}[Indexed(JsonPath="$.ZipCode")][Searchable(JsonPath="$.StreetAddress")]publicAddressAddress{get;set;}}

This methodology can also be used for indexing string and string-like value-types within objects within Arrays and Lists, so for example if we had an array of Addresses, and we wanted to index the cities within those addresses we could do so with the following

[Indexed(JsonPath="$.City")]publicAddress[]Addresses{get;set;}

Those Cities can then be queried with an Any predicate within the main Where clause.

collection.Where(c=>c.Addresses.Any(a=>a.City=="Satellite Beach"))

The way Redis indexes fields within a collection of embedded objects does not allow multiple predictates to be specified to a given document e.g.

collection.Where(c=>c.Addresses.Any(a=>a.City=="Satellite Beach"&&a.ZipCode== "32937))

In the above case the query can only check if the Addresses collection contains an entry that is Satellite Beach, and Contains an entry that has a zip code of 32937, rather than an entry that has both the city of Satellite Beach and a zip code of `32937

Alternatively, you can also embedded models by cascading indexes. In this instance you'd simply decorate the property with Indexed and set the CascadeDepth to whatever to however may levels you want the model to cascade for. The default is 0, so if CascadeDepth is not set, indexing an object will be a no-op:

[Document(StorageType=StorageType.Json)]publicclassCustomer{[Indexed]publicstringFirstName{get;set;}[Indexed]publicstringLastName{get;set;}publicstringEmail{get;set;}[Indexed(Sortable=true)]publicintAge{get;set;}[Indexed]publicstring[]NickNames{get;set;}[Indexed(CascadeDepth=2)]publicAddressAddress{get;set;}}

In the above case, all indexed/searchable fields in Address will be indexed down 2 levels, so the ForwardingAddress field in Address will also be indexed.

Once the index is created, we can:

• Insert Customer objects into Redis
• Get a Customer object by ID from Redis
• Query Customers from Redis
• Run aggregations on Customers in Redis

Let's see how!

As of version 0.4.0, all DateTime objects are indexed as numerics, and they are inserted as numerics into JSON documents. Because of this, you can query them as if they were numerics!

Ids are unique per object, and are used as part of key generation for the primary index in Redis. The natively supported Id type in Redis OM is the ULID. You can bind ids to your model, by explicitly decorating your Id field with the RedisIdField attribute:

[Document(StorageType=StorageType.Json)]publicclassCustomer{[RedisIdField]publicUlidId{get;set;}[Indexed]publicstringFirstName{get;set;}[Indexed]publicstringLastName{get;set;}publicstringEmail{get;set;}[Indexed(Sortable=true)]publicintAge{get;set;}[Indexed]publicstring[]NickNames{get;set;}}

When you call Set on the RedisConnection or call Insert in the RedisCollection, to insert your object into Redis, Redis OM will automatically set the id for you and you will be able to access it in the object. If the Id type is a string, and there is no explicitly overriding IdGenerationStrategy on the object, the ULID for the object will bind to the string.

Redis OM also supports other types of ids, ids must either be strings or value types (e.g. ints, longs, GUIDs etc. . .), if you want a non-ULID id type, you must either set the id on each object prior to insertion, or you must register an IIdGenerationStrategy with the DocumentAttribute class.

To Register an IIdGenerationStrategy with the DocumentAttribute class, simply call DocumentAttribute.RegisterIdGenerationStrategy passing in the strategy name, and the implementation of IIdGenerationStrategy you want to use. Let's say for example you had the StaticIncrementStrategy, which maintains a static counter in memory, and increments ids based off that counter:

publicclassStaticIncrementStrategy:IIdGenerationStrategy{publicstaticintCurrent=0;publicstringGenerateId(){return(Current++).ToString();}}

You would then register that strategy with Redis.OM like so:

DocumentAttribute.RegisterIdGenerationStrategy(nameof(StaticIncrementStrategy),newStaticIncrementStrategy());

Then, when you want to use that strategy for generating the Ids of a document, you can simply set the IdGenerationStrategy of your document attribute to the name of the strategy.

[Document(IdGenerationStrategyName=nameof(StaticIncrementStrategy))]publicclassObjectWithCustomIdGenerationStrategy{[RedisIdField]publicstringId{get;set;}}

The key names are, by default, the fully qualified class name of the object, followed by a colon, followed by the Id. For example, there is a Person class in the Unit Test project, an example id of that person class would be Redis.OM.Unit.Tests.RediSearchTests.Person:01FTHAF0D1EKSN0XG67HYG36GZ, because Redis.OM.Unit.Tests.RediSearchTests.Person is the fully qualified class name, and 01FTHAF0D1EKSN0XG67HYG36GZ is the ULID (the default id type). If you want to change the prefix (the fully qualified class name), you can change that in the DocumentAttribute by setting the Prefixes property, which is an array of strings e.g.

[Document(Prefixes=new[]{"Person"})]publicclass Person

Note: At this time, Redis.OM will only use the first prefix in the prefix list as the prefix when creating a key name. However, when an index is created, it will be created on all prefixes enumerated in the Prefixes property

We can query our domain using expressions in LINQ, like so:

varcustomers=provider.RedisCollection<Customer>();// Insert customercustomers.Insert(newCustomer(){FirstName="James",LastName="Bond",Age=68,Email="bondjamesbond@email.com"});// Find all customers whose last name is "Bond"customers.Where(x =>x.LastName=="Bond");// Find all customers whose last name is Bond OR whose age is greater than 65customers.Where(x =>x.LastName=="Bond"||x.Age>65);// Find all customers whose last name is Bond AND whose first name is Jamescustomers.Where(x =>x.LastName=="Bond"&&x.FirstName=="James");// Find all customers with the nickname of Jimcustomers.Where(x=>x.NickNames.Contains("Jim"));

For advanced scenarios where you need direct control over the filter syntax, Redis OM provides a Raw extension method that lets you write the query filter using the Redis Search query syntax directly:

// Use raw query to find customers named Bond with exact tag matchingvarresults=customers.Raw("@LastName:{Bond}").ToList();

Important: The Raw method ONLY sets the filter portion of the query. For all other query parameters such as sorting, pagination, etc., you should continue to use the LINQ methods:

// Raw for filter + OrderBy for sortingvarresults=customers.Raw("@LastName:{Bond}").OrderBy(c =>c.Age).ToList();// Raw for filter + Skip/Take for paginationvarresults=customers.Raw("@Age:[30 70]").Skip(10).Take(5).ToList();

This is also true for aggregations, where Raw only sets the initial filter, and you should use the full aggregation API for the rest of the pipeline:

varaggSet=customers.AggregationSet();// Raw sets ONLY the filter, then use the aggregation API for operationsvarresults=aggSet.Raw("@Age:[30 70]").GroupBy(x =>x.RecordShell.LastName).Average(x =>x.RecordShell.Age).ToList();

For more details on the Redis Search query syntax for filters, refer to the RediSearch Query Syntax Documentation.

Redis OM .NET also supports storing and querying Vectors stored in Redis.

A Vector<T> is a representation of an object that can be transformed into a vector by a Vectorizer.

A VectorizerAttribute is the abstract class you use to decorate your Vector fields, it is responsible for defining the logic to convert the object's that Vector<T> is a container for into actual vector embeddings needed. In the package Redis.OM.Vectorizers we provide vectorizers for HuggingFace, OpenAI, and AzureOpenAI to allow you to easily integrate them into your workflows.

To define a vector in your model, simply decorate a Vector<T> field with an Indexed attribute which defines the algorithm and algorithmic parameters and a Vectorizer attribute which defines the shape of the vectors, (in this case we'll use OpenAI):

[Document(StorageType=StorageType.Json)]publicclassOpenAICompletionResponse{[RedisIdField]publicstringId{get;set;}[Indexed(DistanceMetric=DistanceMetric.COSINE,Algorithm=VectorAlgorithm.HNSW,M=16)][OpenAIVectorizer]publicVector<string>Prompt{get;set;}publicstringResponse{get;set;}[Indexed]publicstringLanguage{get;set;}[Indexed]publicDateTimeTimeStamp{get;set;}}

With the vector defined in our model, all we need to do is create Vectors of the generic type, and insert them with our model. Using our RedisCollection, you can do this by simply using Insert:

varcollection=_provider.RedisCollection<OpenAICompletionResponse>();varcompletionResult=newOpenAICompletionResponse{Language="en_us",Prompt=Vector.Of("What is the Capital of France?"),Response="Paris",TimeStamp=DateTime.Now-TimeSpan.FromHours(3)};collection.Insert(completionResult);

The Vectorizer will manage the embedding generation for you without you having to intervene.

To query vector fields in Redis, all you need to do is use the VectorRange method on a vector within our normal LINQ queries, and/or use the NearestNeighbors with whatever other filters you want to use, here's some examples:

varprompt="What really is the Capital of France?";// simple vector range, find first within .15varresult=collection.First(x =>x.Prompt.VectorRange(prompt,.15));// simple nearest neighbors query, finds first nearest neighborresult=collection.NearestNeighbors(x =>x.Prompt,1,prompt).First();// hybrid query, pre-filters result set for english responses, then runs a nearest neighbors search.result=collection.Where(x=>x.Language=="en_us").NearestNeighbors(x =>x.Prompt,1,prompt).First();// hybrid query, pre-filters responses newer than 4 hours, and finds first result within .15varts=DateTimeOffset.Now-TimeSpan.FromHours(4);result=collection.First(x=>x.TimeStamp>ts&&x.Prompt.VectorRange(prompt,.15));

With Redis OM, the embeddings can be completely transparent to you, they are generated and bound to the Vector<T> when you query/insert your vectors. If however you needed your embedding after the insertion/Query, they are available at Vector<T>.Embedding, and be queried either as the raw bytes, as an array of doubles or as an array of floats (depending on your vectorizer).

The Vectorizers provided by the Redis.OM.Vectorizers package have some configuration parameters that it will pull in either from your appsettings.json file, or your environment variables (with your appsettings taking precedence).

Redis OM also provides the ability to use Semantic Caching, as well as providers for OpenAI, HuggingFace, and Azure OpenAI to perform semantic caching. To use a Semantic Cache, simply pull one out of the RedisConnectionProvider and use Store to insert items, and GetSimilar to retrieve items. For example:

varcache=_provider.OpenAISemanticCache(token,threshold:.15);cache.Store("What is the capital of France?","Paris");varres=cache.GetSimilar("What really is the capital of France?").First();

We also provide the packages Redis.OM.Vectorizers.ResNet18 and Redis.OM.Vectorizers.AllMiniLML6V2 which have embedded models / ML Pipelines in them to allow you to easily Vectorize Images and Sentences respectively without the need to depend on an external API.

We can also run aggregations on the customer object, again using expressions in LINQ:

// Get our average customer agecustomerAggregations.Average(x =>x.RecordShell.Age);// Format customer full namescustomerAggregations.Apply(x =>string.Format("{0} {1}",x.RecordShell.FirstName,x.RecordShell.LastName),"FullName");// Get each customer's distance from the Mall of AmericacustomerAggregations.Apply(x =>ApplyFunctions.GeoDistance(x.RecordShell.Home,-93.241786,44.853816),"DistanceToMall");

This README just scratches the surface. You can find a full tutorial on the redis.io. All the summary docs for this library can be found on the repo's github page.

If you run into trouble or have any questions, we're here to help!

First, check the FAQ. If you don't find the answer there, hit us up on the Redis Discord Server.

Redis OM can be used with regular Redis for Object mapping and getting objects by their IDs. For more advanced features like indexing, querying, and aggregation, Redis OM is dependent on the Redis Stack platform, a collection of modules that extend Redis.

Without Redis Stack, you can still use Redis OM to create declarative models backed by Redis.

We'll store your model data in Redis as Hashes, and you can retrieve models using their primary keys.

So, what won't work without Redis Stack?

1. You won't be able to nest models inside each other.
2. You won't be able to use our expressive queries to find object -- you'll only be able to query by primary key.

You can use Redis Stack with your self-hosted Redis deployment. Just follow the instructions for Installing Redis Stack.

Don't want to run Redis yourself? Redis Stack is also available on Redis Cloud. Get started here.

We'd love your contributions! If you want to contribute please read our Contributing document.

Redis OM .NET supports connecting to Azure Managed Redis instances using EntraId (formerly Azure AD) authentication. This allows you to securely connect to your Azure Redis instance without embedding connection secrets in your code.

1. Install the required NuGet packages:

   dotnet add package Microsoft.Azure.StackExchangeRedis dotnet add package Azure.Identity 

2. Configure your Azure Redis instance to use EntraId authentication

usingAzure.Identity;usingRedis.OM;usingStackExchange.Redis;// Create configuration options for your Azure Redis endpoint// Standard format for Azure Managed Redis: your-instance-name.region.redis.azure.net:10000ConfigurationOptionsoptions=newConfigurationOptions{EndPoints={"your-instance-name.region.redis.azure.net:10000"}};// Configure for Azure with DefaultAzureCredentialawaitoptions.ConfigureForAzureWithTokenCredentialAsync(newDefaultAzureCredential());// Connect to Redis using EntraId authenticationvarmuxer=ConnectionMultiplexer.Connect(options);// Create Redis OM connection provider using the authenticated connectionvarprovider=newRedisConnectionProvider(muxer);// Define a model for Redis OM[Document(StorageType=StorageType.Json)]publicclassCustomer{[RedisIdField]publicstringId{get;set;}[Indexed]publicstringName{get;set;}[Indexed]publicstringEmail{get;set;}[Indexed(Sortable=true)]publicintAge{get;set;}}// Create index if it doesn't existawaitprovider.Connection.CreateIndexAsync(typeof(Customer));// Get a Redis collection for your modelvarcustomers=provider.RedisCollection<Customer>();// Insert a new customerawaitcustomers.InsertAsync(newCustomer{Name="Jane Smith",Email="jane@example.com",Age=32});// Query customersvaryoungCustomers=awaitcustomers.Where(c =>c.Age<35).ToListAsync();foreach(varcustomerinyoungCustomers){Console.WriteLine($"Name: {customer.Name}, Email: {customer.Email}, Age: {customer.Age}");}

The DefaultAzureCredential class will automatically try various authentication methods, including:

• Environment variables
• Managed Identity
• Visual Studio credentials
• Azure CLI credentials
• Interactive browser login

This approach is particularly useful for services deployed to Azure, as it allows you to use Managed Identity without hardcoding any secrets.

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