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articles/azure-cache-for-redis/cache-aspnet-output-cache-provider.md

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# ASP.NET Output Cache Provider for Azure Cache for Redis
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The Redis Output Cache Provider is an out-of-process storage mechanism for output cache data. This data is specifically for full HTTP responses (page output caching). The provider plugs into the new output cache provider extensibility point that was introduced in ASP.NET 4. For ASP.NET Core applications, read:
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- [Output Caching in ASP.NET core using Redis in .NET 8](/aspnet/core/performance/caching/output?view=aspnetcore-8.0#redis-cache&preserve-view=true)
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The Redis Output Cache Provider is an out-of-process storage mechanism for output cache data. This data is specifically for full HTTP responses (page output caching). The provider plugs into the new output cache provider extensibility point that was introduced in ASP.NET 4.
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For ASP.NET Core applications, see [Output Caching in ASP.NET core using Redis in .NET 8](/aspnet/core/performance/caching/output?view=aspnetcore-8.0#redis-cache&preserve-view=true).
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To use the Redis Output Cache Provider, first configure your cache, and then configure your ASP.NET application using the Redis Output Cache Provider NuGet package. This article provides guidance on configuring your application to use the Redis Output Cache Provider. For more information about creating and configuring an Azure Cache for Redis instance, see [Create a cache](cache-dotnet-how-to-use-azure-redis-cache.md#create-a-cache).
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## Store ASP.NET core page output in Redis
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For a full feature specification, see [AS.NET core output caching](/aspnet/core/performance/caching/output?view=aspnetcore-8.0&preserve-view=true) for full feature spec. You can find a sample application demonstrating the usage can be found at [AS.NET core Output Cache](https://github.com/CawaMS/OutputCacheOpenAI).
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For a full feature specification, see [AS.NET core output caching](/aspnet/core/performance/caching/output?view=aspnetcore-8.0&preserve-view=true).
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For sample application demonstrating the usage, see [.NET 8 Web Application with Redis Output Caching and Azure Open AI](https://github.com/CawaMS/OutputCacheOpenAI).
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## Store ASP.NET page output in Redis
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To configure a client application in Visual Studio using the Azure Cache for Redis Session State NuGet package, select **NuGet Package Manager**, **Package Manager Console** from the **Tools** menu.
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Run the following command from the `Package Manager Console` window.
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Run the following command from the `Package Manager Console` window:
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```powershell
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Install-Package Microsoft.Web.RedisOutputCacheProvider
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</caching>
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```
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Configure the attributes in the first c with the values from your cache in the Microsoft Azure portal. Also, configure the other values you want. For instructions on accessing your cache properties, see [Configure Azure Cache for Redis settings](cache-configure.md#configure-azure-cache-for-redis-settings).
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Configure the attributes in the first column with the values from your cache in the Microsoft Azure portal. Also, configure the other values you want. For instructions on accessing your cache properties, see [Configure Azure Cache for Redis settings](cache-configure.md#configure-azure-cache-for-redis-settings).
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| Attribute | Type | Default | Description |
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| --------- | ---- | ------- | ----------- |
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* [NCache](https://www.alachisoft.com/blogs/how-to-use-a-distributed-cache-for-asp-net-output-cache/)
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* [Apache Ignite](https://apacheignite-net.readme.io/docs/aspnet-output-caching)
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## Next steps
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## Related content
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Check out the [ASP.NET Session State Provider for Azure Cache for Redis](cache-aspnet-session-state-provider.md).

articles/azure-cache-for-redis/cache-overview-vector-similarity.md

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ms.date: 04/24/2024
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# About Vector Embeddings and Vector Search in Azure Cache for Redis
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# What are Vector Embeddings and Vector Search in Azure Cache for Redis
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Vector similarity search (VSS) has become a popular tehnology for AI-powered intelligent applications. Azure Cache for Redis can be used as a vector database together with models like [Azure OpenAI](../ai-services/openai/overview.md) for Retrival-Augmented Generative AI and analysis scenarios. This article is a high-level introduction to the concept of vector embeddings, vector similarity search, and how Redis can be used as a vector database powering intelligent applications.
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Vector similarity search (VSS) has become a popular technology for AI-powered intelligent applications. Azure Cache for Redis can be used as a vector database by combining it models like [Azure OpenAI](../ai-services/openai/overview.md) for Retrieval-Augmented Generative AI and analysis scenarios. This article is a high-level introduction to the concept of vector embeddings, vector similarity search, and how Redis can be used as a vector database powering intelligent applications.
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For tutorials and sample applications on how to use Azure Cache for Redis and Azure OpenAI to perform vector similarity search, see:
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For tutorials and sample applications on how to use Azure Cache for Redis and Azure OpenAI to perform vector similarity search, see the following:
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- [Tutorial: Conduct vector similarity search on Azure OpenAI embeddings using Azure Cache for Redis with Langchain](./cache-tutorial-vector-similarity.md)
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- [Tutorial: Conduct vector similarity search on Azure OpenAI embeddings using Azure Cache for Redis with LangChain](./cache-tutorial-vector-similarity.md)
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- [Sample: Using Redis as vector database in a Chatbot application with .NET Semantic Kernel](https://github.com/CawaMS/chatappredis)
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- [Sample: Using Redis as semantic cache in a Dall-E powered image gallery with Redis OM for .NET](https://github.com/CawaMS/OutputCacheOpenAI)
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|--------- |:------------------:|:----------:|:---------:|:---------:|
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|Available | No | No | Yes | Yes (preview) |
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## What are vector embeddings?
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### Concept
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### Index and search method
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Vector databases need to index data for fast search and retrieval. In addition, it should support built-in search queries for simplified programming experiences.
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Vector databases need to index data for fast search and retrieval. In addition, a vector database should support built-in search queries for simplified programming experiences.
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There are several indexing methods, such as:
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- **FLAT** - Brute-force index
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- **HNSW** - Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
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## Why choose Azure Cache for Redis for storing and searching vectors?
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Azure Cache for Redis can be used effectively as a vector database to store embeddings vectors and to perform vector similarity searches. Support for vector storage and search has been available in many key machine learning frameworks like [Semantic Kernel](https://github.com/microsoft/semantic-kernel), [LangChain](https://python.langchain.com/docs/integrations/vectorstores/redis) and [LlamaIndex](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/RedisIndexDemo.html) feature rich integrations with Redis. For example, the Redis LangChain integration [automatically generates an index schema for metadata](https://python.langchain.com/docs/integrations/vectorstores/redis#inspecting-the-created-index) passed in when using Redis as a vector store. This makes it much easier to filter results based on metadata.
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Azure Cache for Redis can be used effectively as a vector database to store embeddings vectors and to perform vector similarity searches. Support for vector storage and search has been available in many key machine learning frameworks like:
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- [Semantic Kernel](https://github.com/microsoft/semantic-kernel)
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- [LangChain](https://python.langchain.com/docs/integrations/vectorstores/redis)
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- [LlamaIndex](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/RedisIndexDemo.html)
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These frameworks feature rich integrations with Redis. For example, the Redis LangChain integration [automatically generates an index schema for metadata](https://python.langchain.com/docs/integrations/vectorstores/redis#inspecting-the-created-index) passed in when using Redis as a vector store. This makes it much easier to filter results based on metadata.
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Redis has a wide range of search capabilities through the [RediSearch module](cache-redis-modules.md#redisearch), which is available in the Enterprise tier of Azure Cache for Redis. These include:
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articles/azure-cache-for-redis/cache-web-app-aspnet-core-howto.md

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In this quickstart, you incorporate Azure Cache for Redis into an ASP.NET Core web application that connects to Azure Cache for Redis to store and retrieve data from the cache.
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There are also caching providers in .NET core. To quickly start using Redis with minimal changes to your existing code, see:
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- [AS.NET core Output Cache provider](/aspnet/core/performance/caching/output?view=aspnetcore-8.0#redis-cache)
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- [ASP.NET core Output Cache provider](/aspnet/core/performance/caching/output?view=aspnetcore-8.0#redis-cache)
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- [ASP.NET core Distributed Caching provider](/aspnet/core/performance/caching/distributed#distributed-redis-cache)
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- [ASP.NET core Redis session provider](/aspnet/core/fundamentals/app-state#configure-session-state)
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## Skip to the code on GitHub
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Clone the repo [https://github.com/Azure-Samples/azure-cache-redis-samples/tree/main/quickstart/aspnet-core](https://github.com/Azure-Samples/azure-cache-redis-samples/tree/main/quickstart/aspnet-core) on GitHub.
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As next step, there is a real-world scenario eShop application demonstrating the ASP.NET core caching providers: [ASP.NET core eShop using Redis caching providers](https://github.com/Azure-Samples/azure-cache-redis-demos). Features included:
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As a next step, you can see a real-world scenario eShop application demonstrating the ASP.NET core caching providers: [ASP.NET core eShop using Redis caching providers](https://github.com/Azure-Samples/azure-cache-redis-demos).
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Features included:
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- Redis Distributed Caching
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- Redis session state provider
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