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Copy file name to clipboardExpand all lines: articles/azure-cache-for-redis/cache-redis-modules.md
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@@ -6,7 +6,7 @@ author: flang-msft
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ms.author: franlanglois
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ms.service: cache
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ms.topic: conceptual
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ms.date: 03/02/2023
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ms.date: 04/10/2024
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ms.custom: template-concept
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|RedisTimeSeries | No | Yes | No |
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|RedisJSON | No | Yes | Yes |
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> [!NOTE]
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> Currently, you can't manually load any modules into Azure Cache for Redis. Manually updating modules version is also not possible.
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## Using modules with active geo-replication
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Only the `RediSearch` and `RedisJSON` modules can be used concurrently with [active geo-replication](cache-how-to-active-geo-replication.md).
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Only the `RediSearch` and `RedisJSON` modules can be used concurrently with [active geo-replication](cache-how-to-active-geo-replication.md).
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Using these modules, you can implement searches across groups of caches that are synchronized in an active-active configuration. Also, you can search JSON structures in your active-active configuration.
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- Geo-filtering
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- Boolean queries
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Additionally, **RediSearch** can function as a secondary index, expanding your cache beyond a key-value structure and offering more sophisticated queries.
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Additionally, **RediSearch** can function as a secondary index, expanding your cache beyond a key-value structure and offering more sophisticated queries.
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**RediSearch** also includes functionality to perform [vector similarity queries](https://redis.io/docs/stack/search/reference/vectors/) such as K-nearest neighbor (KNN) search. This feature allows Azure Cache for Redis to be used as a vector database, which is useful in AI use-cases like [semantic answer engines or any other application that requires the comparison of embeddings vectors](https://redis.com/blog/rediscover-redis-for-vector-similarity-search/) generated by machine learning models.
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**RediSearch** also includes functionality to perform [vector similarity queries](https://redis.io/docs/stack/search/reference/vectors/) such as K-nearest neighbor (KNN) search. This feature allows Azure Cache for Redis to be used as a vector database, which is useful in AI use-cases like [semantic answer engines or any other application that requires the comparison of embeddings vectors](https://redis.com/blog/rediscover-redis-for-vector-similarity-search/) generated by machine learning models.
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You can use **RediSearch** is used in a wide variety of additional use-cases, including real-time inventory, enterprise search, and in indexing external databases. [For more information, see the RediSearch documentation page](https://redis.io/docs/stack/search/).
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You can use **RediSearch** is used in a wide variety of use-cases, including real-time inventory, enterprise search, and in indexing external databases. [For more information, see the RediSearch documentation page](https://redis.io/docs/stack/search/).
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>[!IMPORTANT]
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> The RediSearch module requires use of the `Enterprise` clustering policy and the `NoEviction` eviction policy. For more information, see [Clustering Policy](quickstart-create-redis-enterprise.md#clustering-policy) and [Memory Policies](cache-configure.md#memory-policies)
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For more information, see [RedisJSON](https://redis.io/docs/stack/json/).
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> [!NOTE]
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> FT.CONFIG Command is not supported for updating any module configuration elements or parameters however these can be done via Management plane API's by specifying the arguments.
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> The `FT.CONFIG` command is not supported for updating module configuration parameters. However, this can be achieved by passing in arguments configuring the modules when using management APIs. For instance, you can see samples of configuring the `ERROR_RATE` and `INITIAL_SIZE` properties of the RedisBloom module using the `args` parameter with the [REST API](/rest/api/redis/redisenterprisecache/databases/create), [Azure CLI](/cli/azure/redisenterprise), or [PowerShell](/powershell/module/az.redisenterprisecache/new-azredisenterprisecache).
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## Next steps
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## Related content
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-[Quickstart: Create a Redis Enterprise cache](quickstart-create-redis-enterprise.md)
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