Merge pull request #271840 from flang-msft/fxl---Update-cache-redis-modules.md-120637

Fxl---update cache redis modules.md 120637

Author: prmerger-automator[bot]

articles/azure-cache-for-redis/cache-redis-modules.md

@@ -1,12 +1,12 @@
 ---
 title: Using Redis modules with Azure Cache for Redis
-description: You can use Redis modules with your Azure Cache for Redis instances.
+description: You can use Redis modules with your Azure Cache for Redis instances to extend your caches on the Enterprise tiers.
 author: flang-msft
 
 ms.author: franlanglois
 ms.service: cache
 ms.topic: conceptual
-ms.date: 03/02/2023
+ms.date: 04/10/2024
 ms.custom: template-concept
 
 ---
@@ -29,13 +29,12 @@ Some popular modules are available for use in the Enterprise tier of Azure Cache
 |RedisTimeSeries |   No    |    Yes   |   No    |
 |RedisJSON  |     No    |  Yes    |   Yes      |
 
-
 > [!NOTE]
 > Currently, you can't manually load any modules into Azure Cache for Redis. Manually updating modules version is also not possible.
 
-
 ## Using modules with active geo-replication
-Only the `RediSearch` and `RedisJSON` modules can be used concurrently with [active geo-replication](cache-how-to-active-geo-replication.md). 
+
+Only the `RediSearch` and `RedisJSON` modules can be used concurrently with [active geo-replication](cache-how-to-active-geo-replication.md).
 
 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.
 
@@ -78,11 +77,11 @@ Features include:
 - Geo-filtering
 - Boolean queries
 
-Additionally, **RediSearch** can function as a secondary index, expanding your cache beyond a key-value structure and offering more sophisticated queries. 
+Additionally, **RediSearch** can function as a secondary index, expanding your cache beyond a key-value structure and offering more sophisticated queries.
 
-**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. 
+**RediSearch** also includes functionality to perform [vector similarity queries](https://redis.io/solutions/vector-search/) 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.
 
-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/).
+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/search/).
 
 >[!IMPORTANT]
 > 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)
@@ -101,7 +100,7 @@ RedisBloom adds four probabilistic data structures to a Redis server: **bloom fi
 **Bloom and Cuckoo** filters are similar to each other, but each has a unique set of advantages and disadvantages that are beyond the scope of
 this documentation.
 
-For more information, see [RedisBloom](https://redis.io/docs/stack/bloom/).
+For more information, see [RedisBloom](https://redis.io/bloom/).
 
 ### RedisTimeSeries
 
@@ -115,7 +114,7 @@ The **RedisTimeSeries** module adds high-throughput time series capabilities to
 
 This module is useful for many applications that involve monitoring streaming data, such as IoT telemetry, application monitoring, and anomaly detection.
 
-For more information, see [RedisTimeSeries](https://redis.io/docs/stack/timeseries/).
+For more information, see [RedisTimeSeries](https://redis.io/timeseries/).
 
 ### RedisJSON
 
@@ -131,9 +130,12 @@ The **RedisJSON** module is also designed for use with the **RediSearch** module
 
 Some common use-cases for **RedisJSON** include applications such as searching product catalogs, managing user profiles, and caching JSON-structured data.
 
-For more information, see [RedisJSON](https://redis.io/docs/stack/json/).
+For more information, see [RedisJSON](https://redis.io/json/).
+
+> [!NOTE]
+> 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).
 
-## Next steps
+## Related content
 
 - [Quickstart: Create a Redis Enterprise cache](quickstart-create-redis-enterprise.md)
 - [Client libraries](cache-best-practices-client-libraries.md)