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

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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/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 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/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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**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
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this documentation.
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For more information, see [RedisBloom](https://redis.io/docs/stack/bloom/).
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For more information, see [RedisBloom](https://redis.io/bloom/).
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### RedisTimeSeries
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This module is useful for many applications that involve monitoring streaming data, such as IoT telemetry, application monitoring, and anomaly detection.
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For more information, see [RedisTimeSeries](https://redis.io/docs/stack/timeseries/).
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For more information, see [RedisTimeSeries](https://redis.io/timeseries/).
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### RedisJSON
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Some common use-cases for **RedisJSON** include applications such as searching product catalogs, managing user profiles, and caching JSON-structured data.
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For more information, see [RedisJSON](https://redis.io/docs/stack/json/).
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For more information, see [RedisJSON](https://redis.io/json/).
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> [!NOTE]
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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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