clean up es redirects pre-assembler

Author: colleenmcginnis

deploy-manage/deploy/cloud-enterprise/add-custom-bundles-plugins.md

@@ -314,7 +314,7 @@ To import a JVM trust store:
             }
     ```
 
-4. To use this bundle, you can refer it in the [GeoIP processor](elasticsearch://reference/ingestion-tools/enrich-processor/geoip-processor.md) of an ingest pipeline as `MyGeoLite2-City.mmdb` under `database_file` such as:
+4. To use this bundle, you can refer it in the [GeoIP processor](elasticsearch://reference/enrich-processor/geoip-processor.md) of an ingest pipeline as `MyGeoLite2-City.mmdb` under `database_file` such as:
 
     ```sh
     ...

deploy-manage/deploy/cloud-on-k8s/custom-configuration-files-plugins.md

@@ -107,7 +107,7 @@ To install custom configuration files you can:
 1. Add the configuration data into a ConfigMap or Secret.
 2. Use volumes and volume mounts in your manifest to mount the contents of the ConfigMap or Secret as files in your {{es}} nodes.
 
-The next example shows how to add a synonyms file for the [synonym token filter](elasticsearch://reference/data-analysis/text-analysis/analysis-synonym-tokenfilter.md) in Elasticsearch. But you can **use the same approach for any kind of file you want to mount into the configuration directory of Elasticsearch**, like adding CA certificates of external systems.
+The next example shows how to add a synonyms file for the [synonym token filter](elasticsearch://reference/text-analysis/analysis-synonym-tokenfilter.md) in Elasticsearch. But you can **use the same approach for any kind of file you want to mount into the configuration directory of Elasticsearch**, like adding CA certificates of external systems.
 
 1. Create the ConfigMap or Secret with the data:
 

deploy-manage/deploy/elastic-cloud/differences-from-other-elasticsearch-offerings.md

@@ -154,6 +154,6 @@ The following features are not available in {{es-serverless}} and are not planne
 
 * [Custom plugins and bundles](/deploy-manage/deploy/elastic-cloud/upload-custom-plugins-bundles.md)
 * [{{es}} for Apache Hadoop](elasticsearch-hadoop://reference/index.md)
-* [Scripted metric aggregations](elasticsearch://reference/data-analysis/aggregations/search-aggregations-metrics-scripted-metric-aggregation.md)
+* [Scripted metric aggregations](elasticsearch://reference/aggregations/search-aggregations-metrics-scripted-metric-aggregation.md)
 * Managed web crawler: You can use the [self-managed web crawler](https://github.com/elastic/crawler) instead.
-* Managed Search connectors: You can use [self-managed Search connectors](elasticsearch://reference/ingestion-tools/search-connectors/self-managed-connectors.md) instead.
+* Managed Search connectors: You can use [self-managed Search connectors](elasticsearch://reference/search-connectors/self-managed-connectors.md) instead.

deploy-manage/deploy/self-managed/air-gapped-install.md

@@ -19,7 +19,7 @@ Air-gapped install of {{es}} may require additional steps in order to access som
 
 Specifically:
 
-* To be able to use the GeoIP processor, refer to [the GeoIP processor documentation](elasticsearch://reference/ingestion-tools/enrich-processor/geoip-processor.md#manually-update-geoip-databases) for instructions on downloading and deploying the required databases.
+* To be able to use the GeoIP processor, refer to [the GeoIP processor documentation](elasticsearch://reference/enrich-processor/geoip-processor.md#manually-update-geoip-databases) for instructions on downloading and deploying the required databases.
 * Refer to [{{ml-cap}}](/deploy-manage/deploy/self-managed/air-gapped-install.md#air-gapped-machine-learning) for instructions on deploying the Elastic Learned Sparse EncodeR (ELSER) natural language processing (NLP) model and other trained {{ml}} models.
 
 

deploy-manage/manage-connectors.md

@@ -12,7 +12,7 @@ applies_to:
 Connectors serve as a central place to store connection information for both Elastic and third-party systems. They enable the linking of actions to rules, which execute as background tasks on the {{kib}} server when rule conditions are met. This allows rules to route actions to various destinations such as log files, ticketing systems, and messaging tools. Different {{kib}} apps may have their own rule types, but they typically share connectors. The **{{stack-manage-app}} > {{connectors-ui}}** provides a central location to view and manage all connectors in the current space.
 
 ::::{note}
-This page is about {{kib}} connectors that integrate with services like generative AI model providers. If you’re looking for Search connectors that synchronize third-party data into {{es}}, refer to [Connector clients](elasticsearch://reference/ingestion-tools/search-connectors/index.md).
+This page is about {{kib}} connectors that integrate with services like generative AI model providers. If you’re looking for Search connectors that synchronize third-party data into {{es}}, refer to [Connector clients](elasticsearch://reference/search-connectors/index.md).
 
 ::::
 

deploy-manage/production-guidance/optimize-performance/search-speed.md

@@ -14,7 +14,7 @@ applies_to:
 This page provides guidance on tuning {{es}} for faster search performance. While hardware and system-level settings play an important role, the structure of your documents and the design of your queries often have the biggest impact. Use these recommendations to optimize field mappings, caching behavior, and query design for high-throughput, low-latency search at scale.
 
 ::::{note}
-Search performance in {{es}} depends on a combination of factors, including how expensive individual queries are, how many searches run in parallel, the number of indices and shards involved, and the overall sharding strategy and shard size. 
+Search performance in {{es}} depends on a combination of factors, including how expensive individual queries are, how many searches run in parallel, the number of indices and shards involved, and the overall sharding strategy and shard size.
 
 These variables influence how the system should be tuned. For example, optimizing for a small number of complex queries differs significantly from optimizing for many lightweight, concurrent searches.
 
@@ -34,7 +34,7 @@ deployment:
 By default, {{es}} automatically sets its [JVM heap size](/deploy-manage/deploy/self-managed/important-settings-configuration.md#heap-size-settings) to follow this best practice. However, in self-managed or {{eck}} deployments, you have the flexibility to allocate even more memory to the filesystem cache, which can lead to performance improvements depending on your workload.
 
 ::::{note}
-On Linux, the filesystem cache uses any memory not actively used by applications. To allocate memory to the cache, ensure that enough system memory remains available and is not consumed by {{es}} or other processes. 
+On Linux, the filesystem cache uses any memory not actively used by applications. To allocate memory to the cache, ensure that enough system memory remains available and is not consumed by {{es}} or other processes.
 ::::
 
 ## Avoid page cache thrashing by using modest readahead values on Linux [_avoid_page_cache_thrashing_by_using_modest_readahead_values_on_linux]
@@ -117,7 +117,7 @@ PUT movies
 
 ## Pre-index data [_pre_index_data]
 
-You should leverage patterns in your queries to optimize the way data is indexed. For instance, if all your documents have a `price` field and most queries run [`range`](elasticsearch://reference/data-analysis/aggregations/search-aggregations-bucket-range-aggregation.md) aggregations on a fixed list of ranges, you could make this aggregation faster by pre-indexing the ranges into the index and using a [`terms`](elasticsearch://reference/data-analysis/aggregations/search-aggregations-bucket-terms-aggregation.md) aggregations.
+You should leverage patterns in your queries to optimize the way data is indexed. For instance, if all your documents have a `price` field and most queries run [`range`](elasticsearch://reference/aggregations/search-aggregations-bucket-range-aggregation.md) aggregations on a fixed list of ranges, you could make this aggregation faster by pre-indexing the ranges into the index and using a [`terms`](elasticsearch://reference/aggregations/search-aggregations-bucket-terms-aggregation.md) aggregations.
 
 For instance, if documents look like:
 

deploy-manage/users-roles/cluster-or-deployment-auth/controlling-access-at-document-field-level.md

@@ -188,15 +188,15 @@ POST /_security/role/example3
 
 ### Pre-processing documents to add security details [set-security-user-processor]
 
-To guarantee that a user reads only their own documents, it makes sense to set up document level security. In this scenario, each document must have the username or role name associated with it, so that this information can be used by the role query for document level security. This is a situation where the [set security user processor](elasticsearch://reference/ingestion-tools/enrich-processor/ingest-node-set-security-user-processor.md) ingest processor can help.
+To guarantee that a user reads only their own documents, it makes sense to set up document level security. In this scenario, each document must have the username or role name associated with it, so that this information can be used by the role query for document level security. This is a situation where the [set security user processor](elasticsearch://reference/enrich-processor/ingest-node-set-security-user-processor.md) ingest processor can help.
 
 ::::{note}
 Document level security doesn’t apply to write APIs. You must use unique ids for each user that uses the same data stream or index, otherwise they might overwrite other users' documents. The ingest processor just adds properties for the current authenticated user to the documents that are being indexed.
 ::::
 
-The [set security user processor](elasticsearch://reference/ingestion-tools/enrich-processor/ingest-node-set-security-user-processor.md) attaches user-related details (such as `username`,  `roles`, `email`, `full_name` and `metadata` ) from the current authenticated user to the current document by pre-processing the ingest. When you index data with an ingest pipeline, user details are automatically attached to the document. If the authenticating credential is an API key, the API key `id`, `name` and `metadata` (if it exists and is non-empty) are also attached to the document.
+The [set security user processor](elasticsearch://reference/enrich-processor/ingest-node-set-security-user-processor.md) attaches user-related details (such as `username`,  `roles`, `email`, `full_name` and `metadata` ) from the current authenticated user to the current document by pre-processing the ingest. When you index data with an ingest pipeline, user details are automatically attached to the document. If the authenticating credential is an API key, the API key `id`, `name` and `metadata` (if it exists and is non-empty) are also attached to the document.
 
-For more information, see [Ingest pipelines](/manage-data/ingest/transform-enrich/ingest-pipelines.md) and [Set security user](elasticsearch://reference/ingestion-tools/enrich-processor/ingest-node-set-security-user-processor.md).
+For more information, see [Ingest pipelines](/manage-data/ingest/transform-enrich/ingest-pipelines.md) and [Set security user](elasticsearch://reference/enrich-processor/ingest-node-set-security-user-processor.md).
 
 
 ## Field level security [field-level-security]

explore-analyze/alerts-cases/alerts/rule-type-es-query.md

@@ -52,7 +52,7 @@ When you create an {{es}} query rule, your choice of query type affects the info
     :   Specify how to calculate the value that is compared to the threshold. The value is calculated by aggregating a numeric field within the time window. The aggregation options are: `count`, `average`, `sum`, `min`, and `max`. When using `count` the document count is used and an aggregation field is not necessary.
 
     Over or Grouped Over
-    :   Specify whether the aggregation is applied over all documents or split into groups using up to four grouping fields. If you choose to use grouping, it’s a [terms](elasticsearch://reference/data-analysis/aggregations/search-aggregations-bucket-terms-aggregation.md) or [multi terms aggregation](elasticsearch://reference/data-analysis/aggregations/search-aggregations-bucket-multi-terms-aggregation.md); an alert will be created for each unique set of values when it meets the condition. To limit the number of alerts on high cardinality fields, you must specify the number of groups to check against the threshold. Only the top groups are checked.
+    :   Specify whether the aggregation is applied over all documents or split into groups using up to four grouping fields. If you choose to use grouping, it’s a [terms](elasticsearch://reference/aggregations/search-aggregations-bucket-terms-aggregation.md) or [multi terms aggregation](elasticsearch://reference/aggregations/search-aggregations-bucket-multi-terms-aggregation.md); an alert will be created for each unique set of values when it meets the condition. To limit the number of alerts on high cardinality fields, you must specify the number of groups to check against the threshold. Only the top groups are checked.
 
     Threshold
     :   Defines a threshold value and a comparison operator  (`is above`, `is above or equals`, `is below`, `is below or equals`, or `is between`). The value calculated by the aggregation is compared to this threshold.

explore-analyze/elastic-inference/inference-api.md

@@ -61,7 +61,7 @@ Your {{es}} deployment contains preconfigured {{infer}} endpoints which makes th
 * `.elser-2-elasticsearch`: uses the [ELSER](../../explore-analyze/machine-learning/nlp/ml-nlp-elser.md) built-in trained model for `sparse_embedding` tasks (recommended for English language tex). The `model_id` is `.elser_model_2_linux-x86_64`.
 * `.multilingual-e5-small-elasticsearch`: uses the [E5](../../explore-analyze/machine-learning/nlp/ml-nlp-e5.md) built-in trained model for `text_embedding` tasks (recommended for non-English language texts). The `model_id` is `.e5_model_2_linux-x86_64`.
 
-Use the `inference_id` of the endpoint in a [`semantic_text`](elasticsearch://reference/elasticsearch/mapping-reference/semantic-text.md) field definition or when creating an [{{infer}} processor](elasticsearch://reference/ingestion-tools/enrich-processor/inference-processor.md). The API call will automatically download and deploy the model which might take a couple of minutes. Default {{infer}} enpoints have adaptive allocations enabled. For these models, the minimum number of allocations is `0`. If there is no {{infer}} activity that uses the endpoint, the number of allocations will scale down to `0` automatically after 15 minutes.
+Use the `inference_id` of the endpoint in a [`semantic_text`](elasticsearch://reference/elasticsearch/mapping-reference/semantic-text.md) field definition or when creating an [{{infer}} processor](elasticsearch://reference/enrich-processor/inference-processor.md). The API call will automatically download and deploy the model which might take a couple of minutes. Default {{infer}} enpoints have adaptive allocations enabled. For these models, the minimum number of allocations is `0`. If there is no {{infer}} activity that uses the endpoint, the number of allocations will scale down to `0` automatically after 15 minutes.
 
 ## Configuring chunking [infer-chunking-config]