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docs/changelog/114002.yaml

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pr: 114002
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summary: Add a `mustache.max_output_size_bytes` setting to limit the length of results from mustache scripts
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area: Infra/Scripting
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type: enhancement
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issues: []

docs/changelog/114231.yaml

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pr: 114231
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summary: Remove cluster state from `/_cluster/reroute` response
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area: Allocation
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type: breaking
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issues:
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- 88978
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breaking:
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title: Remove cluster state from `/_cluster/reroute` response
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area: REST API
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details: >-
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The `POST /_cluster/reroute` API no longer returns the cluster state in its
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response. The `?metric` query parameter to this API now has no effect and
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its use will be forbidden in a future version.
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impact: >-
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Cease usage of the `?metric` query parameter when calling the
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`POST /_cluster/reroute` API.
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notable: false

docs/reference/cluster/reroute.asciidoc

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[[cluster-reroute-api-request]]
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==== {api-request-title}
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`POST /_cluster/reroute?metric=none`
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`POST /_cluster/reroute`
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[[cluster-reroute-api-prereqs]]
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==== {api-prereq-title}
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[source,console]
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--------------------------------------------------
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POST /_cluster/reroute?metric=none
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POST /_cluster/reroute
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{
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"commands": [
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{

docs/reference/commands/shard-tool.asciidoc

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You should run the following command to allocate this shard:
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POST /_cluster/reroute?metric=none
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POST /_cluster/reroute
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{
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"commands" : [
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{

docs/reference/intro.asciidoc

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If you're building a website or app, then you can call Elasticsearch APIs using an https://www.elastic.co/guide/en/elasticsearch/client/index.html[{es} client] in the programming language of your choice. If you use the Python client, then check out the `elasticsearch-labs` repo for various https://github.com/elastic/elasticsearch-labs/tree/main/notebooks/search/python-examples[example notebooks].
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* {kibana-ref}/connect-to-elasticsearch.html#upload-data-kibana[File upload]: Use the {kib} file uploader to index single files for one-off testing and exploration. The GUI guides you through setting up your index and field mappings.
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* https://github.com/elastic/crawler[Web crawler]: Extract and index web page content into {es} documents.
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* {enterprise-search-ref}/connectors.html[Connectors]: Sync data from various third-party data sources to create searchable, read-only replicas in {es}.
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* <<es-connectors,Connectors>>: Sync data from various third-party data sources to create searchable, read-only replicas in {es}.
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[discrete]
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[[es-ingestion-overview-timestamped]]
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* <<use-elasticsearch-for-time-series-data,Tune for time series data>>
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Many {es} options come with different performance considerations and trade-offs. The best way to determine the
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optimal configuration for your use case is through https://www.elastic.co/elasticon/conf/2016/sf/quantitative-cluster-sizing[testing with your own data and queries].
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optimal configuration for your use case is through https://www.elastic.co/elasticon/conf/2016/sf/quantitative-cluster-sizing[testing with your own data and queries].

docs/reference/ml/trained-models/apis/infer-trained-model.asciidoc

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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`deberta_v2`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-deberta-v2]
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+
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.Properties of deberta_v2
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate-deberta-v2]
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=======
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`roberta`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-roberta]
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`deberta_v2`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-deberta-v2]
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+
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.Properties of deberta_v2
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate-deberta-v2]
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=======
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`roberta`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-roberta]
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(Optional, string)
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=======
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`deberta_v2`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-deberta-v2]
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.Properties of deberta_v2
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[%collapsible%open]
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=======
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`span`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-span]
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate-deberta-v2]
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=======
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`roberta`::::
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(Optional, object)
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`deberta_v2`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-deberta-v2]
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+
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.Properties of deberta_v2
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[%collapsible%open]
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=======
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`span`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-span]
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate-deberta-v2]
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=======
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`roberta`::::
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(Optional, object)
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(Optional, string)
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=======
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`deberta_v2`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-deberta-v2]
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+
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.Properties of deberta_v2
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate-deberta-v2]
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=======
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`roberta`::::
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-span]
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`with_special_tokens`::::
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(Optional, boolean)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-bert-with-special-tokens]
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=======
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`deberta_v2`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-deberta-v2]
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.Properties of deberta_v2
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[%collapsible%open]
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=======
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`span`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-span]
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`deberta_v2`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-deberta-v2]
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.Properties of deberta_v2
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate-deberta-v2]
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=======
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docs/reference/rest-api/common-parms.asciidoc

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(Optional, string) The number of shard copies that must be active before
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Default: 1, the primary shard.
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(Optional, string) The number of copies of each shard that must be active
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before proceeding with the operation. Set to `all` or any non-negative integer
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up to the total number of copies of each shard in the index
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(`number_of_replicas+1`). Defaults to `1`, meaning to wait just for each
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docs/reference/search/search-your-data/semantic-search-semantic-text.asciidoc

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Every time you ingest data into the related `semantic_text` field, this endpoint will be used for creating the vector representation of the text.
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[NOTE]
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====
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If you're using web crawlers or connectors to generate indices, you have to
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<<indices-put-mapping,update the index mappings>> for these indices to
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include the `semantic_text` field. Once the mapping is updated, you'll need to run
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a full web crawl or a full connector sync. This ensures that all existing
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documents are reprocessed and updated with the new semantic embeddings,
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[NOTE]
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====
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This step uses the reindex API to simulate data ingestion. If you are working with data that has already been indexed,
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rather than using the test-data set, reindexing is required to ensure that the data is processed by the {infer} endpoint
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and the necessary embeddings are generated.
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====
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docs/reference/troubleshooting/common-issues/red-yellow-cluster-status.asciidoc

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=== Red or yellow cluster health status
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* **Red health status**: The cluster has some unassigned primary shards, which
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* **Yellow health status**: The cluster has no unassigned primary shards but some
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* **Yellow health status**: The cluster has no unassigned primary shards but some
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In many cases, your cluster will recover to green health status automatically.
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[discrete]
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don't exist or are irrecoverable, <<cluster-allocation-explain,Allocation
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* restore the missing data from <<snapshot-restore,snapshot>>
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{

libs/logstash-bridge/src/main/java/org/elasticsearch/logstashbridge/script/ScriptServiceBridge.java

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new MustacheScriptEngine(settings)
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return new ScriptService(settings, scriptEngines, ScriptModule.CORE_CONTEXTS, timeProvider);
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}

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