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javanna authored Jan 21, 2025
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5 changes: 5 additions & 0 deletions docs/changelog/119536.yaml
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pr: 119536
summary: Fix ROUND() with unsigned longs throwing in some edge cases
area: ES|QL
type: bug
issues: []
5 changes: 5 additions & 0 deletions docs/changelog/119580.yaml
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pr: 119580
summary: Do not serialize `EsIndex` in plan
area: ES|QL
type: enhancement
issues: []
5 changes: 5 additions & 0 deletions docs/changelog/119743.yaml
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pr: 119743
summary: POC mark read-only
area: Engine
type: enhancement
issues: []
5 changes: 5 additions & 0 deletions docs/changelog/120192.yaml
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pr: 120192
summary: Extend `TranslationAware` to all pushable expressions
area: ES|QL
type: enhancement
issues: []
6 changes: 6 additions & 0 deletions docs/changelog/120250.yaml
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pr: 120250
summary: "Retry internally when CAS upload is throttled [GCS]"
area: Snapshot/Restore
type: enhancement
issues:
- 116546
5 changes: 5 additions & 0 deletions docs/changelog/120271.yaml
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pr: 120271
summary: Optimize indexing points with index and doc values set to true
area: Geo
type: enhancement
issues: []
6 changes: 6 additions & 0 deletions docs/changelog/120370.yaml
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pr: 120370
summary: "Merge field mappers when updating mappings with [subobjects:false]"
area: Mapping
type: bug
issues:
- 120216
5 changes: 5 additions & 0 deletions docs/changelog/120458.yaml
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pr: 120458
summary: Do not recommend increasing `max_shards_per_node`
area: Health
type: bug
issues: []
90 changes: 90 additions & 0 deletions docs/reference/esql/functions/kibana/definition/round.json

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5 changes: 5 additions & 0 deletions docs/reference/esql/functions/types/round.asciidoc

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12 changes: 4 additions & 8 deletions docs/reference/inference/inference-apis.asciidoc
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Expand Up @@ -16,10 +16,8 @@ models or if you want to use non-NLP models, use the
For the most up-to-date API details, refer to {api-es}/group/endpoint-inference[{infer-cap} APIs].
--

The {infer} APIs enable you to create {infer} endpoints and use {ml} models of
different providers - such as Amazon Bedrock, Anthropic, Azure AI Studio,
Cohere, Google AI, Mistral, OpenAI, or HuggingFace - as a service. Use
the following APIs to manage {infer} models and perform {infer}:
The {infer} APIs enable you to create {infer} endpoints and integrate with {ml} models of different services - such as Amazon Bedrock, Anthropic, Azure AI Studio, Cohere, Google AI, Mistral, OpenAI, or HuggingFace.
Use the following APIs to manage {infer} models and perform {infer}:

* <<delete-inference-api>>
* <<get-inference-api>>
Expand All @@ -37,10 +35,8 @@ An {infer} endpoint enables you to use the corresponding {ml} model without
manual deployment and apply it to your data at ingestion time through
<<semantic-search-semantic-text, semantic text>>.

Choose a model from your provider or use ELSER – a retrieval model trained by
Elastic –, then create an {infer} endpoint by the <<put-inference-api>>.
Now use <<semantic-search-semantic-text, semantic text>> to perform
<<semantic-search, semantic search>> on your data.
Choose a model from your service or use ELSER – a retrieval model trained by Elastic –, then create an {infer} endpoint by the <<put-inference-api>>.
Now use <<semantic-search-semantic-text, semantic text>> to perform <<semantic-search, semantic search>> on your data.

[discrete]
[[adaptive-allocations]]
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16 changes: 8 additions & 8 deletions docs/reference/inference/put-inference.asciidoc
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Expand Up @@ -42,7 +42,7 @@ include::inference-shared.asciidoc[tag=inference-id]
include::inference-shared.asciidoc[tag=task-type]
+
--
Refer to the service list in the <<put-inference-api-desc,API description section>> for the available task types.
Refer to the integration list in the <<put-inference-api-desc,API description section>> for the available task types.
--


Expand All @@ -54,15 +54,15 @@ The create {infer} API enables you to create an {infer} endpoint and configure a

[IMPORTANT]
====
* When creating an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
* When creating an {infer} endpoint, the associated {ml} model is automatically deployed if it is not already running.
* After creating the endpoint, wait for the model deployment to complete before using it. You can verify the deployment status by using the <<get-trained-models-stats, Get trained model statistics>> API. In the response, look for `"state": "fully_allocated"` and ensure the `"allocation_count"` matches the `"target_allocation_count"`.
* Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
====


The following services are available through the {infer} API.
You can find the available task types next to the service name.
Click the links to review the configuration details of the services:
The following integrations are available through the {infer} API.
You can find the available task types next to the integration name.
Click the links to review the configuration details of the integrations:

* <<infer-service-alibabacloud-ai-search,AlibabaCloud AI Search>> (`completion`, `rerank`, `sparse_embedding`, `text_embedding`)
* <<infer-service-amazon-bedrock,Amazon Bedrock>> (`completion`, `text_embedding`)
Expand All @@ -80,14 +80,14 @@ Click the links to review the configuration details of the services:
* <<infer-service-watsonx-ai>> (`text_embedding`)
* <<infer-service-jinaai,JinaAI>> (`text_embedding`, `rerank`)

The {es} and ELSER services run on a {ml} node in your {es} cluster. The rest of
the services connect to external providers.
The {es} and ELSER services run on a {ml} node in your {es} cluster.
The rest of the integrations connect to external services.

[discrete]
[[adaptive-allocations-put-inference]]
==== Adaptive allocations

Adaptive allocations allow inference services to dynamically adjust the number of model allocations based on the current load.
Adaptive allocations allow inference endpoints to dynamically adjust the number of model allocations based on the current load.

When adaptive allocations are enabled:

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[[infer-service-alibabacloud-ai-search]]
=== AlibabaCloud AI Search {infer} service
=== AlibabaCloud AI Search {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-amazon-bedrock.asciidoc
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[[infer-service-amazon-bedrock]]
=== Amazon Bedrock {infer} service
=== Amazon Bedrock {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-anthropic.asciidoc
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[[infer-service-anthropic]]
=== Anthropic {infer} service
=== Anthropic {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-azure-ai-studio.asciidoc
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[[infer-service-azure-ai-studio]]
=== Azure AI studio {infer} service
=== Azure AI studio {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-azure-openai.asciidoc
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[[infer-service-azure-openai]]
=== Azure OpenAI {infer} service
=== Azure OpenAI {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-cohere.asciidoc
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[[infer-service-cohere]]
=== Cohere {infer} service
=== Cohere {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-elasticsearch.asciidoc
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[[infer-service-elasticsearch]]
=== Elasticsearch {infer} service
=== Elasticsearch {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-elser.asciidoc
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[[infer-service-elser]]
=== ELSER {infer} service
=== ELSER {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-google-ai-studio.asciidoc
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[[infer-service-google-ai-studio]]
=== Google AI Studio {infer} service
=== Google AI Studio {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-google-vertex-ai.asciidoc
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[[infer-service-google-vertex-ai]]
=== Google Vertex AI {infer} service
=== Google Vertex AI {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-hugging-face.asciidoc
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[[infer-service-hugging-face]]
=== HuggingFace {infer} service
=== HuggingFace {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-jinaai.asciidoc
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[[infer-service-jinaai]]
=== JinaAI {infer} service
=== JinaAI {infer} integration

Creates an {infer} endpoint to perform an {infer} task with the `jinaai` service.

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2 changes: 1 addition & 1 deletion docs/reference/inference/service-mistral.asciidoc
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[[infer-service-mistral]]
=== Mistral {infer} service
=== Mistral {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-openai.asciidoc
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[[infer-service-openai]]
=== OpenAI {infer} service
=== OpenAI {infer} integration

.New API reference
[sidebar]
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2 changes: 1 addition & 1 deletion docs/reference/inference/service-watsonx-ai.asciidoc
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[[infer-service-watsonx-ai]]
=== Watsonx {infer} service
=== Watsonx {infer} integration

.New API reference
[sidebar]
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18 changes: 11 additions & 7 deletions docs/reference/mapping/types/array.asciidoc
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Expand Up @@ -10,19 +10,16 @@ same data type. For instance:
* an array of arrays: [ `1`, [ `2`, `3` ]] which is the equivalent of [ `1`, `2`, `3` ]
* an array of objects: [ `{ "name": "Mary", "age": 12 }`, `{ "name": "John", "age": 10 }`]

.Arrays of objects
.Arrays with `object` field type vs `nested` type
[NOTE]
====================================================
Arrays of objects do not work as you would expect: you cannot query each
object independently of the other objects in the array. If you need to be
able to do this then you should use the <<nested,`nested`>> data type instead
of the <<object,`object`>> data type.
Arrays of objects in Elasticsearch do not behave as you would expect: queries may match fields across different objects in the array, leading to unexpected results. By default, arrays of objects are <<nested-arrays-flattening-objects,flattened>>
during indexing. To ensure queries match values within the same object, use the <<nested,`nested`>> data type instead of the <<object,`object`>> data type.
This is explained in more detail in <<nested>>.
This behavior is explained in more detail in <<nested-arrays-flattening-objects, `nested`>>.
====================================================


When adding a field dynamically, the first value in the array determines the
field `type`. All subsequent values must be of the same data type or it must
at least be possible to <<coerce,coerce>> subsequent values to the same
Expand Down Expand Up @@ -81,3 +78,10 @@ GET my-index-000001/_search
<3> The second document contains no arrays, but can be indexed into the same fields.
<4> The query looks for `elasticsearch` in the `tags` field, and matches both documents.

[TIP]
====
You can modify arrays using the <<update-api-example,update API>>.
====



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