Regenerate client

Author: flobernd

src/Elastic.Clients.Elasticsearch/_Generated/Api/Cluster/ClusterStatsResponse.g.cs

@@ -28,6 +28,14 @@ namespace Elastic.Clients.Elasticsearch.Cluster;
 
 public sealed partial class ClusterStatsResponse : ElasticsearchResponse
 {
+	/// <summary>
+	/// <para>
+	/// Cross-cluster stats
+	/// </para>
+	/// </summary>
+	[JsonInclude, JsonPropertyName("ccs")]
+	public Elastic.Clients.Elasticsearch.Cluster.CCSStats Ccs { get; init; }
+
 	/// <summary>
 	/// <para>
 	/// Name of the cluster, based on the cluster name setting.

src/Elastic.Clients.Elasticsearch/_Generated/Api/IndexManagement/DeleteTemplateRequest.g.cs

@@ -52,6 +52,7 @@ public sealed partial class DeleteTemplateRequestParameters : RequestParameters
 /// <summary>
 /// <para>
 /// Delete a legacy index template.
+/// IMPORTANT: This documentation is about legacy index templates, which are deprecated and will be replaced by the composable templates introduced in Elasticsearch 7.8.
 /// </para>
 /// </summary>
 public sealed partial class DeleteTemplateRequest : PlainRequest<DeleteTemplateRequestParameters>
@@ -90,6 +91,7 @@ public DeleteTemplateRequest(Elastic.Clients.Elasticsearch.Name name) : base(r =
 /// <summary>
 /// <para>
 /// Delete a legacy index template.
+/// IMPORTANT: This documentation is about legacy index templates, which are deprecated and will be replaced by the composable templates introduced in Elasticsearch 7.8.
 /// </para>
 /// </summary>
 public sealed partial class DeleteTemplateRequestDescriptor : RequestDescriptor<DeleteTemplateRequestDescriptor, DeleteTemplateRequestParameters>

src/Elastic.Clients.Elasticsearch/_Generated/Api/IndexManagement/GetTemplateRequest.g.cs

@@ -57,7 +57,7 @@ public sealed partial class GetTemplateRequestParameters : RequestParameters
 
 /// <summary>
 /// <para>
-/// Get index templates.
+/// Get legacy index templates.
 /// Get information about one or more index templates.
 /// </para>
 /// <para>
@@ -110,7 +110,7 @@ public GetTemplateRequest(Elastic.Clients.Elasticsearch.Names? name) : base(r =>
 
 /// <summary>
 /// <para>
-/// Get index templates.
+/// Get legacy index templates.
 /// Get information about one or more index templates.
 /// </para>
 /// <para>

src/Elastic.Clients.Elasticsearch/_Generated/Api/IndexManagement/PutTemplateRequest.g.cs

@@ -57,7 +57,7 @@ public sealed partial class PutTemplateRequestParameters : RequestParameters
 
 /// <summary>
 /// <para>
-/// Create or update an index template.
+/// Create or update a legacy index template.
 /// Index templates define settings, mappings, and aliases that can be applied automatically to new indices.
 /// Elasticsearch applies templates to new indices based on an index pattern that matches the index name.
 /// </para>
@@ -185,7 +185,7 @@ public PutTemplateRequest(Elastic.Clients.Elasticsearch.Name name) : base(r => r
 
 /// <summary>
 /// <para>
-/// Create or update an index template.
+/// Create or update a legacy index template.
 /// Index templates define settings, mappings, and aliases that can be applied automatically to new indices.
 /// Elasticsearch applies templates to new indices based on an index pattern that matches the index name.
 /// </para>
@@ -426,7 +426,7 @@ protected override void Serialize(Utf8JsonWriter writer, JsonSerializerOptions o
 
 /// <summary>
 /// <para>
-/// Create or update an index template.
+/// Create or update a legacy index template.
 /// Index templates define settings, mappings, and aliases that can be applied automatically to new indices.
 /// Elasticsearch applies templates to new indices based on an index pattern that matches the index name.
 /// </para>

src/Elastic.Clients.Elasticsearch/_Generated/Api/Inference/ChatCompletionUnifiedRequest.g.cs

@@ -44,6 +44,20 @@ public sealed partial class ChatCompletionUnifiedRequestParameters : RequestPara
 /// <para>
 /// Perform chat completion inference
 /// </para>
+/// <para>
+/// The chat completion inference API enables real-time responses for chat completion tasks by delivering answers incrementally, reducing response times during computation.
+/// It only works with the <c>chat_completion</c> task type for <c>openai</c> and <c>elastic</c> inference services.
+/// </para>
+/// <para>
+/// IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
+/// For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
+/// </para>
+/// <para>
+/// NOTE: The <c>chat_completion</c> task type is only available within the _stream API and only supports streaming.
+/// The Chat completion inference API and the Stream inference API differ in their response structure and capabilities.
+/// The Chat completion inference API provides more comprehensive customization options through more fields and function calling support.
+/// If you use the <c>openai</c> service or the <c>elastic</c> service, use the Chat completion inference API.
+/// </para>
 /// </summary>
 public sealed partial class ChatCompletionUnifiedRequest : PlainRequest<ChatCompletionUnifiedRequestParameters>, ISelfSerializable
 {
@@ -79,6 +93,20 @@ void ISelfSerializable.Serialize(Utf8JsonWriter writer, JsonSerializerOptions op
 /// <para>
 /// Perform chat completion inference
 /// </para>
+/// <para>
+/// The chat completion inference API enables real-time responses for chat completion tasks by delivering answers incrementally, reducing response times during computation.
+/// It only works with the <c>chat_completion</c> task type for <c>openai</c> and <c>elastic</c> inference services.
+/// </para>
+/// <para>
+/// IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
+/// For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
+/// </para>
+/// <para>
+/// NOTE: The <c>chat_completion</c> task type is only available within the _stream API and only supports streaming.
+/// The Chat completion inference API and the Stream inference API differ in their response structure and capabilities.
+/// The Chat completion inference API provides more comprehensive customization options through more fields and function calling support.
+/// If you use the <c>openai</c> service or the <c>elastic</c> service, use the Chat completion inference API.
+/// </para>
 /// </summary>
 public sealed partial class ChatCompletionUnifiedRequestDescriptor : RequestDescriptor<ChatCompletionUnifiedRequestDescriptor, ChatCompletionUnifiedRequestParameters>
 {

src/Elastic.Clients.Elasticsearch/_Generated/Api/Inference/PutAlibabacloudRequest.g.cs

@@ -41,13 +41,6 @@ public sealed partial class PutAlibabacloudRequestParameters : RequestParameters
 /// <para>
 /// Create an inference endpoint to perform an inference task with the <c>alibabacloud-ai-search</c> service.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAlibabacloudRequest : PlainRequest<PutAlibabacloudRequestParameters>
 {
@@ -104,13 +97,6 @@ public PutAlibabacloudRequest(Elastic.Clients.Elasticsearch.Inference.AlibabaClo
 /// <para>
 /// Create an inference endpoint to perform an inference task with the <c>alibabacloud-ai-search</c> service.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAlibabacloudRequestDescriptor : RequestDescriptor<PutAlibabacloudRequestDescriptor, PutAlibabacloudRequestParameters>
 {

src/Elastic.Clients.Elasticsearch/_Generated/Api/Inference/PutAmazonbedrockRequest.g.cs

@@ -45,13 +45,6 @@ public sealed partial class PutAmazonbedrockRequestParameters : RequestParameter
 /// info
 /// You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAmazonbedrockRequest : PlainRequest<PutAmazonbedrockRequestParameters>
 {
@@ -112,13 +105,6 @@ public PutAmazonbedrockRequest(Elastic.Clients.Elasticsearch.Inference.AmazonBed
 /// info
 /// You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAmazonbedrockRequestDescriptor : RequestDescriptor<PutAmazonbedrockRequestDescriptor, PutAmazonbedrockRequestParameters>
 {

src/Elastic.Clients.Elasticsearch/_Generated/Api/Inference/PutAnthropicRequest.g.cs

@@ -41,13 +41,6 @@ public sealed partial class PutAnthropicRequestParameters : RequestParameters
 /// <para>
 /// Create an inference endpoint to perform an inference task with the <c>anthropic</c> service.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAnthropicRequest : PlainRequest<PutAnthropicRequestParameters>
 {
@@ -104,13 +97,6 @@ public PutAnthropicRequest(Elastic.Clients.Elasticsearch.Inference.AnthropicTask
 /// <para>
 /// Create an inference endpoint to perform an inference task with the <c>anthropic</c> service.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAnthropicRequestDescriptor : RequestDescriptor<PutAnthropicRequestDescriptor, PutAnthropicRequestParameters>
 {

src/Elastic.Clients.Elasticsearch/_Generated/Api/Inference/PutAzureaistudioRequest.g.cs

@@ -41,13 +41,6 @@ public sealed partial class PutAzureaistudioRequestParameters : RequestParameter
 /// <para>
 /// Create an inference endpoint to perform an inference task with the <c>azureaistudio</c> service.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAzureaistudioRequest : PlainRequest<PutAzureaistudioRequestParameters>
 {
@@ -104,13 +97,6 @@ public PutAzureaistudioRequest(Elastic.Clients.Elasticsearch.Inference.AzureAiSt
 /// <para>
 /// Create an inference endpoint to perform an inference task with the <c>azureaistudio</c> service.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAzureaistudioRequestDescriptor : RequestDescriptor<PutAzureaistudioRequestDescriptor, PutAzureaistudioRequestParameters>
 {

src/Elastic.Clients.Elasticsearch/_Generated/Api/Inference/PutAzureopenaiRequest.g.cs

@@ -59,13 +59,6 @@ public sealed partial class PutAzureopenaiRequestParameters : RequestParameters
 /// <para>
 /// The list of embeddings models that you can choose from in your deployment can be found in the <a href="https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#embeddings">Azure models documentation</a>.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAzureopenaiRequest : PlainRequest<PutAzureopenaiRequestParameters>
 {
@@ -140,13 +133,6 @@ public PutAzureopenaiRequest(Elastic.Clients.Elasticsearch.Inference.AzureOpenAI
 /// <para>
 /// The list of embeddings models that you can choose from in your deployment can be found in the <a href="https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#embeddings">Azure models documentation</a>.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutAzureopenaiRequestDescriptor : RequestDescriptor<PutAzureopenaiRequestDescriptor, PutAzureopenaiRequestParameters>
 {

src/Elastic.Clients.Elasticsearch/_Generated/Api/Inference/PutCohereRequest.g.cs

@@ -41,13 +41,6 @@ public sealed partial class PutCohereRequestParameters : RequestParameters
 /// <para>
 /// Create an inference endpoint to perform an inference task with the <c>cohere</c> service.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutCohereRequest : PlainRequest<PutCohereRequestParameters>
 {
@@ -105,13 +98,6 @@ public PutCohereRequest(Elastic.Clients.Elasticsearch.Inference.CohereTaskType t
 /// <para>
 /// Create an inference endpoint to perform an inference task with the <c>cohere</c> service.
 /// </para>
-/// <para>
-/// When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
-/// After creating the endpoint, wait for the model deployment to complete before using it.
-/// To verify the deployment status, use the get trained model statistics API.
-/// Look for <c>"state": "fully_allocated"</c> in the response and ensure that the <c>"allocation_count"</c> matches the <c>"target_allocation_count"</c>.
-/// Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
-/// </para>
 /// </summary>
 public sealed partial class PutCohereRequestDescriptor : RequestDescriptor<PutCohereRequestDescriptor, PutCohereRequestParameters>
 {