Auto-generated API code (#2716)

Author: elasticmachine

Diff for: docs/reference.asciidoc

@@ -8038,25 +8038,6 @@ client.inference.get({ ... })
 ** *`task_type` (Optional, Enum("sparse_embedding" | "text_embedding" | "rerank" | "completion" | "chat_completion"))*: The task type
 ** *`inference_id` (Optional, string)*: The inference Id
 
-[discrete]
-==== post_eis_chat_completion
-Perform a chat completion task through the Elastic Inference Service (EIS).
-
-Perform a chat completion inference task with the `elastic` service.
-
-{ref}/post-inference-api.html[Endpoint documentation]
-[source,ts]
-----
-client.inference.postEisChatCompletion({ eis_inference_id })
-----
-
-[discrete]
-==== Arguments
-
-* *Request (object):*
-** *`eis_inference_id` (string)*: The unique identifier of the inference endpoint.
-** *`chat_completion_request` (Optional, { messages, model, max_completion_tokens, stop, temperature, tool_choice, tools, top_p })*
-
 [discrete]
 ==== put
 Create an inference endpoint.
@@ -8277,27 +8258,6 @@ These settings are specific to the `cohere` service.
 ** *`task_settings` (Optional, { input_type, return_documents, top_n, truncate })*: Settings to configure the inference task.
 These settings are specific to the task type you specified.
 
-[discrete]
-==== put_eis
-Create an Elastic Inference Service (EIS) inference endpoint.
-
-Create an inference endpoint to perform an inference task through the Elastic Inference Service (EIS).
-
-[source,ts]
-----
-client.inference.putEis({ task_type, eis_inference_id, service, service_settings })
-----
-
-[discrete]
-==== Arguments
-
-* *Request (object):*
-** *`task_type` (Enum("chat_completion"))*: The type of the inference task that the model will perform.
-NOTE: The `chat_completion` task type only supports streaming and only through the _stream API.
-** *`eis_inference_id` (string)*: The unique identifier of the inference endpoint.
-** *`service` (Enum("elastic"))*: The type of service supported for the specified task type. In this case, `elastic`.
-** *`service_settings` ({ model_id, rate_limit })*: Settings used to install the inference model. These settings are specific to the `elastic` service.
-
 [discrete]
 ==== put_elasticsearch
 Create an Elasticsearch inference endpoint.

Diff for: src/api/api/inference.ts

@@ -209,43 +209,6 @@ export default class Inference {
     return await this.transport.request({ path, method, querystring, body, meta }, options)
   }
 
-  /**
-    * Perform a chat completion task through the Elastic Inference Service (EIS). Perform a chat completion inference task with the `elastic` service.
-    * @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/post-inference-api.html | Elasticsearch API documentation}
-    */
-  async postEisChatCompletion (this: That, params: T.InferencePostEisChatCompletionRequest | TB.InferencePostEisChatCompletionRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePostEisChatCompletionResponse>
-  async postEisChatCompletion (this: That, params: T.InferencePostEisChatCompletionRequest | TB.InferencePostEisChatCompletionRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePostEisChatCompletionResponse, unknown>>
-  async postEisChatCompletion (this: That, params: T.InferencePostEisChatCompletionRequest | TB.InferencePostEisChatCompletionRequest, options?: TransportRequestOptions): Promise<T.InferencePostEisChatCompletionResponse>
-  async postEisChatCompletion (this: That, params: T.InferencePostEisChatCompletionRequest | TB.InferencePostEisChatCompletionRequest, options?: TransportRequestOptions): Promise<any> {
-    const acceptedPath: string[] = ['eis_inference_id']
-    const acceptedBody: string[] = ['chat_completion_request']
-    const querystring: Record<string, any> = {}
-    // @ts-expect-error
-    let body: any = params.body ?? undefined
-
-    for (const key in params) {
-      if (acceptedBody.includes(key)) {
-        // @ts-expect-error
-        body = params[key]
-      } else if (acceptedPath.includes(key)) {
-        continue
-      } else if (key !== 'body') {
-        // @ts-expect-error
-        querystring[key] = params[key]
-      }
-    }
-
-    const method = 'POST'
-    const path = `/_inference/chat_completion/${encodeURIComponent(params.eis_inference_id.toString())}/_stream`
-    const meta: TransportRequestMetadata = {
-      name: 'inference.post_eis_chat_completion',
-      pathParts: {
-        eis_inference_id: params.eis_inference_id
-      }
-    }
-    return await this.transport.request({ path, method, querystring, body, meta }, options)
-  }
-
   /**
     * Create an inference endpoint. 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 `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources. 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, Mistral, Azure OpenAI, 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.
     * @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/put-inference-api.html | Elasticsearch API documentation}
@@ -561,51 +524,6 @@ export default class Inference {
     return await this.transport.request({ path, method, querystring, body, meta }, options)
   }
 
-  /**
-    * Create an Elastic Inference Service (EIS) inference endpoint. Create an inference endpoint to perform an inference task through the Elastic Inference Service (EIS).
-    * @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-elastic.html | Elasticsearch API documentation}
-    */
-  async putEis (this: That, params: T.InferencePutEisRequest | TB.InferencePutEisRequest, options?: TransportRequestOptionsWithOutMeta): Promise<T.InferencePutEisResponse>
-  async putEis (this: That, params: T.InferencePutEisRequest | TB.InferencePutEisRequest, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.InferencePutEisResponse, unknown>>
-  async putEis (this: That, params: T.InferencePutEisRequest | TB.InferencePutEisRequest, options?: TransportRequestOptions): Promise<T.InferencePutEisResponse>
-  async putEis (this: That, params: T.InferencePutEisRequest | TB.InferencePutEisRequest, options?: TransportRequestOptions): Promise<any> {
-    const acceptedPath: string[] = ['task_type', 'eis_inference_id']
-    const acceptedBody: string[] = ['service', 'service_settings']
-    const querystring: Record<string, any> = {}
-    // @ts-expect-error
-    const userBody: any = params?.body
-    let body: Record<string, any> | string
-    if (typeof userBody === 'string') {
-      body = userBody
-    } else {
-      body = userBody != null ? { ...userBody } : undefined
-    }
-
-    for (const key in params) {
-      if (acceptedBody.includes(key)) {
-        body = body ?? {}
-        // @ts-expect-error
-        body[key] = params[key]
-      } else if (acceptedPath.includes(key)) {
-        continue
-      } else if (key !== 'body') {
-        // @ts-expect-error
-        querystring[key] = params[key]
-      }
-    }
-
-    const method = 'PUT'
-    const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.eis_inference_id.toString())}`
-    const meta: TransportRequestMetadata = {
-      name: 'inference.put_eis',
-      pathParts: {
-        task_type: params.task_type,
-        eis_inference_id: params.eis_inference_id
-      }
-    }
-    return await this.transport.request({ path, method, querystring, body, meta }, options)
-  }
-
   /**
     * Create an Elasticsearch inference endpoint. Create an inference endpoint to perform an inference task with the `elasticsearch` service. > info > Your Elasticsearch deployment contains preconfigured ELSER and E5 inference endpoints, you only need to create the enpoints using the API if you want to customize the settings. If you use the ELSER or the E5 model through the `elasticsearch` service, the API request will automatically download and deploy the model if it isn't downloaded yet. > info > You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value. 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 `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`. Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
     * @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-elasticsearch.html | Elasticsearch API documentation}

Diff for: src/api/types.ts

@@ -9182,7 +9182,8 @@ export interface ClusterGetSettingsResponse {
 export interface ClusterHealthHealthResponseBody {
   active_primary_shards: integer
   active_shards: integer
-  active_shards_percent_as_number: Percentage
+  active_shards_percent?: string
+  active_shards_percent_as_number: double
   cluster_name: Name
   delayed_unassigned_shards: integer
   indices?: Record<IndexName, ClusterHealthIndexHealthStats>
@@ -9222,7 +9223,7 @@ export interface ClusterHealthRequest extends RequestBase {
   timeout?: Duration
   wait_for_active_shards?: WaitForActiveShards
   wait_for_events?: WaitForEvents
-  wait_for_nodes?: string | integer
+  wait_for_nodes?: ClusterHealthWaitForNodes
   wait_for_no_initializing_shards?: boolean
   wait_for_no_relocating_shards?: boolean
   wait_for_status?: HealthStatus
@@ -9240,6 +9241,8 @@ export interface ClusterHealthShardHealthStats {
   unassigned_primary_shards: integer
 }
 
+export type ClusterHealthWaitForNodes = string | integer
+
 export interface ClusterInfoRequest extends RequestBase {
   target: ClusterInfoTargets
 }
@@ -13014,15 +13017,6 @@ export type InferenceDenseByteVector = byte[]
 
 export type InferenceDenseVector = float[]
 
-export interface InferenceEisServiceSettings {
-  model_id: string
-  rate_limit?: InferenceRateLimitSetting
-}
-
-export type InferenceEisServiceType = 'elastic'
-
-export type InferenceEisTaskType = 'chat_completion'
-
 export interface InferenceElasticsearchServiceSettings {
   adaptive_allocations?: InferenceAdaptiveAllocations
   deployment_id?: string
@@ -13294,13 +13288,6 @@ export interface InferenceGetResponse {
   endpoints: InferenceInferenceEndpointInfo[]
 }
 
-export interface InferencePostEisChatCompletionRequest extends RequestBase {
-  eis_inference_id: Id
-  chat_completion_request?: InferenceRequestChatCompletion
-}
-
-export type InferencePostEisChatCompletionResponse = StreamResult
-
 export interface InferencePutRequest extends RequestBase {
   task_type?: InferenceTaskType
   inference_id: Id
@@ -13375,15 +13362,6 @@ export interface InferencePutCohereRequest extends RequestBase {
 
 export type InferencePutCohereResponse = InferenceInferenceEndpointInfo
 
-export interface InferencePutEisRequest extends RequestBase {
-  task_type: InferenceEisTaskType
-  eis_inference_id: Id
-  service: InferenceEisServiceType
-  service_settings: InferenceEisServiceSettings
-}
-
-export type InferencePutEisResponse = InferenceInferenceEndpointInfo
-
 export interface InferencePutElasticsearchRequest extends RequestBase {
   task_type: InferenceElasticsearchTaskType
   elasticsearch_inference_id: Id
@@ -13765,6 +13743,8 @@ export interface IngestInferenceProcessor extends IngestProcessorBase {
   target_field?: Field
   field_map?: Record<Field, any>
   inference_config?: IngestInferenceConfig
+  input_output?: IngestInputConfig | IngestInputConfig[]
+  ignore_missing?: boolean
 }
 
 export interface IngestIngest {
@@ -13773,6 +13753,11 @@ export interface IngestIngest {
   pipeline?: Name
 }
 
+export interface IngestInputConfig {
+  input_field: string
+  output_field: string
+}
+
 export interface IngestIpLocationProcessor extends IngestProcessorBase {
   database_file?: string
   field: Field

Diff for: src/api/typesWithBodyKey.ts

@@ -9278,7 +9278,8 @@ export interface ClusterGetSettingsResponse {
 export interface ClusterHealthHealthResponseBody {
   active_primary_shards: integer
   active_shards: integer
-  active_shards_percent_as_number: Percentage
+  active_shards_percent?: string
+  active_shards_percent_as_number: double
   cluster_name: Name
   delayed_unassigned_shards: integer
   indices?: Record<IndexName, ClusterHealthIndexHealthStats>
@@ -9318,7 +9319,7 @@ export interface ClusterHealthRequest extends RequestBase {
   timeout?: Duration
   wait_for_active_shards?: WaitForActiveShards
   wait_for_events?: WaitForEvents
-  wait_for_nodes?: string | integer
+  wait_for_nodes?: ClusterHealthWaitForNodes
   wait_for_no_initializing_shards?: boolean
   wait_for_no_relocating_shards?: boolean
   wait_for_status?: HealthStatus
@@ -9336,6 +9337,8 @@ export interface ClusterHealthShardHealthStats {
   unassigned_primary_shards: integer
 }
 
+export type ClusterHealthWaitForNodes = string | integer
+
 export interface ClusterInfoRequest extends RequestBase {
   target: ClusterInfoTargets
 }
@@ -13256,15 +13259,6 @@ export type InferenceDenseByteVector = byte[]
 
 export type InferenceDenseVector = float[]
 
-export interface InferenceEisServiceSettings {
-  model_id: string
-  rate_limit?: InferenceRateLimitSetting
-}
-
-export type InferenceEisServiceType = 'elastic'
-
-export type InferenceEisTaskType = 'chat_completion'
-
 export interface InferenceElasticsearchServiceSettings {
   adaptive_allocations?: InferenceAdaptiveAllocations
   deployment_id?: string
@@ -13540,14 +13534,6 @@ export interface InferenceGetResponse {
   endpoints: InferenceInferenceEndpointInfo[]
 }
 
-export interface InferencePostEisChatCompletionRequest extends RequestBase {
-  eis_inference_id: Id
-  /** @deprecated The use of the 'body' key has been deprecated, use 'chat_completion_request' instead. */
-  body?: InferenceRequestChatCompletion
-}
-
-export type InferencePostEisChatCompletionResponse = StreamResult
-
 export interface InferencePutRequest extends RequestBase {
   task_type?: InferenceTaskType
   inference_id: Id
@@ -13641,18 +13627,6 @@ export interface InferencePutCohereRequest extends RequestBase {
 
 export type InferencePutCohereResponse = InferenceInferenceEndpointInfo
 
-export interface InferencePutEisRequest extends RequestBase {
-  task_type: InferenceEisTaskType
-  eis_inference_id: Id
-  /** @deprecated The use of the 'body' key has been deprecated, move the nested keys to the top level object. */
-  body?: {
-    service: InferenceEisServiceType
-    service_settings: InferenceEisServiceSettings
-  }
-}
-
-export type InferencePutEisResponse = InferenceInferenceEndpointInfo
-
 export interface InferencePutElasticsearchRequest extends RequestBase {
   task_type: InferenceElasticsearchTaskType
   elasticsearch_inference_id: Id
@@ -14077,6 +14051,8 @@ export interface IngestInferenceProcessor extends IngestProcessorBase {
   target_field?: Field
   field_map?: Record<Field, any>
   inference_config?: IngestInferenceConfig
+  input_output?: IngestInputConfig | IngestInputConfig[]
+  ignore_missing?: boolean
 }
 
 export interface IngestIngest {
@@ -14085,6 +14061,11 @@ export interface IngestIngest {
   pipeline?: Name
 }
 
+export interface IngestInputConfig {
+  input_field: string
+  output_field: string
+}
+
 export interface IngestIpLocationProcessor extends IngestProcessorBase {
   database_file?: string
   field: Field