Auto-generated API code

docs/reference.asciidoc

@@ -7283,7 +7283,7 @@ To unset a version, replace the template without specifying one.
 ** *`create` (Optional, boolean)*: If true, this request cannot replace or update existing index templates.
 ** *`master_timeout` (Optional, string | -1 | 0)*: Period to wait for a connection to the master node. If no response is
 received before the timeout expires, the request fails and returns an error.
-** *`cause` (Optional, string)*
+** *`cause` (Optional, string)*: User defined reason for creating/updating the index template
 
 [discrete]
 ==== recovery
@@ -8093,6 +8093,17 @@ NOTE: The `chat_completion` task type only supports streaming and only through t
 ** *`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_mistral
+Configure a Mistral inference endpoint
+
+{ref}/infer-service-mistral.html[Endpoint documentation]
+[source,ts]
+----
+client.inference.putMistral()
+----
+
+
 [discrete]
 ==== put_openai
 Create an OpenAI inference endpoint.

src/api/api/inference.ts

@@ -306,6 +306,39 @@ export default class Inference {
     return await this.transport.request({ path, method, querystring, body, meta }, options)
   }
 
+  /**
+    * Configure a Mistral inference endpoint
+    * @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-mistral.html | Elasticsearch API documentation}
+    */
+  async putMistral (this: That, params?: T.TODO | TB.TODO, options?: TransportRequestOptionsWithOutMeta): Promise<T.TODO>
+  async putMistral (this: That, params?: T.TODO | TB.TODO, options?: TransportRequestOptionsWithMeta): Promise<TransportResult<T.TODO, unknown>>
+  async putMistral (this: That, params?: T.TODO | TB.TODO, options?: TransportRequestOptions): Promise<T.TODO>
+  async putMistral (this: That, params?: T.TODO | TB.TODO, options?: TransportRequestOptions): Promise<any> {
+    const acceptedPath: string[] = ['task_type', 'mistral_inference_id']
+    const querystring: Record<string, any> = {}
+    const body = undefined
+
+    params = params ?? {}
+    for (const key in params) {
+      if (acceptedPath.includes(key)) {
+        continue
+      } else if (key !== 'body') {
+        querystring[key] = params[key]
+      }
+    }
+
+    const method = 'PUT'
+    const path = `/_inference/${encodeURIComponent(params.task_type.toString())}/${encodeURIComponent(params.mistral_inference_id.toString())}`
+    const meta: TransportRequestMetadata = {
+      name: 'inference.put_mistral',
+      pathParts: {
+        task_type: params.task_type,
+        mistral_inference_id: params.mistral_inference_id
+      }
+    }
+    return await this.transport.request({ path, method, querystring, body, meta }, options)
+  }
+
   /**
     * Create an OpenAI inference endpoint. Create an inference endpoint to perform an inference task with the `openai` service. 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.
     * @see {@link https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-openai.html | Elasticsearch API documentation}