From 72ec322742390fe4ebb4548c54e7b7dd1ab30ad0 Mon Sep 17 00:00:00 2001 From: Liam Thompson Date: Tue, 19 Aug 2025 09:18:15 +0200 Subject: [PATCH] [DOCS] Remove instances of added in 0.0.0 + unrelated randomness (#5147) (cherry picked from commit 4401b0b5538f0884407a80d24fa76bbb7364fb86) --- output/openapi/elasticsearch-openapi.json | 20 +- output/schema/schema.json | 279 ++++++++++++++++-- .../PutAmazonSageMakerRequestExample1.yaml | 4 +- .../PutAmazonSageMakerRequestExample3.yaml | 4 +- .../PutAmazonSageMakerRequestExample4.yaml | 4 +- .../request/PutCustomRequestExample5.yaml | 4 +- .../snapshot/create/SnapshotCreateRequest.ts | 2 +- .../SnapshotCreateRepositoryRequest.ts | 2 +- .../SnapshotDeleteRepositoryRequest.ts | 2 +- .../snapshot/get/SnapshotGetRequest.ts | 2 +- .../SnapshotGetRepositoryRequest.ts | 2 +- .../restore/SnapshotRestoreRequest.ts | 2 +- .../SnapshotVerifyRepositoryRequest.ts | 2 +- 13 files changed, 286 insertions(+), 43 deletions(-) diff --git a/output/openapi/elasticsearch-openapi.json b/output/openapi/elasticsearch-openapi.json index 73fae1b644..043afe0ed7 100644 --- a/output/openapi/elasticsearch-openapi.json +++ b/output/openapi/elasticsearch-openapi.json @@ -44187,7 +44187,7 @@ } } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -44228,7 +44228,7 @@ "$ref": "#/components/responses/snapshot.create-200" } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -44269,7 +44269,7 @@ "$ref": "#/components/responses/snapshot.create-200" } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -44379,7 +44379,7 @@ "$ref": "#/components/responses/snapshot.get_repository-200" } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -44420,7 +44420,7 @@ "$ref": "#/components/responses/snapshot.create_repository-200" } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -44461,7 +44461,7 @@ "$ref": "#/components/responses/snapshot.create_repository-200" } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -44521,7 +44521,7 @@ } } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -44551,7 +44551,7 @@ "$ref": "#/components/responses/snapshot.get_repository-200" } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -45095,7 +45095,7 @@ } } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", @@ -45272,7 +45272,7 @@ } } }, - "x-state": "Generally available; Added in 0.0.0", + "x-state": "Generally available", "x-metaTags": [ { "content": "Elasticsearch", diff --git a/output/schema/schema.json b/output/schema/schema.json index b38bc2f699..43630b917f 100644 --- a/output/schema/schema.json +++ b/output/schema/schema.json @@ -21623,7 +21623,6 @@ "visibility": "private" }, "stack": { - "since": "0.0.0", "stability": "stable" } }, @@ -21671,7 +21670,6 @@ "visibility": "private" }, "stack": { - "since": "0.0.0", "stability": "stable" } }, @@ -21760,7 +21758,6 @@ "visibility": "private" }, "stack": { - "since": "0.0.0", "stability": "stable" } }, @@ -21802,7 +21799,6 @@ "visibility": "private" }, "stack": { - "since": "0.0.0", "stability": "stable" } }, @@ -21844,7 +21840,6 @@ "visibility": "private" }, "stack": { - "since": "0.0.0", "stability": "stable" } }, @@ -21970,7 +21965,6 @@ "visibility": "private" }, "stack": { - "since": "0.0.0", "stability": "stable" } }, @@ -22071,7 +22065,6 @@ "visibility": "private" }, "stack": { - "since": "0.0.0", "stability": "stable" } }, @@ -173811,30 +173804,140 @@ "description": "Create an Amazon SageMaker inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `amazon_sagemaker` service.", "examples": { "PutAmazonSageMakerRequestExample1": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"amazon_sagemaker_embeddings\",\n inference_config={\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\",\n \"dimensions\": 384,\n \"element_type\": \"float\"\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"amazon_sagemaker_embeddings\",\n inference_config: {\n service: \"amazon_sagemaker\",\n service_settings: {\n access_key: \"AWS-access-key\",\n secret_key: \"AWS-secret-key\",\n region: \"us-east-1\",\n api: \"elastic\",\n endpoint_name: \"my-endpoint\",\n dimensions: 384,\n element_type: \"float\",\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"amazon_sagemaker_embeddings\",\n body: {\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\",\n \"dimensions\": 384,\n \"element_type\": \"float\"\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"amazon_sagemaker_embeddings\",\n \"body\" => [\n \"service\" => \"amazon_sagemaker\",\n \"service_settings\" => [\n \"access_key\" => \"AWS-access-key\",\n \"secret_key\" => \"AWS-secret-key\",\n \"region\" => \"us-east-1\",\n \"api\" => \"elastic\",\n \"endpoint_name\" => \"my-endpoint\",\n \"dimensions\" => 384,\n \"element_type\" => \"float\",\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"amazon_sagemaker\",\"service_settings\":{\"access_key\":\"AWS-access-key\",\"secret_key\":\"AWS-secret-key\",\"region\":\"us-east-1\",\"api\":\"elastic\",\"endpoint_name\":\"my-endpoint\",\"dimensions\":384,\"element_type\":\"float\"}}' \"$ELASTICSEARCH_URL/_inference/text_embedding/amazon_sagemaker_embeddings\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/text_embedding/amazon_sagemaker_embeddings` to create an inference endpoint that performs a text embedding task.", "method_request": "PUT _inference/text_embedding/amazon_sagemaker_embeddings", "summary": "A text embedding task", "value": "{\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\",\n \"dimensions\": 384,\n \"element_type\": \"float\"\n }\n}" }, "PutAmazonSageMakerRequestExample2": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"completion\",\n inference_id=\"amazon_sagemaker_completion\",\n inference_config={\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"completion\",\n inference_id: \"amazon_sagemaker_completion\",\n inference_config: {\n service: \"amazon_sagemaker\",\n service_settings: {\n access_key: \"AWS-access-key\",\n secret_key: \"AWS-secret-key\",\n region: \"us-east-1\",\n api: \"elastic\",\n endpoint_name: \"my-endpoint\",\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"completion\",\n inference_id: \"amazon_sagemaker_completion\",\n body: {\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"completion\",\n \"inference_id\" => \"amazon_sagemaker_completion\",\n \"body\" => [\n \"service\" => \"amazon_sagemaker\",\n \"service_settings\" => [\n \"access_key\" => \"AWS-access-key\",\n \"secret_key\" => \"AWS-secret-key\",\n \"region\" => \"us-east-1\",\n \"api\" => \"elastic\",\n \"endpoint_name\" => \"my-endpoint\",\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"amazon_sagemaker\",\"service_settings\":{\"access_key\":\"AWS-access-key\",\"secret_key\":\"AWS-secret-key\",\"region\":\"us-east-1\",\"api\":\"elastic\",\"endpoint_name\":\"my-endpoint\"}}' \"$ELASTICSEARCH_URL/_inference/completion/amazon_sagemaker_completion\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/completion/amazon_sagemaker_completion` to create an inference endpoint that performs a completion task.", "method_request": "PUT _inference/completion/amazon_sagemaker_completion", "summary": "A completion task", "value": "{\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n}" }, "PutAmazonSageMakerRequestExample3": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"chat_completion\",\n inference_id=\"amazon_sagemaker_chat_completion\",\n inference_config={\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"chat_completion\",\n inference_id: \"amazon_sagemaker_chat_completion\",\n inference_config: {\n service: \"amazon_sagemaker\",\n service_settings: {\n access_key: \"AWS-access-key\",\n secret_key: \"AWS-secret-key\",\n region: \"us-east-1\",\n api: \"elastic\",\n endpoint_name: \"my-endpoint\",\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"chat_completion\",\n inference_id: \"amazon_sagemaker_chat_completion\",\n body: {\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"chat_completion\",\n \"inference_id\" => \"amazon_sagemaker_chat_completion\",\n \"body\" => [\n \"service\" => \"amazon_sagemaker\",\n \"service_settings\" => [\n \"access_key\" => \"AWS-access-key\",\n \"secret_key\" => \"AWS-secret-key\",\n \"region\" => \"us-east-1\",\n \"api\" => \"elastic\",\n \"endpoint_name\" => \"my-endpoint\",\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"amazon_sagemaker\",\"service_settings\":{\"access_key\":\"AWS-access-key\",\"secret_key\":\"AWS-secret-key\",\"region\":\"us-east-1\",\"api\":\"elastic\",\"endpoint_name\":\"my-endpoint\"}}' \"$ELASTICSEARCH_URL/_inference/chat_completion/amazon_sagemaker_chat_completion\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/chat_completion/amazon_sagemaker_chat_completion` to create an inference endpoint that performs a chat completion task.", "method_request": "PUT _inference/chat_completion/amazon_sagemaker_chat_completion", "summary": "A chat completion task", "value": "{\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n}" }, "PutAmazonSageMakerRequestExample4": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"sparse_embedding\",\n inference_id=\"amazon_sagemaker_sparse_embedding\",\n inference_config={\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"sparse_embedding\",\n inference_id: \"amazon_sagemaker_sparse_embedding\",\n inference_config: {\n service: \"amazon_sagemaker\",\n service_settings: {\n access_key: \"AWS-access-key\",\n secret_key: \"AWS-secret-key\",\n region: \"us-east-1\",\n api: \"elastic\",\n endpoint_name: \"my-endpoint\",\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"sparse_embedding\",\n inference_id: \"amazon_sagemaker_sparse_embedding\",\n body: {\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"sparse_embedding\",\n \"inference_id\" => \"amazon_sagemaker_sparse_embedding\",\n \"body\" => [\n \"service\" => \"amazon_sagemaker\",\n \"service_settings\" => [\n \"access_key\" => \"AWS-access-key\",\n \"secret_key\" => \"AWS-secret-key\",\n \"region\" => \"us-east-1\",\n \"api\" => \"elastic\",\n \"endpoint_name\" => \"my-endpoint\",\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"amazon_sagemaker\",\"service_settings\":{\"access_key\":\"AWS-access-key\",\"secret_key\":\"AWS-secret-key\",\"region\":\"us-east-1\",\"api\":\"elastic\",\"endpoint_name\":\"my-endpoint\"}}' \"$ELASTICSEARCH_URL/_inference/sparse_embedding/amazon_sagemaker_sparse_embedding\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/sparse_embedding/amazon_sagemaker_sparse_embedding` to create an inference endpoint that performs a sparse embedding task.", "method_request": "PUT _inference/sparse_embedding/amazon_sagemaker_sparse_embedding", "summary": "A sparse embedding task", "value": "{\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n}" }, "PutAmazonSageMakerRequestExample5": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"rerank\",\n inference_id=\"amazon_sagemaker_rerank\",\n inference_config={\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"rerank\",\n inference_id: \"amazon_sagemaker_rerank\",\n inference_config: {\n service: \"amazon_sagemaker\",\n service_settings: {\n access_key: \"AWS-access-key\",\n secret_key: \"AWS-secret-key\",\n region: \"us-east-1\",\n api: \"elastic\",\n endpoint_name: \"my-endpoint\",\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"rerank\",\n inference_id: \"amazon_sagemaker_rerank\",\n body: {\n \"service\": \"amazon_sagemaker\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"api\": \"elastic\",\n \"endpoint_name\": \"my-endpoint\"\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"rerank\",\n \"inference_id\" => \"amazon_sagemaker_rerank\",\n \"body\" => [\n \"service\" => \"amazon_sagemaker\",\n \"service_settings\" => [\n \"access_key\" => \"AWS-access-key\",\n \"secret_key\" => \"AWS-secret-key\",\n \"region\" => \"us-east-1\",\n \"api\" => \"elastic\",\n \"endpoint_name\" => \"my-endpoint\",\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"amazon_sagemaker\",\"service_settings\":{\"access_key\":\"AWS-access-key\",\"secret_key\":\"AWS-secret-key\",\"region\":\"us-east-1\",\"api\":\"elastic\",\"endpoint_name\":\"my-endpoint\"}}' \"$ELASTICSEARCH_URL/_inference/rerank/amazon_sagemaker_rerank\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/rerank/amazon_sagemaker_rerank` to create an inference endpoint that performs a rerank task.", "method_request": "PUT _inference/rerank/amazon_sagemaker_rerank", "summary": "A rerank task", @@ -174604,6 +174707,28 @@ "value": "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"api_key\": \"Cohere-API-key\",\n \"model_id\": \"rerank-english-v3.0\"\n },\n \"task_settings\": {\n \"top_n\": 10,\n \"return_documents\": true\n }\n}" }, "PutCohereRequestExample3": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"completion\",\n inference_id=\"cohere-completion\",\n inference_config={\n \"service\": \"cohere\",\n \"service_settings\": {\n \"api_key\": \"Cohere-API-key\",\n \"model_id\": \"command-a-03-2025\"\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"completion\",\n inference_id: \"cohere-completion\",\n inference_config: {\n service: \"cohere\",\n service_settings: {\n api_key: \"Cohere-API-key\",\n model_id: \"command-a-03-2025\",\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"completion\",\n inference_id: \"cohere-completion\",\n body: {\n \"service\": \"cohere\",\n \"service_settings\": {\n \"api_key\": \"Cohere-API-key\",\n \"model_id\": \"command-a-03-2025\"\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"completion\",\n \"inference_id\" => \"cohere-completion\",\n \"body\" => [\n \"service\" => \"cohere\",\n \"service_settings\" => [\n \"api_key\" => \"Cohere-API-key\",\n \"model_id\" => \"command-a-03-2025\",\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"cohere\",\"service_settings\":{\"api_key\":\"Cohere-API-key\",\"model_id\":\"command-a-03-2025\"}}' \"$ELASTICSEARCH_URL/_inference/completion/cohere-completion\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/completion/cohere-completion` to create an inference endpoint that performs a completion task.", "method_request": "PUT _inference/completion/cohere-completion", "summary": "A completion task", @@ -174745,30 +174870,140 @@ "description": "Create a custom inference endpoint.\n\nThe custom service gives more control over how to interact with external inference services that aren't explicitly supported through dedicated integrations.\nThe custom service gives you the ability to define the headers, url, query parameters, request body, and secrets.\nThe custom service supports the template replacement functionality, which enables you to define a template that can be replaced with the value associated with that key.\nTemplates are portions of a string that start with `${` and end with `}`.\nThe parameters `secret_parameters` and `task_settings` are checked for keys for template replacement. Template replacement is supported in the `request`, `headers`, `url`, and `query_parameters`.\nIf the definition (key) is not found for a template, an error message is returned.\nIn case of an endpoint definition like the following:\n```\nPUT _inference/text_embedding/test-text-embedding\n{\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"...endpoints.huggingface.cloud/v1/embeddings\",\n \"headers\": {\n \"Authorization\": \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"input\\\": ${input}}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\":\"$.data[*].embedding[*]\"\n }\n }\n }\n}\n```\nTo replace `${api_key}` the `secret_parameters` and `task_settings` are checked for a key named `api_key`.\n\n> info\n> Templates should not be surrounded by quotes.\n\nPre-defined templates:\n* `${input}` refers to the array of input strings that comes from the `input` field of the subsequent inference requests.\n* `${input_type}` refers to the input type translation values.\n* `${query}` refers to the query field used specifically for reranking tasks.\n* `${top_n}` refers to the `top_n` field available when performing rerank requests.\n* `${return_documents}` refers to the `return_documents` field available when performing rerank requests.", "examples": { "PutCustomRequestExample1": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"custom-embeddings\",\n inference_config={\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.openai.com/v1/embeddings\",\n \"headers\": {\n \"Authorization\": \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json;charset=utf-8\"\n },\n \"request\": \"{\\\"input\\\": ${input}, \\\"model\\\": \\\"text-embedding-3-small\\\"}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\": \"$.data[*].embedding[*]\"\n }\n }\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"custom-embeddings\",\n inference_config: {\n service: \"custom\",\n service_settings: {\n secret_parameters: {\n api_key: \"\",\n },\n url: \"https://api.openai.com/v1/embeddings\",\n headers: {\n Authorization: \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json;charset=utf-8\",\n },\n request: '{\"input\": ${input}, \"model\": \"text-embedding-3-small\"}',\n response: {\n json_parser: {\n text_embeddings: \"$.data[*].embedding[*]\",\n },\n },\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"custom-embeddings\",\n body: {\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.openai.com/v1/embeddings\",\n \"headers\": {\n \"Authorization\": \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json;charset=utf-8\"\n },\n \"request\": \"{\\\"input\\\": ${input}, \\\"model\\\": \\\"text-embedding-3-small\\\"}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\": \"$.data[*].embedding[*]\"\n }\n }\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"custom-embeddings\",\n \"body\" => [\n \"service\" => \"custom\",\n \"service_settings\" => [\n \"secret_parameters\" => [\n \"api_key\" => \"\",\n ],\n \"url\" => \"https://api.openai.com/v1/embeddings\",\n \"headers\" => [\n \"Authorization\" => \"Bearer ${api_key}\",\n \"Content-Type\" => \"application/json;charset=utf-8\",\n ],\n \"request\" => \"{\\\"input\\\": ${input}, \\\"model\\\": \\\"text-embedding-3-small\\\"}\",\n \"response\" => [\n \"json_parser\" => [\n \"text_embeddings\" => \"$.data[*].embedding[*]\",\n ],\n ],\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"custom\",\"service_settings\":{\"secret_parameters\":{\"api_key\":\"\"},\"url\":\"https://api.openai.com/v1/embeddings\",\"headers\":{\"Authorization\":\"Bearer ${api_key}\",\"Content-Type\":\"application/json;charset=utf-8\"},\"request\":\"{\\\"input\\\": ${input}, \\\"model\\\": \\\"text-embedding-3-small\\\"}\",\"response\":{\"json_parser\":{\"text_embeddings\":\"$.data[*].embedding[*]\"}}}}' \"$ELASTICSEARCH_URL/_inference/text_embedding/custom-embeddings\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/text_embedding/custom-embeddings` to create an inference endpoint that performs a text embedding task.", "method_request": "PUT _inference/text_embedding/custom-embeddings", "summary": "Custom text embedding task (OpenAI)", "value": "{\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.openai.com/v1/embeddings\",\n \"headers\": {\n \"Authorization\": \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json;charset=utf-8\"\n },\n \"request\": \"{\\\"input\\\": ${input}, \\\"model\\\": \\\"text-embedding-3-small\\\"}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\": \"$.data[*].embedding[*]\"\n }\n }\n }\n}" }, "PutCustomRequestExample2": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"rerank\",\n inference_id=\"custom-rerank\",\n inference_config={\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.cohere.com/v2/rerank\",\n \"headers\": {\n \"Authorization\": \"bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"documents\\\": ${input}, \\\"query\\\": ${query}, \\\"model\\\": \\\"rerank-v3.5\\\"}\",\n \"response\": {\n \"json_parser\": {\n \"reranked_index\": \"$.results[*].index\",\n \"relevance_score\": \"$.results[*].relevance_score\"\n }\n }\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"rerank\",\n inference_id: \"custom-rerank\",\n inference_config: {\n service: \"custom\",\n service_settings: {\n secret_parameters: {\n api_key: \"\",\n },\n url: \"https://api.cohere.com/v2/rerank\",\n headers: {\n Authorization: \"bearer ${api_key}\",\n \"Content-Type\": \"application/json\",\n },\n request:\n '{\"documents\": ${input}, \"query\": ${query}, \"model\": \"rerank-v3.5\"}',\n response: {\n json_parser: {\n reranked_index: \"$.results[*].index\",\n relevance_score: \"$.results[*].relevance_score\",\n },\n },\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"rerank\",\n inference_id: \"custom-rerank\",\n body: {\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.cohere.com/v2/rerank\",\n \"headers\": {\n \"Authorization\": \"bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"documents\\\": ${input}, \\\"query\\\": ${query}, \\\"model\\\": \\\"rerank-v3.5\\\"}\",\n \"response\": {\n \"json_parser\": {\n \"reranked_index\": \"$.results[*].index\",\n \"relevance_score\": \"$.results[*].relevance_score\"\n }\n }\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"rerank\",\n \"inference_id\" => \"custom-rerank\",\n \"body\" => [\n \"service\" => \"custom\",\n \"service_settings\" => [\n \"secret_parameters\" => [\n \"api_key\" => \"\",\n ],\n \"url\" => \"https://api.cohere.com/v2/rerank\",\n \"headers\" => [\n \"Authorization\" => \"bearer ${api_key}\",\n \"Content-Type\" => \"application/json\",\n ],\n \"request\" => \"{\\\"documents\\\": ${input}, \\\"query\\\": ${query}, \\\"model\\\": \\\"rerank-v3.5\\\"}\",\n \"response\" => [\n \"json_parser\" => [\n \"reranked_index\" => \"$.results[*].index\",\n \"relevance_score\" => \"$.results[*].relevance_score\",\n ],\n ],\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"custom\",\"service_settings\":{\"secret_parameters\":{\"api_key\":\"\"},\"url\":\"https://api.cohere.com/v2/rerank\",\"headers\":{\"Authorization\":\"bearer ${api_key}\",\"Content-Type\":\"application/json\"},\"request\":\"{\\\"documents\\\": ${input}, \\\"query\\\": ${query}, \\\"model\\\": \\\"rerank-v3.5\\\"}\",\"response\":{\"json_parser\":{\"reranked_index\":\"$.results[*].index\",\"relevance_score\":\"$.results[*].relevance_score\"}}}}' \"$ELASTICSEARCH_URL/_inference/rerank/custom-rerank\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/rerank/custom-rerank` to create an inference endpoint that performs a rerank task.", "method_request": "PUT _inference/rerank/custom-rerank", "summary": "Custom rerank task (Cohere APIv2)", "value": "{\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.cohere.com/v2/rerank\",\n \"headers\": {\n \"Authorization\": \"bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"documents\\\": ${input}, \\\"query\\\": ${query}, \\\"model\\\": \\\"rerank-v3.5\\\"}\",\n \"response\": {\n \"json_parser\": {\n \"reranked_index\":\"$.results[*].index\",\n \"relevance_score\":\"$.results[*].relevance_score\"\n }\n }\n }\n}" }, "PutCustomRequestExample3": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"custom-text-embedding\",\n inference_config={\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.cohere.com/v2/embed\",\n \"headers\": {\n \"Authorization\": \"bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"texts\\\": ${input}, \\\"model\\\": \\\"embed-v4.0\\\", \\\"input_type\\\": ${input_type}}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\": \"$.embeddings.float[*]\"\n }\n },\n \"input_type\": {\n \"translation\": {\n \"ingest\": \"search_document\",\n \"search\": \"search_query\"\n },\n \"default\": \"search_document\"\n }\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"custom-text-embedding\",\n inference_config: {\n service: \"custom\",\n service_settings: {\n secret_parameters: {\n api_key: \"\",\n },\n url: \"https://api.cohere.com/v2/embed\",\n headers: {\n Authorization: \"bearer ${api_key}\",\n \"Content-Type\": \"application/json\",\n },\n request:\n '{\"texts\": ${input}, \"model\": \"embed-v4.0\", \"input_type\": ${input_type}}',\n response: {\n json_parser: {\n text_embeddings: \"$.embeddings.float[*]\",\n },\n },\n input_type: {\n translation: {\n ingest: \"search_document\",\n search: \"search_query\",\n },\n default: \"search_document\",\n },\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"custom-text-embedding\",\n body: {\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.cohere.com/v2/embed\",\n \"headers\": {\n \"Authorization\": \"bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"texts\\\": ${input}, \\\"model\\\": \\\"embed-v4.0\\\", \\\"input_type\\\": ${input_type}}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\": \"$.embeddings.float[*]\"\n }\n },\n \"input_type\": {\n \"translation\": {\n \"ingest\": \"search_document\",\n \"search\": \"search_query\"\n },\n \"default\": \"search_document\"\n }\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"custom-text-embedding\",\n \"body\" => [\n \"service\" => \"custom\",\n \"service_settings\" => [\n \"secret_parameters\" => [\n \"api_key\" => \"\",\n ],\n \"url\" => \"https://api.cohere.com/v2/embed\",\n \"headers\" => [\n \"Authorization\" => \"bearer ${api_key}\",\n \"Content-Type\" => \"application/json\",\n ],\n \"request\" => \"{\\\"texts\\\": ${input}, \\\"model\\\": \\\"embed-v4.0\\\", \\\"input_type\\\": ${input_type}}\",\n \"response\" => [\n \"json_parser\" => [\n \"text_embeddings\" => \"$.embeddings.float[*]\",\n ],\n ],\n \"input_type\" => [\n \"translation\" => [\n \"ingest\" => \"search_document\",\n \"search\" => \"search_query\",\n ],\n \"default\" => \"search_document\",\n ],\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"custom\",\"service_settings\":{\"secret_parameters\":{\"api_key\":\"\"},\"url\":\"https://api.cohere.com/v2/embed\",\"headers\":{\"Authorization\":\"bearer ${api_key}\",\"Content-Type\":\"application/json\"},\"request\":\"{\\\"texts\\\": ${input}, \\\"model\\\": \\\"embed-v4.0\\\", \\\"input_type\\\": ${input_type}}\",\"response\":{\"json_parser\":{\"text_embeddings\":\"$.embeddings.float[*]\"}},\"input_type\":{\"translation\":{\"ingest\":\"search_document\",\"search\":\"search_query\"},\"default\":\"search_document\"}}}' \"$ELASTICSEARCH_URL/_inference/text_embedding/custom-text-embedding\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/text_embedding/custom-text-embedding` to create an inference endpoint that performs a text embedding task.", "method_request": "PUT _inference/text_embedding/custom-text-embedding", "summary": "Custom text embedding task (Cohere APIv2)", "value": "{\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.cohere.com/v2/embed\",\n \"headers\": {\n \"Authorization\": \"bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"texts\\\": ${input}, \\\"model\\\": \\\"embed-v4.0\\\", \\\"input_type\\\": ${input_type}}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\":\"$.embeddings.float[*]\"\n }\n },\n \"input_type\": {\n \"translation\": {\n \"ingest\": \"search_document\",\n \"search\": \"search_query\"\n },\n \"default\": \"search_document\"\n }\n }\n}" }, "PutCustomRequestExample4": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"rerank\",\n inference_id=\"custom-rerank-jina\",\n inference_config={\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.jina.ai/v1/rerank\",\n \"headers\": {\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer ${api_key}\"\n },\n \"request\": \"{\\\"model\\\": \\\"jina-reranker-v2-base-multilingual\\\",\\\"query\\\": ${query},\\\"documents\\\":${input}}\",\n \"response\": {\n \"json_parser\": {\n \"relevance_score\": \"$.results[*].relevance_score\",\n \"reranked_index\": \"$.results[*].index\"\n }\n }\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"rerank\",\n inference_id: \"custom-rerank-jina\",\n inference_config: {\n service: \"custom\",\n service_settings: {\n secret_parameters: {\n api_key: \"\",\n },\n url: \"https://api.jina.ai/v1/rerank\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer ${api_key}\",\n },\n request:\n '{\"model\": \"jina-reranker-v2-base-multilingual\",\"query\": ${query},\"documents\":${input}}',\n response: {\n json_parser: {\n relevance_score: \"$.results[*].relevance_score\",\n reranked_index: \"$.results[*].index\",\n },\n },\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"rerank\",\n inference_id: \"custom-rerank-jina\",\n body: {\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"https://api.jina.ai/v1/rerank\",\n \"headers\": {\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer ${api_key}\"\n },\n \"request\": \"{\\\"model\\\": \\\"jina-reranker-v2-base-multilingual\\\",\\\"query\\\": ${query},\\\"documents\\\":${input}}\",\n \"response\": {\n \"json_parser\": {\n \"relevance_score\": \"$.results[*].relevance_score\",\n \"reranked_index\": \"$.results[*].index\"\n }\n }\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"rerank\",\n \"inference_id\" => \"custom-rerank-jina\",\n \"body\" => [\n \"service\" => \"custom\",\n \"service_settings\" => [\n \"secret_parameters\" => [\n \"api_key\" => \"\",\n ],\n \"url\" => \"https://api.jina.ai/v1/rerank\",\n \"headers\" => [\n \"Content-Type\" => \"application/json\",\n \"Authorization\" => \"Bearer ${api_key}\",\n ],\n \"request\" => \"{\\\"model\\\": \\\"jina-reranker-v2-base-multilingual\\\",\\\"query\\\": ${query},\\\"documents\\\":${input}}\",\n \"response\" => [\n \"json_parser\" => [\n \"relevance_score\" => \"$.results[*].relevance_score\",\n \"reranked_index\" => \"$.results[*].index\",\n ],\n ],\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"custom\",\"service_settings\":{\"secret_parameters\":{\"api_key\":\"\"},\"url\":\"https://api.jina.ai/v1/rerank\",\"headers\":{\"Content-Type\":\"application/json\",\"Authorization\":\"Bearer ${api_key}\"},\"request\":\"{\\\"model\\\": \\\"jina-reranker-v2-base-multilingual\\\",\\\"query\\\": ${query},\\\"documents\\\":${input}}\",\"response\":{\"json_parser\":{\"relevance_score\":\"$.results[*].relevance_score\",\"reranked_index\":\"$.results[*].index\"}}}}' \"$ELASTICSEARCH_URL/_inference/rerank/custom-rerank-jina\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/rerank/custom-rerank-jina` to create an inference endpoint that performs a rerank task.", "method_request": "PUT _inference/rerank/custom-rerank-jina", "summary": "Custom rerank task (Jina AI)", "value": "{\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n }, \n \"url\": \"https://api.jina.ai/v1/rerank\",\n \"headers\": {\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer ${api_key}\"\n },\n \"request\": \"{\\\"model\\\": \\\"jina-reranker-v2-base-multilingual\\\",\\\"query\\\": ${query},\\\"documents\\\":${input}}\",\n \"response\": {\n \"json_parser\": {\n \"relevance_score\": \"$.results[*].relevance_score\",\n \"reranked_index\": \"$.results[*].index\"\n }\n }\n }\n}" }, "PutCustomRequestExample5": { + "alternatives": [ + { + "code": "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"custom-text-embedding-hf\",\n inference_config={\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"/v1/embeddings\",\n \"headers\": {\n \"Authorization\": \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"input\\\": ${input}}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\": \"$.data[*].embedding[*]\"\n }\n }\n }\n },\n)", + "language": "Python" + }, + { + "code": "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"custom-text-embedding-hf\",\n inference_config: {\n service: \"custom\",\n service_settings: {\n secret_parameters: {\n api_key: \"\",\n },\n url: \"/v1/embeddings\",\n headers: {\n Authorization: \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json\",\n },\n request: '{\"input\": ${input}}',\n response: {\n json_parser: {\n text_embeddings: \"$.data[*].embedding[*]\",\n },\n },\n },\n },\n});", + "language": "JavaScript" + }, + { + "code": "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"custom-text-embedding-hf\",\n body: {\n \"service\": \"custom\",\n \"service_settings\": {\n \"secret_parameters\": {\n \"api_key\": \"\"\n },\n \"url\": \"/v1/embeddings\",\n \"headers\": {\n \"Authorization\": \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json\"\n },\n \"request\": \"{\\\"input\\\": ${input}}\",\n \"response\": {\n \"json_parser\": {\n \"text_embeddings\": \"$.data[*].embedding[*]\"\n }\n }\n }\n }\n)", + "language": "Ruby" + }, + { + "code": "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"custom-text-embedding-hf\",\n \"body\" => [\n \"service\" => \"custom\",\n \"service_settings\" => [\n \"secret_parameters\" => [\n \"api_key\" => \"\",\n ],\n \"url\" => \"/v1/embeddings\",\n \"headers\" => [\n \"Authorization\" => \"Bearer ${api_key}\",\n \"Content-Type\" => \"application/json\",\n ],\n \"request\" => \"{\\\"input\\\": ${input}}\",\n \"response\" => [\n \"json_parser\" => [\n \"text_embeddings\" => \"$.data[*].embedding[*]\",\n ],\n ],\n ],\n ],\n]);", + "language": "PHP" + }, + { + "code": "curl -X PUT -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"service\":\"custom\",\"service_settings\":{\"secret_parameters\":{\"api_key\":\"\"},\"url\":\"/v1/embeddings\",\"headers\":{\"Authorization\":\"Bearer ${api_key}\",\"Content-Type\":\"application/json\"},\"request\":\"{\\\"input\\\": ${input}}\",\"response\":{\"json_parser\":{\"text_embeddings\":\"$.data[*].embedding[*]\"}}}}' \"$ELASTICSEARCH_URL/_inference/text_embedding/custom-text-embedding-hf\"", + "language": "curl" + } + ], "description": "Run `PUT _inference/text_embedding/custom-text-embedding-hf` to create an inference endpoint that performs a text embedding task by using the Qwen/Qwen3-Embedding-8B model.", "method_request": "PUT _inference/text_embedding/custom-text-embedding-hf", "summary": "Custom text embedding task (Hugging Face)", @@ -247167,23 +247402,23 @@ "SimulateIngestRequestExample1": { "alternatives": [ { - "code": "resp = client.simulate.ingest(\n docs=[\n {\n \"_id\": 123,\n \"_index\": \"my-index\",\n \"_source\": {\n \"foo\": \"bar\"\n }\n },\n {\n \"_id\": 456,\n \"_index\": \"my-index\",\n \"_source\": {\n \"foo\": \"rab\"\n }\n }\n ],\n)", + "code": "resp = client.simulate.ingest(\n docs=[\n {\n \"_id\": \"123\",\n \"_index\": \"my-index\",\n \"_source\": {\n \"foo\": \"bar\"\n }\n },\n {\n \"_id\": \"456\",\n \"_index\": \"my-index\",\n \"_source\": {\n \"foo\": \"rab\"\n }\n }\n ],\n)", "language": "Python" }, { - "code": "const response = await client.simulate.ingest({\n docs: [\n {\n _id: 123,\n _index: \"my-index\",\n _source: {\n foo: \"bar\",\n },\n },\n {\n _id: 456,\n _index: \"my-index\",\n _source: {\n foo: \"rab\",\n },\n },\n ],\n});", + "code": "const response = await client.simulate.ingest({\n docs: [\n {\n _id: \"123\",\n _index: \"my-index\",\n _source: {\n foo: \"bar\",\n },\n },\n {\n _id: \"456\",\n _index: \"my-index\",\n _source: {\n foo: \"rab\",\n },\n },\n ],\n});", "language": "JavaScript" }, { - "code": "response = client.simulate.ingest(\n body: {\n \"docs\": [\n {\n \"_id\": 123,\n \"_index\": \"my-index\",\n \"_source\": {\n \"foo\": \"bar\"\n }\n },\n {\n \"_id\": 456,\n \"_index\": \"my-index\",\n \"_source\": {\n \"foo\": \"rab\"\n }\n }\n ]\n }\n)", + "code": "response = client.simulate.ingest(\n body: {\n \"docs\": [\n {\n \"_id\": \"123\",\n \"_index\": \"my-index\",\n \"_source\": {\n \"foo\": \"bar\"\n }\n },\n {\n \"_id\": \"456\",\n \"_index\": \"my-index\",\n \"_source\": {\n \"foo\": \"rab\"\n }\n }\n ]\n }\n)", "language": "Ruby" }, { - "code": "$resp = $client->simulate()->ingest([\n \"body\" => [\n \"docs\" => array(\n [\n \"_id\" => 123,\n \"_index\" => \"my-index\",\n \"_source\" => [\n \"foo\" => \"bar\",\n ],\n ],\n [\n \"_id\" => 456,\n \"_index\" => \"my-index\",\n \"_source\" => [\n \"foo\" => \"rab\",\n ],\n ],\n ),\n ],\n]);", + "code": "$resp = $client->simulate()->ingest([\n \"body\" => [\n \"docs\" => array(\n [\n \"_id\" => \"123\",\n \"_index\" => \"my-index\",\n \"_source\" => [\n \"foo\" => \"bar\",\n ],\n ],\n [\n \"_id\" => \"456\",\n \"_index\" => \"my-index\",\n \"_source\" => [\n \"foo\" => \"rab\",\n ],\n ],\n ),\n ],\n]);", "language": "PHP" }, { - "code": "curl -X POST -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"docs\":[{\"_id\":123,\"_index\":\"my-index\",\"_source\":{\"foo\":\"bar\"}},{\"_id\":456,\"_index\":\"my-index\",\"_source\":{\"foo\":\"rab\"}}]}' \"$ELASTICSEARCH_URL/_ingest/_simulate\"", + "code": "curl -X POST -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"docs\":[{\"_id\":\"123\",\"_index\":\"my-index\",\"_source\":{\"foo\":\"bar\"}},{\"_id\":\"456\",\"_index\":\"my-index\",\"_source\":{\"foo\":\"rab\"}}]}' \"$ELASTICSEARCH_URL/_ingest/_simulate\"", "language": "curl" }, { @@ -247199,23 +247434,23 @@ "SimulateIngestRequestExample2": { "alternatives": [ { - "code": "resp = client.simulate.ingest(\n docs=[\n {\n \"_index\": \"my-index\",\n \"_id\": 123,\n \"_source\": {\n \"foo\": \"bar\"\n }\n },\n {\n \"_index\": \"my-index\",\n \"_id\": 456,\n \"_source\": {\n \"foo\": \"rab\"\n }\n }\n ],\n pipeline_substitutions={\n \"my-pipeline\": {\n \"processors\": [\n {\n \"uppercase\": {\n \"field\": \"foo\"\n }\n }\n ]\n }\n },\n)", + "code": "resp = client.simulate.ingest(\n docs=[\n {\n \"_index\": \"my-index\",\n \"_id\": \"123\",\n \"_source\": {\n \"foo\": \"bar\"\n }\n },\n {\n \"_index\": \"my-index\",\n \"_id\": \"456\",\n \"_source\": {\n \"foo\": \"rab\"\n }\n }\n ],\n pipeline_substitutions={\n \"my-pipeline\": {\n \"processors\": [\n {\n \"uppercase\": {\n \"field\": \"foo\"\n }\n }\n ]\n }\n },\n)", "language": "Python" }, { - "code": "const response = await client.simulate.ingest({\n docs: [\n {\n _index: \"my-index\",\n _id: 123,\n _source: {\n foo: \"bar\",\n },\n },\n {\n _index: \"my-index\",\n _id: 456,\n _source: {\n foo: \"rab\",\n },\n },\n ],\n pipeline_substitutions: {\n \"my-pipeline\": {\n processors: [\n {\n uppercase: {\n field: \"foo\",\n },\n },\n ],\n },\n },\n});", + "code": "const response = await client.simulate.ingest({\n docs: [\n {\n _index: \"my-index\",\n _id: \"123\",\n _source: {\n foo: \"bar\",\n },\n },\n {\n _index: \"my-index\",\n _id: \"456\",\n _source: {\n foo: \"rab\",\n },\n },\n ],\n pipeline_substitutions: {\n \"my-pipeline\": {\n processors: [\n {\n uppercase: {\n field: \"foo\",\n },\n },\n ],\n },\n },\n});", "language": "JavaScript" }, { - "code": "response = client.simulate.ingest(\n body: {\n \"docs\": [\n {\n \"_index\": \"my-index\",\n \"_id\": 123,\n \"_source\": {\n \"foo\": \"bar\"\n }\n },\n {\n \"_index\": \"my-index\",\n \"_id\": 456,\n \"_source\": {\n \"foo\": \"rab\"\n }\n }\n ],\n \"pipeline_substitutions\": {\n \"my-pipeline\": {\n \"processors\": [\n {\n \"uppercase\": {\n \"field\": \"foo\"\n }\n }\n ]\n }\n }\n }\n)", + "code": "response = client.simulate.ingest(\n body: {\n \"docs\": [\n {\n \"_index\": \"my-index\",\n \"_id\": \"123\",\n \"_source\": {\n \"foo\": \"bar\"\n }\n },\n {\n \"_index\": \"my-index\",\n \"_id\": \"456\",\n \"_source\": {\n \"foo\": \"rab\"\n }\n }\n ],\n \"pipeline_substitutions\": {\n \"my-pipeline\": {\n \"processors\": [\n {\n \"uppercase\": {\n \"field\": \"foo\"\n }\n }\n ]\n }\n }\n }\n)", "language": "Ruby" }, { - "code": "$resp = $client->simulate()->ingest([\n \"body\" => [\n \"docs\" => array(\n [\n \"_index\" => \"my-index\",\n \"_id\" => 123,\n \"_source\" => [\n \"foo\" => \"bar\",\n ],\n ],\n [\n \"_index\" => \"my-index\",\n \"_id\" => 456,\n \"_source\" => [\n \"foo\" => \"rab\",\n ],\n ],\n ),\n \"pipeline_substitutions\" => [\n \"my-pipeline\" => [\n \"processors\" => array(\n [\n \"uppercase\" => [\n \"field\" => \"foo\",\n ],\n ],\n ),\n ],\n ],\n ],\n]);", + "code": "$resp = $client->simulate()->ingest([\n \"body\" => [\n \"docs\" => array(\n [\n \"_index\" => \"my-index\",\n \"_id\" => \"123\",\n \"_source\" => [\n \"foo\" => \"bar\",\n ],\n ],\n [\n \"_index\" => \"my-index\",\n \"_id\" => \"456\",\n \"_source\" => [\n \"foo\" => \"rab\",\n ],\n ],\n ),\n \"pipeline_substitutions\" => [\n \"my-pipeline\" => [\n \"processors\" => array(\n [\n \"uppercase\" => [\n \"field\" => \"foo\",\n ],\n ],\n ),\n ],\n ],\n ],\n]);", "language": "PHP" }, { - "code": "curl -X POST -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"docs\":[{\"_index\":\"my-index\",\"_id\":123,\"_source\":{\"foo\":\"bar\"}},{\"_index\":\"my-index\",\"_id\":456,\"_source\":{\"foo\":\"rab\"}}],\"pipeline_substitutions\":{\"my-pipeline\":{\"processors\":[{\"uppercase\":{\"field\":\"foo\"}}]}}}' \"$ELASTICSEARCH_URL/_ingest/_simulate\"", + "code": "curl -X POST -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"docs\":[{\"_index\":\"my-index\",\"_id\":\"123\",\"_source\":{\"foo\":\"bar\"}},{\"_index\":\"my-index\",\"_id\":\"456\",\"_source\":{\"foo\":\"rab\"}}],\"pipeline_substitutions\":{\"my-pipeline\":{\"processors\":[{\"uppercase\":{\"field\":\"foo\"}}]}}}' \"$ELASTICSEARCH_URL/_ingest/_simulate\"", "language": "curl" }, { @@ -263202,23 +263437,23 @@ "UpdateTransformRequestExample1": { "alternatives": [ { - "code": "resp = client.transform.update_transform(\n transform_id=\"simple-kibana-ecomm-pivot\",\n source={\n \"index\": \"kibana_sample_data_ecommerce\",\n \"query\": {\n \"term\": {\n \"geoip.continent_name\": {\n \"value\": \"Asia\"\n }\n }\n }\n },\n pivot={\n \"group_by\": {\n \"customer_id\": {\n \"terms\": {\n \"field\": \"customer_id\",\n \"missing_bucket\": True\n }\n }\n },\n \"aggregations\": {\n \"max_price\": {\n \"max\": {\n \"field\": \"taxful_total_price\"\n }\n }\n }\n },\n description=\"Maximum priced ecommerce data by customer_id in Asia\",\n dest={\n \"index\": \"kibana_sample_data_ecommerce_transform1\",\n \"pipeline\": \"add_timestamp_pipeline\"\n },\n frequency=\"5m\",\n sync={\n \"time\": {\n \"field\": \"order_date\",\n \"delay\": \"60s\"\n }\n },\n retention_policy={\n \"time\": {\n \"field\": \"order_date\",\n \"max_age\": \"30d\"\n }\n },\n)", + "code": "resp = client.transform.update_transform(\n transform_id=\"simple-kibana-ecomm-pivot\",\n source={\n \"index\": \"kibana_sample_data_ecommerce\",\n \"query\": {\n \"term\": {\n \"geoip.continent_name\": {\n \"value\": \"Asia\"\n }\n }\n }\n },\n description=\"Maximum priced ecommerce data by customer_id in Asia\",\n dest={\n \"index\": \"kibana_sample_data_ecommerce_transform_v2\",\n \"pipeline\": \"add_timestamp_pipeline\"\n },\n frequency=\"15m\",\n sync={\n \"time\": {\n \"field\": \"order_date\",\n \"delay\": \"120s\"\n }\n },\n)", "language": "Python" }, { - "code": "const response = await client.transform.updateTransform({\n transform_id: \"simple-kibana-ecomm-pivot\",\n source: {\n index: \"kibana_sample_data_ecommerce\",\n query: {\n term: {\n \"geoip.continent_name\": {\n value: \"Asia\",\n },\n },\n },\n },\n pivot: {\n group_by: {\n customer_id: {\n terms: {\n field: \"customer_id\",\n missing_bucket: true,\n },\n },\n },\n aggregations: {\n max_price: {\n max: {\n field: \"taxful_total_price\",\n },\n },\n },\n },\n description: \"Maximum priced ecommerce data by customer_id in Asia\",\n dest: {\n index: \"kibana_sample_data_ecommerce_transform1\",\n pipeline: \"add_timestamp_pipeline\",\n },\n frequency: \"5m\",\n sync: {\n time: {\n field: \"order_date\",\n delay: \"60s\",\n },\n },\n retention_policy: {\n time: {\n field: \"order_date\",\n max_age: \"30d\",\n },\n },\n});", + "code": "const response = await client.transform.updateTransform({\n transform_id: \"simple-kibana-ecomm-pivot\",\n source: {\n index: \"kibana_sample_data_ecommerce\",\n query: {\n term: {\n \"geoip.continent_name\": {\n value: \"Asia\",\n },\n },\n },\n },\n description: \"Maximum priced ecommerce data by customer_id in Asia\",\n dest: {\n index: \"kibana_sample_data_ecommerce_transform_v2\",\n pipeline: \"add_timestamp_pipeline\",\n },\n frequency: \"15m\",\n sync: {\n time: {\n field: \"order_date\",\n delay: \"120s\",\n },\n },\n});", "language": "JavaScript" }, { - "code": "response = client.transform.update_transform(\n transform_id: \"simple-kibana-ecomm-pivot\",\n body: {\n \"source\": {\n \"index\": \"kibana_sample_data_ecommerce\",\n \"query\": {\n \"term\": {\n \"geoip.continent_name\": {\n \"value\": \"Asia\"\n }\n }\n }\n },\n \"pivot\": {\n \"group_by\": {\n \"customer_id\": {\n \"terms\": {\n \"field\": \"customer_id\",\n \"missing_bucket\": true\n }\n }\n },\n \"aggregations\": {\n \"max_price\": {\n \"max\": {\n \"field\": \"taxful_total_price\"\n }\n }\n }\n },\n \"description\": \"Maximum priced ecommerce data by customer_id in Asia\",\n \"dest\": {\n \"index\": \"kibana_sample_data_ecommerce_transform1\",\n \"pipeline\": \"add_timestamp_pipeline\"\n },\n \"frequency\": \"5m\",\n \"sync\": {\n \"time\": {\n \"field\": \"order_date\",\n \"delay\": \"60s\"\n }\n },\n \"retention_policy\": {\n \"time\": {\n \"field\": \"order_date\",\n \"max_age\": \"30d\"\n }\n }\n }\n)", + "code": "response = client.transform.update_transform(\n transform_id: \"simple-kibana-ecomm-pivot\",\n body: {\n \"source\": {\n \"index\": \"kibana_sample_data_ecommerce\",\n \"query\": {\n \"term\": {\n \"geoip.continent_name\": {\n \"value\": \"Asia\"\n }\n }\n }\n },\n \"description\": \"Maximum priced ecommerce data by customer_id in Asia\",\n \"dest\": {\n \"index\": \"kibana_sample_data_ecommerce_transform_v2\",\n \"pipeline\": \"add_timestamp_pipeline\"\n },\n \"frequency\": \"15m\",\n \"sync\": {\n \"time\": {\n \"field\": \"order_date\",\n \"delay\": \"120s\"\n }\n }\n }\n)", "language": "Ruby" }, { - "code": "$resp = $client->transform()->updateTransform([\n \"transform_id\" => \"simple-kibana-ecomm-pivot\",\n \"body\" => [\n \"source\" => [\n \"index\" => \"kibana_sample_data_ecommerce\",\n \"query\" => [\n \"term\" => [\n \"geoip.continent_name\" => [\n \"value\" => \"Asia\",\n ],\n ],\n ],\n ],\n \"pivot\" => [\n \"group_by\" => [\n \"customer_id\" => [\n \"terms\" => [\n \"field\" => \"customer_id\",\n \"missing_bucket\" => true,\n ],\n ],\n ],\n \"aggregations\" => [\n \"max_price\" => [\n \"max\" => [\n \"field\" => \"taxful_total_price\",\n ],\n ],\n ],\n ],\n \"description\" => \"Maximum priced ecommerce data by customer_id in Asia\",\n \"dest\" => [\n \"index\" => \"kibana_sample_data_ecommerce_transform1\",\n \"pipeline\" => \"add_timestamp_pipeline\",\n ],\n \"frequency\" => \"5m\",\n \"sync\" => [\n \"time\" => [\n \"field\" => \"order_date\",\n \"delay\" => \"60s\",\n ],\n ],\n \"retention_policy\" => [\n \"time\" => [\n \"field\" => \"order_date\",\n \"max_age\" => \"30d\",\n ],\n ],\n ],\n]);", + "code": "$resp = $client->transform()->updateTransform([\n \"transform_id\" => \"simple-kibana-ecomm-pivot\",\n \"body\" => [\n \"source\" => [\n \"index\" => \"kibana_sample_data_ecommerce\",\n \"query\" => [\n \"term\" => [\n \"geoip.continent_name\" => [\n \"value\" => \"Asia\",\n ],\n ],\n ],\n ],\n \"description\" => \"Maximum priced ecommerce data by customer_id in Asia\",\n \"dest\" => [\n \"index\" => \"kibana_sample_data_ecommerce_transform_v2\",\n \"pipeline\" => \"add_timestamp_pipeline\",\n ],\n \"frequency\" => \"15m\",\n \"sync\" => [\n \"time\" => [\n \"field\" => \"order_date\",\n \"delay\" => \"120s\",\n ],\n ],\n ],\n]);", "language": "PHP" }, { - "code": "curl -X POST -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"source\":{\"index\":\"kibana_sample_data_ecommerce\",\"query\":{\"term\":{\"geoip.continent_name\":{\"value\":\"Asia\"}}}},\"pivot\":{\"group_by\":{\"customer_id\":{\"terms\":{\"field\":\"customer_id\",\"missing_bucket\":true}}},\"aggregations\":{\"max_price\":{\"max\":{\"field\":\"taxful_total_price\"}}}},\"description\":\"Maximum priced ecommerce data by customer_id in Asia\",\"dest\":{\"index\":\"kibana_sample_data_ecommerce_transform1\",\"pipeline\":\"add_timestamp_pipeline\"},\"frequency\":\"5m\",\"sync\":{\"time\":{\"field\":\"order_date\",\"delay\":\"60s\"}},\"retention_policy\":{\"time\":{\"field\":\"order_date\",\"max_age\":\"30d\"}}}' \"$ELASTICSEARCH_URL/_transform/simple-kibana-ecomm-pivot/_update\"", + "code": "curl -X POST -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"source\":{\"index\":\"kibana_sample_data_ecommerce\",\"query\":{\"term\":{\"geoip.continent_name\":{\"value\":\"Asia\"}}}},\"description\":\"Maximum priced ecommerce data by customer_id in Asia\",\"dest\":{\"index\":\"kibana_sample_data_ecommerce_transform_v2\",\"pipeline\":\"add_timestamp_pipeline\"},\"frequency\":\"15m\",\"sync\":{\"time\":{\"field\":\"order_date\",\"delay\":\"120s\"}}}' \"$ELASTICSEARCH_URL/_transform/simple-kibana-ecomm-pivot/_update\"", "language": "curl" }, { diff --git a/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample1.yaml b/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample1.yaml index 006249bbd1..4c93a454d4 100644 --- a/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample1.yaml +++ b/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample1.yaml @@ -1,5 +1,7 @@ summary: A text embedding task -description: Run `PUT _inference/text_embedding/amazon_sagemaker_embeddings` to create an inference endpoint that performs a text embedding task. +description: + Run `PUT _inference/text_embedding/amazon_sagemaker_embeddings` to create an inference endpoint that performs a text + embedding task. method_request: 'PUT _inference/text_embedding/amazon_sagemaker_embeddings' # type: "request" value: |- diff --git a/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample3.yaml b/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample3.yaml index ab12f559a3..88c33789f6 100644 --- a/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample3.yaml +++ b/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample3.yaml @@ -1,5 +1,7 @@ summary: A chat completion task -description: Run `PUT _inference/chat_completion/amazon_sagemaker_chat_completion` to create an inference endpoint that performs a chat completion task. +description: + Run `PUT _inference/chat_completion/amazon_sagemaker_chat_completion` to create an inference endpoint that performs a + chat completion task. method_request: 'PUT _inference/chat_completion/amazon_sagemaker_chat_completion' # type: "request" value: |- diff --git a/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample4.yaml b/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample4.yaml index afac438c22..a26bdef822 100644 --- a/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample4.yaml +++ b/specification/inference/put_amazonsagemaker/examples/request/PutAmazonSageMakerRequestExample4.yaml @@ -1,5 +1,7 @@ summary: A sparse embedding task -description: Run `PUT _inference/sparse_embedding/amazon_sagemaker_sparse_embedding` to create an inference endpoint that performs a sparse embedding task. +description: + Run `PUT _inference/sparse_embedding/amazon_sagemaker_sparse_embedding` to create an inference endpoint that performs a + sparse embedding task. method_request: 'PUT _inference/sparse_embedding/amazon_sagemaker_sparse_embedding' # type: "request" value: |- diff --git a/specification/inference/put_custom/examples/request/PutCustomRequestExample5.yaml b/specification/inference/put_custom/examples/request/PutCustomRequestExample5.yaml index c9f86dad8d..e7896d2bac 100644 --- a/specification/inference/put_custom/examples/request/PutCustomRequestExample5.yaml +++ b/specification/inference/put_custom/examples/request/PutCustomRequestExample5.yaml @@ -1,5 +1,7 @@ summary: Custom text embedding task (Hugging Face) -description: Run `PUT _inference/text_embedding/custom-text-embedding-hf` to create an inference endpoint that performs a text embedding task by using the Qwen/Qwen3-Embedding-8B model. +description: + Run `PUT _inference/text_embedding/custom-text-embedding-hf` to create an inference endpoint that performs a text + embedding task by using the Qwen/Qwen3-Embedding-8B model. method_request: 'PUT _inference/text_embedding/custom-text-embedding-hf' # type: "request" value: |- diff --git a/specification/snapshot/create/SnapshotCreateRequest.ts b/specification/snapshot/create/SnapshotCreateRequest.ts index 5c7ca93bc9..4e449d47db 100644 --- a/specification/snapshot/create/SnapshotCreateRequest.ts +++ b/specification/snapshot/create/SnapshotCreateRequest.ts @@ -25,7 +25,7 @@ import { Duration } from '@_types/Time' * Create a snapshot. * Take a snapshot of a cluster or of data streams and indices. * @rest_spec_name snapshot.create - * @availability stack since=0.0.0 stability=stable + * @availability stack stability=stable * @availability serverless stability=stable visibility=private * @cluster_privileges create_snapshot * @doc_id snapshot-create-api diff --git a/specification/snapshot/create_repository/SnapshotCreateRepositoryRequest.ts b/specification/snapshot/create_repository/SnapshotCreateRepositoryRequest.ts index f1fd161b23..427cbf73a8 100644 --- a/specification/snapshot/create_repository/SnapshotCreateRepositoryRequest.ts +++ b/specification/snapshot/create_repository/SnapshotCreateRepositoryRequest.ts @@ -31,7 +31,7 @@ import { Repository } from '@snapshot/_types/SnapshotRepository' * Several options for this API can be specified using a query parameter or a request body parameter. * If both parameters are specified, only the query parameter is used. * @rest_spec_name snapshot.create_repository - * @availability stack since=0.0.0 stability=stable + * @availability stack stability=stable * @availability serverless stability=stable visibility=private * @cluster_privileges manage * @doc_id snapshot-repo-create diff --git a/specification/snapshot/delete_repository/SnapshotDeleteRepositoryRequest.ts b/specification/snapshot/delete_repository/SnapshotDeleteRepositoryRequest.ts index 022e7c6751..19ee50fc3d 100644 --- a/specification/snapshot/delete_repository/SnapshotDeleteRepositoryRequest.ts +++ b/specification/snapshot/delete_repository/SnapshotDeleteRepositoryRequest.ts @@ -26,7 +26,7 @@ import { Duration } from '@_types/Time' * When a repository is unregistered, Elasticsearch removes only the reference to the location where the repository is storing the snapshots. * The snapshots themselves are left untouched and in place. * @rest_spec_name snapshot.delete_repository - * @availability stack since=0.0.0 stability=stable + * @availability stack stability=stable * @availability serverless stability=stable visibility=private * @cluster_privileges manage * @doc_id snapshot-repo-delete diff --git a/specification/snapshot/get/SnapshotGetRequest.ts b/specification/snapshot/get/SnapshotGetRequest.ts index e509a5e666..a0e7bbfebf 100644 --- a/specification/snapshot/get/SnapshotGetRequest.ts +++ b/specification/snapshot/get/SnapshotGetRequest.ts @@ -32,7 +32,7 @@ import { SnapshotState } from '@snapshot/_types/SnapshotState' * It is guaranteed that any snapshot that exists at the beginning of the iteration and is not concurrently deleted will be seen during the iteration. * Snapshots concurrently created may be seen during an iteration. * @rest_spec_name snapshot.get - * @availability stack since=0.0.0 stability=stable + * @availability stack stability=stable * @availability serverless stability=stable visibility=private * @cluster_privileges monitor_snapshot * @doc_id snapshot-get diff --git a/specification/snapshot/get_repository/SnapshotGetRepositoryRequest.ts b/specification/snapshot/get_repository/SnapshotGetRepositoryRequest.ts index c17c9ef80f..7c01fe1fdb 100644 --- a/specification/snapshot/get_repository/SnapshotGetRepositoryRequest.ts +++ b/specification/snapshot/get_repository/SnapshotGetRepositoryRequest.ts @@ -24,7 +24,7 @@ import { Duration } from '@_types/Time' /** * Get snapshot repository information. * @rest_spec_name snapshot.get_repository - * @availability stack since=0.0.0 stability=stable + * @availability stack stability=stable * @availability serverless stability=stable visibility=private * @cluster_privileges monitor_snapshot * @doc_id snapshot-repo-get diff --git a/specification/snapshot/restore/SnapshotRestoreRequest.ts b/specification/snapshot/restore/SnapshotRestoreRequest.ts index 2e55e7f6a2..2b5d1ff29d 100644 --- a/specification/snapshot/restore/SnapshotRestoreRequest.ts +++ b/specification/snapshot/restore/SnapshotRestoreRequest.ts @@ -42,7 +42,7 @@ import { IndexSettings } from '@indices/_types/IndexSettings' * * If your snapshot contains data from App Search or Workplace Search, you must restore the Enterprise Search encryption key before you restore the snapshot. * @rest_spec_name snapshot.restore - * @availability stack since=0.0.0 stability=stable + * @availability stack stability=stable * @availability serverless stability=stable visibility=private * @cluster_privileges manage * @doc_id snapshot-restore-api diff --git a/specification/snapshot/verify_repository/SnapshotVerifyRepositoryRequest.ts b/specification/snapshot/verify_repository/SnapshotVerifyRepositoryRequest.ts index bd15e40869..b3ac543a90 100644 --- a/specification/snapshot/verify_repository/SnapshotVerifyRepositoryRequest.ts +++ b/specification/snapshot/verify_repository/SnapshotVerifyRepositoryRequest.ts @@ -25,7 +25,7 @@ import { Duration } from '@_types/Time' * Verify a snapshot repository. * Check for common misconfigurations in a snapshot repository. * @rest_spec_name snapshot.verify_repository - * @availability stack since=0.0.0 stability=stable + * @availability stack stability=stable * @availability serverless stability=stable visibility=private * @cluster_privileges manage * @doc_id snapshot-repo-verify