diff --git a/output/openapi/elasticsearch-openapi.json b/output/openapi/elasticsearch-openapi.json index 043afe0ed7..1c2e7ecf62 100644 --- a/output/openapi/elasticsearch-openapi.json +++ b/output/openapi/elasticsearch-openapi.json @@ -9551,6 +9551,9 @@ }, "slice": { "$ref": "#/components/schemas/_types.SlicedScroll" + }, + "sort": { + "$ref": "#/components/schemas/_types.Sort" } } }, @@ -126895,7 +126898,7 @@ "examples": { "InferencePutExample1": { "description": "An example body for a `PUT _inference/rerank/my-rerank-model` request.", - "value": "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n \"chunking_settings\": {\n \"strategy\": \"recursive\",\n \"max_chunk_size\": 200,\n \"separator_group\": \"markdown\"\n}" + "value": "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n },\n \"chunking_settings\": {\n \"strategy\": \"recursive\",\n \"max_chunk_size\": 200,\n \"separator_group\": \"markdown\"\n }\n}" } } } diff --git a/output/openapi/elasticsearch-serverless-openapi.json b/output/openapi/elasticsearch-serverless-openapi.json index d857f609cd..767d281829 100644 --- a/output/openapi/elasticsearch-serverless-openapi.json +++ b/output/openapi/elasticsearch-serverless-openapi.json @@ -5225,6 +5225,9 @@ }, "slice": { "$ref": "#/components/schemas/_types.SlicedScroll" + }, + "sort": { + "$ref": "#/components/schemas/_types.Sort" } } }, @@ -76009,7 +76012,7 @@ "examples": { "InferencePutExample1": { "description": "An example body for a `PUT _inference/rerank/my-rerank-model` request.", - "value": "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n \"chunking_settings\": {\n \"strategy\": \"recursive\",\n \"max_chunk_size\": 200,\n \"separator_group\": \"markdown\"\n}" + "value": "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n },\n \"chunking_settings\": {\n \"strategy\": \"recursive\",\n \"max_chunk_size\": 200,\n \"separator_group\": \"markdown\"\n }\n}" } } } diff --git a/output/schema/schema.json b/output/schema/schema.json index 43630b917f..2e6093fd73 100644 --- a/output/schema/schema.json +++ b/output/schema/schema.json @@ -26714,6 +26714,18 @@ "namespace": "_types" } } + }, + { + "description": "A sort object that specifies the order of deleted documents.", + "name": "sort", + "required": false, + "type": { + "kind": "instance_of", + "type": { + "name": "Sort", + "namespace": "_types" + } + } } ] }, @@ -27245,7 +27257,7 @@ } } ], - "specLocation": "_global/delete_by_query/DeleteByQueryRequest.ts#L36-L314" + "specLocation": "_global/delete_by_query/DeleteByQueryRequest.ts#L37-L319" }, { "kind": "response", @@ -173204,7 +173216,7 @@ ], "description": "An example body for a `PUT _inference/rerank/my-rerank-model` request.", "method_request": "PUT _inference/rerank/my-rerank-model", - "value": "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n \"chunking_settings\": {\n \"strategy\": \"recursive\",\n \"max_chunk_size\": 200,\n \"separator_group\": \"markdown\"\n}" + "value": "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n },\n \"chunking_settings\": {\n \"strategy\": \"recursive\",\n \"max_chunk_size\": 200,\n \"separator_group\": \"markdown\"\n }\n}" } }, "inherits": { @@ -173804,140 +173816,30 @@ "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", @@ -174707,28 +174609,6 @@ "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", @@ -174870,140 +174750,30 @@ "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)", @@ -247402,23 +247172,23 @@ "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" }, { @@ -247434,23 +247204,23 @@ "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" }, { @@ -263437,23 +263207,23 @@ "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 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)", + "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)", "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 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});", + "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});", "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 \"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)", + "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)", "language": "Ruby" }, { - "code": "$resp = $client->transform()->updateTransform([\n \"transform_id\" => \"simple-kibana-ecomm-pivot\",\n \"body\" => [\n \"source\" => [\n \"index\" => \"kibana_sample_data_ecommerce\",\n \"query\" => [\n \"term\" => [\n \"geoip.continent_name\" => [\n \"value\" => \"Asia\",\n ],\n ],\n ],\n ],\n \"description\" => \"Maximum priced ecommerce data by customer_id in Asia\",\n \"dest\" => [\n \"index\" => \"kibana_sample_data_ecommerce_transform_v2\",\n \"pipeline\" => \"add_timestamp_pipeline\",\n ],\n \"frequency\" => \"15m\",\n \"sync\" => [\n \"time\" => [\n \"field\" => \"order_date\",\n \"delay\" => \"120s\",\n ],\n ],\n ],\n]);", + "code": "$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]);", "language": "PHP" }, { - "code": "curl -X POST -H \"Authorization: ApiKey $ELASTIC_API_KEY\" -H \"Content-Type: application/json\" -d '{\"source\":{\"index\":\"kibana_sample_data_ecommerce\",\"query\":{\"term\":{\"geoip.continent_name\":{\"value\":\"Asia\"}}}},\"description\":\"Maximum priced ecommerce data by customer_id in Asia\",\"dest\":{\"index\":\"kibana_sample_data_ecommerce_transform_v2\",\"pipeline\":\"add_timestamp_pipeline\"},\"frequency\":\"15m\",\"sync\":{\"time\":{\"field\":\"order_date\",\"delay\":\"120s\"}}}' \"$ELASTICSEARCH_URL/_transform/simple-kibana-ecomm-pivot/_update\"", + "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\"", "language": "curl" }, { diff --git a/output/typescript/types.ts b/output/typescript/types.ts index d245aa2bef..392fe691b3 100644 --- a/output/typescript/types.ts +++ b/output/typescript/types.ts @@ -224,6 +224,7 @@ export interface DeleteByQueryRequest extends RequestBase { max_docs?: long query?: QueryDslQueryContainer slice?: SlicedScroll + sort?: Sort } } diff --git a/specification/inference/put/examples/request/InferencePutExample1.yaml b/specification/inference/put/examples/request/InferencePutExample1.yaml index 68d00bdca9..4b33705804 100644 --- a/specification/inference/put/examples/request/InferencePutExample1.yaml +++ b/specification/inference/put/examples/request/InferencePutExample1.yaml @@ -6,9 +6,10 @@ value: |- "service_settings": { "model_id": "rerank-english-v3.0", "api_key": "{{COHERE_API_KEY}}" - } + }, "chunking_settings": { "strategy": "recursive", "max_chunk_size": 200, "separator_group": "markdown" + } }