From e6f86dfd9a5c881e9727c94b83fe938b8e62b664 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Istv=C3=A1n=20Zolt=C3=A1n=20Szab=C3=B3?= Date: Mon, 24 Mar 2025 16:48:02 +0100 Subject: [PATCH] Modifies the description of PUT inference OpenAI. (#4066) (cherry picked from commit ae9958471166c553322a0cf92445729027a60438) --- output/openapi/elasticsearch-openapi.json | 2 +- output/openapi/elasticsearch-serverless-openapi.json | 2 +- output/schema/schema-serverless.json | 4 ++-- output/schema/schema.json | 4 ++-- specification/inference/put_openai/PutOpenAiRequest.ts | 2 +- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/output/openapi/elasticsearch-openapi.json b/output/openapi/elasticsearch-openapi.json index d7c45ea928..6fb67a452b 100644 --- a/output/openapi/elasticsearch-openapi.json +++ b/output/openapi/elasticsearch-openapi.json @@ -17901,7 +17901,7 @@ "inference" ], "summary": "Create an OpenAI inference endpoint", - "description": "Create an inference endpoint to perform an inference task with the `openai` service.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", + "description": "Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", "operationId": "inference-put-openai", "parameters": [ { diff --git a/output/openapi/elasticsearch-serverless-openapi.json b/output/openapi/elasticsearch-serverless-openapi.json index 729211bdd2..1b376414f4 100644 --- a/output/openapi/elasticsearch-serverless-openapi.json +++ b/output/openapi/elasticsearch-serverless-openapi.json @@ -9867,7 +9867,7 @@ "inference" ], "summary": "Create an OpenAI inference endpoint", - "description": "Create an inference endpoint to perform an inference task with the `openai` service.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", + "description": "Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", "operationId": "inference-put-openai", "parameters": [ { diff --git a/output/schema/schema-serverless.json b/output/schema/schema-serverless.json index ebb73589b9..c60d36dee1 100644 --- a/output/schema/schema-serverless.json +++ b/output/schema/schema-serverless.json @@ -4815,7 +4815,7 @@ "visibility": "public" } }, - "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", + "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", "docId": "inference-api-put-openai", "docUrl": "https://www.elastic.co/guide/en/elasticsearch/reference/{branch}/infer-service-openai.html", "name": "inference.put_openai", @@ -27627,7 +27627,7 @@ } ] }, - "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", + "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", "examples": { "PutOpenAiRequestExample1": { "description": "Run `PUT _inference/text_embedding/openai-embeddings` to create an inference endpoint that performs a `text_embedding` task. The embeddings created by requests to this endpoint will have 128 dimensions.", diff --git a/output/schema/schema.json b/output/schema/schema.json index d51718d6f9..ac2631c140 100644 --- a/output/schema/schema.json +++ b/output/schema/schema.json @@ -9436,7 +9436,7 @@ "visibility": "public" } }, - "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", + "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", "docId": "inference-api-put-openai", "docUrl": "https://www.elastic.co/guide/en/elasticsearch/reference/{branch}/infer-service-openai.html", "name": "inference.put_openai", @@ -150764,7 +150764,7 @@ } ] }, - "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", + "description": "Create an OpenAI inference endpoint.\n\nCreate an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.\n\nWhen you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.\nAfter creating the endpoint, wait for the model deployment to complete before using it.\nTo verify the deployment status, use the get trained model statistics API.\nLook for `\"state\": \"fully_allocated\"` in the response and ensure that the `\"allocation_count\"` matches the `\"target_allocation_count\"`.\nAvoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.", "examples": { "PutOpenAiRequestExample1": { "description": "Run `PUT _inference/text_embedding/openai-embeddings` to create an inference endpoint that performs a `text_embedding` task. The embeddings created by requests to this endpoint will have 128 dimensions.", diff --git a/specification/inference/put_openai/PutOpenAiRequest.ts b/specification/inference/put_openai/PutOpenAiRequest.ts index 886905600e..0d1c03b005 100644 --- a/specification/inference/put_openai/PutOpenAiRequest.ts +++ b/specification/inference/put_openai/PutOpenAiRequest.ts @@ -28,7 +28,7 @@ import { integer } from '@_types/Numeric' /** * Create an OpenAI inference endpoint. * - * Create an inference endpoint to perform an inference task with the `openai` service. + * Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs. * * When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running. * After creating the endpoint, wait for the model deployment to complete before using it.