From d24a8fdc22d265773f4cd887dd9099738b0e1f68 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 14:21:02 +0100 Subject: [PATCH] Modifies the description of PUT inference OpenAI. --- 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 6c45fd480d..053717213c 100644 --- a/output/openapi/elasticsearch-openapi.json +++ b/output/openapi/elasticsearch-openapi.json @@ -17822,7 +17822,7 @@ "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 ef165f4ddf..dd119ec2d3 100644 --- a/output/openapi/elasticsearch-serverless-openapi.json +++ b/output/openapi/elasticsearch-serverless-openapi.json @@ -9644,7 +9644,7 @@ "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 aa66f92b23..4840521fd9 100644 --- a/output/schema/schema-serverless.json +++ b/output/schema/schema-serverless.json @@ -4603,7 +4603,7 @@ "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/current/infer-service-openai.html", "name": "inference.put_openai", @@ -27161,7 +27161,7 @@ } ] }, - "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 8671596959..b8dda61890 100644 --- a/output/schema/schema.json +++ b/output/schema/schema.json @@ -9343,7 +9343,7 @@ "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/current/infer-service-openai.html", "name": "inference.put_openai", @@ -150670,7 +150670,7 @@ } ] }, - "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.