# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand. overlay: 1.0.0 info: title: API Evangelist conversational phrasing for Elasticsearch Request & Response Specification Inference API version: 1.0.0 extends: openapi/elk-stack-inference-api-openapi.yml actions: - target: $.info update: x-apievangelist-phrasing: method: generated generated: '2026-09-26' generator: build-phrasing.py label: Generated by API Evangelist operations: 48 - target: $.paths['/_inference/chat_completion/{inference_id}/_stream'].post update: x-apievangelist-phrasing: intent: Stream a chat completion from an inference endpoint effect: write questions: - Can I stream a multi-turn chat conversation through an Elasticsearch chat_completion endpoint? - Does the streaming chat API let me pass tools and a tool_choice to the model? - What controls do I get over temperature and max completion tokens when streaming a chat reply? instructions: - text: 'Stream a chat reply from chat_completion endpoint {inference_id} for these messages: {messages}.' slots: inference_id: path.inference_id messages: requestBody.messages - text: Send {messages} to chat endpoint {inference_id} with temperature {temperature} and stream the answer back. slots: messages: requestBody.messages inference_id: path.inference_id temperature: requestBody.temperature method: generated generated: '2026-09-26' - target: $.paths['/_inference/completion/{inference_id}'].post update: x-apievangelist-phrasing: intent: Get a text completion from an inference endpoint effect: write questions: - How do I get a non-streaming text completion from a completion inference endpoint? - Can I send a prompt to a completion endpoint and get the whole answer back in one response? instructions: - text: Complete the prompt {input} using completion endpoint {inference_id} and return the full response. slots: input: requestBody.input inference_id: path.inference_id - text: Run a one-shot completion on {inference_id} for {input}. slots: inference_id: path.inference_id input: requestBody.input method: generated generated: '2026-09-26' - target: $.paths['/_inference/{inference_id}'].get update: x-apievangelist-phrasing: intent: Look up an inference endpoint by ID effect: read questions: - What service and settings is a given inference endpoint configured with? - Can I look up one inference endpoint by its ID without knowing its task type? instructions: - text: Show me the configuration of inference endpoint {inference_id}. slots: inference_id: path.inference_id - text: Look up inference endpoint {inference_id} by ID alone. slots: inference_id: path.inference_id method: generated generated: '2026-09-26' - target: $.paths['/_inference/{inference_id}'].put update: x-apievangelist-phrasing: intent: Create an inference endpoint without a task type effect: write questions: - Can I create an inference endpoint just by ID, letting the service determine the task type? - What do I need to supply in service and service_settings to register a generic inference endpoint? instructions: - text: Create inference endpoint {inference_id} using service {service} with settings {service_settings}, no task type in the path. slots: inference_id: path.inference_id service: requestBody.service service_settings: requestBody.service_settings - text: Register a new inference endpoint named {inference_id} on the {service} service. slots: inference_id: path.inference_id service: requestBody.service method: generated generated: '2026-09-26' - target: $.paths['/_inference/{inference_id}'].post update: x-apievangelist-phrasing: intent: Run inference against an endpoint by ID effect: write questions: - Can I send input to an inference endpoint by ID and let it perform whatever task it was configured for? - Which input do I pass to run a generic inference call when I don't want to name the task type? instructions: - text: Run inference endpoint {inference_id} on the input {input}. slots: inference_id: path.inference_id input: requestBody.input - text: Call {inference_id} with {input} using its configured task, no task type given. slots: inference_id: path.inference_id input: requestBody.input method: generated generated: '2026-09-26' - target: $.paths['/_inference/{inference_id}'].delete update: x-apievangelist-phrasing: intent: Delete an inference endpoint by ID effect: destructive questions: - How do I remove an inference endpoint I no longer need? - Can I do a dry run to see which ingest pipelines still reference an inference endpoint before deleting it? instructions: - text: Delete inference endpoint {inference_id}. slots: inference_id: path.inference_id - text: Dry-run deleting inference endpoint {inference_id} to see what references it. slots: inference_id: path.inference_id method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{inference_id}'].get update: x-apievangelist-phrasing: intent: Look up an inference endpoint by task type and ID effect: read questions: - Can I fetch an inference endpoint scoped to a specific task type like text_embedding? - What configuration does my rerank endpoint have when I look it up under its task type? instructions: - text: Get the {task_type} inference endpoint {inference_id}. slots: task_type: path.task_type inference_id: path.inference_id - text: Show the settings for {inference_id} under task type {task_type}. slots: inference_id: path.inference_id task_type: path.task_type method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{inference_id}'].put update: x-apievangelist-phrasing: intent: Create an inference endpoint for a task type effect: write questions: - How do I create an inference endpoint for a specific task type such as sparse_embedding or completion? - Can I set chunking settings when creating an inference endpoint for a given task type? instructions: - text: Create a {task_type} inference endpoint {inference_id} on service {service}. slots: task_type: path.task_type inference_id: path.inference_id service: requestBody.service - text: Set up {inference_id} for task type {task_type} with service settings {service_settings}. slots: inference_id: path.inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{inference_id}'].post update: x-apievangelist-phrasing: intent: Run inference for a given task type effect: write questions: - Can I run inference while naming the task type explicitly in the request path? - What happens if I call an endpoint with a task type it wasn't created for? instructions: - text: Run {task_type} inference on endpoint {inference_id} with input {input}. slots: task_type: path.task_type inference_id: path.inference_id input: requestBody.input - text: Use the {task_type} endpoint {inference_id} to process {input}. slots: task_type: path.task_type inference_id: path.inference_id input: requestBody.input method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{inference_id}'].delete update: x-apievangelist-phrasing: intent: Delete an inference endpoint of a task type effect: destructive questions: - Can I delete an inference endpoint by naming both its task type and ID? - Is there a force option to delete a task-typed inference endpoint that pipelines still use? instructions: - text: Delete the {task_type} inference endpoint {inference_id}. slots: task_type: path.task_type inference_id: path.inference_id - text: Force-delete {inference_id} under task type {task_type} even if it is referenced. slots: inference_id: path.inference_id task_type: path.task_type method: generated generated: '2026-09-26' - target: $.paths['/_inference/_region_policy'].get update: x-apievangelist-phrasing: intent: Get the inference region policy effect: read questions: - Which geographic regions is inference currently restricted to on my cluster? - Is there a region policy set for inference right now? instructions: - text: Show me the current inference region policy. - text: Check which regions inference is allowed to run in. method: generated generated: '2026-09-26' - target: $.paths['/_inference/_region_policy'].put update: x-apievangelist-phrasing: intent: Set the inference region policy effect: write questions: - How do I restrict inference to certain cloud regions or geographic areas? - Can I change the allowed inference regions after a policy already exists? instructions: - text: Restrict inference to the regions in {region_policy}. slots: region_policy: requestBody.region_policy - text: Update the inference region policy to {region_policy}, forcing the change. slots: region_policy: requestBody.region_policy method: generated generated: '2026-09-26' - target: $.paths['/_inference/_region_policy'].delete update: x-apievangelist-phrasing: intent: Remove the inference region policy effect: destructive questions: - How can I lift the geographic restriction on where inference runs? - What removes the inference region policy entirely? instructions: - text: Delete the inference region policy. - text: Remove all regional restrictions on inference. method: generated generated: '2026-09-26' - target: $.paths['/_inference/embedding/{inference_id}'].post update: x-apievangelist-phrasing: intent: Generate dense embeddings for input effect: write questions: - Can I get dense vector embeddings from an embedding task endpoint? - Does the dense embedding call accept an input_type to mark the text as a query or a document? instructions: - text: Generate dense embeddings for {input} using embedding endpoint {inference_id}. slots: input: requestBody.input inference_id: path.inference_id - text: Embed {input} as dense vectors with {inference_id}, input type {input_type}. slots: input: requestBody.input inference_id: path.inference_id input_type: requestBody.input_type method: generated generated: '2026-09-26' - target: $.paths['/_inference'].get update: x-apievangelist-phrasing: intent: List all inference endpoints effect: read questions: - What inference endpoints exist on my cluster across every task type? - Can I see every inference endpoint, including the preconfigured ELSER and E5 ones? instructions: - text: List all my inference endpoints. - text: Show every inference endpoint on the cluster regardless of task type. method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/_all'].get update: x-apievangelist-phrasing: intent: List inference endpoints for one task type effect: read questions: - Which inference endpoints do I have for text_embedding? - Can I list only the rerank endpoints on my cluster? instructions: - text: List all {task_type} inference endpoints. slots: task_type: path.task_type - text: Show every endpoint configured for task type {task_type}. slots: task_type: path.task_type method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{ai21_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an AI21 inference endpoint effect: write questions: - How do I connect Elasticsearch to AI21 models for completion or chat? - What service settings does an ai21 inference endpoint need? instructions: - text: Create an AI21 {task_type} inference endpoint named {ai21_inference_id}. slots: task_type: path.task_type ai21_inference_id: path.ai21_inference_id - text: Set up AI21 endpoint {ai21_inference_id} for {task_type} with settings {service_settings}. slots: ai21_inference_id: path.ai21_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{alibabacloud_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an AlibabaCloud AI Search inference endpoint effect: write questions: - Can I use AlibabaCloud AI Search models for embeddings or reranking in Elasticsearch? - What's needed to register an alibabacloud-ai-search inference endpoint? instructions: - text: Create an AlibabaCloud AI Search {task_type} endpoint {alibabacloud_inference_id}. slots: task_type: path.task_type alibabacloud_inference_id: path.alibabacloud_inference_id - text: Register AlibabaCloud AI Search endpoint {alibabacloud_inference_id} for {task_type} using {service_settings}. slots: alibabacloud_inference_id: path.alibabacloud_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{amazonbedrock_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an Amazon Bedrock inference endpoint effect: write questions: - How do I hook Amazon Bedrock models into Elasticsearch inference? - Do I have to re-enter my Amazon Bedrock access and secret keys after creating the endpoint? instructions: - text: Create an Amazon Bedrock {task_type} endpoint called {amazonbedrock_inference_id}. slots: task_type: path.task_type amazonbedrock_inference_id: path.amazonbedrock_inference_id - text: Connect Amazon Bedrock as {amazonbedrock_inference_id} for {task_type} with settings {service_settings}. slots: amazonbedrock_inference_id: path.amazonbedrock_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{amazonsagemaker_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an Amazon SageMaker inference endpoint effect: write questions: - Can I point an Elasticsearch inference endpoint at a model hosted on Amazon SageMaker? - What settings does the amazon_sagemaker inference service expect? instructions: - text: Create an Amazon SageMaker {task_type} endpoint {amazonsagemaker_inference_id}. slots: task_type: path.task_type amazonsagemaker_inference_id: path.amazonsagemaker_inference_id - text: Wire my SageMaker model in as {amazonsagemaker_inference_id} for {task_type} using {service_settings}. slots: amazonsagemaker_inference_id: path.amazonsagemaker_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{anthropic_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an Anthropic inference endpoint effect: write questions: - How do I use Anthropic models for completion through Elasticsearch? - What does an anthropic inference endpoint need in its service settings? instructions: - text: Create an Anthropic {task_type} endpoint named {anthropic_inference_id}. slots: task_type: path.task_type anthropic_inference_id: path.anthropic_inference_id - text: Register Anthropic endpoint {anthropic_inference_id} for {task_type} with {service_settings}. slots: anthropic_inference_id: path.anthropic_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{azureaistudio_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an Azure AI Studio inference endpoint effect: write questions: - Can I use models deployed in Azure AI Studio for embeddings or completion in Elasticsearch? - What's required to create an azureaistudio inference endpoint? instructions: - text: Create an Azure AI Studio {task_type} endpoint {azureaistudio_inference_id}. slots: task_type: path.task_type azureaistudio_inference_id: path.azureaistudio_inference_id - text: Connect Azure AI Studio as {azureaistudio_inference_id} for {task_type} using {service_settings}. slots: azureaistudio_inference_id: path.azureaistudio_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{azureopenai_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an Azure OpenAI inference endpoint effect: write questions: - How do I use my Azure OpenAI deployment for embeddings or chat completion in Elasticsearch? - Which Azure OpenAI chat models can an azureopenai inference endpoint point at? instructions: - text: Create an Azure OpenAI {task_type} endpoint named {azureopenai_inference_id}. slots: task_type: path.task_type azureopenai_inference_id: path.azureopenai_inference_id - text: Connect my Azure OpenAI deployment as {azureopenai_inference_id} for {task_type} with {service_settings}. slots: azureopenai_inference_id: path.azureopenai_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{cohere_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Cohere inference endpoint effect: write questions: - Can I use Cohere embeddings or rerank models inside Elasticsearch? - What goes in service_settings for a cohere inference endpoint? instructions: - text: Create a Cohere {task_type} endpoint {cohere_inference_id}. slots: task_type: path.task_type cohere_inference_id: path.cohere_inference_id - text: Register Cohere as {cohere_inference_id} for {task_type} using {service_settings}. slots: cohere_inference_id: path.cohere_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{contextualai_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Contextual AI inference endpoint effect: write questions: - How do I use Contextual AI rerank models from Elasticsearch? - What settings does a Contextual AI rerank endpoint need? instructions: - text: Create a Contextual AI {task_type} endpoint named {contextualai_inference_id}. slots: task_type: path.task_type contextualai_inference_id: path.contextualai_inference_id - text: Set up Contextual AI reranking as {contextualai_inference_id} for {task_type} with {service_settings}. slots: contextualai_inference_id: path.contextualai_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{custom_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a custom inference endpoint effect: write questions: - Can I connect Elasticsearch to an inference service that has no dedicated integration? - How do I define my own request and response format for an external model with the custom service? instructions: - text: Create a custom {task_type} inference endpoint {custom_inference_id} for an unsupported external service. slots: task_type: path.task_type custom_inference_id: path.custom_inference_id - text: Define custom endpoint {custom_inference_id} for {task_type} with request and response settings {service_settings}. slots: custom_inference_id: path.custom_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{deepseek_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a DeepSeek inference endpoint effect: write questions: - Can I use DeepSeek models for completion through Elasticsearch inference? - What does a deepseek inference endpoint need to be configured? instructions: - text: Create a DeepSeek {task_type} endpoint {deepseek_inference_id}. slots: task_type: path.task_type deepseek_inference_id: path.deepseek_inference_id - text: Register DeepSeek as {deepseek_inference_id} for {task_type} with {service_settings}. slots: deepseek_inference_id: path.deepseek_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{elasticsearch_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an Elasticsearch-hosted model inference endpoint effect: write questions: - How do I deploy a model running inside my own cluster, like E5 or one uploaded with Eland, as an inference endpoint? - Do I need to create an endpoint for ELSER or E5 if the deployment already has preconfigured ones? instructions: - text: Create an elasticsearch-service {task_type} endpoint {elasticsearch_inference_id} for a model in my cluster. slots: task_type: path.task_type elasticsearch_inference_id: path.elasticsearch_inference_id - text: Deploy the in-cluster model as {elasticsearch_inference_id} for {task_type} with {service_settings}. slots: elasticsearch_inference_id: path.elasticsearch_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{elser_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an ELSER inference endpoint effect: write questions: - How do I set up an ELSER endpoint for sparse semantic search? - Is creating an ELSER endpoint through the elser service still the recommended way to deploy ELSER? instructions: - text: Create an ELSER {task_type} endpoint named {elser_inference_id}. slots: task_type: path.task_type elser_inference_id: path.elser_inference_id - text: Deploy ELSER as {elser_inference_id} for {task_type} with allocation settings {service_settings}. slots: elser_inference_id: path.elser_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{fireworksai_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Fireworks AI inference endpoint effect: write questions: - Can I call Fireworks AI models from an Elasticsearch inference endpoint? - What settings does the fireworksai service require? instructions: - text: Create a Fireworks AI {task_type} endpoint {fireworksai_inference_id}. slots: task_type: path.task_type fireworksai_inference_id: path.fireworksai_inference_id - text: Register Fireworks AI as {fireworksai_inference_id} for {task_type} with {service_settings}. slots: fireworksai_inference_id: path.fireworksai_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{googleaistudio_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Google AI Studio inference endpoint effect: write questions: - How do I use Google AI Studio models for completion or embeddings in Elasticsearch? - What does a googleaistudio endpoint need in service_settings? instructions: - text: Create a Google AI Studio {task_type} endpoint {googleaistudio_inference_id}. slots: task_type: path.task_type googleaistudio_inference_id: path.googleaistudio_inference_id - text: Connect Google AI Studio as {googleaistudio_inference_id} for {task_type} using {service_settings}. slots: googleaistudio_inference_id: path.googleaistudio_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{googlevertexai_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Google Vertex AI inference endpoint effect: write questions: - Can I use Google Vertex AI embeddings or rerankers from Elasticsearch? - Which settings are required for a googlevertexai inference endpoint? instructions: - text: Create a Google Vertex AI {task_type} endpoint {googlevertexai_inference_id}. slots: task_type: path.task_type googlevertexai_inference_id: path.googlevertexai_inference_id - text: Connect Vertex AI as {googlevertexai_inference_id} for {task_type} with {service_settings}. slots: googlevertexai_inference_id: path.googlevertexai_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{groq_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Groq inference endpoint effect: write questions: - How can I run Groq-hosted models through Elasticsearch inference? - What does the groq service need to create an endpoint? instructions: - text: Create a Groq {task_type} endpoint {groq_inference_id}. slots: task_type: path.task_type groq_inference_id: path.groq_inference_id - text: Register Groq as {groq_inference_id} for {task_type} with {service_settings}. slots: groq_inference_id: path.groq_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{huggingface_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Hugging Face inference endpoint effect: write questions: - Can I use a Hugging Face Inference Endpoint for text embeddings in Elasticsearch? - Does the hugging_face service support chat_completion as well as text_embedding? instructions: - text: Create a Hugging Face {task_type} endpoint {huggingface_inference_id}. slots: task_type: path.task_type huggingface_inference_id: path.huggingface_inference_id - text: Connect my Hugging Face endpoint URL as {huggingface_inference_id} for {task_type} with {service_settings}. slots: huggingface_inference_id: path.huggingface_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{jinaai_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a JinaAI inference endpoint effect: write questions: - Can I use JinaAI rerankers or embedding models in Elasticsearch? - What goes in the settings for a jinaai inference endpoint? instructions: - text: Create a JinaAI {task_type} endpoint {jinaai_inference_id}. slots: task_type: path.task_type jinaai_inference_id: path.jinaai_inference_id - text: Register JinaAI as {jinaai_inference_id} for {task_type} with {service_settings}. slots: jinaai_inference_id: path.jinaai_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{llama_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Llama inference endpoint effect: write questions: - How do I connect Llama models to Elasticsearch inference? - What service settings does a llama endpoint need? instructions: - text: Create a Llama {task_type} endpoint {llama_inference_id}. slots: task_type: path.task_type llama_inference_id: path.llama_inference_id - text: Register Llama as {llama_inference_id} for {task_type} with {service_settings}. slots: llama_inference_id: path.llama_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{mistral_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a Mistral inference endpoint effect: write questions: - Can I use Mistral embeddings or completion models in Elasticsearch? - What does the mistral service need in service_settings? instructions: - text: Create a Mistral {task_type} endpoint {mistral_inference_id}. slots: task_type: path.task_type mistral_inference_id: path.mistral_inference_id - text: Register Mistral as {mistral_inference_id} for {task_type} with {service_settings}. slots: mistral_inference_id: path.mistral_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{nvidia_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an Nvidia inference endpoint effect: write questions: - How do I use Nvidia-hosted models for inference in Elasticsearch? - What settings does an nvidia inference endpoint require? instructions: - text: Create an Nvidia {task_type} endpoint {nvidia_inference_id}. slots: task_type: path.task_type nvidia_inference_id: path.nvidia_inference_id - text: Register Nvidia as {nvidia_inference_id} for {task_type} with {service_settings}. slots: nvidia_inference_id: path.nvidia_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{openai_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an OpenAI inference endpoint effect: write questions: - Can I use OpenAI or an OpenAI-compatible API for embeddings and chat in Elasticsearch? - What goes in service_settings for an openai inference endpoint? instructions: - text: Create an OpenAI {task_type} endpoint {openai_inference_id}. slots: task_type: path.task_type openai_inference_id: path.openai_inference_id - text: Register an OpenAI-compatible API as {openai_inference_id} for {task_type} with {service_settings}. slots: openai_inference_id: path.openai_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{openshiftai_inference_id}'].put update: x-apievangelist-phrasing: intent: Create an OpenShift AI inference endpoint effect: write questions: - How do I connect models served on OpenShift AI to Elasticsearch inference? - What does the openshift_ai service need to be configured? instructions: - text: Create an OpenShift AI {task_type} endpoint {openshiftai_inference_id}. slots: task_type: path.task_type openshiftai_inference_id: path.openshiftai_inference_id - text: Register OpenShift AI as {openshiftai_inference_id} for {task_type} with {service_settings}. slots: openshiftai_inference_id: path.openshiftai_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{voyageai_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a VoyageAI inference endpoint effect: write questions: - Can I use VoyageAI embedding or rerank models in Elasticsearch? - Why shouldn't I create several VoyageAI endpoints for the same model? instructions: - text: Create a VoyageAI {task_type} endpoint {voyageai_inference_id}. slots: task_type: path.task_type voyageai_inference_id: path.voyageai_inference_id - text: Register VoyageAI as {voyageai_inference_id} for {task_type} with {service_settings}. slots: voyageai_inference_id: path.voyageai_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{watsonx_inference_id}'].put update: x-apievangelist-phrasing: intent: Create a watsonx inference endpoint effect: write questions: - How do I use IBM watsonx.ai models for inference in Elasticsearch? - Do I need an IBM Cloud Databases for Elasticsearch deployment to use the watsonxai service? instructions: - text: Create a watsonx {task_type} endpoint {watsonx_inference_id}. slots: task_type: path.task_type watsonx_inference_id: path.watsonx_inference_id - text: Register watsonx.ai as {watsonx_inference_id} for {task_type} with {service_settings}. slots: watsonx_inference_id: path.watsonx_inference_id task_type: path.task_type service_settings: requestBody.service_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/rerank/{inference_id}'].post update: x-apievangelist-phrasing: intent: Rerank documents against a query effect: read questions: - Can I rerank a list of candidate documents by relevance to a query? - Is there a way to only get back the top N results after reranking? - Does the rerank call return the document text or just the scores? instructions: - text: Rerank {input} against the query {query} with rerank endpoint {inference_id}. slots: input: requestBody.input query: requestBody.query inference_id: path.inference_id - text: Rerank these documents for {query} using {inference_id} and keep only the top {top_n}. slots: query: requestBody.query inference_id: path.inference_id top_n: requestBody.top_n method: generated generated: '2026-09-26' - target: $.paths['/_inference/sparse_embedding/{inference_id}'].post update: x-apievangelist-phrasing: intent: Generate sparse embeddings for text effect: write questions: - How do I get sparse token-weight vectors for text, like ELSER produces? - Can I generate sparse embeddings for several strings in one request? instructions: - text: Generate sparse embeddings for {input} with endpoint {inference_id}. slots: input: requestBody.input inference_id: path.inference_id - text: Expand {input} into sparse token weights using {inference_id}. slots: input: requestBody.input inference_id: path.inference_id method: generated generated: '2026-09-26' - target: $.paths['/_inference/completion/{inference_id}/_stream'].post update: x-apievangelist-phrasing: intent: Stream a text completion incrementally effect: write questions: - Can I stream a completion answer token by token instead of waiting for all of it? - Does streaming completion work with endpoints of the completion task type only? instructions: - text: Stream the completion for {input} from endpoint {inference_id} as it is generated. slots: input: requestBody.input inference_id: path.inference_id - text: Incrementally stream a completion of {input} using {inference_id}. slots: input: requestBody.input inference_id: path.inference_id method: generated generated: '2026-09-26' - target: $.paths['/_inference/text_embedding/{inference_id}'].post update: x-apievangelist-phrasing: intent: Generate text embeddings for input effect: write questions: - How do I turn text into embedding vectors with a text_embedding endpoint? - Can I tell the text embedding endpoint whether my input is a search query or an ingested document? instructions: - text: Create text embeddings for {input} with text_embedding endpoint {inference_id}. slots: input: requestBody.input inference_id: path.inference_id - text: Vectorize {input} using {inference_id} with input type {input_type}. slots: input: requestBody.input inference_id: path.inference_id input_type: requestBody.input_type method: generated generated: '2026-09-26' - target: $.paths['/_inference/{inference_id}/_update'].put update: x-apievangelist-phrasing: intent: Update an inference endpoint's settings effect: write questions: - Can I rotate the API key stored in an existing inference endpoint? - How can I change task settings or the number of allocations on an endpoint without recreating it? instructions: - text: Update inference endpoint {inference_id} with new service settings {service_settings}. slots: inference_id: path.inference_id service_settings: requestBody.service_settings - text: Change the task settings of {inference_id} to {task_settings}. slots: inference_id: path.inference_id task_settings: requestBody.task_settings method: generated generated: '2026-09-26' - target: $.paths['/_inference/{task_type}/{inference_id}/_update'].put update: x-apievangelist-phrasing: intent: Update a task-typed inference endpoint effect: write questions: - Can I update an inference endpoint while naming its task type in the path? - What parts of a text_embedding endpoint can I modify after creation? instructions: - text: Update the {task_type} endpoint {inference_id} with service settings {service_settings}. slots: task_type: path.task_type inference_id: path.inference_id service_settings: requestBody.service_settings - text: Modify task settings on {task_type} endpoint {inference_id} to {task_settings}. slots: task_type: path.task_type inference_id: path.inference_id task_settings: requestBody.task_settings method: generated generated: '2026-09-26'