you specified and override the task settings specified when initializing the service.
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- input
examples:
CompletionRequestExample1:
summary: Completion task
description: Run `POST _inference/completion/openai_completions` to perform a completion on the example question.
value: "{\n \"input\": \"What is Elastic?\"\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.CompletionInferenceResult'
examples:
CompletionResponseExample1:
summary: Completion task
description: 'A successful response from `POST _inference/completion/openai_completions`.
'
value: "{\n \"completion\": [\n {\n \"result\": \"Elastic is a company that provides a range of software solutions for search, logging, security, and analytics. Their flagship product is Elasticsearch, an open-source, distributed search engine that allows users to search, analyze, and visualize large volumes of data in real-time. Elastic also offers products such as Kibana, a data visualization tool, and Logstash, a log management and pipeline tool, as well as various other tools and solutions for data analysis and management.\"\n }\n ]\n}"
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n POST\n /_inference/completion/{inference_id}\n
\n "
x-api: completion.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "POST _inference/completion/openai_completions\n{\n \"input\": \"What is Elastic?\"\n}"
- lang: Python
source: "resp = client.inference.completion(\n inference_id=\"openai_completions\",\n input=\"What is Elastic?\",\n)"
- lang: JavaScript
source: "const response = await client.inference.completion({\n inference_id: \"openai_completions\",\n input: \"What is Elastic?\",\n});"
- lang: Ruby
source: "response = client.inference.completion(\n inference_id: \"openai_completions\",\n body: {\n \"input\": \"What is Elastic?\"\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->completion([\n \"inference_id\" => \"openai_completions\",\n \"body\" => [\n \"input\" => \"What is Elastic?\",\n ],\n]);"
- lang: curl
source: 'curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"input":"What is Elastic?"}'' "$ELASTICSEARCH_URL/_inference/completion/openai_completions"'
- lang: Java
source: "client.inference().completion(c -> c\n .inferenceId(\"openai_completions\")\n .input(\"What is Elastic?\")\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{inference_id}:
get:
tags:
- Inference
summary: Get an inference endpoint
description: This API requires the `monitor_inference` cluster privilege (the built-in `inference_admin` and `inference_user` roles grant this privilege).
operationId: inference-get-1
parameters:
- in: path
name: inference_id
description: The inference Id
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
responses:
'200':
description: ''
content:
application/json:
schema:
type: object
properties:
endpoints:
type: array
items:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfo'
required:
- endpoints
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n GET\n /_inference/{inference_id}\n
\n "
x-api: get.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: 'GET _inference/sparse_embedding/my-elser-model
'
- lang: Python
source: "resp = client.inference.get(\n task_type=\"sparse_embedding\",\n inference_id=\"my-elser-model\",\n)"
- lang: JavaScript
source: "const response = await client.inference.get({\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n});"
- lang: Ruby
source: "response = client.inference.get(\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\"\n)"
- lang: PHP
source: "$resp = $client->inference()->get([\n \"task_type\" => \"sparse_embedding\",\n \"inference_id\" => \"my-elser-model\",\n]);"
- lang: curl
source: 'curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"'
- lang: Java
source: "client.inference().get(g -> g\n .inferenceId(\"my-elser-model\")\n .taskType(TaskType.SparseEmbedding)\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
put:
tags:
- Inference
summary: Create an inference endpoint
description: 'IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.
However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
The following integrations are available through the inference API. You can find the available task types next to the integration name:
* AI21 (`chat_completion`, `completion`)
* AlibabaCloud AI Search (`completion`, `rerank`, `sparse_embedding`, `text_embedding`)
* Amazon Bedrock (`chat_completion`, `completion`, `text_embedding`)
* Amazon SageMaker (`chat_completion`, `completion`, `rerank`, `sparse_embedding`, `text_embedding`)
* Anthropic (`completion`)
* Azure AI Studio (`completion`, `rerank`, `text_embedding`)
* Azure OpenAI (`chat_completion`, `completion`, `text_embedding`)
* Cohere (`completion`, `rerank`, `text_embedding`)
* DeepSeek (`chat_completion`, `completion`)
* Elasticsearch (`rerank`, `sparse_embedding`, `text_embedding` - this service is for built-in models and models uploaded through Eland)
* ELSER (`sparse_embedding`)
* Google AI Studio (`completion`, `text_embedding`)
* Google Vertex AI (`chat_completion`, `completion`, `rerank`, `text_embedding`)
* Groq (`chat_completion`)
* Hugging Face (`chat_completion`, `completion`, `rerank`, `text_embedding`)
* JinaAI (`embedding`, `rerank`, `text_embedding`)
* Llama (`chat_completion`, `completion`, `text_embedding`)
* Mistral (`chat_completion`, `completion`, `text_embedding`)
* Nvidia (`chat_completion`, `completion`, `text_embedding`, `rerank`)
* OpenAI (`chat_completion`, `completion`, `text_embedding`)
* OpenShift AI (`chat_completion`, `completion`, `rerank`, `text_embedding`)
* VoyageAI (`rerank`, `text_embedding`)
* Watsonx (`chat_completion`, `completion`, `rerank`, `text_embedding`)'
operationId: inference-put
parameters:
- in: path
name: inference_id
description: The inference Id
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpoint'
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}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfo'
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n PUT\n /_inference/{inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/rerank/my-rerank-model\n{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"rerank\",\n inference_id=\"my-rerank-model\",\n inference_config={\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"rerank\",\n inference_id: \"my-rerank-model\",\n inference_config: {\n service: \"cohere\",\n service_settings: {\n model_id: \"rerank-english-v3.0\",\n api_key: \"{{COHERE_API_KEY}}\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"rerank\",\n inference_id: \"my-rerank-model\",\n body: {\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"rerank\",\n \"inference_id\" => \"my-rerank-model\",\n \"body\" => [\n \"service\" => \"cohere\",\n \"service_settings\" => [\n \"model_id\" => \"rerank-english-v3.0\",\n \"api_key\" => \"{{COHERE_API_KEY}}\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"cohere","service_settings":{"model_id":"rerank-english-v3.0","api_key":"{{COHERE_API_KEY}}"}}'' "$ELASTICSEARCH_URL/_inference/rerank/my-rerank-model"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"my-rerank-model\")\n .taskType(TaskType.Rerank)\n .inferenceConfig(i -> i\n .service(\"cohere\")\n .serviceSettings(JsonData.fromJson(\"{\\\"model_id\\\":\\\"rerank-english-v3.0\\\",\\\"api_key\\\":\\\"{{COHERE_API_KEY}}\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
post:
tags:
- Inference
summary: Perform inference on the service
description: 'This API enables you to use machine learning models to perform specific tasks on data that you provide as an input.
It returns a response with the results of the tasks.
The inference endpoint you use can perform one specific task that has been defined when the endpoint was created with the create inference API.
For details about using this API with a service, such as Amazon Bedrock, Anthropic, or HuggingFace, refer to the service-specific documentation.
> info
> The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.'
operationId: inference-inference
parameters:
- in: path
name: inference_id
description: The unique identifier for the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: The amount of time to wait for the inference request to complete.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
query:
description: 'The query input, which is required only for the `rerank` task.
It is not required for other tasks.'
type: string
input:
description: 'The text on which you want to perform the inference task.
It can be a single string or an array.
> info
> Inference endpoints for the `completion` task type currently only support a single string as input.'
oneOf:
- type: string
- type: array
items:
type: string
input_type:
description: 'Specifies the input data type for the embedding model. The `input_type` parameter only applies to Inference Endpoints with the `embedding` or `text_embedding` task type. Possible values include:
* `SEARCH`
* `INGEST`
* `CLASSIFICATION`
* `CLUSTERING`
Not all services support all values. Unsupported values will trigger a validation exception.
Accepted values depend on the configured inference service, refer to the relevant service-specific documentation for more info.
> info
> The `input_type` parameter specified on the root level of the request body will take precedence over the `input_type` parameter specified in `task_settings`.'
type: string
task_settings:
description: 'Task settings for the individual inference request.
These settings are specific to the task type you specified and override the task settings specified when initializing the service.'
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- input
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceResult'
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n POST\n /_inference/{inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `monitor_inference`
'
x-api: inference.inference
x-category: ai/ml
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
delete:
tags:
- Inference
summary: Delete an inference endpoint
description: This API requires the manage_inference cluster privilege (the built-in `inference_admin` role grants this privilege).
operationId: inference-delete
parameters:
- in: path
name: inference_id
description: The inference identifier.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: dry_run
description: When true, checks the semantic_text fields and inference processors that reference the endpoint and returns them in a list, but does not delete the endpoint.
deprecated: false
schema:
type: boolean
style: form
- in: query
name: force
description: When true, the inference endpoint is forcefully deleted even if it is still being used by ingest processors or semantic text fields.
deprecated: false
schema:
type: boolean
style: form
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.DeleteInferenceEndpointResult'
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n DELETE\n /_inference/{inference_id}\n
\n "
x-api: delete.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: 'DELETE /_inference/sparse_embedding/my-elser-model
'
- lang: Python
source: "resp = client.inference.delete(\n task_type=\"sparse_embedding\",\n inference_id=\"my-elser-model\",\n)"
- lang: JavaScript
source: "const response = await client.inference.delete({\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n});"
- lang: Ruby
source: "response = client.inference.delete(\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\"\n)"
- lang: PHP
source: "$resp = $client->inference()->delete([\n \"task_type\" => \"sparse_embedding\",\n \"inference_id\" => \"my-elser-model\",\n]);"
- lang: curl
source: 'curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"'
- lang: Java
source: "client.inference().delete(d -> d\n .inferenceId(\"my-elser-model\")\n .taskType(TaskType.SparseEmbedding)\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{inference_id}:
get:
tags:
- Inference
summary: Get an inference endpoint
description: This API requires the `monitor_inference` cluster privilege (the built-in `inference_admin` and `inference_user` roles grant this privilege).
operationId: inference-get-2
parameters:
- in: path
name: task_type
description: The task type
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.TaskType'
style: simple
- in: path
name: inference_id
description: The inference Id
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
responses:
'200':
description: ''
content:
application/json:
schema:
type: object
properties:
endpoints:
type: array
items:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfo'
required:
- endpoints
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n GET\n /_inference/{task_type}/{inference_id}\n
\n "
x-api: get.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: 'GET _inference/sparse_embedding/my-elser-model
'
- lang: Python
source: "resp = client.inference.get(\n task_type=\"sparse_embedding\",\n inference_id=\"my-elser-model\",\n)"
- lang: JavaScript
source: "const response = await client.inference.get({\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n});"
- lang: Ruby
source: "response = client.inference.get(\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\"\n)"
- lang: PHP
source: "$resp = $client->inference()->get([\n \"task_type\" => \"sparse_embedding\",\n \"inference_id\" => \"my-elser-model\",\n]);"
- lang: curl
source: 'curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"'
- lang: Java
source: "client.inference().get(g -> g\n .inferenceId(\"my-elser-model\")\n .taskType(TaskType.SparseEmbedding)\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
put:
tags:
- Inference
summary: Create an inference endpoint
description: 'IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.
However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
The following integrations are available through the inference API. You can find the available task types next to the integration name:
* AI21 (`chat_completion`, `completion`)
* AlibabaCloud AI Search (`completion`, `rerank`, `sparse_embedding`, `text_embedding`)
* Amazon Bedrock (`chat_completion`, `completion`, `text_embedding`)
* Amazon SageMaker (`chat_completion`, `completion`, `rerank`, `sparse_embedding`, `text_embedding`)
* Anthropic (`completion`)
* Azure AI Studio (`completion`, `rerank`, `text_embedding`)
* Azure OpenAI (`chat_completion`, `completion`, `text_embedding`)
* Cohere (`completion`, `rerank`, `text_embedding`)
* DeepSeek (`chat_completion`, `completion`)
* Elasticsearch (`rerank`, `sparse_embedding`, `text_embedding` - this service is for built-in models and models uploaded through Eland)
* ELSER (`sparse_embedding`)
* Google AI Studio (`completion`, `text_embedding`)
* Google Vertex AI (`chat_completion`, `completion`, `rerank`, `text_embedding`)
* Groq (`chat_completion`)
* Hugging Face (`chat_completion`, `completion`, `rerank`, `text_embedding`)
* JinaAI (`embedding`, `rerank`, `text_embedding`)
* Llama (`chat_completion`, `completion`, `text_embedding`)
* Mistral (`chat_completion`, `completion`, `text_embedding`)
* Nvidia (`chat_completion`, `completion`, `text_embedding`, `rerank`)
* OpenAI (`chat_completion`, `completion`, `text_embedding`)
* OpenShift AI (`chat_completion`, `completion`, `rerank`, `text_embedding`)
* VoyageAI (`rerank`, `text_embedding`)
* Watsonx (`chat_completion`, `completion`, `rerank`, `text_embedding`)'
operationId: inference-put-1
parameters:
- in: path
name: task_type
description: The task type. Refer to the integration list in the API description for the available task types.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.TaskType'
style: simple
- in: path
name: inference_id
description: The inference Id
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpoint'
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}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfo'
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/rerank/my-rerank-model\n{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"rerank\",\n inference_id=\"my-rerank-model\",\n inference_config={\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"rerank\",\n inference_id: \"my-rerank-model\",\n inference_config: {\n service: \"cohere\",\n service_settings: {\n model_id: \"rerank-english-v3.0\",\n api_key: \"{{COHERE_API_KEY}}\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"rerank\",\n inference_id: \"my-rerank-model\",\n body: {\n \"service\": \"cohere\",\n \"service_settings\": {\n \"model_id\": \"rerank-english-v3.0\",\n \"api_key\": \"{{COHERE_API_KEY}}\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"rerank\",\n \"inference_id\" => \"my-rerank-model\",\n \"body\" => [\n \"service\" => \"cohere\",\n \"service_settings\" => [\n \"model_id\" => \"rerank-english-v3.0\",\n \"api_key\" => \"{{COHERE_API_KEY}}\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"cohere","service_settings":{"model_id":"rerank-english-v3.0","api_key":"{{COHERE_API_KEY}}"}}'' "$ELASTICSEARCH_URL/_inference/rerank/my-rerank-model"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"my-rerank-model\")\n .taskType(TaskType.Rerank)\n .inferenceConfig(i -> i\n .service(\"cohere\")\n .serviceSettings(JsonData.fromJson(\"{\\\"model_id\\\":\\\"rerank-english-v3.0\\\",\\\"api_key\\\":\\\"{{COHERE_API_KEY}}\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
post:
tags:
- Inference
summary: Perform inference on the service
description: 'This API enables you to use machine learning models to perform specific tasks on data that you provide as an input.
It returns a response with the results of the tasks.
The inference endpoint you use can perform one specific task that has been defined when the endpoint was created with the create inference API.
For details about using this API with a service, such as Amazon Bedrock, Anthropic, or HuggingFace, refer to the service-specific documentation.
> info
> The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.'
operationId: inference-inference-1
parameters:
- in: path
name: task_type
description: The type of inference task that the model performs.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.TaskType'
style: simple
- in: path
name: inference_id
description: The unique identifier for the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: The amount of time to wait for the inference request to complete.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
query:
description: 'The query input, which is required only for the `rerank` task.
It is not required for other tasks.'
type: string
input:
description: 'The text on which you want to perform the inference task.
It can be a single string or an array.
> info
> Inference endpoints for the `completion` task type currently only support a single string as input.'
oneOf:
- type: string
- type: array
items:
type: string
input_type:
description: 'Specifies the input data type for the embedding model. The `input_type` parameter only applies to Inference Endpoints with the `embedding` or `text_embedding` task type. Possible values include:
* `SEARCH`
* `INGEST`
* `CLASSIFICATION`
* `CLUSTERING`
Not all services support all values. Unsupported values will trigger a validation exception.
Accepted values depend on the configured inference service, refer to the relevant service-specific documentation for more info.
> info
> The `input_type` parameter specified on the root level of the request body will take precedence over the `input_type` parameter specified in `task_settings`.'
type: string
task_settings:
description: 'Task settings for the individual inference request.
These settings are specific to the task type you specified and override the task settings specified when initializing the service.'
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- input
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceResult'
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n POST\n /_inference/{task_type}/{inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `monitor_inference`
'
x-api: inference.inference
x-category: ai/ml
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
delete:
tags:
- Inference
summary: Delete an inference endpoint
description: This API requires the manage_inference cluster privilege (the built-in `inference_admin` role grants this privilege).
operationId: inference-delete-1
parameters:
- in: path
name: task_type
description: The task type
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.TaskType'
style: simple
- in: path
name: inference_id
description: The inference identifier.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: dry_run
description: When true, checks the semantic_text fields and inference processors that reference the endpoint and returns them in a list, but does not delete the endpoint.
deprecated: false
schema:
type: boolean
style: form
- in: query
name: force
description: When true, the inference endpoint is forcefully deleted even if it is still being used by ingest processors or semantic text fields.
deprecated: false
schema:
type: boolean
style: form
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.DeleteInferenceEndpointResult'
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n DELETE\n /_inference/{task_type}/{inference_id}\n
\n "
x-api: delete.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: 'DELETE /_inference/sparse_embedding/my-elser-model
'
- lang: Python
source: "resp = client.inference.delete(\n task_type=\"sparse_embedding\",\n inference_id=\"my-elser-model\",\n)"
- lang: JavaScript
source: "const response = await client.inference.delete({\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n});"
- lang: Ruby
source: "response = client.inference.delete(\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\"\n)"
- lang: PHP
source: "$resp = $client->inference()->delete([\n \"task_type\" => \"sparse_embedding\",\n \"inference_id\" => \"my-elser-model\",\n]);"
- lang: curl
source: 'curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"'
- lang: Java
source: "client.inference().delete(d -> d\n .inferenceId(\"my-elser-model\")\n .taskType(TaskType.SparseEmbedding)\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/embedding/{inference_id}:
post:
tags:
- Inference
summary: Perform dense embedding inference on the service
operationId: inference-embedding
parameters:
- in: path
name: inference_id
description: The inference Id
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference request to complete.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.RequestEmbedding'
examples:
EmbeddingRequestExample1:
summary: Multimodal embedding task
description: Run `POST _inference/embedding/my-multimodal-endpoint` to generate embeddings from the example text and image
value: "{\n \"input\": [\n {\n \"content\": {\n \"type\": \"image\",\n \"format\": \"base64\",\n \"value\": \"data:image/jpg;base64,...\"\n }\n },\n {\n \"content\": {\n \"type\": \"text\",\n \"value\": \"Some text to create an embedding\"\n }\n }\n ]\n}"
EmbeddingRequestExample2:
summary: Text-only embedding task
description: Run `POST _inference/embedding/my-text-only-endpoint` to generate embeddings from the example text
value: "{\n \"input\": [\"The first text\", \"The second text\"]\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.EmbeddingInferenceResult'
examples:
EmbeddingResponseExample1:
summary: Multimodal embedding task
description: 'An abbreviated response from `POST _inference/embedding/my-multimodal-endpoint`.
'
value: "{\n \"embeddings\": [\n {\n \"embedding\": [\n -0.0189209,\n -0.04174805,\n 0.00854492,\n 0.01556396,\n 0.01928711,\n -0.00616455,\n -0.00460815,\n 0.01477051,\n -0.00656128,\n 0.05419922\n ]\n },\n {\n \"embedding\": [\n -0.01379395,\n -0.02368164,\n 0.01068115,\n 0.0279541,\n 0.01043701,\n -7.7057E-4,\n 0.04150391,\n 0.00836182,\n -0.01135254,\n 0.0246582\n ]\n }\n ]\n}"
EmbeddingResponseExample2:
summary: Text-only embedding task
description: 'An abbreviated response from `POST _inference/embedding/my-text-only-endpoint`.
'
value: "{\n \"embeddings\": [\n {\n \"embedding\": [\n 0.00854492,\n -0.00616455,\n -0.0189209,\n 0.01556396,\n -0.00460815,\n 0.01477051,\n -0.04174805,\n 0.01928711,\n -0.00656128,\n 0.05419922\n ]\n },\n {\n \"embedding\": [\n -0.01135254,\n 0.0279541,\n -0.02368164,\n 0.01068115,\n 0.01043701,\n 0.04150391,\n 0.00836182,\n -7.7057E-4,\n -0.01379395,\n 0.0246582\n ]\n }\n ]\n}"
x-state: Technical preview; Added in 9.4.0
x-variations:
- "\n POST\n /_inference/embedding/{inference_id}\n
\n "
x-api: embedding.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "POST _inference/embedding/my-multimodal-endpoint\n{\n \"input\": [\n {\n \"content\": {\n \"type\": \"image\",\n \"format\": \"base64\",\n \"value\": \"data:image/jpg;base64,...\"\n }\n },\n {\n \"content\": {\n \"type\": \"text\",\n \"value\": \"Some text to create an embedding\"\n }\n }\n ]\n}"
- lang: Python
source: "resp = client.inference.embedding(\n inference_id=\"my-multimodal-endpoint\",\n embedding={\n \"input\": [\n {\n \"content\": {\n \"type\": \"image\",\n \"format\": \"base64\",\n \"value\": \"data:image/jpg;base64,...\"\n }\n },\n {\n \"content\": {\n \"type\": \"text\",\n \"value\": \"Some text to create an embedding\"\n }\n }\n ]\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.embedding({\n inference_id: \"my-multimodal-endpoint\",\n embedding: {\n input: [\n {\n content: {\n type: \"image\",\n format: \"base64\",\n value: \"data:image/jpg;base64,...\",\n },\n },\n {\n content: {\n type: \"text\",\n value: \"Some text to create an embedding\",\n },\n },\n ],\n },\n});"
- lang: Ruby
source: "response = client.inference.embedding(\n inference_id: \"my-multimodal-endpoint\",\n body: {\n \"input\": [\n {\n \"content\": {\n \"type\": \"image\",\n \"format\": \"base64\",\n \"value\": \"data:image/jpg;base64,...\"\n }\n },\n {\n \"content\": {\n \"type\": \"text\",\n \"value\": \"Some text to create an embedding\"\n }\n }\n ]\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->embedding([\n \"inference_id\" => \"my-multimodal-endpoint\",\n \"body\" => [\n \"input\" => array(\n [\n \"content\" => [\n \"type\" => \"image\",\n \"format\" => \"base64\",\n \"value\" => \"data:image/jpg;base64,...\",\n ],\n ],\n [\n \"content\" => [\n \"type\" => \"text\",\n \"value\" => \"Some text to create an embedding\",\n ],\n ],\n ),\n ],\n]);"
- lang: curl
source: 'curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"input":[{"content":{"type":"image","format":"base64","value":"data:image/jpg;base64,..."}},{"content":{"type":"text","value":"Some text to create an embedding"}}]}'' "$ELASTICSEARCH_URL/_inference/embedding/my-multimodal-endpoint"'
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference:
get:
tags:
- Inference
summary: Get an inference endpoint
description: This API requires the `monitor_inference` cluster privilege (the built-in `inference_admin` and `inference_user` roles grant this privilege).
operationId: inference-get
responses:
'200':
description: ''
content:
application/json:
schema:
type: object
properties:
endpoints:
type: array
items:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfo'
required:
- endpoints
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n GET\n /_inference\n
\n "
x-api: get.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: 'GET _inference/sparse_embedding/my-elser-model
'
- lang: Python
source: "resp = client.inference.get(\n task_type=\"sparse_embedding\",\n inference_id=\"my-elser-model\",\n)"
- lang: JavaScript
source: "const response = await client.inference.get({\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n});"
- lang: Ruby
source: "response = client.inference.get(\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\"\n)"
- lang: PHP
source: "$resp = $client->inference()->get([\n \"task_type\" => \"sparse_embedding\",\n \"inference_id\" => \"my-elser-model\",\n]);"
- lang: curl
source: 'curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"'
- lang: Java
source: "client.inference().get(g -> g\n .inferenceId(\"my-elser-model\")\n .taskType(TaskType.SparseEmbedding)\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{ai21_inference_id}:
put:
tags:
- Inference
summary: Create a AI21 inference endpoint
description: Create an inference endpoint to perform an inference task with the `ai21` service.
operationId: inference-put-ai21
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.Ai21TaskType'
style: simple
- in: path
name: ai21_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
service:
description: The type of service supported for the specified task type. In this case, `ai21`.
allOf:
- $ref: '#/components/schemas/inference._types.Ai21ServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `ai21` service.
allOf:
- $ref: '#/components/schemas/inference._types.Ai21ServiceSettings'
required:
- service
- service_settings
examples:
PutAi21RequestExample1:
description: Run `PUT _inference/completion/ai21-completion` to create an AI21 inference endpoint that performs a `completion` task.
value: "{\n \"service\": \"ai21\",\n \"service_settings\": {\n \"api_key\": \"ai21-api-key\",\n \"model_id\": \"jamba-large\" \n }\n}"
PutAi21RequestExample2:
description: Run `PUT _inference/chat-completion/ai21-chat-completion` to create a AI21 inference endpoint that performs a `chat_completion` task.
value: "{\n \"service\": \"ai21\",\n \"service_settings\": {\n \"api_key\": \"ai21-api-key\",\n \"model_id\": \"jamba-mini\" \n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoAi21'
x-state: Generally available; Added in 9.2.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{ai21_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_ai21.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/completion/ai21-completion\n{\n \"service\": \"ai21\",\n \"service_settings\": {\n \"api_key\": \"ai21-api-key\",\n \"model_id\": \"jamba-large\" \n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"completion\",\n inference_id=\"ai21-completion\",\n inference_config={\n \"service\": \"ai21\",\n \"service_settings\": {\n \"api_key\": \"ai21-api-key\",\n \"model_id\": \"jamba-large\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"completion\",\n inference_id: \"ai21-completion\",\n inference_config: {\n service: \"ai21\",\n service_settings: {\n api_key: \"ai21-api-key\",\n model_id: \"jamba-large\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"completion\",\n inference_id: \"ai21-completion\",\n body: {\n \"service\": \"ai21\",\n \"service_settings\": {\n \"api_key\": \"ai21-api-key\",\n \"model_id\": \"jamba-large\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"completion\",\n \"inference_id\" => \"ai21-completion\",\n \"body\" => [\n \"service\" => \"ai21\",\n \"service_settings\" => [\n \"api_key\" => \"ai21-api-key\",\n \"model_id\" => \"jamba-large\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"ai21","service_settings":{"api_key":"ai21-api-key","model_id":"jamba-large"}}'' "$ELASTICSEARCH_URL/_inference/completion/ai21-completion"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"ai21-completion\")\n .taskType(TaskType.Completion)\n .inferenceConfig(i -> i\n .service(\"ai21\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"ai21-api-key\\\",\\\"model_id\\\":\\\"jamba-large\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{alibabacloud_inference_id}:
put:
tags:
- Inference
summary: Create an AlibabaCloud AI Search inference endpoint
description: Create an inference endpoint to perform an inference task with the `alibabacloud-ai-search` service.
operationId: inference-put-alibabacloud
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.AlibabaCloudTaskType'
style: simple
- in: path
name: alibabacloud_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `sparse_embedding` or `text_embedding` task types.
Not applicable to the `rerank` or `completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `alibabacloud-ai-search`.
allOf:
- $ref: '#/components/schemas/inference._types.AlibabaCloudServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `alibabacloud-ai-search` service.
allOf:
- $ref: '#/components/schemas/inference._types.AlibabaCloudServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.AlibabaCloudTaskSettings'
required:
- service
- service_settings
examples:
PutAlibabaCloudRequestExample1:
summary: A completion task
description: Run `PUT _inference/completion/alibabacloud_ai_search_completion` to create an inference endpoint that performs a completion task.
value: "{\n \"service\": \"alibabacloud-ai-search\",\n \"service_settings\": {\n \"host\" : \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n \"api_key\": \"AlibabaCloud-API-Key\",\n \"service_id\": \"ops-qwen-turbo\",\n \"workspace\" : \"default\"\n }\n}"
PutAlibabaCloudRequestExample2:
summary: A rerank task
description: Run `PUT _inference/rerank/alibabacloud_ai_search_rerank` to create an inference endpoint that performs a rerank task.
value: "{\n \"service\": \"alibabacloud-ai-search\",\n \"service_settings\": {\n \"api_key\": \"AlibabaCloud-API-Key\",\n \"service_id\": \"ops-bge-reranker-larger\",\n \"host\": \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n \"workspace\": \"default\"\n }\n}"
PutAlibabaCloudRequestExample3:
summary: A sparse embedding task
description: Run `PUT _inference/sparse_embedding/alibabacloud_ai_search_sparse` to create an inference endpoint that performs perform a sparse embedding task.
value: "{\n \"service\": \"alibabacloud-ai-search\",\n \"service_settings\": {\n \"api_key\": \"AlibabaCloud-API-Key\",\n \"service_id\": \"ops-text-sparse-embedding-001\",\n \"host\": \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n \"workspace\": \"default\"\n }\n}"
PutAlibabaCloudRequestExample4:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/alibabacloud_ai_search_embeddings` to create an inference endpoint that performs a text embedding task.
value: "{\n \"service\": \"alibabacloud-ai-search\",\n \"service_settings\": {\n \"api_key\": \"AlibabaCloud-API-Key\",\n \"service_id\": \"ops-text-embedding-001\",\n \"host\": \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n \"workspace\": \"default\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoAlibabaCloudAI'
x-state: Generally available; Added in 8.16.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{alibabacloud_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_alibabacloud.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/completion/alibabacloud_ai_search_completion\n{\n \"service\": \"alibabacloud-ai-search\",\n \"service_settings\": {\n \"host\" : \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n \"api_key\": \"AlibabaCloud-API-Key\",\n \"service_id\": \"ops-qwen-turbo\",\n \"workspace\" : \"default\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"completion\",\n inference_id=\"alibabacloud_ai_search_completion\",\n inference_config={\n \"service\": \"alibabacloud-ai-search\",\n \"service_settings\": {\n \"host\": \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n \"api_key\": \"AlibabaCloud-API-Key\",\n \"service_id\": \"ops-qwen-turbo\",\n \"workspace\": \"default\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"completion\",\n inference_id: \"alibabacloud_ai_search_completion\",\n inference_config: {\n service: \"alibabacloud-ai-search\",\n service_settings: {\n host: \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n api_key: \"AlibabaCloud-API-Key\",\n service_id: \"ops-qwen-turbo\",\n workspace: \"default\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"completion\",\n inference_id: \"alibabacloud_ai_search_completion\",\n body: {\n \"service\": \"alibabacloud-ai-search\",\n \"service_settings\": {\n \"host\": \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n \"api_key\": \"AlibabaCloud-API-Key\",\n \"service_id\": \"ops-qwen-turbo\",\n \"workspace\": \"default\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"completion\",\n \"inference_id\" => \"alibabacloud_ai_search_completion\",\n \"body\" => [\n \"service\" => \"alibabacloud-ai-search\",\n \"service_settings\" => [\n \"host\" => \"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\n \"api_key\" => \"AlibabaCloud-API-Key\",\n \"service_id\" => \"ops-qwen-turbo\",\n \"workspace\" => \"default\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"alibabacloud-ai-search","service_settings":{"host":"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com","api_key":"AlibabaCloud-API-Key","service_id":"ops-qwen-turbo","workspace":"default"}}'' "$ELASTICSEARCH_URL/_inference/completion/alibabacloud_ai_search_completion"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"alibabacloud_ai_search_completion\")\n .taskType(TaskType.Completion)\n .inferenceConfig(i -> i\n .service(\"alibabacloud-ai-search\")\n .serviceSettings(JsonData.fromJson(\"{\\\"host\\\":\\\"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\\\",\\\"api_key\\\":\\\"AlibabaCloud-API-Key\\\",\\\"service_id\\\":\\\"ops-qwen-turbo\\\",\\\"workspace\\\":\\\"default\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{amazonbedrock_inference_id}:
put:
tags:
- Inference
summary: Create an Amazon Bedrock inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `amazonbedrock` service.
>info
> You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys.'
operationId: inference-put-amazonbedrock
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.AmazonBedrockTaskType'
style: simple
- in: path
name: amazonbedrock_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `chat_completion` and `completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `amazonbedrock`.
allOf:
- $ref: '#/components/schemas/inference._types.AmazonBedrockServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `amazonbedrock` service.
allOf:
- $ref: '#/components/schemas/inference._types.AmazonBedrockServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.AmazonBedrockTaskSettings'
required:
- service
- service_settings
examples:
PutAmazonBedrockRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/amazon_bedrock_embeddings` to create an inference endpoint that performs a text embedding task.
value: "{\n \"service\": \"amazonbedrock\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"provider\": \"amazontitan\",\n \"model\": \"amazon.titan-embed-text-v2:0\"\n }\n}"
PutAmazonBedrockRequestExample2:
summary: A completion task
description: Run `PUT _inference/completion/amazon_bedrock_completion` to create an inference endpoint to perform a completion task.
value: "{\n \"service\": \"amazonbedrock\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"provider\": \"amazontitan\",\n \"model\": \"amazon.titan-text-premier-v1:0\"\n }\n}"
PutAmazonBedrockRequestExample3:
summary: A chat completion task
description: Run `PUT _inference/chat_completion/amazon_bedrock_chat_completion` to create an inference endpoint to perform a chat completion task.
value: "{\n \"service\": \"amazonbedrock\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"provider\": \"amazontitan\",\n \"model\": \"amazon.titan-text-premier-v1:0\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoAmazonBedrock'
x-state: Generally available; Added in 8.12.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{amazonbedrock_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_amazonbedrock.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/amazon_bedrock_embeddings\n{\n \"service\": \"amazonbedrock\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"provider\": \"amazontitan\",\n \"model\": \"amazon.titan-embed-text-v2:0\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"amazon_bedrock_embeddings\",\n inference_config={\n \"service\": \"amazonbedrock\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"provider\": \"amazontitan\",\n \"model\": \"amazon.titan-embed-text-v2:0\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"amazon_bedrock_embeddings\",\n inference_config: {\n service: \"amazonbedrock\",\n service_settings: {\n access_key: \"AWS-access-key\",\n secret_key: \"AWS-secret-key\",\n region: \"us-east-1\",\n provider: \"amazontitan\",\n model: \"amazon.titan-embed-text-v2:0\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"amazon_bedrock_embeddings\",\n body: {\n \"service\": \"amazonbedrock\",\n \"service_settings\": {\n \"access_key\": \"AWS-access-key\",\n \"secret_key\": \"AWS-secret-key\",\n \"region\": \"us-east-1\",\n \"provider\": \"amazontitan\",\n \"model\": \"amazon.titan-embed-text-v2:0\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"amazon_bedrock_embeddings\",\n \"body\" => [\n \"service\" => \"amazonbedrock\",\n \"service_settings\" => [\n \"access_key\" => \"AWS-access-key\",\n \"secret_key\" => \"AWS-secret-key\",\n \"region\" => \"us-east-1\",\n \"provider\" => \"amazontitan\",\n \"model\" => \"amazon.titan-embed-text-v2:0\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"amazonbedrock","service_settings":{"access_key":"AWS-access-key","secret_key":"AWS-secret-key","region":"us-east-1","provider":"amazontitan","model":"amazon.titan-embed-text-v2:0"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/amazon_bedrock_embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"amazon_bedrock_embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"amazonbedrock\")\n .serviceSettings(JsonData.fromJson(\"{\\\"access_key\\\":\\\"AWS-access-key\\\",\\\"secret_key\\\":\\\"AWS-secret-key\\\",\\\"region\\\":\\\"us-east-1\\\",\\\"provider\\\":\\\"amazontitan\\\",\\\"model\\\":\\\"amazon.titan-embed-text-v2:0\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{amazonsagemaker_inference_id}:
put:
tags:
- Inference
summary: Create an Amazon SageMaker inference endpoint
description: Create an inference endpoint to perform an inference task with the `amazon_sagemaker` service.
operationId: inference-put-amazonsagemaker
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.TaskTypeAmazonSageMaker'
style: simple
- in: path
name: amazonsagemaker_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `sparse_embedding` or `text_embedding` task types.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `amazon_sagemaker`.
allOf:
- $ref: '#/components/schemas/inference._types.AmazonSageMakerServiceType'
service_settings:
description: 'Settings used to install the inference model.
These settings are specific to the `amazon_sagemaker` service and `service_settings.api` you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.AmazonSageMakerServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type and `service_settings.api` you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.AmazonSageMakerTaskSettings'
required:
- service
- service_settings
examples:
PutAmazonSageMakerRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/amazon_sagemaker_embeddings` to create an inference endpoint that performs a text embedding task.
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:
summary: A completion task
description: Run `PUT _inference/completion/amazon_sagemaker_completion` to create an inference endpoint that performs 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:
summary: A chat completion task
description: Run `PUT _inference/chat_completion/amazon_sagemaker_chat_completion` to create an inference endpoint that performs a chat completion task.
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:
summary: A sparse embedding task
description: Run `PUT _inference/sparse_embedding/amazon_sagemaker_sparse_embedding` to create an inference endpoint that performs a sparse embedding task.
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:
summary: A rerank task
description: Run `PUT _inference/rerank/amazon_sagemaker_rerank` to create an inference endpoint that performs a rerank 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}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoAmazonSageMaker'
x-state: Generally available; Added in 9.1.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{amazonsagemaker_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_amazonsagemaker.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/amazon_sagemaker_embeddings\n{\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}"
- lang: Python
source: "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)"
- lang: JavaScript
source: "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});"
- lang: Ruby
source: "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)"
- lang: PHP
source: "$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]);"
- lang: curl
source: '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"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"amazon_sagemaker_embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"amazon_sagemaker\")\n .serviceSettings(JsonData.fromJson(\"{\\\"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\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{anthropic_inference_id}:
put:
tags:
- Inference
summary: Create an Anthropic inference endpoint
description: Create an inference endpoint to perform an inference task with the `anthropic` service.
operationId: inference-put-anthropic
parameters:
- in: path
name: task_type
description: 'The task type.
The only valid task type for the model to perform is `completion`.'
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.AnthropicTaskType'
style: simple
- in: path
name: anthropic_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
service:
description: The type of service supported for the specified task type. In this case, `anthropic`.
allOf:
- $ref: '#/components/schemas/inference._types.AnthropicServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `anthropic` service.
allOf:
- $ref: '#/components/schemas/inference._types.AnthropicServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.AnthropicTaskSettings'
required:
- service
- service_settings
examples:
PutAnthropicRequestExample1:
description: Run `PUT _inference/completion/anthropic_completion` to create an inference endpoint that performs a completion task.
value: "{\n \"service\": \"anthropic\",\n \"service_settings\": {\n \"api_key\": \"Anthropic-Api-Key\",\n \"model_id\": \"Model-ID\"\n },\n \"task_settings\": {\n \"max_tokens\": 1024\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoAnthropic'
x-state: Generally available; Added in 8.16.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{anthropic_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_anthropic.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/completion/anthropic_completion\n{\n \"service\": \"anthropic\",\n \"service_settings\": {\n \"api_key\": \"Anthropic-Api-Key\",\n \"model_id\": \"Model-ID\"\n },\n \"task_settings\": {\n \"max_tokens\": 1024\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"completion\",\n inference_id=\"anthropic_completion\",\n inference_config={\n \"service\": \"anthropic\",\n \"service_settings\": {\n \"api_key\": \"Anthropic-Api-Key\",\n \"model_id\": \"Model-ID\"\n },\n \"task_settings\": {\n \"max_tokens\": 1024\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"completion\",\n inference_id: \"anthropic_completion\",\n inference_config: {\n service: \"anthropic\",\n service_settings: {\n api_key: \"Anthropic-Api-Key\",\n model_id: \"Model-ID\",\n },\n task_settings: {\n max_tokens: 1024,\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"completion\",\n inference_id: \"anthropic_completion\",\n body: {\n \"service\": \"anthropic\",\n \"service_settings\": {\n \"api_key\": \"Anthropic-Api-Key\",\n \"model_id\": \"Model-ID\"\n },\n \"task_settings\": {\n \"max_tokens\": 1024\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"completion\",\n \"inference_id\" => \"anthropic_completion\",\n \"body\" => [\n \"service\" => \"anthropic\",\n \"service_settings\" => [\n \"api_key\" => \"Anthropic-Api-Key\",\n \"model_id\" => \"Model-ID\",\n ],\n \"task_settings\" => [\n \"max_tokens\" => 1024,\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"anthropic","service_settings":{"api_key":"Anthropic-Api-Key","model_id":"Model-ID"},"task_settings":{"max_tokens":1024}}'' "$ELASTICSEARCH_URL/_inference/completion/anthropic_completion"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"anthropic_completion\")\n .taskType(TaskType.Completion)\n .inferenceConfig(i -> i\n .service(\"anthropic\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"Anthropic-Api-Key\\\",\\\"model_id\\\":\\\"Model-ID\\\"}\"))\n .taskSettings(JsonData.fromJson(\"{\\\"max_tokens\\\":1024}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{azureaistudio_inference_id}:
put:
tags:
- Inference
summary: Create an Azure AI studio inference endpoint
description: Create an inference endpoint to perform an inference task with the `azureaistudio` service.
operationId: inference-put-azureaistudio
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.AzureAiStudioTaskType'
style: simple
- in: path
name: azureaistudio_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank` or `completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `azureaistudio`.
allOf:
- $ref: '#/components/schemas/inference._types.AzureAiStudioServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `azureaistudio` service.
allOf:
- $ref: '#/components/schemas/inference._types.AzureAiStudioServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.AzureAiStudioTaskSettings'
required:
- service
- service_settings
examples:
PutAzureAiStudioRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/azure_ai_studio_embeddings` to create an inference endpoint that performs a text_embedding task. Note that you do not specify a model here, as it is defined already in the Azure AI Studio deployment.
value: "{\n \"service\": \"azureaistudio\",\n \"service_settings\": {\n \"api_key\": \"Azure-AI-Studio-API-key\",\n \"target\": \"Target-Uri\",\n \"provider\": \"openai\",\n \"endpoint_type\": \"token\"\n }\n}"
PutAzureAiStudioRequestExample2:
summary: A completion task
description: Run `PUT _inference/completion/azure_ai_studio_completion` to create an inference endpoint that performs a completion task.
value: "{\n \"service\": \"azureaistudio\",\n \"service_settings\": {\n \"api_key\": \"Azure-AI-Studio-API-key\",\n \"target\": \"Target-URI\",\n \"provider\": \"databricks\",\n \"endpoint_type\": \"realtime\"\n }\n}"
PutAzureAiStudioRequestExample3:
summary: A rerank task
description: Run `PUT _inference/rerank/azure_ai_studio_rerank` to create an inference endpoint that performs a rerank task.
value: "{\n \"service\": \"azureaistudio\",\n \"service_settings\": {\n \"api_key\": \"Azure-AI-Studio-API-key\",\n \"target\": \"Target-URI\",\n \"provider\": \"cohere\",\n \"endpoint_type\": \"token\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoAzureAIStudio'
x-state: Generally available; Added in 8.14.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{azureaistudio_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_azureaistudio.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/azure_ai_studio_embeddings\n{\n \"service\": \"azureaistudio\",\n \"service_settings\": {\n \"api_key\": \"Azure-AI-Studio-API-key\",\n \"target\": \"Target-Uri\",\n \"provider\": \"openai\",\n \"endpoint_type\": \"token\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"azure_ai_studio_embeddings\",\n inference_config={\n \"service\": \"azureaistudio\",\n \"service_settings\": {\n \"api_key\": \"Azure-AI-Studio-API-key\",\n \"target\": \"Target-Uri\",\n \"provider\": \"openai\",\n \"endpoint_type\": \"token\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"azure_ai_studio_embeddings\",\n inference_config: {\n service: \"azureaistudio\",\n service_settings: {\n api_key: \"Azure-AI-Studio-API-key\",\n target: \"Target-Uri\",\n provider: \"openai\",\n endpoint_type: \"token\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"azure_ai_studio_embeddings\",\n body: {\n \"service\": \"azureaistudio\",\n \"service_settings\": {\n \"api_key\": \"Azure-AI-Studio-API-key\",\n \"target\": \"Target-Uri\",\n \"provider\": \"openai\",\n \"endpoint_type\": \"token\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"azure_ai_studio_embeddings\",\n \"body\" => [\n \"service\" => \"azureaistudio\",\n \"service_settings\" => [\n \"api_key\" => \"Azure-AI-Studio-API-key\",\n \"target\" => \"Target-Uri\",\n \"provider\" => \"openai\",\n \"endpoint_type\" => \"token\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"azureaistudio","service_settings":{"api_key":"Azure-AI-Studio-API-key","target":"Target-Uri","provider":"openai","endpoint_type":"token"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/azure_ai_studio_embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"azure_ai_studio_embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"azureaistudio\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"Azure-AI-Studio-API-key\\\",\\\"target\\\":\\\"Target-Uri\\\",\\\"provider\\\":\\\"openai\\\",\\\"endpoint_type\\\":\\\"token\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{azureopenai_inference_id}:
put:
tags:
- Inference
summary: Create an Azure OpenAI inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `azureopenai` service.
The list of chat completion models that you can choose from in your Azure OpenAI deployment include:
* GPT-4 and GPT-4 Turbo models
* GPT-3.5
The list of embeddings models that you can choose from in your deployment can be found in the Azure models documentation.'
operationId: inference-put-azureopenai
parameters:
- in: path
name: task_type
description: 'The type of the inference task that the model will perform.
NOTE: The `chat_completion` task type only supports streaming and only through the _stream API.'
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.AzureOpenAITaskType'
style: simple
- in: path
name: azureopenai_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `completion` and `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `azureopenai`.
allOf:
- $ref: '#/components/schemas/inference._types.AzureOpenAIServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `azureopenai` service.
allOf:
- $ref: '#/components/schemas/inference._types.AzureOpenAIServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.AzureOpenAITaskSettings'
required:
- service
- service_settings
examples:
PutAzureOpenAiRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/azure_openai_embeddings` to create an inference endpoint that performs a `text_embedding` task. You do not specify a model, as it is defined already in the Azure OpenAI deployment.
value: "{\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"api_key\": \"Api-Key\",\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\"\n }\n}"
PutAzureOpenAiRequestExample2:
summary: A completion task
description: Run `PUT _inference/completion/azure_openai_completion` to create an inference endpoint that performs a `completion` task.
value: "{\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"api_key\": \"Api-Key\",\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\"\n }\n}"
PutAzureOpenAiRequestExample3:
summary: A chat completion task
description: Run `PUT _inference/chat_completion/azure_openai_chat_completion` to create an inference endpoint that performs a `chat_completion` task.
value: "{\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"api_key\": \"Api-Key\",\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoAzureOpenAI'
examples:
PutAzureOpenAiResponseExample1:
summary: A text embedding task
description: A successful response when creating an Azure OpenAI `text_embedding` inference endpoint.
value: "{\n \"inference_id\": \"azure_openai_embeddings\",\n \"task_type\": \"text_embedding\",\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\",\n \"rate_limit\": {\n \"requests_per_minute\": 1140\n },\n \"dimensions\": 1536,\n \"similarity\": \"dot_product\"\n },\n \"chunking_settings\": {\n \"strategy\": \"sentence\",\n \"max_chunk_size\": 250,\n \"sentence_overlap\": 1\n }\n}"
PutAzureOpenAiResponseExample2:
summary: A completion task
description: A successful response when creating an Azure OpenAI `completion` inference endpoint.
value: "{\n \"inference_id\": \"azure_openai_completion\",\n \"task_type\": \"completion\",\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\",\n \"rate_limit\": {\n \"requests_per_minute\": 120\n }\n }\n}"
PutAzureOpenAiResponseExample3:
summary: A chat completion task
description: A successful response when creating an Azure OpenAI `chat_completion` inference endpoint.
value: "{\n \"inference_id\": \"azure_openai_chat_completion\",\n \"task_type\": \"chat_completion\",\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\",\n \"rate_limit\": {\n \"requests_per_minute\": 120\n }\n }\n}"
x-state: Generally available; Added in 8.14.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{azureopenai_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_azureopenai.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/azure_openai_embeddings\n{\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"api_key\": \"Api-Key\",\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"azure_openai_embeddings\",\n inference_config={\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"api_key\": \"Api-Key\",\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"azure_openai_embeddings\",\n inference_config: {\n service: \"azureopenai\",\n service_settings: {\n api_key: \"Api-Key\",\n resource_name: \"Resource-name\",\n deployment_id: \"Deployment-id\",\n api_version: \"2024-02-01\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"azure_openai_embeddings\",\n body: {\n \"service\": \"azureopenai\",\n \"service_settings\": {\n \"api_key\": \"Api-Key\",\n \"resource_name\": \"Resource-name\",\n \"deployment_id\": \"Deployment-id\",\n \"api_version\": \"2024-02-01\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"azure_openai_embeddings\",\n \"body\" => [\n \"service\" => \"azureopenai\",\n \"service_settings\" => [\n \"api_key\" => \"Api-Key\",\n \"resource_name\" => \"Resource-name\",\n \"deployment_id\" => \"Deployment-id\",\n \"api_version\" => \"2024-02-01\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"azureopenai","service_settings":{"api_key":"Api-Key","resource_name":"Resource-name","deployment_id":"Deployment-id","api_version":"2024-02-01"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/azure_openai_embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"azure_openai_embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"azureopenai\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"Api-Key\\\",\\\"resource_name\\\":\\\"Resource-name\\\",\\\"deployment_id\\\":\\\"Deployment-id\\\",\\\"api_version\\\":\\\"2024-02-01\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{cohere_inference_id}:
put:
tags:
- Inference
summary: Create a Cohere inference endpoint
description: Create an inference endpoint to perform an inference task with the `cohere` service.
operationId: inference-put-cohere
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.CohereTaskType'
style: simple
- in: path
name: cohere_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank` or `completion` task type.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `cohere`.
allOf:
- $ref: '#/components/schemas/inference._types.CohereServiceType'
service_settings:
description: 'Settings used to install the inference model.
These settings are specific to the `cohere` service.'
allOf:
- $ref: '#/components/schemas/inference._types.CohereServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.CohereTaskSettings'
required:
- service
- service_settings
examples:
PutCohereRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/cohere-embeddings` to create an inference endpoint that performs a text embedding task.
value: "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"api_key\": \"Cohere-Api-key\",\n \"model_id\": \"embed-english-light-v3.0\",\n \"embedding_type\": \"byte\"\n }\n}"
PutCohereRequestExample2:
summary: A rerank task
description: Run `PUT _inference/rerank/cohere-rerank` to create an inference endpoint that performs a rerank task.
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:
summary: A completion task
description: Run `PUT _inference/completion/cohere-completion` to create an inference endpoint that performs a completion task.
value: "{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"api_key\": \"Cohere-API-key\",\n \"model_id\": \"command-a-03-2025\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoCohere'
x-state: Generally available; Added in 8.13.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{cohere_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_cohere.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/cohere-embeddings\n{\n \"service\": \"cohere\",\n \"service_settings\": {\n \"api_key\": \"Cohere-Api-key\",\n \"model_id\": \"embed-english-light-v3.0\",\n \"embedding_type\": \"byte\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"cohere-embeddings\",\n inference_config={\n \"service\": \"cohere\",\n \"service_settings\": {\n \"api_key\": \"Cohere-Api-key\",\n \"model_id\": \"embed-english-light-v3.0\",\n \"embedding_type\": \"byte\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"cohere-embeddings\",\n inference_config: {\n service: \"cohere\",\n service_settings: {\n api_key: \"Cohere-Api-key\",\n model_id: \"embed-english-light-v3.0\",\n embedding_type: \"byte\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"cohere-embeddings\",\n body: {\n \"service\": \"cohere\",\n \"service_settings\": {\n \"api_key\": \"Cohere-Api-key\",\n \"model_id\": \"embed-english-light-v3.0\",\n \"embedding_type\": \"byte\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"cohere-embeddings\",\n \"body\" => [\n \"service\" => \"cohere\",\n \"service_settings\" => [\n \"api_key\" => \"Cohere-Api-key\",\n \"model_id\" => \"embed-english-light-v3.0\",\n \"embedding_type\" => \"byte\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"cohere","service_settings":{"api_key":"Cohere-Api-key","model_id":"embed-english-light-v3.0","embedding_type":"byte"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/cohere-embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"cohere-embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"cohere\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"Cohere-Api-key\\\",\\\"model_id\\\":\\\"embed-english-light-v3.0\\\",\\\"embedding_type\\\":\\\"byte\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{contextualai_inference_id}:
put:
tags:
- Inference
summary: Create an Contextual AI inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `contexualai` service.
To review the available `rerank` models, refer to .'
operationId: inference-put-contextualai
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.TaskTypeContextualAI'
style: simple
- in: path
name: contextualai_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
service:
description: The type of service supported for the specified task type. In this case, `contextualai`.
allOf:
- $ref: '#/components/schemas/inference._types.ContextualAIServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `contextualai` service.
allOf:
- $ref: '#/components/schemas/inference._types.ContextualAIServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.ContextualAITaskSettings'
required:
- service
- service_settings
examples:
PutContextualAiRequestExample1:
summary: A rerank task
description: Run `PUT _inference/rerank/contextualai-rerank` to create an inference endpoint for rerank tasks using the Contextual AI service.
value: "{\n \"service\": \"contextualai\",\n \"service_settings\": {\n \"api_key\": \"ContextualAI-Api-key\",\n \"model_id\": \"ctxl-rerank-v2-instruct-multilingual-mini\"\n },\n \"task_settings\": {\n \"instruction\": \"Rerank the following documents based on their relevance to the query.\",\n \"top_k\": 3\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoContextualAi'
x-state: Generally available; Added in 9.2.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{contextualai_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_contextualai.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/rerank/contextualai-rerank\n{\n \"service\": \"contextualai\",\n \"service_settings\": {\n \"api_key\": \"ContextualAI-Api-key\",\n \"model_id\": \"ctxl-rerank-v2-instruct-multilingual-mini\"\n },\n \"task_settings\": {\n \"instruction\": \"Rerank the following documents based on their relevance to the query.\",\n \"top_k\": 3\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"rerank\",\n inference_id=\"contextualai-rerank\",\n inference_config={\n \"service\": \"contextualai\",\n \"service_settings\": {\n \"api_key\": \"ContextualAI-Api-key\",\n \"model_id\": \"ctxl-rerank-v2-instruct-multilingual-mini\"\n },\n \"task_settings\": {\n \"instruction\": \"Rerank the following documents based on their relevance to the query.\",\n \"top_k\": 3\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"rerank\",\n inference_id: \"contextualai-rerank\",\n inference_config: {\n service: \"contextualai\",\n service_settings: {\n api_key: \"ContextualAI-Api-key\",\n model_id: \"ctxl-rerank-v2-instruct-multilingual-mini\",\n },\n task_settings: {\n instruction:\n \"Rerank the following documents based on their relevance to the query.\",\n top_k: 3,\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"rerank\",\n inference_id: \"contextualai-rerank\",\n body: {\n \"service\": \"contextualai\",\n \"service_settings\": {\n \"api_key\": \"ContextualAI-Api-key\",\n \"model_id\": \"ctxl-rerank-v2-instruct-multilingual-mini\"\n },\n \"task_settings\": {\n \"instruction\": \"Rerank the following documents based on their relevance to the query.\",\n \"top_k\": 3\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"rerank\",\n \"inference_id\" => \"contextualai-rerank\",\n \"body\" => [\n \"service\" => \"contextualai\",\n \"service_settings\" => [\n \"api_key\" => \"ContextualAI-Api-key\",\n \"model_id\" => \"ctxl-rerank-v2-instruct-multilingual-mini\",\n ],\n \"task_settings\" => [\n \"instruction\" => \"Rerank the following documents based on their relevance to the query.\",\n \"top_k\" => 3,\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"contextualai","service_settings":{"api_key":"ContextualAI-Api-key","model_id":"ctxl-rerank-v2-instruct-multilingual-mini"},"task_settings":{"instruction":"Rerank the following documents based on their relevance to the query.","top_k":3}}'' "$ELASTICSEARCH_URL/_inference/rerank/contextualai-rerank"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"contextualai-rerank\")\n .taskType(TaskType.Rerank)\n .inferenceConfig(i -> i\n .service(\"contextualai\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"ContextualAI-Api-key\\\",\\\"model_id\\\":\\\"ctxl-rerank-v2-instruct-multilingual-mini\\\"}\"))\n .taskSettings(JsonData.fromJson(\"{\\\"instruction\\\":\\\"Rerank the following documents based on their relevance to the query.\\\",\\\"top_k\\\":3}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{custom_inference_id}:
put:
tags:
- Inference
summary: Create a custom inference endpoint
description: 'The custom service gives more control over how to interact with external inference services that aren''t explicitly supported through dedicated integrations.
The custom service gives you the ability to define the headers, url, query parameters, request body, and secrets.
The 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.
Templates are portions of a string that start with `${` and end with `}`.
The 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`.
If the definition (key) is not found for a template, an error message is returned.
In case of an endpoint definition like the following:
```
PUT _inference/text_embedding/test-text-embedding
{
"service": "custom",
"service_settings": {
"secret_parameters": {
"api_key": ""
},
"url": "...endpoints.huggingface.cloud/v1/embeddings",
"headers": {
"Authorization": "Bearer ${api_key}",
"Content-Type": "application/json"
},
"request": "{\"input\": ${input}}",
"response": {
"json_parser": {
"text_embeddings":"$.data[*].embedding[*]"
}
}
}
}
```
To replace `${api_key}` the `secret_parameters` and `task_settings` are checked for a key named `api_key`.
> info
> Templates should not be surrounded by quotes.
Pre-defined templates:
* `${input}` refers to the array of input strings that comes from the `input` field of the subsequent inference requests.
* `${input_type}` refers to the input type translation values.
* `${query}` refers to the query field used specifically for reranking tasks.
* `${top_n}` refers to the `top_n` field available when performing rerank requests.
* `${return_documents}` refers to the `return_documents` field available when performing rerank requests.'
operationId: inference-put-custom
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.CustomTaskType'
style: simple
- in: path
name: custom_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `sparse_embedding` or `text_embedding` task types.
Not applicable to the `rerank` or `completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `custom`.
allOf:
- $ref: '#/components/schemas/inference._types.CustomServiceType'
service_settings:
description: 'Settings used to install the inference model.
These settings are specific to the `custom` service.'
allOf:
- $ref: '#/components/schemas/inference._types.CustomServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.CustomTaskSettings'
required:
- service
- service_settings
examples:
PutCustomRequestExample1:
summary: Custom text embedding task (OpenAI)
description: Run `PUT _inference/text_embedding/custom-embeddings` to create an inference endpoint that performs a text embedding task.
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:
summary: Custom rerank task (Cohere APIv2)
description: Run `PUT _inference/rerank/custom-rerank` to create an inference endpoint that performs a rerank task.
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:
summary: Custom text embedding task (Cohere APIv2)
description: Run `PUT _inference/text_embedding/custom-text-embedding` to create an inference endpoint that performs a text embedding task.
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:
summary: Custom rerank task (Jina AI)
description: Run `PUT _inference/rerank/custom-rerank-jina` to create an inference endpoint that performs a rerank task.
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:
summary: Custom text embedding task (Hugging Face)
description: Run `PUT _inference/text_embedding/custom-text-embedding-hf` to create an inference endpoint that performs a text embedding task by using the Qwen/Qwen3-Embedding-8B model.
value: "{\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}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoCustom'
x-state: Generally available; Added in 8.19.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{custom_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_custom.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/custom-embeddings\n{\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}"
- lang: Python
source: "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)"
- lang: JavaScript
source: "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});"
- lang: Ruby
source: "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)"
- lang: PHP
source: "$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]);"
- lang: curl
source: '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"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"custom-embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"custom\")\n .serviceSettings(JsonData.fromJson(\"{\\\"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[*]\\\"}}}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{deepseek_inference_id}:
put:
tags:
- Inference
summary: Create a DeepSeek inference endpoint
description: Create an inference endpoint to perform an inference task with the `deepseek` service.
operationId: inference-put-deepseek
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.TaskTypeDeepSeek'
style: simple
- in: path
name: deepseek_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
service:
description: The type of service supported for the specified task type. In this case, `deepseek`.
allOf:
- $ref: '#/components/schemas/inference._types.DeepSeekServiceType'
service_settings:
description: 'Settings used to install the inference model.
These settings are specific to the `deepseek` service.'
allOf:
- $ref: '#/components/schemas/inference._types.DeepSeekServiceSettings'
required:
- service
- service_settings
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoDeepSeek'
x-state: Generally available; Added in 9.1.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{deepseek_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_deepseek.inference
x-category: ai/ml
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{elasticsearch_inference_id}:
put:
tags:
- Inference
summary: Create an Elasticsearch inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `elasticsearch` service.
> info
> Your Elasticsearch deployment contains preconfigured ELSER and E5 inference endpoints, you only need to create the enpoints using the API if you want to customize the settings.
If you use the ELSER or the E5 model through the `elasticsearch` service, the API request will automatically download and deploy the model if it isn''t downloaded yet.
> info
> You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.
After creating the endpoint, wait for the model deployment to complete before using it.
To verify the deployment status, use the get trained model statistics API.
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.'
operationId: inference-put-elasticsearch
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.ElasticsearchTaskType'
style: simple
- in: path
name: elasticsearch_inference_id
description: 'The unique identifier of the inference endpoint.
The must not match the `model_id`.'
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `sparse_embedding` and `text_embedding` task types.
Not applicable to the `rerank` task type.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `elasticsearch`.
allOf:
- $ref: '#/components/schemas/inference._types.ElasticsearchServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `elasticsearch` service.
allOf:
- $ref: '#/components/schemas/inference._types.ElasticsearchServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.ElasticsearchTaskSettings'
required:
- service
- service_settings
examples:
PutElasticsearchRequestExample1:
summary: ELSER sparse embedding task
description: Run `PUT _inference/sparse_embedding/my-elser-model` to create an inference endpoint that performs a `sparse_embedding` task. The `model_id` must be the ID of one of the built-in ELSER models. The API will automatically download the ELSER model if it isn't already downloaded and then deploy the model.
value: "{\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"adaptive_allocations\": { \n \"enabled\": true,\n \"min_number_of_allocations\": 1,\n \"max_number_of_allocations\": 4\n },\n \"num_threads\": 1,\n \"model_id\": \".elser_model_2\" \n }\n}"
PutElasticsearchRequestExample2:
summary: Elastic rerank task
description: Run `PUT _inference/rerank/my-elastic-rerank` to create an inference endpoint that performs a rerank task using the built-in Elastic Rerank cross-encoder model. The `model_id` must be `.rerank-v1`, which is the ID of the built-in Elastic Rerank model. The API will automatically download the Elastic Rerank model if it isn't already downloaded and then deploy the model. Once deployed, the model can be used for semantic re-ranking with a `text_similarity_reranker` retriever.
value: "{\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"model_id\": \".rerank-v1\", \n \"num_threads\": 1,\n \"adaptive_allocations\": { \n \"enabled\": true,\n \"min_number_of_allocations\": 1,\n \"max_number_of_allocations\": 4\n }\n }\n}"
PutElasticsearchRequestExample3:
summary: E5 text embedding task
description: Run `PUT _inference/text_embedding/my-e5-model` to create an inference endpoint that performs a `text_embedding` task. The `model_id` must be the ID of one of the built-in E5 models. The API will automatically download the E5 model if it isn't already downloaded and then deploy the model.
value: "{\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"num_allocations\": 1,\n \"num_threads\": 1,\n \"model_id\": \".multilingual-e5-small\" \n }\n}"
PutElasticsearchRequestExample4:
summary: Eland text embedding task
description: Run `PUT _inference/text_embedding/my-msmarco-minilm-model` to create an inference endpoint that performs a `text_embedding` task with a model that was uploaded by Eland.
value: "{\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"num_allocations\": 1,\n \"num_threads\": 1,\n \"model_id\": \"msmarco-MiniLM-L12-cos-v5\" \n }\n}"
PutElasticsearchRequestExample5:
summary: Adaptive allocation
description: Run `PUT _inference/text_embedding/my-e5-model` to create an inference endpoint that performs a `text_embedding` task and to configure adaptive allocations. The API request will automatically download the E5 model if it isn't already downloaded and then deploy the model.
value: "{\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"adaptive_allocations\": {\n \"enabled\": true,\n \"min_number_of_allocations\": 3,\n \"max_number_of_allocations\": 10\n },\n \"num_threads\": 1,\n \"model_id\": \".multilingual-e5-small\"\n }\n}"
PutElasticsearchRequestExample6:
summary: Existing model deployment
description: Run `PUT _inference/sparse_embedding/use_existing_deployment` to use an already existing model deployment when creating an inference endpoint.
value: "{\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"deployment_id\": \".elser_model_2\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoElasticsearch'
examples:
PutElasticsearchResponseExample1:
description: 'A successful response from `PUT _inference/sparse_embedding/use_existing_deployment`. It contains the model ID and the threads and allocations settings from the model deployment.
'
value: "{\n \"inference_id\": \"use_existing_deployment\",\n \"task_type\": \"sparse_embedding\",\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"num_allocations\": 2,\n \"num_threads\": 1,\n \"model_id\": \".elser_model_2\",\n \"deployment_id\": \".elser_model_2\"\n },\n \"chunking_settings\": {\n \"strategy\": \"sentence\",\n \"max_chunk_size\": 250,\n \"sentence_overlap\": 1\n }\n}"
x-state: Generally available; Added in 8.13.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{elasticsearch_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_elasticsearch.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/sparse_embedding/my-elser-model\n{\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"adaptive_allocations\": { \n \"enabled\": true,\n \"min_number_of_allocations\": 1,\n \"max_number_of_allocations\": 4\n },\n \"num_threads\": 1,\n \"model_id\": \".elser_model_2\" \n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"sparse_embedding\",\n inference_id=\"my-elser-model\",\n inference_config={\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"adaptive_allocations\": {\n \"enabled\": True,\n \"min_number_of_allocations\": 1,\n \"max_number_of_allocations\": 4\n },\n \"num_threads\": 1,\n \"model_id\": \".elser_model_2\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n inference_config: {\n service: \"elasticsearch\",\n service_settings: {\n adaptive_allocations: {\n enabled: true,\n min_number_of_allocations: 1,\n max_number_of_allocations: 4,\n },\n num_threads: 1,\n model_id: \".elser_model_2\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n body: {\n \"service\": \"elasticsearch\",\n \"service_settings\": {\n \"adaptive_allocations\": {\n \"enabled\": true,\n \"min_number_of_allocations\": 1,\n \"max_number_of_allocations\": 4\n },\n \"num_threads\": 1,\n \"model_id\": \".elser_model_2\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"sparse_embedding\",\n \"inference_id\" => \"my-elser-model\",\n \"body\" => [\n \"service\" => \"elasticsearch\",\n \"service_settings\" => [\n \"adaptive_allocations\" => [\n \"enabled\" => true,\n \"min_number_of_allocations\" => 1,\n \"max_number_of_allocations\" => 4,\n ],\n \"num_threads\" => 1,\n \"model_id\" => \".elser_model_2\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"elasticsearch","service_settings":{"adaptive_allocations":{"enabled":true,"min_number_of_allocations":1,"max_number_of_allocations":4},"num_threads":1,"model_id":".elser_model_2"}}'' "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"my-elser-model\")\n .taskType(TaskType.SparseEmbedding)\n .inferenceConfig(i -> i\n .service(\"elasticsearch\")\n .serviceSettings(JsonData.fromJson(\"{\\\"adaptive_allocations\\\":{\\\"enabled\\\":true,\\\"min_number_of_allocations\\\":1,\\\"max_number_of_allocations\\\":4},\\\"num_threads\\\":1,\\\"model_id\\\":\\\".elser_model_2\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{elser_inference_id}:
put:
tags:
- Inference
summary: Create an ELSER inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `elser` service.
You can also deploy ELSER by using the Elasticsearch inference integration.
> info
> Your Elasticsearch deployment contains a preconfigured ELSER inference endpoint, you only need to create the enpoint using the API if you want to customize the settings.
The API request will automatically download and deploy the ELSER model if it isn''t already downloaded.
> info
> You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.
After creating the endpoint, wait for the model deployment to complete before using it.
To verify the deployment status, use the get trained model statistics API.
Look for `"state": "fully_allocated"` in the response and ensure that the `"allocation_count"` matches the `"target_allocation_count"`.
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.'
operationId: inference-put-elser
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.ElserTaskType'
style: simple
- in: path
name: elser_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Note that for ELSER endpoints, the max_chunk_size may not exceed `300`.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `elser`.
allOf:
- $ref: '#/components/schemas/inference._types.ElserServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `elser` service.
allOf:
- $ref: '#/components/schemas/inference._types.ElserServiceSettings'
required:
- service
- service_settings
examples:
PutElserRequestExample1:
summary: A sparse embedding task
description: Run `PUT _inference/sparse_embedding/my-elser-model` to create an inference endpoint that performs a `sparse_embedding` task. The request will automatically download the ELSER model if it isn't already downloaded and then deploy the model.
value: "{\n \"service\": \"elser\",\n \"service_settings\": {\n \"num_allocations\": 1,\n \"num_threads\": 1\n }\n}"
PutElserRequestExample2:
summary: Adaptive allocations
description: Run `PUT _inference/sparse_embedding/my-elser-model` to create an inference endpoint that performs a `sparse_embedding` task with adaptive allocations. When adaptive allocations are enabled, the number of allocations of the model is set automatically based on the current load.
value: "{\n \"service\": \"elser\",\n \"service_settings\": {\n \"adaptive_allocations\": {\n \"enabled\": true,\n \"min_number_of_allocations\": 3,\n \"max_number_of_allocations\": 10\n },\n \"num_threads\": 1\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoELSER'
examples:
PutElserResponseExample1:
description: A successful response when creating an ELSER inference endpoint.
value: "{\n \"inference_id\": \"my-elser-model\",\n \"task_type\": \"sparse_embedding\",\n \"service\": \"elser\",\n \"service_settings\": {\n \"num_allocations\": 1,\n \"num_threads\": 1\n },\n \"task_settings\": {}\n}"
deprecated: true
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{elser_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_elser.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/sparse_embedding/my-elser-model\n{\n \"service\": \"elser\",\n \"service_settings\": {\n \"num_allocations\": 1,\n \"num_threads\": 1\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"sparse_embedding\",\n inference_id=\"my-elser-model\",\n inference_config={\n \"service\": \"elser\",\n \"service_settings\": {\n \"num_allocations\": 1,\n \"num_threads\": 1\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n inference_config: {\n service: \"elser\",\n service_settings: {\n num_allocations: 1,\n num_threads: 1,\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"sparse_embedding\",\n inference_id: \"my-elser-model\",\n body: {\n \"service\": \"elser\",\n \"service_settings\": {\n \"num_allocations\": 1,\n \"num_threads\": 1\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"sparse_embedding\",\n \"inference_id\" => \"my-elser-model\",\n \"body\" => [\n \"service\" => \"elser\",\n \"service_settings\" => [\n \"num_allocations\" => 1,\n \"num_threads\" => 1,\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"elser","service_settings":{"num_allocations":1,"num_threads":1}}'' "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"my-elser-model\")\n .taskType(TaskType.SparseEmbedding)\n .inferenceConfig(i -> i\n .service(\"elser\")\n .serviceSettings(JsonData.fromJson(\"{\\\"num_allocations\\\":1,\\\"num_threads\\\":1}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{googleaistudio_inference_id}:
put:
tags:
- Inference
summary: Create an Google AI Studio inference endpoint
description: Create an inference endpoint to perform an inference task with the `googleaistudio` service.
operationId: inference-put-googleaistudio
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.GoogleAiStudioTaskType'
style: simple
- in: path
name: googleaistudio_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `completion` task type.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `googleaistudio`.
allOf:
- $ref: '#/components/schemas/inference._types.GoogleAiServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `googleaistudio` service.
allOf:
- $ref: '#/components/schemas/inference._types.GoogleAiStudioServiceSettings'
required:
- service
- service_settings
examples:
PutGoogleAiStudioRequestExample1:
summary: A completion task
description: Run `PUT _inference/completion/google_ai_studio_completion` to create an inference endpoint to perform a `completion` task type.
value: "{\n \"service\": \"googleaistudio\",\n \"service_settings\": {\n \"api_key\": \"api-key\",\n \"model_id\": \"model-id\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoGoogleAIStudio'
x-state: Generally available; Added in 8.15.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{googleaistudio_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_googleaistudio.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/completion/google_ai_studio_completion\n{\n \"service\": \"googleaistudio\",\n \"service_settings\": {\n \"api_key\": \"api-key\",\n \"model_id\": \"model-id\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"completion\",\n inference_id=\"google_ai_studio_completion\",\n inference_config={\n \"service\": \"googleaistudio\",\n \"service_settings\": {\n \"api_key\": \"api-key\",\n \"model_id\": \"model-id\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"completion\",\n inference_id: \"google_ai_studio_completion\",\n inference_config: {\n service: \"googleaistudio\",\n service_settings: {\n api_key: \"api-key\",\n model_id: \"model-id\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"completion\",\n inference_id: \"google_ai_studio_completion\",\n body: {\n \"service\": \"googleaistudio\",\n \"service_settings\": {\n \"api_key\": \"api-key\",\n \"model_id\": \"model-id\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"completion\",\n \"inference_id\" => \"google_ai_studio_completion\",\n \"body\" => [\n \"service\" => \"googleaistudio\",\n \"service_settings\" => [\n \"api_key\" => \"api-key\",\n \"model_id\" => \"model-id\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"googleaistudio","service_settings":{"api_key":"api-key","model_id":"model-id"}}'' "$ELASTICSEARCH_URL/_inference/completion/google_ai_studio_completion"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"google_ai_studio_completion\")\n .taskType(TaskType.Completion)\n .inferenceConfig(i -> i\n .service(\"googleaistudio\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"api-key\\\",\\\"model_id\\\":\\\"model-id\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{googlevertexai_inference_id}:
put:
tags:
- Inference
summary: Create a Google Vertex AI inference endpoint
description: Create an inference endpoint to perform an inference task with the `googlevertexai` service.
operationId: inference-put-googlevertexai
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.GoogleVertexAITaskType'
style: simple
- in: path
name: googlevertexai_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `googlevertexai`.
allOf:
- $ref: '#/components/schemas/inference._types.GoogleVertexAIServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `googlevertexai` service.
allOf:
- $ref: '#/components/schemas/inference._types.GoogleVertexAIServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.GoogleVertexAITaskSettings'
required:
- service
- service_settings
examples:
PutGoogleVertexAiRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/google_vertex_ai_embeddings` to create an inference endpoint to perform a `text_embedding` task type.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"service_account_json\": \"service-account-json\",\n \"model_id\": \"model-id\",\n \"location\": \"location\",\n \"project_id\": \"project-id\"\n }\n}"
PutGoogleVertexAiRequestExample10:
summary: A chat_completion task for Google Model Garden Meta shared endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_meta_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Meta's model hosted on Google Model Garden shared endpoint with single streaming URL provided. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"meta\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample11:
summary: A completion task for Google Model Garden Hugging Face dedicated endpoint with single URL provided for both streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_hugging_face_completion` to create an inference endpoint to perform a `completion` task using Hugging Face's model hosted on Google Model Garden dedicated endpoint with single URL provided for both streaming and non-streaming tasks. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"hugging_face\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%ENDPOINT_ID%.%LOCATION_ID%-%PROJECT_ID%.prediction.vertexai.goog/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample12:
summary: A chat_completion task for Google Model Garden Hugging Face dedicated endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_hugging_face_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Hugging Face's model hosted on Google Model Garden dedicated endpoint with single streaming URL provided. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"hugging_face\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%ENDPOINT_ID%.%LOCATION_ID%-%PROJECT_ID%.prediction.vertexai.goog/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample13:
summary: A completion task for Google Model Garden Hugging Face shared endpoint with single URL provided for both streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_hugging_face_completion` to create an inference endpoint to perform a `completion` task using Hugging Face's model hosted on Google Model Garden shared endpoint with single URL provided for both streaming and non-streaming tasks. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"hugging_face\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample14:
summary: A chat_completion task for Google Model Garden Hugging Face shared endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_hugging_face_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Hugging Face's model hosted on Google Model Garden shared endpoint with single streaming URL provided. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"hugging_face\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample15:
summary: A completion task for Google Model Garden Mistral serverless endpoint with separate URLs for streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_mistral_completion` to create an inference endpoint to perform a `completion` task using Mistral's serverless model hosted on Google Model Garden with separate URLs for streaming and non-streaming tasks. See the Mistral model documentation for instructions on how to construct URLs.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"mistral\",\n \"model_id\": \"mistral-small-2503\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/mistralai/models/%MODEL_ID%:rawPredict\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/mistralai/models/%MODEL_ID%:streamRawPredict\"\n }\n}"
PutGoogleVertexAiRequestExample16:
summary: A chat_completion task for Google Model Garden Mistral serverless endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_mistral_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Mistral's serverless model hosted on Google Model Garden with single streaming URL provided. See the Mistral model documentation for instructions on how to construct the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"mistral\",\n \"model_id\": \"mistral-small-2503\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/mistralai/models/%MODEL_ID%:streamRawPredict\"\n }\n}"
PutGoogleVertexAiRequestExample17:
summary: A completion task for Google Model Garden Mistral dedicated endpoint with single URL provided for both streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_mistral_completion` to create an inference endpoint to perform a `completion` task using Mistral's model hosted on Google Model Garden dedicated endpoint with single URL provided for both streaming and non-streaming tasks. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"mistral\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%ENDPOINT_ID%.%LOCATION_ID%-%PROJECT_ID%.prediction.vertexai.goog/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample18:
summary: A chat_completion task for Google Model Garden Mistral dedicated endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_mistral_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Mistral's model hosted on Google Model Garden dedicated endpoint with single streaming URL provided. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"mistral\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%ENDPOINT_ID%.%LOCATION_ID%-%PROJECT_ID%.prediction.vertexai.goog/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample19:
summary: A completion task for Google Model Garden Mistral shared endpoint with single URL provided for both streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_mistral_completion` to create an inference endpoint to perform a `completion` task using Mistral's model hosted on Google Model Garden shared endpoint with single URL provided for both streaming and non-streaming tasks. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"mistral\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample2:
summary: A rerank task
description: Run `PUT _inference/rerank/google_vertex_ai_rerank` to create an inference endpoint to perform a `rerank` task type.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"service_account_json\": \"service-account-json\",\n \"project_id\": \"project-id\"\n }\n}"
PutGoogleVertexAiRequestExample20:
summary: A chat_completion task for Google Model Garden Mistral shared endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_mistral_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Mistral's model hosted on Google Model Garden shared endpoint with single streaming URL provided. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"mistral\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample21:
summary: A completion task for Google Model Garden AI21 serverless endpoint with separate URLs for streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_ai21_completion` to create an inference endpoint to perform a `completion` task using AI21's model hosted on Google Model Garden serverless endpoint with separate URLs for streaming and non-streaming tasks. See the AI21 model documentation for instructions on how to construct URLs.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"ai21\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/ai21/models/%MODEL_ID%:rawPredict\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/ai21/models/%MODEL_ID%:streamRawPredict\"\n }\n}"
PutGoogleVertexAiRequestExample22:
summary: A chat_completion task for Google Model Garden AI21 serverless endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_ai21_chat_completion` to create an inference endpoint to perform a `chat_completion` task using AI21's model hosted on Google Model Garden serverless endpoint with single streaming URL provided. See the AI21 model documentation for instructions on how to construct URLs.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"ai21\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/ai21/models/%MODEL_ID%:streamRawPredict\"\n }\n}"
PutGoogleVertexAiRequestExample3:
summary: A completion task for Google Model Garden Anthropic serverless endpoint with separate URLs for streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_anthropic_completion` to create an inference endpoint to perform a `completion` task using Anthropic's serverless model hosted on Google Model Garden with separate URLs for streaming and non-streaming tasks. See the Anthropic model documentation for instructions on how to construct URLs.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"anthropic\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/anthropic/models/%MODEL_ID%:rawPredict\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/anthropic/models/%MODEL_ID%:streamRawPredict\"\n },\n \"task_settings\": {\n \"max_tokens\": 128\n }\n}"
PutGoogleVertexAiRequestExample4:
summary: A chat_completion task for Google Model Garden Anthropic serverless endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_anthropic_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Anthropic's serverless model hosted on Google Model Garden with single streaming URL provided. See the Anthropic model documentation for instructions on how to construct the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"anthropic\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/publishers/anthropic/models/%MODEL_ID%:streamRawPredict\"\n },\n \"task_settings\": {\n \"max_tokens\": 128\n }\n}"
PutGoogleVertexAiRequestExample5:
summary: A completion task for Google Model Garden Meta serverless endpoint with single URL provided for both streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_meta_completion` to create an inference endpoint to perform a `completion` task using Meta's serverless model hosted on Google Model Garden with single URL provided for both streaming and non-streaming tasks. See the Meta model documentation for instructions on how to construct the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"meta\",\n \"model_id\": \"meta/llama-3.3-70b-instruct-maas\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/openapi/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample6:
summary: A chat_completion task for Google Model Garden Meta serverless endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_meta_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Meta's serverless model hosted on Google Model Garden with single streaming URL provided. See the Meta model documentation for instructions on how to construct the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"meta\",\n \"model_id\": \"meta/llama-3.3-70b-instruct-maas\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/openapi/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample7:
summary: A completion task for Google Model Garden Meta dedicated endpoint with single URL provided for both streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_meta_completion` to create an inference endpoint to perform a `completion` task using Meta's model hosted on Google Model Garden dedicated endpoint with single URL provided for both streaming and non-streaming tasks. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"meta\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%ENDPOINT_ID%.%LOCATION_ID%-fasttryout.prediction.vertexai.goog/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample8:
summary: A chat_completion task for Google Model Garden Meta dedicated endpoint with single streaming URL provided
description: Run `PUT _inference/chat_completion/google_model_garden_meta_chat_completion` to create an inference endpoint to perform a `chat_completion` task using Meta's model hosted on Google Model Garden dedicated endpoint with single streaming URL provided. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"meta\",\n \"service_account_json\": \"service-account-json\",\n \"streaming_url\": \"https://%ENDPOINT_ID%.%LOCATION_ID%-fasttryout.prediction.vertexai.goog/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
PutGoogleVertexAiRequestExample9:
summary: A completion task for Google Model Garden Meta shared endpoint with single URL provided for both streaming and non-streaming tasks
description: Run `PUT _inference/completion/google_model_garden_meta_completion` to create an inference endpoint to perform a `completion` task using Meta's model hosted on Google Model Garden shared endpoint with single URL provided for both streaming and non-streaming tasks. See the endpoint's `Sample request` page for the variable values used in the URL.
value: "{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"provider\": \"meta\",\n \"service_account_json\": \"service-account-json\",\n \"url\": \"https://%LOCATION_ID%-aiplatform.googleapis.com/v1/projects/%PROJECT_ID%/locations/%LOCATION_ID%/endpoints/%ENDPOINT_ID%/chat/completions\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoGoogleVertexAI'
x-state: Generally available; Added in 8.15.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{googlevertexai_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_googlevertexai.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/google_vertex_ai_embeddingss\n{\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"service_account_json\": \"service-account-json\",\n \"model_id\": \"model-id\",\n \"location\": \"location\",\n \"project_id\": \"project-id\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"google_vertex_ai_embeddingss\",\n inference_config={\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"service_account_json\": \"service-account-json\",\n \"model_id\": \"model-id\",\n \"location\": \"location\",\n \"project_id\": \"project-id\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"google_vertex_ai_embeddingss\",\n inference_config: {\n service: \"googlevertexai\",\n service_settings: {\n service_account_json: \"service-account-json\",\n model_id: \"model-id\",\n location: \"location\",\n project_id: \"project-id\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"google_vertex_ai_embeddingss\",\n body: {\n \"service\": \"googlevertexai\",\n \"service_settings\": {\n \"service_account_json\": \"service-account-json\",\n \"model_id\": \"model-id\",\n \"location\": \"location\",\n \"project_id\": \"project-id\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"google_vertex_ai_embeddingss\",\n \"body\" => [\n \"service\" => \"googlevertexai\",\n \"service_settings\" => [\n \"service_account_json\" => \"service-account-json\",\n \"model_id\" => \"model-id\",\n \"location\" => \"location\",\n \"project_id\" => \"project-id\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"googlevertexai","service_settings":{"service_account_json":"service-account-json","model_id":"model-id","location":"location","project_id":"project-id"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/google_vertex_ai_embeddingss"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"google_vertex_ai_embeddingss\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"googlevertexai\")\n .serviceSettings(JsonData.fromJson(\"{\\\"service_account_json\\\":\\\"service-account-json\\\",\\\"model_id\\\":\\\"model-id\\\",\\\"location\\\":\\\"location\\\",\\\"project_id\\\":\\\"project-id\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{groq_inference_id}:
put:
tags:
- Inference
summary: Create a Groq inference endpoint
description: Create an inference endpoint to perform an inference task with the `groq` service.
operationId: inference-put-groq
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.GroqTaskType'
style: simple
- in: path
name: groq_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
service:
description: The type of service supported for the specified task type. In this case, `groq`.
allOf:
- $ref: '#/components/schemas/inference._types.GroqServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `groq` service.
allOf:
- $ref: '#/components/schemas/inference._types.GroqServiceSettings'
required:
- service
- service_settings
examples:
PutGroqRequestExample1:
description: Run `PUT _inference/chat_completion/groq-chat-completion` to create a Groq inference endpoint that performs a `chat_completion` task.
value: "{\n \"service\": \"groq\",\n \"service_settings\": {\n \"api_key\": \"groq-api-key\",\n \"model_id\": \"llama-3.3-70b-versatile\" \n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoGroq'
x-state: Generally available; Added in 9.3.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{groq_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_groq.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/chat_completion/groq-chat-completion\n{\n \"service\": \"groq\",\n \"service_settings\": {\n \"api_key\": \"groq-api-key\",\n \"model_id\": \"llama-3.3-70b-versatile\" \n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"chat_completion\",\n inference_id=\"groq-chat-completion\",\n inference_config={\n \"service\": \"groq\",\n \"service_settings\": {\n \"api_key\": \"groq-api-key\",\n \"model_id\": \"llama-3.3-70b-versatile\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"chat_completion\",\n inference_id: \"groq-chat-completion\",\n inference_config: {\n service: \"groq\",\n service_settings: {\n api_key: \"groq-api-key\",\n model_id: \"llama-3.3-70b-versatile\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"chat_completion\",\n inference_id: \"groq-chat-completion\",\n body: {\n \"service\": \"groq\",\n \"service_settings\": {\n \"api_key\": \"groq-api-key\",\n \"model_id\": \"llama-3.3-70b-versatile\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"chat_completion\",\n \"inference_id\" => \"groq-chat-completion\",\n \"body\" => [\n \"service\" => \"groq\",\n \"service_settings\" => [\n \"api_key\" => \"groq-api-key\",\n \"model_id\" => \"llama-3.3-70b-versatile\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"groq","service_settings":{"api_key":"groq-api-key","model_id":"llama-3.3-70b-versatile"}}'' "$ELASTICSEARCH_URL/_inference/chat_completion/groq-chat-completion"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"groq-chat-completion\")\n .taskType(TaskType.ChatCompletion)\n .inferenceConfig(i -> i\n .service(\"groq\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"groq-api-key\\\",\\\"model_id\\\":\\\"llama-3.3-70b-versatile\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{huggingface_inference_id}:
put:
tags:
- Inference
summary: Create a Hugging Face inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `hugging_face` service.
Supported tasks include: `text_embedding`, `completion`, and `chat_completion`.
To configure the endpoint, first visit the Hugging Face Inference Endpoints page and create a new endpoint.
Select a model that supports the task you intend to use.
For Elastic''s `text_embedding` task:
The selected model must support the `Sentence Embeddings` task. On the new endpoint creation page, select the `Sentence Embeddings` task under the `Advanced Configuration` section.
After the endpoint has initialized, copy the generated endpoint URL.
Recommended models for `text_embedding` task:
* `all-MiniLM-L6-v2`
* `all-MiniLM-L12-v2`
* `all-mpnet-base-v2`
* `e5-base-v2`
* `e5-small-v2`
* `multilingual-e5-base`
* `multilingual-e5-small`
For Elastic''s `chat_completion` and `completion` tasks:
The selected model must support the `Text Generation` task and expose OpenAI API. HuggingFace supports both serverless and dedicated endpoints for `Text Generation`. When creating dedicated endpoint select the `Text Generation` task.
After the endpoint is initialized (for dedicated) or ready (for serverless), ensure it supports the OpenAI API and includes `/v1/chat/completions` part in URL. Then, copy the full endpoint URL for use.
Recommended models for `chat_completion` and `completion` tasks:
* `Mistral-7B-Instruct-v0.2`
* `QwQ-32B`
* `Phi-3-mini-128k-instruct`
For Elastic''s `rerank` task:
The selected model must support the `sentence-ranking` task and expose OpenAI API.
HuggingFace supports only dedicated (not serverless) endpoints for `Rerank` so far.
After the endpoint is initialized, copy the full endpoint URL for use.
Tested models for `rerank` task:
* `bge-reranker-base`
* `jina-reranker-v1-turbo-en-GGUF`'
operationId: inference-put-hugging-face
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.HuggingFaceTaskType'
style: simple
- in: path
name: huggingface_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `hugging_face`.
allOf:
- $ref: '#/components/schemas/inference._types.HuggingFaceServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `hugging_face` service.
allOf:
- $ref: '#/components/schemas/inference._types.HuggingFaceServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.HuggingFaceTaskSettings'
required:
- service
- service_settings
examples:
PutHuggingFaceRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/hugging-face-embeddings` to create an inference endpoint that performs a `text_embedding` task type.
value: "{\n \"service\": \"hugging_face\",\n \"service_settings\": {\n \"api_key\": \"hugging-face-access-token\", \n \"url\": \"url-endpoint\" \n }\n}"
PutHuggingFaceRequestExample2:
summary: A rerank task
description: Run `PUT _inference/rerank/hugging-face-rerank` to create an inference endpoint that performs a `rerank` task type.
value: "{\n \"service\": \"hugging_face\",\n \"service_settings\": {\n \"api_key\": \"hugging-face-access-token\", \n \"url\": \"url-endpoint\" \n },\n \"task_settings\": {\n \"return_documents\": true,\n \"top_n\": 3\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoHuggingFace'
x-state: Generally available; Added in 8.12.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{huggingface_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_hugging_face.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/hugging-face-embeddings\n{\n \"service\": \"hugging_face\",\n \"service_settings\": {\n \"api_key\": \"hugging-face-access-token\", \n \"url\": \"url-endpoint\" \n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"hugging-face-embeddings\",\n inference_config={\n \"service\": \"hugging_face\",\n \"service_settings\": {\n \"api_key\": \"hugging-face-access-token\",\n \"url\": \"url-endpoint\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"hugging-face-embeddings\",\n inference_config: {\n service: \"hugging_face\",\n service_settings: {\n api_key: \"hugging-face-access-token\",\n url: \"url-endpoint\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"hugging-face-embeddings\",\n body: {\n \"service\": \"hugging_face\",\n \"service_settings\": {\n \"api_key\": \"hugging-face-access-token\",\n \"url\": \"url-endpoint\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"hugging-face-embeddings\",\n \"body\" => [\n \"service\" => \"hugging_face\",\n \"service_settings\" => [\n \"api_key\" => \"hugging-face-access-token\",\n \"url\" => \"url-endpoint\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"hugging_face","service_settings":{"api_key":"hugging-face-access-token","url":"url-endpoint"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/hugging-face-embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"hugging-face-embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"hugging_face\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"hugging-face-access-token\\\",\\\"url\\\":\\\"url-endpoint\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{jinaai_inference_id}:
put:
tags:
- Inference
summary: Create an JinaAI inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `jinaai` service.
To review the available `rerank` models, refer to .
To review the available `embedding` and `text_embedding` models, refer to .'
operationId: inference-put-jinaai
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.JinaAITaskType'
style: simple
- in: path
name: jinaai_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `embedding` and text_embedding` task types.
Not applicable to the `rerank` task type.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `jinaai`.
allOf:
- $ref: '#/components/schemas/inference._types.JinaAIServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `jinaai` service.
allOf:
- $ref: '#/components/schemas/inference._types.JinaAIServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.JinaAITaskSettings'
required:
- service
- service_settings
examples:
PutJinaAiRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/jinaai-embeddings` to create an inference endpoint for text embedding tasks using the JinaAI service.
value: "{\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v3\",\n \"api_key\": \"JinaAi-Api-key\"\n }\n}"
PutJinaAiRequestExample2:
summary: A rerank task
description: Run `PUT _inference/rerank/jinaai-rerank` to create an inference endpoint for rerank tasks using the JinaAI service.
value: "{\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"api_key\": \"JinaAI-Api-key\",\n \"model_id\": \"jina-reranker-v2-base-multilingual\"\n },\n \"task_settings\": {\n \"top_n\": 10,\n \"return_documents\": true\n }\n}"
PutJinaAiRequestExample3:
summary: An embedding task using a multimodal model
description: Run `PUT _inference/embedding/jinaai-embeddings-multimodal` to create an inference endpoint for embedding tasks using an embedding model that supports multimodal inputs (e.g. `jina-embeddings-v4`) via the JinaAI service.
value: "{\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v4\",\n \"api_key\": \"JinaAi-Api-key\"\n }\n}"
PutJinaAiRequestExample4:
summary: An embedding task using a non-multimodal model
description: Run `PUT _inference/embedding/jinaai-embeddings-text-only` to create an inference endpoint for embedding tasks using an embedding model that does not support multimodal inputs (e.g. `jina-embeddings-v3`) via the JinaAI service.
value: "{\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v3\",\n \"api_key\": \"JinaAi-Api-key\",\n \"multimodal_model\": false\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoJinaAi'
examples:
PutJinaAiResponseExample1:
summary: A text embedding task
description: A successful response when creating a JinaAI `text_embedding` inference endpoint.
value: "{\n \"inference_id\": \"jinaai-embeddings\",\n \"task_type\": \"text_embedding\",\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v3\",\n \"rate_limit\": {\n \"requests_per_minute\": 2000\n },\n \"dimensions\": 1024,\n \"embedding_type\": \"float\",\n \"similarity\": \"dot_product\"\n },\n \"chunking_settings\": {\n \"strategy\": \"sentence\",\n \"max_chunk_size\": 250,\n \"sentence_overlap\": 1\n }\n}"
PutJinaAiResponseExample2:
summary: A rerank task
description: A successful response when creating a JinaAI `rerank` inference endpoint.
value: "{\n \"inference_id\": \"jinaai-rerank\",\n \"task_type\": \"rerank\",\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-reranker-v2-base-multilingual\",\n \"rate_limit\": {\n \"requests_per_minute\": 2000\n }\n },\n \"task_settings\": {\n \"top_n\": 10,\n \"return_documents\": true\n }\n}"
PutJinaAiResponseExample3:
summary: An embedding task using a multimodal model
description: A successful response when creating a JinaAI `embedding` inference endpoint.
value: "{\n \"inference_id\": \"jinaai-embeddings-multimodal\",\n \"task_type\": \"embedding\",\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v4\",\n \"rate_limit\": {\n \"requests_per_minute\": 2000\n },\n \"dimensions\": 2048,\n \"embedding_type\": \"float\",\n \"similarity\": \"dot_product\",\n \"multimodal_model\": true\n },\n \"chunking_settings\": {\n \"strategy\": \"sentence\",\n \"max_chunk_size\": 250,\n \"sentence_overlap\": 1\n }\n}"
PutJinaAiResponseExample4:
summary: An embedding task using a non-multimodal model
description: A successful response when creating a JinaAI `embedding` inference endpoint with a model that does not support multimodal inputs.
value: "{\n \"inference_id\": \"jinaai-embeddings-text-only\",\n \"task_type\": \"embedding\",\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v3\",\n \"rate_limit\": {\n \"requests_per_minute\": 2000\n },\n \"dimensions\": 1024,\n \"embedding_type\": \"float\",\n \"similarity\": \"dot_product\",\n \"multimodal_model\": false\n },\n \"chunking_settings\": {\n \"strategy\": \"sentence\",\n \"max_chunk_size\": 250,\n \"sentence_overlap\": 1\n }\n}"
x-state: Generally available; Added in 8.18.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{jinaai_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_jinaai.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/jinaai-embeddings\n{\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v3\",\n \"api_key\": \"JinaAi-Api-key\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"jinaai-embeddings\",\n inference_config={\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v3\",\n \"api_key\": \"JinaAi-Api-key\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"jinaai-embeddings\",\n inference_config: {\n service: \"jinaai\",\n service_settings: {\n model_id: \"jina-embeddings-v3\",\n api_key: \"JinaAi-Api-key\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"jinaai-embeddings\",\n body: {\n \"service\": \"jinaai\",\n \"service_settings\": {\n \"model_id\": \"jina-embeddings-v3\",\n \"api_key\": \"JinaAi-Api-key\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"jinaai-embeddings\",\n \"body\" => [\n \"service\" => \"jinaai\",\n \"service_settings\" => [\n \"model_id\" => \"jina-embeddings-v3\",\n \"api_key\" => \"JinaAi-Api-key\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"jinaai","service_settings":{"model_id":"jina-embeddings-v3","api_key":"JinaAi-Api-key"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/jinaai-embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"jinaai-embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"jinaai\")\n .serviceSettings(JsonData.fromJson(\"{\\\"model_id\\\":\\\"jina-embeddings-v3\\\",\\\"api_key\\\":\\\"JinaAi-Api-key\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{llama_inference_id}:
put:
tags:
- Inference
summary: Create a Llama inference endpoint
description: Create an inference endpoint to perform an inference task with the `llama` service.
operationId: inference-put-llama
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.LlamaTaskType'
style: simple
- in: path
name: llama_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `completion` or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `llama`.
allOf:
- $ref: '#/components/schemas/inference._types.LlamaServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `llama` service.
allOf:
- $ref: '#/components/schemas/inference._types.LlamaServiceSettings'
required:
- service
- service_settings
examples:
PutLlamaRequestExample1:
description: Run `PUT _inference/text_embedding/llama-text-embedding` to create a Llama inference endpoint that performs a `text_embedding` task.
value: "{\n \"service\": \"llama\",\n \"service_settings\": {\n \"url\": \"http://localhost:8321/v1/inference/embeddings\",\n \"dimensions\": 384,\n \"model_id\": \"all-MiniLM-L6-v2\" \n }\n}"
PutLlamaRequestExample2:
description: Run `PUT _inference/completion/llama-completion` to create a Llama inference endpoint that performs a `completion` task.
value: "{\n \"service\": \"llama\",\n \"service_settings\": {\n \"url\": \"http://localhost:8321/v1/openai/v1/chat/completions\",\n \"model_id\": \"llama3.2:3b\" \n }\n}"
PutLlamaRequestExample3:
description: Run `PUT _inference/chat_completion/llama-chat-completion` to create a Llama inference endpoint that performs a `chat_completion` task.
value: "{\n \"service\": \"llama\",\n \"service_settings\": {\n \"url\": \"http://localhost:8321/v1/openai/v1/chat/completions\",\n \"model_id\": \"llama3.2:3b\" \n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoLlama'
x-state: Generally available; Added in 9.2.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{llama_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_llama.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/llama-text-embedding\n{\n \"service\": \"llama\",\n \"service_settings\": {\n \"url\": \"http://localhost:8321/v1/inference/embeddings\",\n \"dimensions\": 384,\n \"model_id\": \"all-MiniLM-L6-v2\" \n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"llama-text-embedding\",\n inference_config={\n \"service\": \"llama\",\n \"service_settings\": {\n \"url\": \"http://localhost:8321/v1/inference/embeddings\",\n \"dimensions\": 384,\n \"model_id\": \"all-MiniLM-L6-v2\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"llama-text-embedding\",\n inference_config: {\n service: \"llama\",\n service_settings: {\n url: \"http://localhost:8321/v1/inference/embeddings\",\n dimensions: 384,\n model_id: \"all-MiniLM-L6-v2\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"llama-text-embedding\",\n body: {\n \"service\": \"llama\",\n \"service_settings\": {\n \"url\": \"http://localhost:8321/v1/inference/embeddings\",\n \"dimensions\": 384,\n \"model_id\": \"all-MiniLM-L6-v2\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"llama-text-embedding\",\n \"body\" => [\n \"service\" => \"llama\",\n \"service_settings\" => [\n \"url\" => \"http://localhost:8321/v1/inference/embeddings\",\n \"dimensions\" => 384,\n \"model_id\" => \"all-MiniLM-L6-v2\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"llama","service_settings":{"url":"http://localhost:8321/v1/inference/embeddings","dimensions":384,"model_id":"all-MiniLM-L6-v2"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/llama-text-embedding"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"llama-text-embedding\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"llama\")\n .serviceSettings(JsonData.fromJson(\"{\\\"url\\\":\\\"http://localhost:8321/v1/inference/embeddings\\\",\\\"dimensions\\\":384,\\\"model_id\\\":\\\"all-MiniLM-L6-v2\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{mistral_inference_id}:
put:
tags:
- Inference
summary: Create a Mistral inference endpoint
description: Create an inference endpoint to perform an inference task with the `mistral` service.
operationId: inference-put-mistral
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.MistralTaskType'
style: simple
- in: path
name: mistral_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `completion` or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `mistral`.
allOf:
- $ref: '#/components/schemas/inference._types.MistralServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `mistral` service.
allOf:
- $ref: '#/components/schemas/inference._types.MistralServiceSettings'
required:
- service
- service_settings
examples:
PutMistralRequestExample1:
description: Run `PUT _inference/text_embedding/mistral-embeddings-test` to create a Mistral inference endpoint that performs a text embedding task.
value: "{\n \"service\": \"mistral\",\n \"service_settings\": {\n \"api_key\": \"Mistral-API-Key\",\n \"model\": \"mistral-embed\" \n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoMistral'
x-state: Generally available; Added in 8.15.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{mistral_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_mistral.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/mistral-embeddings-test\n{\n \"service\": \"mistral\",\n \"service_settings\": {\n \"api_key\": \"Mistral-API-Key\",\n \"model\": \"mistral-embed\" \n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"mistral-embeddings-test\",\n inference_config={\n \"service\": \"mistral\",\n \"service_settings\": {\n \"api_key\": \"Mistral-API-Key\",\n \"model\": \"mistral-embed\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"mistral-embeddings-test\",\n inference_config: {\n service: \"mistral\",\n service_settings: {\n api_key: \"Mistral-API-Key\",\n model: \"mistral-embed\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"mistral-embeddings-test\",\n body: {\n \"service\": \"mistral\",\n \"service_settings\": {\n \"api_key\": \"Mistral-API-Key\",\n \"model\": \"mistral-embed\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"mistral-embeddings-test\",\n \"body\" => [\n \"service\" => \"mistral\",\n \"service_settings\" => [\n \"api_key\" => \"Mistral-API-Key\",\n \"model\" => \"mistral-embed\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"mistral","service_settings":{"api_key":"Mistral-API-Key","model":"mistral-embed"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/mistral-embeddings-test"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"mistral-embeddings-test\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"mistral\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"Mistral-API-Key\\\",\\\"model\\\":\\\"mistral-embed\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{nvidia_inference_id}:
put:
tags:
- Inference
summary: Create an Nvidia inference endpoint
description: Create an inference endpoint to perform an inference task with the `nvidia` service.
operationId: inference-put-nvidia
parameters:
- in: path
name: task_type
description: 'The type of the inference task that the model will perform.
NOTE: The `chat_completion` task type only supports streaming and only through the _stream API.'
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.NvidiaTaskType'
style: simple
- in: path
name: nvidia_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `nvidia`.
allOf:
- $ref: '#/components/schemas/inference._types.NvidiaServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `nvidia` service.
allOf:
- $ref: '#/components/schemas/inference._types.NvidiaServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.NvidiaTaskSettings'
required:
- service
- service_settings
examples:
PutNvidiaRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/nvidia-text-embedding` to create an inference endpoint that performs a `text_embedding` task.
value: "{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"url\": \"nvidia-embeddings-url\",\n \"api_key\": \"nvidia-embeddings-token\",\n \"model_id\": \"nvidia/llama-3.2-nv-embedqa-1b-v2\"\n }\n}"
PutNvidiaRequestExample2:
summary: A text embedding task with custom `task_settings` and no `url` parameter
description: Run `PUT _inference/text_embedding/nvidia-text-embedding` to create an inference endpoint that performs a `text_embedding` task, specifying custom `task_settings` and omitting the `url` parameter if model is accessible via default NVIDIA endpoint.
value: "{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"nvidia/llama-3.2-nv-embedqa-1b-v2\",\n \"api_key\": \"nvidia-text-embeddings-token\"\n },\n \"task_settings\": {\n \"input_type\": \"ingest\",\n \"truncate\": \"start\"\n }\n}"
PutNvidiaRequestExample3:
summary: A completion task
description: Run `PUT _inference/completion/nvidia-completion` to create an inference endpoint that performs a `completion` task.
value: "{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"url\": \"nvidia-completion-url\",\n \"api_key\": \"nvidia-completion-token\",\n \"model_id\": \"microsoft/phi-3-mini-128k-instruct\"\n }\n}"
PutNvidiaRequestExample4:
summary: A completion task without `url` parameter
description: Run `PUT _inference/completion/nvidia-completion` to create an inference endpoint that performs a `completion` task, omitting the `url` parameter if model is accessible via default NVIDIA endpoint.
value: "{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"api_key\": \"nvidia-completion-token\",\n \"model_id\": \"microsoft/phi-3-mini-128k-instruct\"\n }\n}"
PutNvidiaRequestExample5:
summary: A chat completion task
description: Run `PUT _inference/chat_completion/nvidia-chat-completion` to create an inference endpoint that performs a `chat_completion` task.
value: "{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"url\": \"nvidia-chat-completion-url\",\n \"api_key\": \"nvidia-chat-completion-token\",\n \"model_id\": \"microsoft/phi-3-mini-128k-instruct\"\n }\n}"
PutNvidiaRequestExample6:
summary: A chat completion task without `url` parameter
description: Run `PUT _inference/chat_completion/nvidia-chat-completion` to create an inference endpoint that performs a `chat_completion` task, omitting the `url` parameter if model is accessible via default NVIDIA endpoint.
value: "{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"api_key\": \"nvidia-chat-completion-token\",\n \"model_id\": \"microsoft/phi-3-mini-128k-instruct\"\n }\n}"
PutNvidiaRequestExample7:
summary: A rerank task
description: Run `PUT _inference/rerank/nvidia-rerank` to create an inference endpoint that performs a `rerank` task.
value: "{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"url\": \"nvidia-rerank-url\",\n \"api_key\": \"nvidia-rerank-token\",\n \"model_id\": \"nv-rerank-qa-mistral-4b:1\"\n }\n}"
PutNvidiaRequestExample8:
summary: A rerank task without `url` parameter
description: Run `PUT _inference/rerank/nvidia-rerank` to create an inference endpoint that performs a `rerank` task, omitting the `url` parameter if model is accessible via default NVIDIA endpoint.
value: "{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"api_key\": \"nvidia-rerank-token\",\n \"model_id\": \"nv-rerank-qa-mistral-4b:1\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoNvidia'
examples:
PutNvidiaResponseExample1:
summary: A text embedding task
description: A successful response when creating an Nvidia `text_embedding` inference endpoint.
value: "{\n \"inference_id\": \"nvidia-text-embedding\",\n \"task_type\": \"text_embedding\",\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"nvidia/llama-3.2-nv-embedqa-1b-v2\",\n \"url\": \"nvidia-embeddings-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n },\n \"dimensions\": 2048,\n \"similarity\": \"dot_product\"\n },\n \"chunking_settings\": {\n \"strategy\": \"sentence\",\n \"max_chunk_size\": 250,\n \"sentence_overlap\": 1\n }\n}"
PutNvidiaResponseExample2:
summary: A text embedding task with custom `task_settings` and no `url` parameter
description: A successful response when creating an Nvidia `text_embedding` inference endpoint with custom `task_settings` and no `url` parameter.
value: "{\n \"inference_id\": \"nvidia-text-embedding\",\n \"task_type\": \"text_embedding\",\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"nvidia/llama-3.2-nv-embedqa-1b-v2\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n },\n \"dimensions\": 2048,\n \"similarity\": \"dot_product\"\n },\n \"task_settings\": {\n \"input_type\": \"ingest\",\n \"truncate\": \"start\"\n },\n \"chunking_settings\": {\n \"strategy\": \"sentence\",\n \"max_chunk_size\": 250,\n \"sentence_overlap\": 1\n }\n}"
PutNvidiaResponseExample3:
summary: A completion task
description: A successful response when creating an Nvidia `completion` inference endpoint.
value: "{\n \"inference_id\": \"nvidia-completion\",\n \"task_type\": \"completion\",\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"microsoft/phi-3-mini-128k-instruct\",\n \"url\": \"nvidia-completion-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
PutNvidiaResponseExample4:
summary: A completion task without `url` parameter
description: A successful response when creating an Nvidia `completion` inference endpoint without `url` parameter.
value: "{\n \"inference_id\": \"nvidia-completion\",\n \"task_type\": \"completion\",\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"microsoft/phi-3-mini-128k-instruct\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
PutNvidiaResponseExample5:
summary: A chat completion task
description: A successful response when creating an Nvidia `chat_completion` inference endpoint.
value: "{\n \"inference_id\": \"nvidia-chat-completion\",\n \"task_type\": \"chat_completion\",\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"microsoft/phi-3-mini-128k-instruct\",\n \"url\": \"nvidia-chat-completion-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
PutNvidiaResponseExample6:
summary: A chat completion task without `url` parameter
description: A successful response when creating an Nvidia `chat_completion` inference endpoint without `url` parameter.
value: "{\n \"inference_id\": \"nvidia-chat-completion\",\n \"task_type\": \"chat_completion\",\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"microsoft/phi-3-mini-128k-instruct\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
PutNvidiaResponseExample7:
summary: A rerank task
description: A successful response when creating an Nvidia `rerank` inference endpoint.
value: "{\n \"inference_id\": \"nvidia-rerank\",\n \"task_type\": \"rerank\",\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"nv-rerank-qa-mistral-4b:1\",\n \"url\": \"nvidia-rerank-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
PutNvidiaResponseExample8:
summary: A rerank task without `url` parameter
description: A successful response when creating an Nvidia `rerank` inference endpoint without `url` parameter.
value: "{\n \"inference_id\": \"nvidia-rerank\",\n \"task_type\": \"rerank\",\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"model_id\": \"nv-rerank-qa-mistral-4b:1\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
x-state: Generally available; Added in 9.3.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{nvidia_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_nvidia.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/nvidia-text-embedding\n{\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"url\": \"nvidia-embeddings-url\",\n \"api_key\": \"nvidia-embeddings-token\",\n \"model_id\": \"nvidia/llama-3.2-nv-embedqa-1b-v2\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"nvidia-text-embedding\",\n inference_config={\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"url\": \"nvidia-embeddings-url\",\n \"api_key\": \"nvidia-embeddings-token\",\n \"model_id\": \"nvidia/llama-3.2-nv-embedqa-1b-v2\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"nvidia-text-embedding\",\n inference_config: {\n service: \"nvidia\",\n service_settings: {\n url: \"nvidia-embeddings-url\",\n api_key: \"nvidia-embeddings-token\",\n model_id: \"nvidia/llama-3.2-nv-embedqa-1b-v2\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"nvidia-text-embedding\",\n body: {\n \"service\": \"nvidia\",\n \"service_settings\": {\n \"url\": \"nvidia-embeddings-url\",\n \"api_key\": \"nvidia-embeddings-token\",\n \"model_id\": \"nvidia/llama-3.2-nv-embedqa-1b-v2\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"nvidia-text-embedding\",\n \"body\" => [\n \"service\" => \"nvidia\",\n \"service_settings\" => [\n \"url\" => \"nvidia-embeddings-url\",\n \"api_key\" => \"nvidia-embeddings-token\",\n \"model_id\" => \"nvidia/llama-3.2-nv-embedqa-1b-v2\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"nvidia","service_settings":{"url":"nvidia-embeddings-url","api_key":"nvidia-embeddings-token","model_id":"nvidia/llama-3.2-nv-embedqa-1b-v2"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/nvidia-text-embedding"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"nvidia-text-embedding\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"nvidia\")\n .serviceSettings(JsonData.fromJson(\"{\\\"url\\\":\\\"nvidia-embeddings-url\\\",\\\"api_key\\\":\\\"nvidia-embeddings-token\\\",\\\"model_id\\\":\\\"nvidia/llama-3.2-nv-embedqa-1b-v2\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{openai_inference_id}:
put:
tags:
- Inference
summary: Create an OpenAI inference endpoint
description: Create an inference endpoint to perform an inference task with the `openai` service or `openai` compatible APIs.
operationId: inference-put-openai
parameters:
- in: path
name: task_type
description: 'The type of the inference task that the model will perform.
NOTE: The `chat_completion` task type only supports streaming and only through the _stream API.'
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.OpenAITaskType'
style: simple
- in: path
name: openai_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `completion` or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `openai`.
allOf:
- $ref: '#/components/schemas/inference._types.OpenAIServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `openai` service.
allOf:
- $ref: '#/components/schemas/inference._types.OpenAIServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.OpenAITaskSettings'
required:
- service
- service_settings
examples:
PutOpenAiRequestExample1:
summary: A text embedding task
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.
value: "{\n \"service\": \"openai\",\n \"service_settings\": {\n \"api_key\": \"OpenAI-API-Key\",\n \"model_id\": \"text-embedding-3-small\",\n \"dimensions\": 128\n }\n}"
PutOpenAiRequestExample2:
summary: A completion task
description: Run `PUT _inference/completion/openai-completion` to create an inference endpoint to perform a `completion` task type.
value: "{\n \"service\": \"openai\",\n \"service_settings\": {\n \"api_key\": \"OpenAI-API-Key\",\n \"model_id\": \"gpt-3.5-turbo\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoOpenAI'
x-state: Generally available; Added in 8.12.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{openai_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_openai.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/openai-embeddings\n{\n \"service\": \"openai\",\n \"service_settings\": {\n \"api_key\": \"OpenAI-API-Key\",\n \"model_id\": \"text-embedding-3-small\",\n \"dimensions\": 128\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"openai-embeddings\",\n inference_config={\n \"service\": \"openai\",\n \"service_settings\": {\n \"api_key\": \"OpenAI-API-Key\",\n \"model_id\": \"text-embedding-3-small\",\n \"dimensions\": 128\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"openai-embeddings\",\n inference_config: {\n service: \"openai\",\n service_settings: {\n api_key: \"OpenAI-API-Key\",\n model_id: \"text-embedding-3-small\",\n dimensions: 128,\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"openai-embeddings\",\n body: {\n \"service\": \"openai\",\n \"service_settings\": {\n \"api_key\": \"OpenAI-API-Key\",\n \"model_id\": \"text-embedding-3-small\",\n \"dimensions\": 128\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"openai-embeddings\",\n \"body\" => [\n \"service\" => \"openai\",\n \"service_settings\" => [\n \"api_key\" => \"OpenAI-API-Key\",\n \"model_id\" => \"text-embedding-3-small\",\n \"dimensions\" => 128,\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"openai","service_settings":{"api_key":"OpenAI-API-Key","model_id":"text-embedding-3-small","dimensions":128}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/openai-embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"openai-embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"openai\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"OpenAI-API-Key\\\",\\\"model_id\\\":\\\"text-embedding-3-small\\\",\\\"dimensions\\\":128}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{openshiftai_inference_id}:
put:
tags:
- Inference
summary: Create an OpenShift AI inference endpoint
description: Create an inference endpoint to perform an inference task with the `openshift_ai` service.
operationId: inference-put-openshift-ai
parameters:
- in: path
name: task_type
description: 'The type of the inference task that the model will perform.
NOTE: The `chat_completion` task type only supports streaming and only through the _stream API.'
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.OpenShiftAiTaskType'
style: simple
- in: path
name: openshiftai_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `openshift_ai`.
allOf:
- $ref: '#/components/schemas/inference._types.OpenShiftAiServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `openshift_ai` service.
allOf:
- $ref: '#/components/schemas/inference._types.OpenShiftAiServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
Applies only to the `rerank` task type.
Not applicable to the `text_embedding`, `completion`, or `chat_completion` task types.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.OpenShiftAiTaskSettings'
required:
- service
- service_settings
examples:
PutOpenShiftAiRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/openshift-ai-text-embedding` to create an inference endpoint that performs a `text_embedding` task.
value: "{\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-embeddings-url\",\n \"api_key\": \"openshift-ai-embeddings-token\",\n \"model_id\": \"gritlm-7b\"\n }\n}"
PutOpenShiftAiRequestExample2:
summary: A completion task
description: Run `PUT _inference/completion/openshift-ai-completion` to create an inference endpoint that performs a `completion` task.
value: "{\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-completion-url\",\n \"api_key\": \"openshift-ai-completion-token\",\n \"model_id\": \"llama-31-8b-instruct\"\n }\n}"
PutOpenShiftAiRequestExample3:
summary: A chat completion task
description: Run `PUT _inference/chat_completion/openshift-ai-chat-completion` to create an inference endpoint that performs a `chat_completion` task.
value: "{\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-chat-completion-url\",\n \"api_key\": \"openshift-ai-chat-completion-token\",\n \"model_id\": \"llama-31-8b-instruct\"\n }\n}"
PutOpenShiftAiRequestExample4:
summary: A rerank task
description: Run `PUT _inference/rerank/openshift-ai-rerank` to create an inference endpoint that performs a `rerank` task.
value: "{\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-rerank-url\",\n \"api_key\": \"openshift-ai-rerank-token\",\n \"model_id\": \"bge-reranker-v2-m3\"\n }\n}"
PutOpenShiftAiRequestExample5:
summary: A rerank task with custom `task_settings` and omitted `model_id`
description: Run `PUT _inference/rerank/openshift-ai-rerank` to create an inference endpoint that performs a `rerank` task, specifying custom `task_settings` and omitting the `model_id` if deployed model doesn't require it.
value: "{\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-rerank-url\",\n \"api_key\": \"openshift-ai-rerank-token\"\n },\n \"task_settings\": {\n \"return_documents\": true,\n \"top_n\": 2\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoOpenShiftAi'
examples:
PutOpenShiftAiResponseExample1:
summary: A text embedding task
description: A successful response when creating an OpenShift AI `text_embedding` inference endpoint.
value: "{\n \"inference_id\": \"openshift-ai-text-embedding\",\n \"task_type\": \"text_embedding\",\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"model_id\": \"gritlm-7b\",\n \"url\": \"openshift-ai-embeddings-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n },\n \"dimensions\": 4096,\n \"similarity\": \"dot_product\",\n \"dimensions_set_by_user\": false\n },\n \"chunking_settings\": {\n \"strategy\": \"sentence\",\n \"max_chunk_size\": 250,\n \"sentence_overlap\": 1\n }\n}"
PutOpenShiftAiResponseExample2:
summary: A completion task
description: A successful response when creating an OpenShift AI `completion` inference endpoint.
value: "{\n \"inference_id\": \"openshift-ai-completion\",\n \"task_type\": \"completion\",\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"model_id\": \"llama-31-8b-instruct\",\n \"url\": \"openshift-ai-completion-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
PutOpenShiftAiResponseExample3:
summary: A chat completion task
description: A successful response when creating an OpenShift AI `chat_completion` inference endpoint.
value: "{\n \"inference_id\": \"openshift-ai-chat-completion\",\n \"task_type\": \"chat_completion\",\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"model_id\": \"llama-31-8b-instruct\",\n \"url\": \"openshift-ai-chat-completion-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
PutOpenShiftAiResponseExample4:
summary: A rerank task
description: A successful response when creating an OpenShift AI `rerank` inference endpoint.
value: "{\n \"inference_id\": \"openshift-ai-rerank\",\n \"task_type\": \"rerank\",\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"model_id\": \"bge-reranker-v2-m3\",\n \"url\": \"openshift-ai-rerank-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n }\n}"
PutOpenShiftAiResponseExample5:
summary: A rerank task with custom `task_settings` and omitted `model_id`
description: A successful response when creating an OpenShift AI `rerank` inference endpoint with custom `task_settings` and omitted `model_id`
value: "{\n \"inference_id\": \"openshift-ai-rerank\",\n \"task_type\": \"rerank\",\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-rerank-url\",\n \"rate_limit\": {\n \"requests_per_minute\": 3000\n }\n },\n \"task_settings\": {\n \"return_documents\": true,\n \"top_n\": 2\n }\n}"
x-state: Generally available; Added in 9.3.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{openshiftai_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_openshift_ai.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/openshift-ai-text-embedding\n{\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-embeddings-url\",\n \"api_key\": \"openshift-ai-embeddings-token\",\n \"model_id\": \"gritlm-7b\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"openshift-ai-text-embedding\",\n inference_config={\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-embeddings-url\",\n \"api_key\": \"openshift-ai-embeddings-token\",\n \"model_id\": \"gritlm-7b\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"openshift-ai-text-embedding\",\n inference_config: {\n service: \"openshift_ai\",\n service_settings: {\n url: \"openshift-ai-embeddings-url\",\n api_key: \"openshift-ai-embeddings-token\",\n model_id: \"gritlm-7b\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"openshift-ai-text-embedding\",\n body: {\n \"service\": \"openshift_ai\",\n \"service_settings\": {\n \"url\": \"openshift-ai-embeddings-url\",\n \"api_key\": \"openshift-ai-embeddings-token\",\n \"model_id\": \"gritlm-7b\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"openshift-ai-text-embedding\",\n \"body\" => [\n \"service\" => \"openshift_ai\",\n \"service_settings\" => [\n \"url\" => \"openshift-ai-embeddings-url\",\n \"api_key\" => \"openshift-ai-embeddings-token\",\n \"model_id\" => \"gritlm-7b\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"openshift_ai","service_settings":{"url":"openshift-ai-embeddings-url","api_key":"openshift-ai-embeddings-token","model_id":"gritlm-7b"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/openshift-ai-text-embedding"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"openshift-ai-text-embedding\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"openshift_ai\")\n .serviceSettings(JsonData.fromJson(\"{\\\"url\\\":\\\"openshift-ai-embeddings-url\\\",\\\"api_key\\\":\\\"openshift-ai-embeddings-token\\\",\\\"model_id\\\":\\\"gritlm-7b\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{voyageai_inference_id}:
put:
tags:
- Inference
summary: Create a VoyageAI inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `voyageai` service.
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.'
operationId: inference-put-voyageai
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.VoyageAITaskType'
style: simple
- in: path
name: voyageai_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank` task type.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `voyageai`.
allOf:
- $ref: '#/components/schemas/inference._types.VoyageAIServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `voyageai` service.
allOf:
- $ref: '#/components/schemas/inference._types.VoyageAIServiceSettings'
task_settings:
description: 'Settings to configure the inference task.
These settings are specific to the task type you specified.'
allOf:
- $ref: '#/components/schemas/inference._types.VoyageAITaskSettings'
required:
- service
- service_settings
examples:
PutVoyageAIRequestExample1:
summary: A text embedding task
description: Run `PUT _inference/text_embedding/voyageai-embeddings` to create an inference endpoint that performs a `text_embedding` task. The embeddings created by requests to this endpoint will have 512 dimensions.
value: "{\n \"service\": \"voyageai\",\n \"service_settings\": {\n \"model_id\": \"voyage-3-large\",\n \"dimensions\": 512\n }\n}"
PutVoyageAIRequestExample2:
summary: A rerank task
description: Run `PUT _inference/rerank/voyageai-rerank` to create an inference endpoint that performs a `rerank` task.
value: "{\n \"service\": \"voyageai\",\n \"service_settings\": {\n \"model_id\": \"rerank-2\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoVoyageAI'
x-state: Generally available; Added in 8.19.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{voyageai_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_voyageai.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/openai-embeddings\n{\n \"service\": \"voyageai\",\n \"service_settings\": {\n \"model_id\": \"voyage-3-large\",\n \"dimensions\": 512\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"openai-embeddings\",\n inference_config={\n \"service\": \"voyageai\",\n \"service_settings\": {\n \"model_id\": \"voyage-3-large\",\n \"dimensions\": 512\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"openai-embeddings\",\n inference_config: {\n service: \"voyageai\",\n service_settings: {\n model_id: \"voyage-3-large\",\n dimensions: 512,\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"openai-embeddings\",\n body: {\n \"service\": \"voyageai\",\n \"service_settings\": {\n \"model_id\": \"voyage-3-large\",\n \"dimensions\": 512\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"openai-embeddings\",\n \"body\" => [\n \"service\" => \"voyageai\",\n \"service_settings\" => [\n \"model_id\" => \"voyage-3-large\",\n \"dimensions\" => 512,\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"voyageai","service_settings":{"model_id":"voyage-3-large","dimensions":512}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/openai-embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"openai-embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"voyageai\")\n .serviceSettings(JsonData.fromJson(\"{\\\"model_id\\\":\\\"voyage-3-large\\\",\\\"dimensions\\\":512}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{watsonx_inference_id}:
put:
tags:
- Inference
summary: Create a Watsonx inference endpoint
description: 'Create an inference endpoint to perform an inference task with the `watsonxai` service.
You need an IBM Cloud Databases for Elasticsearch deployment to use the `watsonxai` inference service.
You can provision one through the IBM catalog, the Cloud Databases CLI plug-in, the Cloud Databases API, or Terraform.'
operationId: inference-put-watsonx
parameters:
- in: path
name: task_type
description: The type of the inference task that the model will perform.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.WatsonxTaskType'
style: simple
- in: path
name: watsonx_inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference endpoint to be created.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
chunking_settings:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#infer-chunking-config
description: 'The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank`, `completion` or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The type of service supported for the specified task type. In this case, `watsonxai`.
allOf:
- $ref: '#/components/schemas/inference._types.WatsonxServiceType'
service_settings:
description: Settings used to install the inference model. These settings are specific to the `watsonxai` service.
allOf:
- $ref: '#/components/schemas/inference._types.WatsonxServiceSettings'
required:
- service
- service_settings
examples:
PutWatsonxRequestExample1:
description: Run `PUT _inference/text_embedding/watsonx-embeddings` to create an Watonsx inference endpoint that performs a text embedding task.
value: "{\n \"service\": \"watsonxai\",\n \"service_settings\": {\n \"api_key\": \"Watsonx-API-Key\", \n \"url\": \"Wastonx-URL\", \n \"model_id\": \"ibm/slate-30m-english-rtrvr\",\n \"project_id\": \"IBM-Cloud-ID\", \n \"api_version\": \"2024-03-14\"\n }\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfoWatsonx'
x-state: Generally available; Added in 8.16.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{watsonx_inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: put_watsonx.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/text_embedding/watsonx-embeddings\n{\n \"service\": \"watsonxai\",\n \"service_settings\": {\n \"api_key\": \"Watsonx-API-Key\", \n \"url\": \"Wastonx-URL\", \n \"model_id\": \"ibm/slate-30m-english-rtrvr\",\n \"project_id\": \"IBM-Cloud-ID\", \n \"api_version\": \"2024-03-14\"\n }\n}"
- lang: Python
source: "resp = client.inference.put(\n task_type=\"text_embedding\",\n inference_id=\"watsonx-embeddings\",\n inference_config={\n \"service\": \"watsonxai\",\n \"service_settings\": {\n \"api_key\": \"Watsonx-API-Key\",\n \"url\": \"Wastonx-URL\",\n \"model_id\": \"ibm/slate-30m-english-rtrvr\",\n \"project_id\": \"IBM-Cloud-ID\",\n \"api_version\": \"2024-03-14\"\n }\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.put({\n task_type: \"text_embedding\",\n inference_id: \"watsonx-embeddings\",\n inference_config: {\n service: \"watsonxai\",\n service_settings: {\n api_key: \"Watsonx-API-Key\",\n url: \"Wastonx-URL\",\n model_id: \"ibm/slate-30m-english-rtrvr\",\n project_id: \"IBM-Cloud-ID\",\n api_version: \"2024-03-14\",\n },\n },\n});"
- lang: Ruby
source: "response = client.inference.put(\n task_type: \"text_embedding\",\n inference_id: \"watsonx-embeddings\",\n body: {\n \"service\": \"watsonxai\",\n \"service_settings\": {\n \"api_key\": \"Watsonx-API-Key\",\n \"url\": \"Wastonx-URL\",\n \"model_id\": \"ibm/slate-30m-english-rtrvr\",\n \"project_id\": \"IBM-Cloud-ID\",\n \"api_version\": \"2024-03-14\"\n }\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->put([\n \"task_type\" => \"text_embedding\",\n \"inference_id\" => \"watsonx-embeddings\",\n \"body\" => [\n \"service\" => \"watsonxai\",\n \"service_settings\" => [\n \"api_key\" => \"Watsonx-API-Key\",\n \"url\" => \"Wastonx-URL\",\n \"model_id\" => \"ibm/slate-30m-english-rtrvr\",\n \"project_id\" => \"IBM-Cloud-ID\",\n \"api_version\" => \"2024-03-14\",\n ],\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service":"watsonxai","service_settings":{"api_key":"Watsonx-API-Key","url":"Wastonx-URL","model_id":"ibm/slate-30m-english-rtrvr","project_id":"IBM-Cloud-ID","api_version":"2024-03-14"}}'' "$ELASTICSEARCH_URL/_inference/text_embedding/watsonx-embeddings"'
- lang: Java
source: "client.inference().put(p -> p\n .inferenceId(\"watsonx-embeddings\")\n .taskType(TaskType.TextEmbedding)\n .inferenceConfig(i -> i\n .service(\"watsonxai\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"Watsonx-API-Key\\\",\\\"url\\\":\\\"Wastonx-URL\\\",\\\"model_id\\\":\\\"ibm/slate-30m-english-rtrvr\\\",\\\"project_id\\\":\\\"IBM-Cloud-ID\\\",\\\"api_version\\\":\\\"2024-03-14\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/rerank/{inference_id}:
post:
tags:
- Inference
summary: Perform reranking inference on the service
operationId: inference-rerank
parameters:
- in: path
name: inference_id
description: The unique identifier for the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: The amount of time to wait for the inference request to complete.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
query:
description: Query input.
type: string
input:
description: The documents to rank.
type: array
items:
type: string
return_documents:
description: Include the document text in the response.
type: boolean
top_n:
description: Limit the response to the top N documents.
type: number
task_settings:
description: 'Task settings for the individual inference request.
These settings are specific to the task type you specified and override the task settings specified when initializing the service.'
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- query
- input
examples:
RerankRequestExample1:
summary: Rerank task
description: Run `POST _inference/rerank/cohere_rerank` to perform reranking on the example input.
value: "{\n \"input\": [\"luke\", \"like\", \"leia\", \"chewy\",\"r2d2\", \"star\", \"wars\"],\n \"query\": \"star wars main character\"\n}"
RerankRequestExample2:
summary: Rerank task
description: Run `POST _inference/rerank/bge-reranker-base-mkn` to perform reranking on the example input via Hugging Face
value: "{\n \"input\": [\"luke\", \"like\", \"leia\", \"chewy\",\"r2d2\", \"star\", \"wars\"],\n \"query\": \"star wars main character\",\n \"return_documents\": false,\n \"top_n\": 2\n}"
RerankRequestExample3:
summary: Rerank task
description: Run `POST _inference/rerank/bge-reranker-base-mkn` to perform reranking on the example input via Hugging Face
value: "{\n \"input\": [\"luke\", \"like\", \"leia\", \"chewy\",\"r2d2\", \"star\", \"wars\"],\n \"query\": \"star wars main character\",\n \"return_documents\": true,\n \"top_n\": 3\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.RerankedInferenceResult'
examples:
RerankResponseExample1:
summary: Rerank task
description: 'A successful response from `POST _inference/rerank/cohere_rerank`.
'
value: "{\n \"rerank\": [\n {\n \"index\": \"2\",\n \"relevance_score\": \"0.011597361\",\n \"text\": \"leia\"\n },\n {\n \"index\": \"0\",\n \"relevance_score\": \"0.006338922\",\n \"text\": \"luke\"\n },\n {\n \"index\": \"5\",\n \"relevance_score\": \"0.0016166499\",\n \"text\": \"star\"\n },\n {\n \"index\": \"4\",\n \"relevance_score\": \"0.0011695103\",\n \"text\": \"r2d2\"\n },\n {\n \"index\": \"1\",\n \"relevance_score\": \"5.614787E-4\",\n \"text\": \"like\"\n },\n {\n \"index\": \"6\",\n \"relevance_score\": \"3.7850367E-4\",\n \"text\": \"wars\"\n },\n {\n \"index\": \"3\",\n \"relevance_score\": \"1.2508839E-5\",\n \"text\": \"chewy\"\n }\n ]\n}"
RerankResponseExample2:
summary: Rerank task
description: 'A successful response from `POST _inference/rerank/bge-reranker-base-mkn`.
'
value: "{\n \"rerank\": [\n {\n \"index\": 6,\n \"relevance_score\": 0.50955844\n },\n {\n \"index\": 5,\n \"relevance_score\": 0.084341794\n }\n ]\n}"
RerankResponseExample3:
summary: Rerank task
description: 'A successful response from `POST _inference/rerank/bge-reranker-base-mkn`.
'
value: "{\n \"rerank\": [\n {\n \"index\": 6,\n \"relevance_score\": 0.50955844,\n \"text\": \"wars\"\n },\n {\n \"index\": 5,\n \"relevance_score\": 0.084341794,\n \"text\": \"star\"\n },\n {\n \"index\": 3,\n \"relevance_score\": 0.004520818,\n \"text\": \"chewy\"\n }\n ]\n}"
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n POST\n /_inference/rerank/{inference_id}\n
\n "
x-req-auth:
- 'Cluster privileges: `monitor_inference`
'
x-api: rerank.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "POST _inference/rerank/cohere_rerank\n{\n \"input\": [\"luke\", \"like\", \"leia\", \"chewy\",\"r2d2\", \"star\", \"wars\"],\n \"query\": \"star wars main character\"\n}"
- lang: Python
source: "resp = client.inference.rerank(\n inference_id=\"cohere_rerank\",\n input=[\n \"luke\",\n \"like\",\n \"leia\",\n \"chewy\",\n \"r2d2\",\n \"star\",\n \"wars\"\n ],\n query=\"star wars main character\",\n)"
- lang: JavaScript
source: "const response = await client.inference.rerank({\n inference_id: \"cohere_rerank\",\n input: [\"luke\", \"like\", \"leia\", \"chewy\", \"r2d2\", \"star\", \"wars\"],\n query: \"star wars main character\",\n});"
- lang: Ruby
source: "response = client.inference.rerank(\n inference_id: \"cohere_rerank\",\n body: {\n \"input\": [\n \"luke\",\n \"like\",\n \"leia\",\n \"chewy\",\n \"r2d2\",\n \"star\",\n \"wars\"\n ],\n \"query\": \"star wars main character\"\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->rerank([\n \"inference_id\" => \"cohere_rerank\",\n \"body\" => [\n \"input\" => array(\n \"luke\",\n \"like\",\n \"leia\",\n \"chewy\",\n \"r2d2\",\n \"star\",\n \"wars\",\n ),\n \"query\" => \"star wars main character\",\n ],\n]);"
- lang: curl
source: 'curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"input":["luke","like","leia","chewy","r2d2","star","wars"],"query":"star wars main character"}'' "$ELASTICSEARCH_URL/_inference/rerank/cohere_rerank"'
- lang: Java
source: "client.inference().rerank(r -> r\n .inferenceId(\"cohere_rerank\")\n .input(List.of(\"luke\",\"like\",\"leia\",\"chewy\",\"r2d2\",\"star\",\"wars\"))\n .query(\"star wars main character\")\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/sparse_embedding/{inference_id}:
post:
tags:
- Inference
summary: Perform sparse embedding inference on the service
operationId: inference-sparse-embedding
parameters:
- in: path
name: inference_id
description: The inference Id
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference request to complete.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
input:
description: 'Inference input.
Either a string or an array of strings.'
oneOf:
- type: string
- type: array
items:
type: string
task_settings:
description: Task settings for the individual inference request. These settings are specific to the you specified and override the task settings specified when initializing the service.
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- input
examples:
SparseEmbeddingRequestExample1:
summary: Sparse embedding task
description: Run `POST _inference/sparse_embedding/my-elser-model` to perform sparse embedding on the example sentence.
value: "{\n \"input\": \"The sky above the port was the color of television tuned to a dead channel.\"\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.SparseEmbeddingInferenceResult'
examples:
SparseEmbeddingResponseExample1:
summary: Sparse embedding task
description: 'An abbreviated response from `POST _inference/sparse_embedding/my-elser-model`.
'
value: "{\n \"sparse_embedding\": [\n {\n \"is_truncated\": false,\n \"embedding\": {\n \"port\": 2.1259406,\n \"sky\": 1.7073475,\n \"color\": 1.6922266,\n \"dead\": 1.6247464,\n \"television\": 1.3525393,\n \"above\": 1.2425821,\n \"tuned\": 1.1440028,\n \"colors\": 1.1218185,\n \"tv\": 1.0111054,\n \"ports\": 1.0067928,\n \"poem\": 1.0042328,\n \"channel\": 0.99471164,\n \"tune\": 0.96235967,\n \"scene\": 0.9020516\n }\n }\n ]\n}"
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n POST\n /_inference/sparse_embedding/{inference_id}\n
\n "
x-api: sparse_embedding.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "POST _inference/sparse_embedding/my-elser-model\n{\n \"input\": \"The sky above the port was the color of television tuned to a dead channel.\"\n}"
- lang: Python
source: "resp = client.inference.sparse_embedding(\n inference_id=\"my-elser-model\",\n input=\"The sky above the port was the color of television tuned to a dead channel.\",\n)"
- lang: JavaScript
source: "const response = await client.inference.sparseEmbedding({\n inference_id: \"my-elser-model\",\n input:\n \"The sky above the port was the color of television tuned to a dead channel.\",\n});"
- lang: Ruby
source: "response = client.inference.sparse_embedding(\n inference_id: \"my-elser-model\",\n body: {\n \"input\": \"The sky above the port was the color of television tuned to a dead channel.\"\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->sparseEmbedding([\n \"inference_id\" => \"my-elser-model\",\n \"body\" => [\n \"input\" => \"The sky above the port was the color of television tuned to a dead channel.\",\n ],\n]);"
- lang: curl
source: 'curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"input":"The sky above the port was the color of television tuned to a dead channel."}'' "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"'
- lang: Java
source: "client.inference().sparseEmbedding(s -> s\n .inferenceId(\"my-elser-model\")\n .input(\"The sky above the port was the color of television tuned to a dead channel.\")\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/completion/{inference_id}/_stream:
post:
tags:
- Inference
summary: Perform streaming completion inference on the service
description: 'Get real-time responses for completion tasks by delivering answers incrementally, reducing response times during computation.
This API works only with the completion task type.
IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
This API requires the `monitor_inference` cluster privilege (the built-in `inference_admin` and `inference_user` roles grant this privilege). You must use a client that supports streaming.'
operationId: inference-stream-completion
parameters:
- in: path
name: inference_id
description: The unique identifier for the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: The amount of time to wait for the inference request to complete.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
input:
description: 'The text on which you want to perform the inference task.
It can be a single string or an array.
NOTE: Inference endpoints for the completion task type currently only support a single string as input.'
oneOf:
- type: string
- type: array
items:
type: string
task_settings:
description: Task settings for the individual inference request. These settings are specific to the you specified and override the task settings specified when initializing the service.
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- input
examples:
StreamInferenceRequestExample1:
summary: Perform a completion task
description: Run `POST _inference/completion/openai-completion/_stream` to perform a completion on the example question with streaming.
value: "{\n \"input\": \"What is Elastic?\"\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/_types.StreamResult'
x-state: Generally available; Added in 8.16.0
x-variations:
- "\n POST\n /_inference/completion/{inference_id}/_stream\n
\n "
x-req-auth:
- 'Cluster privileges: `monitor_inference`
'
x-api: stream_completion.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "POST _inference/completion/openai-completion/_stream\n{\n \"input\": \"What is Elastic?\"\n}"
- lang: Python
source: "resp = client.inference.stream_completion(\n inference_id=\"openai-completion\",\n input=\"What is Elastic?\",\n)"
- lang: JavaScript
source: "const response = await client.inference.streamCompletion({\n inference_id: \"openai-completion\",\n input: \"What is Elastic?\",\n});"
- lang: Ruby
source: "response = client.inference.stream_completion(\n inference_id: \"openai-completion\",\n body: {\n \"input\": \"What is Elastic?\"\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->streamCompletion([\n \"inference_id\" => \"openai-completion\",\n \"body\" => [\n \"input\" => \"What is Elastic?\",\n ],\n]);"
- lang: curl
source: 'curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"input":"What is Elastic?"}'' "$ELASTICSEARCH_URL/_inference/completion/openai-completion/_stream"'
- lang: Java
source: "client.inference().streamCompletion(s -> s\n .inferenceId(\"openai-completion\")\n .input(\"What is Elastic?\")\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/text_embedding/{inference_id}:
post:
tags:
- Inference
summary: Perform text embedding inference on the service
operationId: inference-text-embedding
parameters:
- in: path
name: inference_id
description: The inference Id
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
- in: query
name: timeout
description: Specifies the amount of time to wait for the inference request to complete.
deprecated: false
schema:
$ref: '#/components/schemas/_types.Duration'
style: form
requestBody:
content:
application/json:
schema:
type: object
properties:
input:
description: 'Inference input.
Either a string or an array of strings.'
oneOf:
- type: string
- type: array
items:
type: string
input_type:
description: 'The input data type for the text embedding model. Possible values include:
* `SEARCH`
* `INGEST`
* `CLASSIFICATION`
* `CLUSTERING`
Not all services support all values. Unsupported values will trigger a validation exception.
Accepted values depend on the configured inference service, refer to the relevant service-specific documentation for more info.
> info
> The `input_type` parameter specified on the root level of the request body will take precedence over the `input_type` parameter specified in `task_settings`.'
type: string
task_settings:
description: Task settings for the individual inference request. These settings are specific to the you specified and override the task settings specified when initializing the service.
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- input
examples:
TextEmbeddingRequestExample1:
summary: Text embedding task
description: Run `POST _inference/text_embedding/my-cohere-endpoint` to perform text embedding on the example sentence using the Cohere integration,
value: "{\n \"input\": \"The sky above the port was the color of television tuned to a dead channel.\",\n \"input_type\": \"ingest\"\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.TextEmbeddingInferenceResult'
examples:
TextEmbeddingResponseExample1:
summary: Text embedding task
description: 'An abbreviated response from `POST _inference/text_embedding/my-text-embedding-endpoint`.
'
value: "{\n \"text_embedding\": [\n {\n \"embedding\": [\n 0.018569946,\n -0.036895752,\n 0.01486969,\n -0.0045204163,\n -0.04385376,\n 0.0075950623,\n 0.04260254,\n -0.004005432,\n 0.007865906,\n 0.030792236,\n -0.050476074,\n 0.011795044,\n -0.011642456,\n -0.010070801\n ]\n }\n ]\n}"
x-state: Generally available; Added in 8.11.0
x-variations:
- "\n POST\n /_inference/text_embedding/{inference_id}\n
\n "
x-api: text_embedding.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "POST _inference/text_embedding/my-cohere-endpoint\n{\n \"input\": \"The sky above the port was the color of television tuned to a dead channel.\",\n \"input_type\": \"ingest\"\n}"
- lang: Python
source: "resp = client.inference.text_embedding(\n inference_id=\"my-cohere-endpoint\",\n input=\"The sky above the port was the color of television tuned to a dead channel.\",\n input_type=\"ingest\",\n)"
- lang: JavaScript
source: "const response = await client.inference.textEmbedding({\n inference_id: \"my-cohere-endpoint\",\n input:\n \"The sky above the port was the color of television tuned to a dead channel.\",\n input_type: \"ingest\",\n});"
- lang: Ruby
source: "response = client.inference.text_embedding(\n inference_id: \"my-cohere-endpoint\",\n body: {\n \"input\": \"The sky above the port was the color of television tuned to a dead channel.\",\n \"input_type\": \"ingest\"\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->textEmbedding([\n \"inference_id\" => \"my-cohere-endpoint\",\n \"body\" => [\n \"input\" => \"The sky above the port was the color of television tuned to a dead channel.\",\n \"input_type\" => \"ingest\",\n ],\n]);"
- lang: curl
source: 'curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"input":"The sky above the port was the color of television tuned to a dead channel.","input_type":"ingest"}'' "$ELASTICSEARCH_URL/_inference/text_embedding/my-cohere-endpoint"'
- lang: Java
source: "client.inference().textEmbedding(t -> t\n .inferenceId(\"my-cohere-endpoint\")\n .input(\"The sky above the port was the color of television tuned to a dead channel.\")\n .inputType(\"ingest\")\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{inference_id}/_update:
put:
tags:
- Inference
summary: Update an inference endpoint
description: 'Modify `task_settings`, secrets (within `service_settings`), or `num_allocations` for an inference endpoint, depending on the specific endpoint service and `task_type`.
IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.
However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.'
operationId: inference-update
parameters:
- in: path
name: inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpoint'
examples:
InferenceUpdateExample1:
description: An example body for a `PUT _inference/my-inference-endpoint/_update` request.
value: "{\n \"service_settings\": {\n \"api_key\": \"\"\n },\n\"service\": \"example-service\"\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfo'
x-state: Generally available; Added in 8.17.0
x-variations:
- "\n PUT\n /_inference/{inference_id}/_update\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: update.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/my-inference-endpoint/_update\n{\n \"service_settings\": {\n \"api_key\": \"\"\n },\n\"service\": \"example-service\"\n}"
- lang: Python
source: "resp = client.inference.update(\n inference_id=\"my-inference-endpoint\",\n inference_config={\n \"service_settings\": {\n \"api_key\": \"\"\n },\n \"service\": \"example-service\"\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.update({\n inference_id: \"my-inference-endpoint\",\n inference_config: {\n service_settings: {\n api_key: \"\",\n },\n service: \"example-service\",\n },\n});"
- lang: Ruby
source: "response = client.inference.update(\n inference_id: \"my-inference-endpoint\",\n body: {\n \"service_settings\": {\n \"api_key\": \"\"\n },\n \"service\": \"example-service\"\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->update([\n \"inference_id\" => \"my-inference-endpoint\",\n \"body\" => [\n \"service_settings\" => [\n \"api_key\" => \"\",\n ],\n \"service\" => \"example-service\",\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service_settings":{"api_key":""},"service":"example-service"}'' "$ELASTICSEARCH_URL/_inference/my-inference-endpoint/_update"'
- lang: Java
source: "client.inference().update(u -> u\n .inferenceId(\"my-inference-endpoint\")\n .inferenceConfig(i -> i\n .service(\"example-service\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
/_inference/{task_type}/{inference_id}/_update:
put:
tags:
- Inference
summary: Update an inference endpoint
description: 'Modify `task_settings`, secrets (within `service_settings`), or `num_allocations` for an inference endpoint, depending on the specific endpoint service and `task_type`.
IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face.
For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models.
However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.'
operationId: inference-update-1
parameters:
- in: path
name: task_type
description: The type of inference task that the model performs.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/inference._types.TaskType'
style: simple
- in: path
name: inference_id
description: The unique identifier of the inference endpoint.
required: true
deprecated: false
schema:
$ref: '#/components/schemas/_types.Id'
style: simple
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpoint'
examples:
InferenceUpdateExample1:
description: An example body for a `PUT _inference/my-inference-endpoint/_update` request.
value: "{\n \"service_settings\": {\n \"api_key\": \"\"\n },\n\"service\": \"example-service\"\n}"
required: true
responses:
'200':
description: ''
content:
application/json:
schema:
$ref: '#/components/schemas/inference._types.InferenceEndpointInfo'
x-state: Generally available; Added in 8.17.0
x-variations:
- "\n PUT\n /_inference/{task_type}/{inference_id}/_update\n
\n "
x-req-auth:
- 'Cluster privileges: `manage_inference`
'
x-api: update.inference
x-category: ai/ml
x-codeSamples:
- lang: Console
source: "PUT _inference/my-inference-endpoint/_update\n{\n \"service_settings\": {\n \"api_key\": \"\"\n },\n\"service\": \"example-service\"\n}"
- lang: Python
source: "resp = client.inference.update(\n inference_id=\"my-inference-endpoint\",\n inference_config={\n \"service_settings\": {\n \"api_key\": \"\"\n },\n \"service\": \"example-service\"\n },\n)"
- lang: JavaScript
source: "const response = await client.inference.update({\n inference_id: \"my-inference-endpoint\",\n inference_config: {\n service_settings: {\n api_key: \"\",\n },\n service: \"example-service\",\n },\n});"
- lang: Ruby
source: "response = client.inference.update(\n inference_id: \"my-inference-endpoint\",\n body: {\n \"service_settings\": {\n \"api_key\": \"\"\n },\n \"service\": \"example-service\"\n }\n)"
- lang: PHP
source: "$resp = $client->inference()->update([\n \"inference_id\" => \"my-inference-endpoint\",\n \"body\" => [\n \"service_settings\" => [\n \"api_key\" => \"\",\n ],\n \"service\" => \"example-service\",\n ],\n]);"
- lang: curl
source: 'curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d ''{"service_settings":{"api_key":""},"service":"example-service"}'' "$ELASTICSEARCH_URL/_inference/my-inference-endpoint/_update"'
- lang: Java
source: "client.inference().update(u -> u\n .inferenceId(\"my-inference-endpoint\")\n .inferenceConfig(i -> i\n .service(\"example-service\")\n .serviceSettings(JsonData.fromJson(\"{\\\"api_key\\\":\\\"\\\"}\"))\n )\n);\n"
x-metaTags:
- content: Elasticsearch, Machine Learning
name: product_name
components:
schemas:
inference._types.EmbeddingInput:
description: 'Inference input.
Either a string, an array of strings, a `content` object, or an array of `content` objects.'
oneOf:
- $ref: '#/components/schemas/inference._types.EmbeddingStringInput'
- $ref: '#/components/schemas/inference._types.EmbeddingContentInput'
inference._types.JinaAITextEmbeddingTask:
type: string
enum:
- classification
- clustering
- ingest
- search
inference._types.AmazonBedrockTaskType:
type: string
enum:
- chat_completion
- completion
- text_embedding
inference._types.InferenceEndpointInfoCohere:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeCohere'
required:
- inference_id
- task_type
inference._types.InferenceEndpointInfoVoyageAI:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeVoyageAI'
required:
- inference_id
- task_type
inference._types.InferenceEndpointInfoNvidia:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference ID
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeNvidia'
required:
- inference_id
- task_type
inference._types.InferenceEndpointInfoOpenAI:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeOpenAI'
required:
- inference_id
- task_type
inference._types.MistralTaskType:
type: string
enum:
- text_embedding
- completion
- chat_completion
inference._types.DenseByteVector:
description: 'Dense Embedding results containing bytes are represented as Dense
Vectors of bytes.'
type: array
items:
$ref: '#/components/schemas/_types.byte'
inference._types.EmbeddingContentFormat:
type: string
enum:
- text
- base64
inference._types.AlibabaCloudTaskSettings:
type: object
properties:
input_type:
description: 'For a `sparse_embedding` or `text_embedding` task, specify the type of input passed to the model.
Valid values are:
* `ingest` for storing document embeddings in a vector database.
* `search` for storing embeddings of search queries run against a vector database to find relevant documents.'
type: string
return_token:
description: 'For a `sparse_embedding` task, it affects whether the token name will be returned in the response.
It defaults to `false`, which means only the token ID will be returned in the response.'
type: boolean
inference._types.InferenceEndpointInfoWatsonx:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeWatsonx'
required:
- inference_id
- task_type
inference._types.CustomTaskType:
type: string
enum:
- text_embedding
- sparse_embedding
- rerank
- completion
inference._types.InferenceEndpointInfoGoogleVertexAI:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeGoogleVertexAI'
required:
- inference_id
- task_type
inference._types.InferenceEndpointInfoAmazonSageMaker:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeAmazonSageMaker'
required:
- inference_id
- task_type
inference._types.OpenShiftAiServiceSettings:
type: object
properties:
api_key:
description: 'A valid API key for your OpenShift AI endpoint.
Can be found in `Token authentication` section of model related information.'
type: string
url:
description: The URL of the OpenShift AI hosted model endpoint.
type: string
model_id:
description: 'The name of the model to use for the inference task.
Refer to the hosted model''s documentation for the name if needed.
Service has been tested and confirmed to be working with the following models:
* For `text_embedding` task - `gritlm-7b`.
* For `completion` and `chat_completion` tasks - `llama-31-8b-instruct`.
* For `rerank` task - `bge-reranker-v2-m3`.'
type: string
max_input_tokens:
description: For a `text_embedding` task, the maximum number of tokens per input before chunking occurs.
type: number
similarity:
description: 'For a `text_embedding` task, the similarity measure. One of cosine, dot_product, l2_norm.
If not specified, the default dot_product value is used.'
allOf:
- $ref: '#/components/schemas/inference._types.OpenShiftAiSimilarityType'
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from the OpenShift AI API.
By default, the `openshift_ai` service sets the number of requests allowed per minute to 3000.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- api_key
- url
inference._types.CustomRequestParams:
type: object
properties:
content:
description: 'The body structure of the request. It requires passing in the string-escaped result of the JSON format HTTP request body.
For example:
```
"request": "{\"input\":${input}}"
```
> info
> The content string needs to be a single line except when using the Kibana console.'
type: string
required:
- content
inference._types.AmazonSageMakerServiceType:
type: string
enum:
- amazon_sagemaker
inference._types.TaskTypeJinaAi:
type: string
enum:
- embedding
- text_embedding
- rerank
inference._types.SparseEmbeddingInferenceResult:
description: The response format for the sparse embedding request.
type: object
properties:
sparse_embedding:
type: array
items:
$ref: '#/components/schemas/inference._types.SparseEmbeddingResult'
required:
- sparse_embedding
inference._types.TaskTypeWatsonx:
type: string
enum:
- text_embedding
- chat_completion
- completion
inference._types.DeleteInferenceEndpointResult:
description: Acknowledged response. For dry_run, contains the list of pipelines which reference the inference endpoint
allOf:
- $ref: '#/components/schemas/_types.AcknowledgedResponseBase'
- type: object
properties:
pipelines:
type: array
items:
type: string
required:
- pipelines
inference._types.InferenceEndpointInfoHuggingFace:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeHuggingFace'
required:
- inference_id
- task_type
inference._types.OpenShiftAiSimilarityType:
type: string
enum:
- cosine
- dot_product
- l2_norm
inference._types.EmbeddingContentObject:
description: A wrapper object which contains the fields required to specify multimodal inputs
type: object
properties:
content:
description: An object containing the input data for the model to embed
allOf:
- $ref: '#/components/schemas/inference._types.EmbeddingContentObjectContents'
required:
- content
_types.AcknowledgedResponseBase:
type: object
properties:
acknowledged:
description: For a successful response, this value is always true. On failure, an exception is returned instead.
type: boolean
required:
- acknowledged
inference._types.ContextualAIServiceType:
type: string
enum:
- contextualai
inference._types.Ai21TaskType:
type: string
enum:
- completion
- chat_completion
inference._types.ToolCallFunction:
description: The function that the model called.
type: object
properties:
arguments:
description: The arguments to call the function with in JSON format.
type: string
name:
description: The name of the function to call.
type: string
required:
- arguments
- name
inference._types.OpenShiftAiTaskType:
type: string
enum:
- text_embedding
- completion
- chat_completion
- rerank
inference._types.JinaAIServiceType:
type: string
enum:
- jinaai
inference._types.TaskTypeVoyageAI:
type: string
enum:
- text_embedding
- rerank
inference._types.OpenAIServiceType:
type: string
enum:
- openai
inference._types.AzureOpenAIServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#authentication
description: 'A valid API key for your Azure OpenAI account.
You must specify either `api_key` or `entra_id`.
If you do not provide either or you provide both, you will receive an error when you try to create your model.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
api_version:
description: 'The Azure API version ID to use.
It is recommended to use the latest supported non-preview version.'
type: string
deployment_id:
externalDocs:
url: https://oai.azure.com/
description: 'The deployment name of your deployed models.
Your Azure OpenAI deployments can be found though the Azure OpenAI Studio portal that is linked to your subscription.'
type: string
entra_id:
externalDocs:
url: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#authentication
description: 'A valid Microsoft Entra token.
You must specify either `api_key` or `entra_id`.
If you do not provide either or you provide both, you will receive an error when you try to create your model.'
type: string
rate_limit:
externalDocs:
url: https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits
description: 'This setting helps to minimize the number of rate limit errors returned from Azure.
The `azureopenai` service sets a default number of requests allowed per minute depending on the task type.
For `text_embedding`, it is set to `1440`.
For `completion` and `chat_completion`, it is set to `120`.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
resource_name:
externalDocs:
url: https://portal.azure.com/#view/HubsExtension/BrowseAll
description: 'The name of your Azure OpenAI resource.
You can find this from the list of resources in the Azure Portal for your subscription.'
type: string
required:
- api_version
- deployment_id
- resource_name
inference._types.InferenceEndpointInfoDeepSeek:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeDeepSeek'
required:
- inference_id
- task_type
inference._types.CompletionToolChoice:
description: Controls which tool is called by the model.
type: object
properties:
type:
description: The type of the tool.
type: string
function:
description: The tool choice function.
allOf:
- $ref: '#/components/schemas/inference._types.CompletionToolChoiceFunction'
required:
- type
- function
inference._types.CustomServiceType:
type: string
enum:
- custom
inference._types.OpenAITaskSettings:
type: object
properties:
user:
description: 'For a `completion` or `text_embedding` task, specify the user issuing the request.
This information can be used for abuse detection.'
type: string
headers:
description: "Specifies custom HTTP header parameters.\nFor example:\n```\n\"headers\":{\n \"Custom-Header\": \"Some-Value\",\n \"Another-Custom-Header\": \"Another-Value\"\n}\n```"
type: object
inference._types.TaskTypeELSER:
type: string
enum:
- sparse_embedding
inference._types.TaskTypeCustom:
type: string
enum:
- text_embedding
- sparse_embedding
- rerank
- completion
inference._types.ElasticsearchTaskSettings:
type: object
properties:
return_documents:
description: For a `rerank` task, return the document instead of only the index.
default: true
type: boolean
inference._types.OpenAITaskType:
type: string
enum:
- chat_completion
- completion
- text_embedding
inference._types.RequestEmbedding:
type: object
properties:
input:
description: "Inference input.\nEither a string, an array of strings, a `content` object, or an array of `content` objects.\n\nstring example:\n```\n\"input\": \"Some text\"\n```\nstring array example:\n```\n\"input\": [\"Some text\", \"Some more text\"]\n```\n`content` object example:\n```\n\"input\": {\n \"content\": {\n \"type\": \"image\",\n \"format\": \"base64\",\n \"value\": \"data:image/jpg;base64,...\"\n }\n }\n```\n`content` object array example:\n```\n\"input\": [\n {\n \"content\": {\n \"type\": \"text\",\n \"format\": \"text\",\n \"value\": \"Some text to generate an embedding\"\n }\n },\n {\n \"content\": {\n \"type\": \"image\",\n \"format\": \"base64\",\n \"value\": \"data:image/jpg;base64,...\"\n }\n }\n]\n```"
allOf:
- $ref: '#/components/schemas/inference._types.EmbeddingInput'
input_type:
description: 'The input data type for the embedding model. Possible values include:
* `SEARCH`
* `INGEST`
* `CLASSIFICATION`
* `CLUSTERING`
Not all models support all values. Unsupported values will trigger a validation exception.
Accepted values depend on the configured inference service, refer to the relevant service-specific documentation for more info.
> info
> The `input_type` parameter specified on the root level of the request body will take precedence over the `input_type` parameter specified in `task_settings`.'
type: string
task_settings:
description: Task settings for the individual inference request. These settings are specific to the you specified and override the task settings specified when initializing the service.
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- input
inference._types.LlamaSimilarityType:
type: string
enum:
- cosine
- dot_product
- l2_norm
inference._types.TaskTypeOpenShiftAi:
type: string
enum:
- text_embedding
- chat_completion
- completion
- rerank
inference._types.InferenceResult:
description: InferenceResult is an aggregation of mutually exclusive variants
type: object
properties:
embeddings_bytes:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingByteResult'
embeddings_bits:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingByteResult'
embeddings:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingResult'
text_embedding_bytes:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingByteResult'
text_embedding_bits:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingByteResult'
text_embedding:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingResult'
sparse_embedding:
type: array
items:
$ref: '#/components/schemas/inference._types.SparseEmbeddingResult'
completion:
type: array
items:
$ref: '#/components/schemas/inference._types.CompletionResult'
rerank:
type: array
items:
$ref: '#/components/schemas/inference._types.RankedDocument'
minProperties: 1
maxProperties: 1
inference._types.HuggingFaceTaskType:
type: string
enum:
- chat_completion
- completion
- rerank
- text_embedding
inference._types.GoogleVertexAITaskType:
type: string
enum:
- rerank
- text_embedding
- completion
- chat_completion
inference._types.JinaAISimilarityType:
type: string
enum:
- cosine
- dot_product
- l2_norm
inference._types.TaskTypeAnthropic:
type: string
enum:
- completion
inference._types.CohereSimilarityType:
type: string
enum:
- cosine
- dot_product
- l2_norm
inference._types.SparseVector:
description: 'Sparse Embedding tokens are represented as a dictionary
of string to double.'
type: object
additionalProperties:
type: number
inference._types.InferenceEndpointInfoGroq:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeGroq'
required:
- inference_id
- task_type
inference._types.InferenceEndpointInfoOpenShiftAi:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeOpenShiftAi'
required:
- inference_id
- task_type
_types.StreamResult:
type: object
inference._types.GroqServiceSettings:
type: object
properties:
model_id:
externalDocs:
url: https://console.groq.com/docs/models
description: 'The name of the model to use for the inference task.
Refer to the Groq model documentation for the list of supported models and versions.
Service has been tested and confirmed to be working for `completion` and `chat_completion` tasks with the following models:
* `llama-3.3-70b-versatile`'
type: string
api_key:
description: 'A valid API key for accessing Groq API.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
rate_limit:
externalDocs:
url: https://console.groq.com/docs/rate-limits
description: 'This setting helps to minimize the number of rate limit errors returned from the Groq API.
By default, the `groq` service sets the number of requests allowed per minute to 1000. Refer to Groq documentation for more details.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- model_id
inference._types.RankedDocument:
description: 'The rerank result object representing a single ranked document
id: the original index of the document in the request
relevance_score: the relevance_score of the document relative to the query
text: Optional, the text of the document, if requested'
type: object
properties:
index:
type: number
relevance_score:
type: number
text:
type: string
required:
- index
- relevance_score
inference._types.ThinkingConfig:
type: object
properties:
thinking_budget:
description: Indicates the desired thinking budget in tokens.
type: number
inference._types.CohereServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://dashboard.cohere.com/api-keys
description: 'A valid API key for your Cohere account.
You can find or create your Cohere API keys on the Cohere API key settings page.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
embedding_type:
description: 'For a `text_embedding` task, the types of embeddings you want to get back.
Use `binary` for binary embeddings, which are encoded as bytes with signed int8 precision.
Use `bit` for binary embeddings, which are encoded as bytes with signed int8 precision (this is a synonym of `binary`).
Use `byte` for signed int8 embeddings (this is a synonym of `int8`).
Use `float` for the default float embeddings.
Use `int8` for signed int8 embeddings.'
default: float
allOf:
- $ref: '#/components/schemas/inference._types.CohereEmbeddingType'
model_id:
description: 'For a `completion`, `rerank`, or `text_embedding` task, the name of the model to use for the inference task.
* For the available `completion` models, refer to the [Cohere command docs](https://docs.cohere.com/docs/models#command).
* For the available `rerank` models, refer to the [Cohere rerank docs](https://docs.cohere.com/reference/rerank-1).
* For the available `text_embedding` models, refer to [Cohere embed docs](https://docs.cohere.com/reference/embed).'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Cohere.
By default, the `cohere` service sets the number of requests allowed per minute to 10000.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
similarity:
description: 'The similarity measure.
If the `embedding_type` is `float`, the default value is `dot_product`.
If the `embedding_type` is `int8` or `byte`, the default value is `cosine`.'
allOf:
- $ref: '#/components/schemas/inference._types.CohereSimilarityType'
required:
- api_key
- model_id
inference._types.AnthropicServiceSettings:
type: object
properties:
api_key:
description: A valid API key for the Anthropic API.
type: string
model_id:
description: 'The name of the model to use for the inference task.
Refer to the Anthropic documentation for the list of supported models.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Anthropic.
By default, the `anthropic` service sets the number of requests allowed per minute to 50.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- api_key
- model_id
inference._types.AmazonBedrockServiceSettings:
type: object
properties:
access_key:
description: A valid AWS access key that has permissions to use Amazon Bedrock and access to models for inference requests.
type: string
model:
externalDocs:
url: https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
description: 'The base model ID or an ARN to a custom model based on a foundational model.
The base model IDs can be found in the Amazon Bedrock documentation.
Note that the model ID must be available for the provider chosen and your IAM user must have access to the model.'
type: string
provider:
description: 'The model provider for your deployment.
Note that some providers may support only certain task types.
Supported providers include:
* `amazontitan` - available for `text_embedding` and `completion` task types
* `anthropic` - available for `chat_completion` and `completion` task types
* `ai21labs` - available for `chat_completion` and `completion` task types
* `cohere` - available for `chat_completion`, `completion` and `text_embedding` task types
* `meta` - available for `chat_completion` and `completion` task types
* `mistral` - available for `chat_completion` and `completion` task types'
type: string
region:
externalDocs:
url: https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
description: 'The region that your model or ARN is deployed in.
The list of available regions per model can be found in the Amazon Bedrock documentation.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Amazon Bedrock.
By default, the `amazonbedrock` service sets the number of requests allowed per minute to 240.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
secret_key:
externalDocs:
url: https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html
description: 'A valid AWS secret key that is paired with the `access_key`.
For informationg about creating and managing access and secret keys, refer to the AWS documentation.'
type: string
required:
- access_key
- model
- region
- secret_key
inference._types.AzureAiStudioTaskSettings:
type: object
properties:
do_sample:
description: 'For a `completion` task, instruct the inference process to perform sampling.
It has no effect unless `temperature` or `top_p` is specified.'
type: number
max_new_tokens:
description: For a `completion` task, provide a hint for the maximum number of output tokens to be generated.
default: 64.0
type: number
temperature:
description: 'For a `completion` task, control the apparent creativity of generated completions with a sampling temperature.
It must be a number in the range of 0.0 to 2.0.
It should not be used if `top_p` is specified.'
type: number
top_p:
description: 'For a `completion` task, make the model consider the results of the tokens with nucleus sampling probability.
It is an alternative value to `temperature` and must be a number in the range of 0.0 to 2.0.
It should not be used if `temperature` is specified.'
type: number
user:
description: 'For a `text_embedding` task, specify the user issuing the request.
This information can be used for abuse detection.'
type: string
return_documents:
description: For a `rerank` task, return doc text within the results.
type: boolean
top_n:
description: 'For a `rerank` task, the number of most relevant documents to return.
It defaults to the number of the documents.'
type: number
inference._types.WatsonxTaskType:
type: string
enum:
- text_embedding
- rerank
- chat_completion
- completion
inference._types.CompletionInferenceResult:
description: Defines the completion result.
type: object
properties:
completion:
type: array
items:
$ref: '#/components/schemas/inference._types.CompletionResult'
required:
- completion
inference._types.OpenShiftAiTaskSettings:
type: object
properties:
return_documents:
description: For a `rerank` task, whether to return the source documents in the response.
type: boolean
top_n:
description: For a `rerank` task, the number of most relevant documents to return.
type: number
inference._types.NvidiaSimilarityType:
type: string
enum:
- cosine
- dot_product
- l2_norm
inference._types.VoyageAITaskType:
type: string
enum:
- text_embedding
- rerank
inference._types.TaskTypeLlama:
type: string
enum:
- text_embedding
- chat_completion
- completion
inference._types.InferenceEndpointInfoAmazonBedrock:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeAmazonBedrock'
required:
- inference_id
- task_type
inference._types.GoogleVertexAIServiceType:
type: string
enum:
- googlevertexai
inference._types.OpenShiftAiServiceType:
type: string
enum:
- openshift_ai
inference._types.TaskTypeAmazonSageMaker:
type: string
enum:
- text_embedding
- completion
- chat_completion
- sparse_embedding
- rerank
inference._types.GoogleVertexAITaskSettings:
type: object
properties:
auto_truncate:
description: For a `text_embedding` task, truncate inputs longer than the maximum token length automatically.
type: boolean
top_n:
description: For a `rerank` task, the number of the top N documents that should be returned.
type: number
thinking_config:
externalDocs:
url: https://ai.google.dev/gemini-api/docs/thinking#set-budget
description: 'For a `completion` or `chat_completion` task, allows configuration of the thinking features for the model.
Refer to the Google documentation for the allowable configurations for each model type.'
allOf:
- $ref: '#/components/schemas/inference._types.ThinkingConfig'
max_tokens:
externalDocs:
url: https://docs.claude.com/en/api/messages#body-max-tokens
description: 'For `completion` and `chat_completion` tasks, specifies the `max_tokens` value for requests sent to the Google Model Garden `anthropic` provider.
If `provider` is not set to `anthropic`, this field is ignored.
If `max_tokens` is specified - it must be a positive integer. If not specified, the default value of 1024 is used.
Anthropic models require `max_tokens` to be set for each request. Please refer to the Anthropic documentation for more information.'
type: number
inference._types.InferenceChunkingSettings:
description: Chunking configuration object
type: object
properties:
max_chunk_size:
description: 'The maximum size of a chunk in words.
This value cannot be lower than `20` (for `sentence` strategy) or `10` (for `word` strategy).
This value should not exceed the window size for the associated model.'
default: 250.0
type: number
overlap:
description: 'The number of overlapping words for chunks.
It is applicable only to a `word` chunking strategy.
This value cannot be higher than half the `max_chunk_size` value.'
default: 100.0
type: number
sentence_overlap:
description: 'The number of overlapping sentences for chunks.
It is applicable only for a `sentence` chunking strategy.
It can be either `1` or `0`.'
default: 1.0
type: number
separator_group:
description: 'Only applicable to the `recursive` strategy and required when using it.
Sets a predefined list of separators in the saved chunking settings based on the selected text type.
Values can be `markdown` or `plaintext`.
Using this parameter is an alternative to manually specifying a custom `separators` list.'
type: string
separators:
description: 'Only applicable to the `recursive` strategy and required when using it.
A list of strings used as possible split points when chunking text.
Each string can be a plain string or a regular expression (regex) pattern.
The system tries each separator in order to split the text, starting from the first item in the list.
After splitting, it attempts to recombine smaller pieces into larger chunks that stay within
the `max_chunk_size` limit, to reduce the total number of chunks generated.'
type: array
items:
type: string
strategy:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/elastic-inference/inference-api#chunking-strategies
description: "The chunking strategy: `sentence`, `word`, `none` or `recursive`.\n\n * If `strategy` is set to `recursive`, you must also specify:\n\n- `max_chunk_size`\n- either `separators` or`separator_group`\n\nLearn more about different chunking strategies in the linked documentation."
default: sentence
type: string
inference._types.HuggingFaceTaskSettings:
type: object
properties:
return_documents:
description: For a `rerank` task, return doc text within the results.
type: boolean
top_n:
description: 'For a `rerank` task, the number of most relevant documents to return.
It defaults to the number of the documents.'
type: number
inference._types.AmazonSageMakerTaskSettings:
type: object
properties:
custom_attributes:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: 'The AWS custom attributes passed verbatim through to the model running in the SageMaker Endpoint.
Values will be returned in the `X-elastic-sagemaker-custom-attributes` header.'
type: string
enable_explanations:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: The optional JMESPath expression used to override the EnableExplanations provided during endpoint creation.
type: string
inference_id:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: The capture data ID when enabled in the endpoint.
type: string
session_id:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: 'The stateful session identifier for a new or existing session.
New sessions will be returned in the `X-elastic-sagemaker-new-session-id` header.
Closed sessions will be returned in the `X-elastic-sagemaker-closed-session-id` header.'
type: string
target_variant:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: Specifies the variant when running with multi-variant Endpoints.
type: string
inference._types.ContextualAITaskSettings:
type: object
properties:
instruction:
description: 'Instructions for the reranking model. Refer to
Only for the `rerank` task type.'
type: string
return_documents:
description: 'Whether to return the source documents in the response.
Only for the `rerank` task type.'
default: false
type: boolean
top_k:
description: 'The number of most relevant documents to return.
If not specified, the reranking results of all documents will be returned.
Only for the `rerank` task type.'
type: number
inference._types.RequestChatCompletion:
type: object
properties:
messages:
description: 'A list of objects representing the conversation.
Requests should generally only add new messages from the user (role `user`).
The other message roles (`assistant`, `system`, or `tool`) should generally only be copied from the response to a previous completion request, such that the messages array is built up throughout a conversation.'
type: array
items:
$ref: '#/components/schemas/inference._types.Message'
model:
description: The ID of the model to use. By default, the model ID is set to the value included when creating the inference endpoint.
type: string
max_completion_tokens:
description: The upper bound limit for the number of tokens that can be generated for a completion request.
type: number
stop:
description: A sequence of strings to control when the model should stop generating additional tokens.
type: array
items:
type: string
temperature:
description: The sampling temperature to use.
type: number
tool_choice:
description: "Controls which tool is called by the model.\nString representation: One of `auto`, `none`, or `requrired`. `auto` allows the model to choose between calling tools and generating a message. `none` causes the model to not call any tools. `required` forces the model to call one or more tools.\nExample (object representation):\n```\n{\n \"tool_choice\": {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_current_weather\"\n }\n }\n}\n```"
allOf:
- $ref: '#/components/schemas/inference._types.CompletionToolType'
tools:
description: "A list of tools that the model can call.\nExample:\n```\n{\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_price_of_item\",\n \"description\": \"Get the current price of an item\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"item\": {\n \"id\": \"12345\"\n },\n \"unit\": {\n \"type\": \"currency\"\n }\n }\n }\n }\n }\n ]\n}\n```"
type: array
items:
$ref: '#/components/schemas/inference._types.CompletionTool'
top_p:
description: Nucleus sampling, an alternative to sampling with temperature.
type: number
required:
- messages
inference._types.GoogleAiStudioTaskType:
type: string
enum:
- completion
- text_embedding
inference._types.CompletionTool:
description: A list of tools that the model can call.
type: object
properties:
type:
description: The type of tool.
type: string
function:
description: The function definition.
allOf:
- $ref: '#/components/schemas/inference._types.CompletionToolFunction'
required:
- type
- function
inference._types.TaskTypeHuggingFace:
type: string
enum:
- chat_completion
- completion
- rerank
- text_embedding
inference._types.VoyageAIServiceType:
type: string
enum:
- voyageai
inference._types.HuggingFaceServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://huggingface.co/settings/tokens
description: 'A valid access token for your HuggingFace account.
You can create or find your access tokens on the HuggingFace settings page.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Hugging Face.
By default, the `hugging_face` service sets the number of requests allowed per minute to 3000 for all supported tasks.
Hugging Face does not publish a universal rate limit — actual limits may vary.
It is recommended to adjust this value based on the capacity and limits of your specific deployment environment.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
url:
externalDocs:
url: https://huggingface.co/docs/inference-providers/en/tasks/chat-completion#conversational-large-language-models-llms
description: 'The URL endpoint to use for the requests.
For `completion` and `chat_completion` tasks, the deployed model must be compatible with the Hugging Face Chat Completion interface (see the linked external documentation for details). The endpoint URL for the request must include `/v1/chat/completions`.
If the model supports the OpenAI Chat Completion schema, a toggle should appear in the interface. Enabling this toggle doesn''t change any model behavior, it reveals the full endpoint URL needed (which should include `/v1/chat/completions`) when configuring the inference endpoint in Elasticsearch. If the model doesn''t support this schema, the toggle may not be shown.'
type: string
model_id:
description: 'The name of the HuggingFace model to use for the inference task.
For `completion` and `chat_completion` tasks, this field is optional but may be required for certain models — particularly when using serverless inference endpoints.
For the `text_embedding` task, this field should not be included. Otherwise, the request will fail.'
type: string
required:
- api_key
- url
inference._types.GoogleAiStudioServiceSettings:
type: object
properties:
api_key:
description: A valid API key of your Google Gemini account.
type: string
model_id:
externalDocs:
url: https://ai.google.dev/gemini-api/docs/models
description: 'The name of the model to use for the inference task.
Refer to the Google documentation for the list of supported models.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Google AI Studio.
By default, the `googleaistudio` service sets the number of requests allowed per minute to 360.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- api_key
- model_id
inference._types.InferenceEndpointInfoMistral:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeMistral'
required:
- inference_id
- task_type
_types.Duration:
externalDocs:
url: https://www.elastic.co/docs/reference/elasticsearch/rest-apis/api-conventions#time-units
description: 'A duration. Units can be `nanos`, `micros`, `ms` (milliseconds), `s` (seconds), `m` (minutes), `h` (hours) and
`d` (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.'
oneOf:
- type: string
- type: string
enum:
- '-1'
- type: string
enum:
- '0'
inference._types.EmbeddingStringInput:
description: Allows specifying text-only inputs for the `embedding` task.
oneOf:
- type: string
- type: array
items:
type: string
inference._types.OpenAISimilarityType:
type: string
enum:
- cosine
- dot_product
- l2_norm
inference._types.TaskTypeAzureOpenAI:
type: string
enum:
- text_embedding
- completion
- chat_completion
inference._types.LlamaServiceType:
type: string
enum:
- llama
inference._types.HuggingFaceServiceType:
type: string
enum:
- hugging_face
inference._types.TaskTypeCohere:
type: string
enum:
- text_embedding
- rerank
- completion
inference._types.InferenceEndpointInfoContextualAi:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeContextualAI'
required:
- inference_id
- task_type
inference._types.TaskTypeMistral:
type: string
enum:
- text_embedding
- chat_completion
- completion
inference._types.CohereEmbeddingType:
type: string
enum:
- binary
- bit
- byte
- float
- int8
inference._types.TaskTypeGoogleVertexAI:
type: string
enum:
- chat_completion
- completion
- text_embedding
- rerank
inference._types.ContentObject:
description: An object style representation of a single portion of a conversation.
type: object
properties:
text:
description: The text content.
type: string
type:
description: The type of content.
type: string
required:
- text
- type
_types.byte:
type: number
_types.Id:
type: string
inference._types.RateLimitSetting:
description: This setting helps to minimize the number of rate limit errors returned from the service.
type: object
properties:
requests_per_minute:
description: 'The number of requests allowed per minute.
By default, the number of requests allowed per minute is set by each service as follows:
* `alibabacloud-ai-search` service: `1000`
* `amazonbedrock` service: `240`
* `anthropic` service: `50`
* `azureaistudio` service: `240`
* `azureopenai` service and task type `text_embedding`: `1440`
* `azureopenai` service and task types `completion` or `chat_completion`: `120`
* `cohere` service: `10000`
* `contextualai` service: `1000`
* `elastic` service and task type `chat_completion`: `240`
* `googleaistudio` service: `360`
* `googlevertexai` service: `30000`
* `hugging_face` service: `3000`
* `jinaai` service: `2000`
* `llama` service: `3000`
* `mistral` service: `240`
* `openai` service and task type `text_embedding`: `3000`
* `openai` service and task type `completion`: `500`
* `openshift_ai` service: `3000`
* `voyageai` service: `2000`
* `watsonxai` service: `120`'
type: number
inference._types.GoogleAiServiceType:
type: string
enum:
- googleaistudio
inference._types.TaskTypeElasticsearch:
type: string
enum:
- sparse_embedding
- text_embedding
- rerank
inference._types.AzureOpenAIServiceType:
type: string
enum:
- azureopenai
inference._types.ElasticsearchTaskType:
type: string
enum:
- rerank
- sparse_embedding
- text_embedding
inference._types.NvidiaTaskSettings:
type: object
properties:
input_type:
description: 'For a `text_embedding` task, type of input sent to the Nvidia endpoint.
Valid values are:
* `ingest`: Mapped to Nvidia''s `passage` value in request. Used when generating embeddings during indexing.
* `search`: Mapped to Nvidia''s `query` value in request. Used when generating embeddings during querying.
IMPORTANT: For Nvidia endpoints, if the `input_type` field is not specified, it defaults to `query`.'
allOf:
- $ref: '#/components/schemas/inference._types.NvidiaInputType'
truncate:
description: 'For a `text_embedding` task, the method used by the Nvidia model to handle inputs longer than the maximum token length.
Valid values are:
* `END`: When the input exceeds the maximum input token length, the end of the input is discarded.
* `NONE`: When the input exceeds the maximum input token length, an error is returned.
* `START`: When the input exceeds the maximum input token length, the start of the input is discarded.'
allOf:
- $ref: '#/components/schemas/inference._types.CohereTruncateType'
inference._types.InferenceEndpoint:
description: Configuration options when storing the inference endpoint
type: object
properties:
chunking_settings:
description: 'The chunking configuration object.
Applies only to the `embedding`, `sparse_embedding` and `text_embedding` task types.
Not applicable to the `rerank`, `completion`, or `chat_completion` task types.'
allOf:
- $ref: '#/components/schemas/inference._types.InferenceChunkingSettings'
service:
description: The service type
type: string
service_settings:
description: Settings specific to the service
allOf:
- $ref: '#/components/schemas/inference._types.ServiceSettings'
task_settings:
description: Task settings specific to the service and task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskSettings'
required:
- service
- service_settings
inference._types.DeepSeekServiceType:
type: string
enum:
- deepseek
inference._types.CompletionToolChoiceFunction:
description: The tool choice function.
type: object
properties:
name:
description: The name of the function to call.
type: string
required:
- name
inference._types.MessageContent:
oneOf:
- type: string
- type: array
items:
$ref: '#/components/schemas/inference._types.ContentObject'
inference._types.DenseVector:
description: 'Dense Embedding results are represented as Dense Vectors
of floats.'
type: array
items:
type: number
inference._types.LlamaServiceSettings:
type: object
properties:
url:
description: 'The URL endpoint of the Llama stack endpoint.
URL must contain:
* For `text_embedding` task - `/v1/inference/embeddings`.
* For `completion` and `chat_completion` tasks - `/v1/openai/v1/chat/completions`.'
type: string
model_id:
externalDocs:
url: https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html/
description: 'The name of the model to use for the inference task.
Refer to the Llama downloading models documentation for different ways of getting a list of available models and downloading them.
Service has been tested and confirmed to be working with the following models:
* For `text_embedding` task - `all-MiniLM-L6-v2`.
* For `completion` and `chat_completion` tasks - `llama3.2:3b`.'
type: string
max_input_tokens:
description: For a `text_embedding` task, the maximum number of tokens per input before chunking occurs.
type: number
similarity:
description: For a `text_embedding` task, the similarity measure. One of cosine, dot_product, l2_norm.
allOf:
- $ref: '#/components/schemas/inference._types.LlamaSimilarityType'
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from the Llama API.
By default, the `llama` service sets the number of requests allowed per minute to 3000.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- url
- model_id
inference._types.AzureAiStudioTaskType:
type: string
enum:
- completion
- rerank
- text_embedding
inference._types.AnthropicTaskType:
type: string
enum:
- completion
inference._types.TaskTypeAlibabaCloudAI:
type: string
enum:
- text_embedding
- rerank
- completion
- sparse_embedding
inference._types.InferenceEndpointInfoAzureOpenAI:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeAzureOpenAI'
required:
- inference_id
- task_type
inference._types.CompletionResult:
description: The completion result object
type: object
properties:
result:
type: string
required:
- result
inference._types.CustomServiceSettings:
type: object
properties:
batch_size:
externalDocs:
url: https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/semantic-text#auto-text-chunking
description: 'Specifies the batch size used for the semantic_text field. If the field is not provided, the default is 10.
The batch size is the maximum number of inputs in a single request to the upstream service.
The chunk within the batch are controlled by the selected chunking strategy for the semantic_text field.'
type: number
headers:
description: "Specifies the HTTP header parameters – such as `Authentication` or `Content-Type` – that are required to access the custom service.\nFor example:\n```\n\"headers\":{\n \"Authorization\": \"Bearer ${api_key}\",\n \"Content-Type\": \"application/json;charset=utf-8\"\n}\n```"
type: object
input_type:
description: "Specifies the input type translation values that are used to replace the `${input_type}` template in the request body.\nFor example:\n```\n\"input_type\": {\n \"translation\": {\n \"ingest\": \"do_ingest\",\n \"search\": \"do_search\"\n },\n \"default\": \"a_default\"\n},\n```\nIf the subsequent inference requests come from a search context, the `search` key will be used and the template will be replaced with `do_search`.\nIf it comes from the ingest context `do_ingest` is used. If it's a different context that is not specified, the default value will be used. If no default is specified an empty string is used.\n`translation` can be:\n* `classification`\n* `clustering`\n* `ingest`\n* `search`"
type: object
query_parameters:
description: "Specifies the query parameters as a list of tuples. The arrays inside the `query_parameters` must have two items, a key and a value.\nFor example:\n```\n\"query_parameters\":[\n [\"param_key\", \"some_value\"],\n [\"param_key\", \"another_value\"],\n [\"other_key\", \"other_value\"]\n]\n```\nIf the base url is `https://www.elastic.co` it results in: `https://www.elastic.co?param_key=some_value¶m_key=another_value&other_key=other_value`."
type: object
request:
description: The request configuration object.
allOf:
- $ref: '#/components/schemas/inference._types.CustomRequestParams'
response:
description: The response configuration object.
allOf:
- $ref: '#/components/schemas/inference._types.CustomResponseParams'
secret_parameters:
description: "Specifies secret parameters, like `api_key` or `api_token`, that are required to access the custom service.\nFor example:\n```\n\"secret_parameters\":{\n \"api_key\":\"\"\n}\n```"
type: object
url:
description: The URL endpoint to use for the requests.
type: string
required:
- request
- response
- secret_parameters
inference._types.DenseEmbeddingResult:
description: The dense embedding result object for float representation
type: object
properties:
embedding:
allOf:
- $ref: '#/components/schemas/inference._types.DenseVector'
required:
- embedding
inference._types.VoyageAIServiceSettings:
type: object
properties:
dimensions:
externalDocs:
url: https://docs.voyageai.com/docs/embeddings
description: 'The number of dimensions for resulting output embeddings.
This setting maps to `output_dimension` in the VoyageAI documentation.
Only for the `text_embedding` task type.'
type: number
model_id:
externalDocs:
url: https://docs.voyageai.com/docs/reranker
description: 'The name of the model to use for the inference task.
Refer to the VoyageAI documentation for the list of available text embedding and rerank models.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from VoyageAI.
The `voyageai` service sets a default number of requests allowed per minute depending on the task type.
For both `text_embedding` and `rerank`, it is set to `2000`.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
embedding_type:
externalDocs:
url: https://docs.voyageai.com/docs/embeddings
description: 'The data type for the embeddings to be returned.
This setting maps to `output_dtype` in the VoyageAI documentation.
Permitted values: float, int8, bit.
`int8` is a synonym of `byte` in the VoyageAI documentation.
`bit` is a synonym of `binary` in the VoyageAI documentation.
Only for the `text_embedding` task type.'
type: number
required:
- model_id
inference._types.EmbeddingContentType:
type: string
enum:
- text
- image
inference._types.AmazonSageMakerApi:
type: string
enum:
- openai
- elastic
inference._types.TaskTypeContextualAI:
type: string
enum:
- rerank
inference._types.WatsonxServiceType:
type: string
enum:
- watsonxai
inference._types.MistralServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://console.mistral.ai/api-keys/
description: 'A valid API key of your Mistral account.
You can find your Mistral API keys or you can create a new one on the API Keys page.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
max_input_tokens:
description: The maximum number of tokens per input before chunking occurs.
type: number
model:
externalDocs:
url: https://docs.mistral.ai/getting-started/models/
description: 'The name of the model to use for the inference task.
Refer to the Mistral models documentation for the list of available models.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from the Mistral API.
By default, the `mistral` service sets the number of requests allowed per minute to 240.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- api_key
- model
inference._types.GoogleVertexAIServiceSettings:
type: object
properties:
provider:
description: 'The name of the Google Model Garden Provider for `completion` and `chat_completion` tasks.
In order for a Google Model Garden endpoint to be used `provider` must be defined and be other than `google`.
Modes:
- Google Model Garden (third-party models): set `provider` to a supported non-`google` value and provide `url` and/or `streaming_url`.
- Google Vertex AI: omit `provider` or set it to `google`. In this mode, do not set `url` or `streaming_url` and Elastic will construct the endpoint url from `location`, `model_id`, and `project_id` parameters.'
allOf:
- $ref: '#/components/schemas/inference._types.GoogleModelGardenProvider'
url:
description: 'The URL for non-streaming `completion` requests to a Google Model Garden provider endpoint.
If both `url` and `streaming_url` are provided, each is used for its respective mode.
If `streaming_url` is not provided, `url` is also used for streaming `completion` and `chat_completion`.
If `provider` is not provided or set to `google` (Google Vertex AI), do not set `url` (or `streaming_url`).
At least one of `url` or `streaming_url` must be provided for Google Model Garden endpoint usage.
Certain providers require separate URLs for streaming and non-streaming operations (e.g., Anthropic, Mistral, AI21). Others support both operation types through a single URL (e.g., Meta, Hugging Face).
Information on constructing the URL for various providers can be found in the Google Model Garden documentation for the model, or on the endpoint’s `Sample request` page. The request examples also illustrate the proper formatting for the `url`.'
type: string
streaming_url:
description: 'The URL for streaming `completion` and `chat_completion` requests to a Google Model Garden provider endpoint.
If both `streaming_url` and `url` are provided, each is used for its respective mode.
If `url` is not provided, `streaming_url` is also used for non-streaming `completion` requests.
If `provider` is not provided or set to `google` (Google Vertex AI), do not set `streaming_url` (or `url`).
At least one of `streaming_url` or `url` must be provided for Google Model Garden endpoint usage.
Certain providers require separate URLs for streaming and non-streaming operations (e.g., Anthropic, Mistral, AI21). Others support both operation types through a single URL (e.g., Meta, Hugging Face).
Information on constructing the URL for various providers can be found in the Google Model Garden documentation for the model, or on the endpoint’s `Sample request` page. The request examples also illustrate the proper formatting for the `streaming_url`.'
type: string
location:
externalDocs:
url: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations
description: 'The name of the location to use for the inference task for the Google Vertex AI inference task.
For Google Vertex AI, when `provider` is omitted or `google` `location` is mandatory.
For Google Model Garden''s `completion` and `chat_completion` tasks, when `provider` is a supported non-`google` value - `location` is ignored.
Refer to the Google documentation for the list of supported locations.'
type: string
model_id:
externalDocs:
url: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api
description: 'The name of the model to use for the inference task.
For Google Vertex AI `model_id` is mandatory.
For Google Model Garden''s `completion` and `chat_completion` tasks, when `provider` is a supported non-`google` value - `model_id` will be used for some providers that require it, otherwise - ignored.
Refer to the Google documentation for the list of supported models for Google Vertex AI.'
type: string
project_id:
description: 'The name of the project to use for the Google Vertex AI inference task.
For Google Vertex AI `project_id` is mandatory.
For Google Model Garden''s `completion` and `chat_completion` tasks, when `provider` is a supported non-`google` value - `project_id` is ignored.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Google Vertex AI.
By default, the `googlevertexai` service sets the number of requests allowed per minute to 30.000.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
service_account_json:
description: A valid service account in JSON format for the Google Vertex AI API.
type: string
dimensions:
externalDocs:
url: https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings
description: 'For a `text_embedding` task, the number of dimensions the resulting output embeddings should have.
By default, the model''s standard output dimension is used.
Refer to the Google documentation for more information.'
type: number
max_batch_size:
description: 'Only applicable for the `text_embedding` task type.
Controls the batch size of chunked inference requests sent to Google Vertex AI.
Setting this parameter lower reduces the risk of exceeding token limits but may result in more API calls. Setting it higher increases throughput but may risk hitting token limits.
To estimate a safe `max_batch_size` value, you can use it together with the `max_chunk_size` parameter using the following formula:
`max_batch_size ≈ max_chunk_size × 1.3 × 512 ÷ 20000`
Where:
- `1.3` is an approximate tokens-per-word ratio
- `512` is the maximum number of chunks that can be generated per document
- `20000` is the Google Vertex AI token limit per request
This estimate assumes the worst-case scenario with a document generating the maximum 512 chunks.'
default: 250.0
type: number
required:
- service_account_json
inference._types.InferenceEndpointInfoLlama:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeLlama'
required:
- inference_id
- task_type
inference._types.LlamaTaskType:
type: string
enum:
- text_embedding
- completion
- chat_completion
inference._types.ToolCall:
description: A tool call generated by the model.
type: object
properties:
id:
description: The identifier of the tool call.
allOf:
- $ref: '#/components/schemas/_types.Id'
function:
description: The function that the model called.
allOf:
- $ref: '#/components/schemas/inference._types.ToolCallFunction'
type:
description: The type of the tool call.
type: string
required:
- id
- function
- type
inference._types.TaskTypeGroq:
type: string
enum:
- chat_completion
inference._types.TaskTypeAmazonBedrock:
type: string
enum:
- chat_completion
- completion
- text_embedding
inference._types.InferenceEndpointInfoElasticsearch:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeElasticsearch'
required:
- inference_id
- task_type
inference._types.NvidiaServiceSettings:
type: object
properties:
api_key:
description: 'A valid API key for your Nvidia endpoint.
Can be found in `API Keys` section of Nvidia account settings.'
type: string
url:
description: 'The URL of the Nvidia model endpoint. If not provided, the default endpoint URL is used depending on the task type:
* For `text_embedding` task - `https://integrate.api.nvidia.com/v1/embeddings`.
* For `completion` and `chat_completion` tasks - `https://integrate.api.nvidia.com/v1/chat/completions`.
* For `rerank` task - `https://ai.api.nvidia.com/v1/retrieval/nvidia/reranking`.'
type: string
model_id:
description: 'The name of the model to use for the inference task.
Refer to the model''s documentation for the name if needed.
Service has been tested and confirmed to be working with the following models:
* For `text_embedding` task - `nvidia/llama-3.2-nv-embedqa-1b-v2`.
* For `completion` and `chat_completion` tasks - `microsoft/phi-3-mini-128k-instruct`.
* For `rerank` task - `nv-rerank-qa-mistral-4b:1`.
Service doesn''t support `text_embedding` task `baai/bge-m3` and `nvidia/nvclip` models due to them not recognizing the `input_type` parameter.'
type: string
max_input_tokens:
description: For a `text_embedding` task, the maximum number of tokens per input. Inputs exceeding this value are truncated prior to sending to the Nvidia API.
type: number
similarity:
description: For a `text_embedding` task, the similarity measure. One of cosine, dot_product, l2_norm.
allOf:
- $ref: '#/components/schemas/inference._types.NvidiaSimilarityType'
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from the Nvidia API.
By default, the `nvidia` service sets the number of requests allowed per minute to 3000.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- api_key
- model_id
inference._types.TaskTypeGoogleAIStudio:
type: string
enum:
- text_embedding
- completion
inference._types.VoyageAITaskSettings:
type: object
properties:
input_type:
description: 'Type of the input text.
Permitted values: `ingest` (maps to `document` in the VoyageAI documentation), `search` (maps to `query` in the VoyageAI documentation).
Only for the `text_embedding` task type.'
type: string
return_documents:
description: 'Whether to return the source documents in the response.
Only for the `rerank` task type.'
default: false
type: boolean
top_k:
description: 'The number of most relevant documents to return.
If not specified, the reranking results of all documents will be returned.
Only for the `rerank` task type.'
type: number
truncation:
description: Whether to truncate the input texts to fit within the context length.
default: true
type: boolean
inference._types.CohereInputType:
type: string
enum:
- classification
- clustering
- ingest
- search
inference._types.NvidiaTaskType:
type: string
enum:
- chat_completion
- completion
- rerank
- text_embedding
inference._types.InferenceEndpointInfoGoogleAIStudio:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeGoogleAIStudio'
required:
- inference_id
- task_type
inference._types.CohereTaskSettings:
type: object
properties:
input_type:
description: 'For a `text_embedding` task, the type of input passed to the model.
Valid values are:
* `classification`: Use it for embeddings passed through a text classifier.
* `clustering`: Use it for the embeddings run through a clustering algorithm.
* `ingest`: Use it for storing document embeddings in a vector database.
* `search`: Use it for storing embeddings of search queries run against a vector database to find relevant documents.
IMPORTANT: The `input_type` field is required when using embedding models `v3` and higher.'
allOf:
- $ref: '#/components/schemas/inference._types.CohereInputType'
return_documents:
description: For a `rerank` task, return doc text within the results.
type: boolean
top_n:
description: 'For a `rerank` task, the number of most relevant documents to return.
It defaults to the number of the documents.
If this inference endpoint is used in a `text_similarity_reranker` retriever query and `top_n` is set, it must be greater than or equal to `rank_window_size` in the query.'
type: number
truncate:
description: 'For a `text_embedding` task, the method to handle inputs longer than the maximum token length.
Valid values are:
* `END`: When the input exceeds the maximum input token length, the end of the input is discarded.
* `NONE`: When the input exceeds the maximum input token length, an error is returned.
* `START`: When the input exceeds the maximum input token length, the start of the input is discarded.'
allOf:
- $ref: '#/components/schemas/inference._types.CohereTruncateType'
required:
- input_type
inference._types.AdaptiveAllocations:
type: object
properties:
enabled:
description: Turn on `adaptive_allocations`.
default: false
type: boolean
max_number_of_allocations:
description: 'The maximum number of allocations to scale to.
If set, it must be greater than or equal to `min_number_of_allocations`.'
type: number
min_number_of_allocations:
description: 'The minimum number of allocations to scale to.
If set, it must be greater than or equal to 0.
If not defined, the deployment scales to 0.'
type: number
inference._types.EmbeddingInferenceResult:
description: EmbeddingInferenceResult is an aggregation of mutually exclusive embeddings variants
type: object
properties:
embeddings_bytes:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingByteResult'
embeddings_bits:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingByteResult'
embeddings:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingResult'
minProperties: 1
maxProperties: 1
inference._types.Ai21ServiceSettings:
type: object
properties:
model_id:
externalDocs:
url: https://docs.ai21.com/docs/jamba-foundation-models
description: 'The name of the model to use for the inference task.
Refer to the AI21 models documentation for the list of supported models and versions.
Service has been tested and confirmed to be working for `completion` and `chat_completion` tasks with the following models:
* `jamba-mini`
* `jamba-large`'
type: string
api_key:
description: 'A valid API key for accessing AI21 API.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
rate_limit:
externalDocs:
url: https://docs.ai21.com/reference/api-rate-limits
description: 'This setting helps to minimize the number of rate limit errors returned from the AI21 API.
By default, the `ai21` service sets the number of requests allowed per minute to 200. Please refer to AI21 documentation for more details.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- model_id
inference._types.CohereTaskType:
type: string
enum:
- completion
- rerank
- text_embedding
inference._types.AlibabaCloudServiceType:
type: string
enum:
- alibabacloud-ai-search
inference._types.Ai21ServiceType:
type: string
enum:
- ai21
inference._types.JinaAITaskType:
type: string
enum:
- embedding
- rerank
- text_embedding
inference._types.GoogleModelGardenProvider:
type: string
enum:
- google
- anthropic
- meta
- hugging_face
- mistral
- ai21
inference._types.InferenceEndpointInfo:
description: Represents an inference endpoint as returned by the GET API
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskType'
required:
- inference_id
- task_type
inference._types.EmbeddingContentObjectContents:
description: An object containing the input data for the model to embed.
type: object
properties:
type:
description: The type of input to embed.
allOf:
- $ref: '#/components/schemas/inference._types.EmbeddingContentType'
format:
description: The format of the input. For the `text` type this defaults to `text`. For the `image` type, this defaults to `base64`.
allOf:
- $ref: '#/components/schemas/inference._types.EmbeddingContentFormat'
value:
description: The value of the input to embed.
type: string
required:
- type
- value
inference._types.AnthropicServiceType:
type: string
enum:
- anthropic
inference._types.ServiceSettings:
type: object
inference._types.EmbeddingContentInput:
description: Allows specifying multimodal inputs for the `embedding` task.
oneOf:
- $ref: '#/components/schemas/inference._types.EmbeddingContentObject'
- type: array
items:
$ref: '#/components/schemas/inference._types.EmbeddingContentObject'
inference._types.TaskTypeOpenAI:
type: string
enum:
- text_embedding
- chat_completion
- completion
inference._types.AzureAiStudioServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://ai.azure.com/
description: 'A valid API key of your Azure AI Studio model deployment.
This key can be found on the overview page for your deployment in the management section of your Azure AI Studio account.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
endpoint_type:
externalDocs:
url: https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/deployments-overview#billing-for-deploying-and-inferencing-llms-in-azure-ai-studio
description: 'The type of endpoint that is available for deployment through Azure AI Studio: `token` or `realtime`.
The `token` endpoint type is for "pay as you go" endpoints that are billed per token.
The `realtime` endpoint type is for "real-time" endpoints that are billed per hour of usage.'
type: string
target:
description: 'The target URL of your Azure AI Studio model deployment.
This can be found on the overview page for your deployment in the management section of your Azure AI Studio account.'
type: string
provider:
description: 'The model provider for your deployment.
Note that some providers may support only certain task types.
Supported providers include:
* `cohere` - available for `text_embedding`, `rerank` and `completion` task types
* `databricks` - available for `completion` task type only
* `meta` - available for `completion` task type only
* `microsoft_phi` - available for `completion` task type only
* `mistral` - available for `completion` task type only
* `openai` - available for `text_embedding` and `completion` task types'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Azure AI Studio.
By default, the `azureaistudio` service sets the number of requests allowed per minute to 240.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- api_key
- endpoint_type
- target
- provider
inference._types.NvidiaServiceType:
type: string
enum:
- nvidia
inference._types.WatsonxServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://cloud.ibm.com/iam/apikeys
description: 'A valid API key of your Watsonx account.
You can find your Watsonx API keys or you can create a new one on the API keys page.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
api_version:
externalDocs:
url: https://cloud.ibm.com/apidocs/watsonx-ai#active-version-dates
description: 'A version parameter that takes a version date in the format of `YYYY-MM-DD`.
For the active version data parameters, refer to the Wastonx documentation.'
type: string
model_id:
externalDocs:
url: https://www.ibm.com/products/watsonx-ai/foundation-models
description: 'The name of the model to use for the inference task.
Refer to the IBM Embedding Models section in the Watsonx documentation for the list of available text embedding models.
Refer to the IBM library - Foundation models in Watsonx.ai.'
type: string
project_id:
description: The identifier of the IBM Cloud project to use for the inference task.
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Watsonx.
By default, the `watsonxai` service sets the number of requests allowed per minute to 120.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
url:
description: The URL of the inference endpoint that you created on Watsonx.
type: string
required:
- api_key
- api_version
- model_id
- project_id
- url
inference._types.ContextualAIServiceSettings:
type: object
properties:
api_key:
description: 'A valid API key for your Contexutual AI account.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
model_id:
description: 'The name of the model to use for the inference task.
Refer to the Contextual AI documentation for the list of available rerank models.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from Contextual AI.
The `contextualai` service sets a default number of requests allowed per minute depending on the task type.
For `rerank`, it is set to `1000`.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
required:
- api_key
- model_id
inference._types.NvidiaInputType:
type: string
enum:
- ingest
- search
inference._types.MistralServiceType:
type: string
enum:
- mistral
inference._types.InferenceEndpointInfoAlibabaCloudAI:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeAlibabaCloudAI'
required:
- inference_id
- task_type
inference._types.JinaAIServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://jina.ai/embeddings/
description: 'A valid API key of your JinaAI account.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
model_id:
description: The name of the model to use for the inference task.
type: string
rate_limit:
externalDocs:
url: https://jina.ai/contact-sales/#rate-limit
description: 'This setting helps to minimize the number of rate limit errors returned from JinaAI.
By default, the `jinaai` service sets the number of requests allowed per minute to 2000 for all task types.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
similarity:
description: 'For an `embedding` or `text_embedding` task, the similarity measure. One of cosine, dot_product, l2_norm.
The default values varies with the embedding type.
For example, a float embedding type uses a `dot_product` similarity measure by default.'
allOf:
- $ref: '#/components/schemas/inference._types.JinaAISimilarityType'
dimensions:
externalDocs:
url: https://jina.ai/embeddings/
description: 'For an `embedding` or `text_embedding` task, the number of dimensions the resulting output embeddings should have.
By default, the model''s standard output dimension is used.
Refer to the Jina documentation for more information.'
type: number
element_type:
description: 'For an `embedding` or `text_embedding` task, the data type returned by the model.
Use `bit` for binary embeddings, which are encoded as bytes with signed int8 precision.
Use `binary` for binary embeddings, which are encoded as bytes with signed int8 precision (this is a synonym of `bit`).
Use `float` for the default float embeddings.'
default: float
allOf:
- $ref: '#/components/schemas/inference._types.JinaAIElementType'
multimodal_model:
description: 'For the `embedding` task, whether the model supports multimodal inputs. If true, requests sent to the Jina model
will use the multimodal request format (a list of objects). If false, requests sent to the model will use the same
format as the `text_embedding` task (a list of strings). Setting this to `false` allows the `embedding` task to be
used with models that do not support multimodal requests.'
default: true
type: boolean
required:
- api_key
- model_id
inference._types.CustomTaskSettings:
type: object
properties:
parameters:
description: "Specifies parameters that are required to run the custom service. The parameters depend on the model your custom service uses.\nFor example:\n```\n\"task_settings\":{\n \"parameters\":{\n \"input_type\":\"query\",\n \"return_token\":true\n }\n}\n```"
type: object
inference._types.AzureOpenAITaskType:
type: string
enum:
- completion
- chat_completion
- text_embedding
inference._types.DenseEmbeddingByteResult:
description: The dense embedding result object for byte representation
type: object
properties:
embedding:
allOf:
- $ref: '#/components/schemas/inference._types.DenseByteVector'
required:
- embedding
inference._types.InferenceEndpointInfoAzureAIStudio:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeAzureAIStudio'
required:
- inference_id
- task_type
inference._types.JinaAIElementType:
type: string
enum:
- binary
- bit
- float
inference._types.TaskTypeAi21:
type: string
enum:
- completion
- chat_completion
inference._types.GroqTaskType:
type: string
enum:
- chat_completion
inference._types.SparseEmbeddingResult:
type: object
properties:
is_truncated:
description: Indicates if the text input was truncated in the request sent to the service
type: boolean
embedding:
allOf:
- $ref: '#/components/schemas/inference._types.SparseVector'
required:
- is_truncated
- embedding
inference._types.TaskTypeAzureAIStudio:
type: string
enum:
- text_embedding
- completion
- rerank
inference._types.InferenceEndpointInfoCustom:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeCustom'
required:
- inference_id
- task_type
inference._types.RerankedInferenceResult:
description: Defines the response for a rerank request.
type: object
properties:
rerank:
type: array
items:
$ref: '#/components/schemas/inference._types.RankedDocument'
required:
- rerank
inference._types.AnthropicTaskSettings:
type: object
properties:
max_tokens:
description: For a `completion` task, it is the maximum number of tokens to generate before stopping.
type: number
temperature:
externalDocs:
url: https://docs.anthropic.com/en/api/messages
description: 'For a `completion` task, it is the amount of randomness injected into the response.
For more details about the supported range, refer to Anthropic documentation.'
type: number
top_k:
description: 'For a `completion` task, it specifies to only sample from the top K options for each subsequent token.
It is recommended for advanced use cases only.
You usually only need to use `temperature`.'
type: number
top_p:
description: 'For a `completion` task, it specifies to use Anthropic''s nucleus sampling.
In nucleus sampling, Anthropic computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches the specified probability.
You should either alter `temperature` or `top_p`, but not both.
It is recommended for advanced use cases only.
You usually only need to use `temperature`.'
type: number
required:
- max_tokens
inference._types.AlibabaCloudTaskType:
type: string
enum:
- completion
- rerank
- sparse_embedding
- text_embedding
inference._types.AlibabaCloudServiceSettings:
type: object
properties:
api_key:
description: A valid API key for the AlibabaCloud AI Search API.
type: string
host:
externalDocs:
url: https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key
description: 'The name of the host address used for the inference task.
You can find the host address in the API keys section of the documentation.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from AlibabaCloud AI Search.
By default, the `alibabacloud-ai-search` service sets the number of requests allowed per minute to `1000`.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
service_id:
description: 'The name of the model service to use for the inference task.
The following service IDs are available for the `completion` task:
* `ops-qwen-turbo`
* `qwen-turbo`
* `qwen-plus`
* `qwen-max ÷ qwen-max-longcontext`
The following service ID is available for the `rerank` task:
* `ops-bge-reranker-larger`
The following service ID is available for the `sparse_embedding` task:
* `ops-text-sparse-embedding-001`
The following service IDs are available for the `text_embedding` task:
`ops-text-embedding-001`
`ops-text-embedding-zh-001`
`ops-text-embedding-en-001`
`ops-text-embedding-002`'
type: string
workspace:
description: The name of the workspace used for the inference task.
type: string
required:
- api_key
- host
- service_id
- workspace
inference._types.CustomResponseParams:
type: object
properties:
json_parser:
description: "Specifies the JSON parser that is used to parse the response from the custom service.\nDifferent task types require different json_parser parameters.\nFor example:\n```\n# text_embedding\n# For a response like this:\n\n{\n \"object\": \"list\",\n \"data\": [\n {\n \"object\": \"embedding\",\n \"index\": 0,\n \"embedding\": [\n 0.014539449,\n -0.015288644\n ]\n }\n ],\n \"model\": \"text-embedding-ada-002-v2\",\n \"usage\": {\n \"prompt_tokens\": 8,\n \"total_tokens\": 8\n }\n}\n\n# the json_parser definition should look like this:\n\n\"response\":{\n \"json_parser\":{\n \"text_embeddings\":\"$.data[*].embedding[*]\"\n }\n}\n\n# Elasticsearch supports the following embedding types:\n* float\n* byte\n* bit (or binary)\n\nTo specify the embedding type for the response, the `embedding_type`\nfield should be added in the `json_parser` object. Here's an example:\n\"response\":{\n \"json_parser\":{\n \"text_embeddings\":\"$.data[*].embedding[*]\",\n \"embedding_type\":\"bit\"\n }\n}\n\nIf `embedding_type` is not specified, it defaults to `float`.\n\n# sparse_embedding\n# For a response like this:\n\n{\n \"request_id\": \"75C50B5B-E79E-4930-****-F48DBB392231\",\n \"latency\": 22,\n \"usage\": {\n \"token_count\": 11\n },\n \"result\": {\n \"sparse_embeddings\": [\n {\n \"index\": 0,\n \"embedding\": [\n {\n \"token_id\": 6,\n \"weight\": 0.101\n },\n {\n \"token_id\": 163040,\n \"weight\": 0.28417\n }\n ]\n }\n ]\n }\n}\n\n# the json_parser definition should look like this:\n\n\"response\":{\n \"json_parser\":{\n \"token_path\":\"$.result.sparse_embeddings[*].embedding[*].token_id\",\n \"weight_path\":\"$.result.sparse_embeddings[*].embedding[*].weight\"\n }\n}\n\n# rerank\n# For a response like this:\n\n{\n \"results\": [\n {\n \"index\": 3,\n \"relevance_score\": 0.999071,\n \"document\": \"abc\"\n },\n {\n \"index\": 4,\n \"relevance_score\": 0.7867867,\n \"document\": \"123\"\n },\n {\n \"index\": 0,\n \"relevance_score\": 0.32713068,\n \"document\": \"super\"\n }\n ],\n}\n\n# the json_parser definition should look like this:\n\n\"response\":{\n \"json_parser\":{\n \"reranked_index\":\"$.result.scores[*].index\", // optional\n \"relevance_score\":\"$.result.scores[*].score\",\n \"document_text\":\"xxx\" // optional\n }\n}\n\n# completion\n# For a response like this:\n\n{\n \"id\": \"chatcmpl-B9MBs8CjcvOU2jLn4n570S5qMJKcT\",\n \"object\": \"chat.completion\",\n \"created\": 1741569952,\n \"model\": \"gpt-4.1-2025-04-14\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"Hello! How can I assist you today?\",\n \"refusal\": null,\n \"annotations\": []\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n }\n ]\n}\n\n# the json_parser definition should look like this:\n\n\"response\":{\n \"json_parser\":{\n \"completion_result\":\"$.choices[*].message.content\"\n }\n}"
type: object
required:
- json_parser
inference._types.ElasticsearchServiceType:
type: string
enum:
- elasticsearch
inference._types.ElserServiceType:
type: string
enum:
- elser
inference._types.CohereServiceType:
type: string
enum:
- cohere
inference._types.JinaAITaskSettings:
type: object
properties:
return_documents:
description: For a `rerank` task, return the doc text within the results.
type: boolean
input_type:
description: 'For an `embedding` or `text_embedding` task, the task passed to the model.
Valid values are:
* `classification`: Use it for embeddings passed through a classifier.
* `clustering`: Use it for the embeddings run through a clustering algorithm.
* `ingest`: Use it for storing document embeddings in a vector database.
* `search`: Use it for storing embeddings of search queries run against a vector database to find relevant documents.'
allOf:
- $ref: '#/components/schemas/inference._types.JinaAITextEmbeddingTask'
late_chunking:
description: 'For an `embedding` or `text_embedding` task, controls when text is split into chunks.
When set to `true`, a request from Elasticsearch contains only chunks related to a single document. Instead of batching chunks across documents, Elasticsearch sends them in separate requests. This ensures that chunk embeddings retain context from the entire document, improving semantic quality.
If a document exceeds the model''s context limits, or if the document contains non-text inputs (relevant when using the multimodal `embedding` task), late chunking is automatically disabled for that document only and standard chunking is used instead.
If not specified, defaults to `false`.'
type: boolean
top_n:
description: 'For a `rerank` task, the number of most relevant documents to return.
It defaults to the number of the documents.
If this inference endpoint is used in a `text_similarity_reranker` retriever query and `top_n` is set, it must be greater than or equal to `rank_window_size` in the query.'
type: number
inference._types.InferenceEndpointInfoJinaAi:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeJinaAi'
required:
- inference_id
- task_type
inference._types.TaskTypeNvidia:
type: string
enum:
- chat_completion
- completion
- rerank
- text_embedding
inference._types.ElserServiceSettings:
type: object
properties:
adaptive_allocations:
description: 'Adaptive allocations configuration details.
If `enabled` is true, the number of allocations of the model is set based on the current load the process gets.
When the load is high, a new model allocation is automatically created, respecting the value of `max_number_of_allocations` if it''s set.
When the load is low, a model allocation is automatically removed, respecting the value of `min_number_of_allocations` if it''s set.
If `enabled` is true, do not set the number of allocations manually.'
allOf:
- $ref: '#/components/schemas/inference._types.AdaptiveAllocations'
num_allocations:
description: 'The total number of allocations this model is assigned across machine learning nodes.
Increasing this value generally increases the throughput.
If adaptive allocations is enabled, do not set this value because it''s automatically set.'
type: number
num_threads:
description: 'The number of threads used by each model allocation during inference.
Increasing this value generally increases the speed per inference request.
The inference process is a compute-bound process; `threads_per_allocations` must not exceed the number of available allocated processors per node.
The value must be a power of 2.
The maximum value is 32.
> info
> If you want to optimize your ELSER endpoint for ingest, set the number of threads to 1. If you want to optimize your ELSER endpoint for search, set the number of threads to greater than 1.'
type: number
required:
- num_allocations
- num_threads
inference._types.CompletionToolType:
oneOf:
- type: string
- $ref: '#/components/schemas/inference._types.CompletionToolChoice'
inference._types.AzureAiStudioServiceType:
type: string
enum:
- azureaistudio
inference._types.ElserTaskType:
type: string
enum:
- sparse_embedding
inference._types.GroqServiceType:
type: string
enum:
- groq
inference._types.ElasticsearchServiceSettings:
type: object
properties:
adaptive_allocations:
description: 'Adaptive allocations configuration details.
If `enabled` is true, the number of allocations of the model is set based on the current load the process gets.
When the load is high, a new model allocation is automatically created, respecting the value of `max_number_of_allocations` if it''s set.
When the load is low, a model allocation is automatically removed, respecting the value of `min_number_of_allocations` if it''s set.
If `enabled` is true, do not set the number of allocations manually.'
allOf:
- $ref: '#/components/schemas/inference._types.AdaptiveAllocations'
deployment_id:
description: 'The deployment identifier for a trained model deployment.
When `deployment_id` is used the `model_id` is optional.'
type: string
model_id:
externalDocs:
url: https://www.elastic.co/docs/explore-analyze/machine-learning/nlp/ml-nlp-import-model#ml-nlp-import-script
description: 'The name of the model to use for the inference task.
It can be the ID of a built-in model (for example, `.multilingual-e5-small` for E5) or a text embedding model that was uploaded by using the Eland client.'
type: string
num_allocations:
description: 'The total number of allocations that are assigned to the model across machine learning nodes.
Increasing this value generally increases the throughput.
If adaptive allocations are enabled, do not set this value because it''s automatically set.'
type: number
num_threads:
description: 'The number of threads used by each model allocation during inference.
This setting generally increases the speed per inference request.
The inference process is a compute-bound process; `threads_per_allocations` must not exceed the number of available allocated processors per node.
The value must be a power of 2.
The maximum value is 32.'
type: number
long_document_strategy:
description: 'Available only for the `rerank` task type using the Elastic reranker model.
Controls the strategy used for processing long documents during inference.
Possible values:
- `truncate` (default): Processes only the beginning of each document.
- `chunk`: Splits long documents into smaller parts (chunks) before inference.
When `long_document_strategy` is set to `chunk`, Elasticsearch splits each document into smaller parts but still returns a single score per document.
That score reflects the highest relevance score among all chunks.'
x-state: Technical preview
type: string
max_chunks_per_doc:
description: 'Only for the `rerank` task type.
Limits the number of chunks per document that are sent for inference when chunking is enabled.
If not set, all chunks generated for the document are processed.'
x-state: Technical preview
type: number
required:
- model_id
- num_threads
inference._types.TaskTypeDeepSeek:
type: string
enum:
- completion
- chat_completion
inference._types.AzureOpenAITaskSettings:
type: object
properties:
user:
description: 'For a `completion`, `chat_completion` or `text_embedding` task, specify the user issuing the request.
This information can be used for abuse detection.'
type: string
inference._types.InferenceEndpointInfoAnthropic:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeAnthropic'
required:
- inference_id
- task_type
inference._types.TextEmbeddingInferenceResult:
description: TextEmbeddingInferenceResult is an aggregation of mutually exclusive text_embedding variants
type: object
properties:
text_embedding_bytes:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingByteResult'
text_embedding_bits:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingByteResult'
text_embedding:
type: array
items:
$ref: '#/components/schemas/inference._types.DenseEmbeddingResult'
minProperties: 1
maxProperties: 1
inference._types.OpenAIServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://platform.openai.com/api-keys
description: 'A valid API key of your OpenAI account.
You can find your OpenAI API keys in your OpenAI account under the API keys section.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
dimensions:
description: 'The number of dimensions the resulting output embeddings should have.
It is supported only in `text-embedding-3` and later models.
If it is not set, the OpenAI defined default for the model is used.'
type: number
model_id:
externalDocs:
url: https://platform.openai.com/docs/guides/embeddings/what-are-embeddings
description: 'The name of the model to use for the inference task.
Refer to the OpenAI documentation for the list of available text embedding models.'
type: string
organization_id:
description: 'The unique identifier for your organization.
You can find the Organization ID in your OpenAI account under *Settings > Organizations*.'
type: string
rate_limit:
description: 'This setting helps to minimize the number of rate limit errors returned from OpenAI.
The `openai` service sets a default number of requests allowed per minute depending on the task type.
For `text_embedding`, it is set to `3000`.
For `completion`, it is set to `500`.'
allOf:
- $ref: '#/components/schemas/inference._types.RateLimitSetting'
similarity:
description: For a `text_embedding` task, the similarity measure. One of cosine, dot_product, l2_norm. Defaults to `dot_product`.
allOf:
- $ref: '#/components/schemas/inference._types.OpenAISimilarityType'
url:
description: 'The URL endpoint to use for the requests.
It can be changed for testing purposes.'
default: https://api.openai.com/v1/embeddings.
type: string
required:
- api_key
- model_id
inference._types.DeepSeekServiceSettings:
type: object
properties:
api_key:
externalDocs:
url: https://api-docs.deepseek.com/
description: 'A valid API key for your DeepSeek account.
You can find or create your DeepSeek API keys on the DeepSeek API key page.
IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.'
type: string
model_id:
description: 'For a `completion` or `chat_completion` task, the name of the model to use for the inference task.
For the available `completion` and `chat_completion` models, refer to the [DeepSeek Models & Pricing docs](https://api-docs.deepseek.com/quick_start/pricing).'
type: string
url:
description: The URL endpoint to use for the requests. Defaults to `https://api.deepseek.com/chat/completions`.
type: string
required:
- api_key
- model_id
inference._types.InferenceEndpointInfoELSER:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeELSER'
required:
- inference_id
- task_type
inference._types.Message:
description: An object representing part of the conversation.
type: object
properties:
content:
description: "The content of the message.\n\nString example:\n```\n{\n \"content\": \"Some string\"\n}\n```\n\nObject example:\n```\n{\n \"content\": [\n {\n \"text\": \"Some text\",\n \"type\": \"text\"\n }\n ]\n}\n```"
allOf:
- $ref: '#/components/schemas/inference._types.MessageContent'
role:
description: The role of the message author. Valid values are `user`, `assistant`, `system`, and `tool`.
type: string
tool_call_id:
description: Only for `tool` role messages. The tool call that this message is responding to.
allOf:
- $ref: '#/components/schemas/_types.Id'
tool_calls:
description: "Only for `assistant` role messages. The tool calls generated by the model. If it's specified, the `content` field is optional.\nExample:\n```\n{\n \"tool_calls\": [\n {\n \"id\": \"call_KcAjWtAww20AihPHphUh46Gd\",\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_current_weather\",\n \"arguments\": \"{\\\"location\\\":\\\"Boston, MA\\\"}\"\n }\n }\n ]\n}\n```"
type: array
items:
$ref: '#/components/schemas/inference._types.ToolCall'
required:
- role
inference._types.CohereTruncateType:
type: string
enum:
- END
- NONE
- START
inference._types.TaskType:
type: string
enum:
- sparse_embedding
- text_embedding
- rerank
- completion
- chat_completion
- embedding
inference._types.TaskSettings:
type: object
inference._types.AmazonBedrockTaskSettings:
type: object
properties:
max_new_tokens:
description: For `chat_completion` and `completion` tasks, it sets the maximum number for the output tokens to be generated.
default: 64.0
type: number
temperature:
description: 'For `chat_completion` and `completion` tasks, it is a number between 0.0 and 1.0 that controls the apparent creativity of the results.
At temperature 0.0 the model is most deterministic, at temperature 1.0 most random.
It should not be used if `top_p` or `top_k` is specified.'
type: number
top_k:
description: 'For `chat_completion` and `completion` tasks, it limits samples to the top-K most likely words, balancing coherence and variability.
It is only available for anthropic, cohere, and mistral providers.
It is an alternative to `temperature`; it should not be used if `temperature` is specified.'
type: number
top_p:
description: 'For `chat_completion` and `completion` tasks, it is a number in the range of 0.0 to 1.0, to eliminate low-probability tokens.
Top-p uses nucleus sampling to select top tokens whose sum of likelihoods does not exceed a certain value, ensuring both variety and coherence.
It is an alternative to `temperature`; it should not be used if `temperature` is specified.'
type: number
inference._types.CompletionToolFunction:
description: The completion tool function definition.
type: object
properties:
description:
description: 'A description of what the function does.
This is used by the model to choose when and how to call the function.'
type: string
name:
description: The name of the function.
type: string
parameters:
description: The parameters the functional accepts. This should be formatted as a JSON object.
type: object
strict:
description: Whether to enable schema adherence when generating the function call.
type: boolean
required:
- name
inference._types.InferenceEndpointInfoAi21:
allOf:
- $ref: '#/components/schemas/inference._types.InferenceEndpoint'
- type: object
properties:
inference_id:
description: The inference Id
type: string
task_type:
description: The task type
allOf:
- $ref: '#/components/schemas/inference._types.TaskTypeAi21'
required:
- inference_id
- task_type
inference._types.AmazonBedrockServiceType:
type: string
enum:
- amazonbedrock
inference._types.AmazonSageMakerServiceSettings:
type: object
properties:
access_key:
description: A valid AWS access key that has permissions to use Amazon SageMaker and access to models for invoking requests.
type: string
endpoint_name:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: The name of the SageMaker endpoint.
type: string
api:
description: 'The API format to use when calling SageMaker.
Elasticsearch will convert the POST _inference request to this data format when invoking the SageMaker endpoint.'
allOf:
- $ref: '#/components/schemas/inference._types.AmazonSageMakerApi'
region:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: 'The region that your endpoint or Amazon Resource Name (ARN) is deployed in.
The list of available regions per model can be found in the Amazon SageMaker documentation.'
type: string
secret_key:
externalDocs:
url: https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html
description: 'A valid AWS secret key that is paired with the `access_key`.
For information about creating and managing access and secret keys, refer to the AWS documentation.'
type: string
target_model:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: The model ID when calling a multi-model endpoint.
type: string
target_container_hostname:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: The container to directly invoke when calling a multi-container endpoint.
type: string
inference_component_name:
externalDocs:
url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
description: The inference component to directly invoke when calling a multi-component endpoint.
type: string
batch_size:
description: 'The maximum number of inputs in each batch. This value is used by inference ingestion pipelines
when processing semantic values. It correlates to the number of times the SageMaker endpoint is
invoked (one per batch of input).'
default: 256.0
type: number
dimensions:
description: 'The number of dimensions returned by the text embedding models. If this value is not provided, then
it is guessed by making invoking the endpoint for the `text_embedding` task.'
type: number
required:
- access_key
- endpoint_name
- api
- region
- secret_key
securitySchemes:
apiKeyAuth:
type: apiKey
in: header
name: Authorization
description: "Elasticsearch APIs support key-based authentication.\nYou must create an API key and use the encoded value in the request header.\nFor example:\n\n```\ncurl -X GET \"${ES_URL}/_cat/indices?v=true\" \\\n -H \"Authorization: ApiKey ${API_KEY}\"\n```\n\nTo get API keys, use the `/_security/api_key` APIs."
basicAuth:
type: http
scheme: basic
bearerAuth:
type: http
scheme: bearer
description: 'Elasticsearch APIs support the use of bearer tokens in the `Authorization` HTTP header to authenticate with the API.
For examples, refer to [Token-based authentication services](https://www.elastic.co/docs/deploy-manage/users-roles/cluster-or-deployment-auth/token-based-authentication-services)'
x-tagGroups:
- name: AI & Machine Learning
tags:
- analytics
- graph
- inference
- ml
- ml anomaly
- ml data frame
- ml trained model
- query_rules
- text_structure
- name: Cluster Management
tags:
- ccr
- cluster
- connector
- data stream
- ilm
- indices
- rollup
- script
- search_application
- searchable_snapshots
- slm
- snapshot
- name: Data Processing
tags:
- enrich
- fleet
- ingest
- logstash
- synonyms
- transform
- name: Information & Monitoring
tags:
- cat
- features
- health_report
- info
- license
- migration
- tasks
- watcher
- xpack
- name: Search & Document APIs
tags:
- document
- eql
- esql
- search
- sql
- name: Security
tags:
- security