All methods and paths for this operation:
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 (
chat_completion,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) - Fireworks AI (
chat_completion,completion,text_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)
Required authorization
- Cluster privileges:
manage_inference
Path parameters
-
The task type. Refer to the integration list in the API description for the available task types.
Values are
sparse_embedding,text_embedding,rerank,completion,chat_completion,embedding,document_extraction, ordecision. -
The inference Id
Query parameters
-
Specifies the amount of time to wait for the inference endpoint to be created. The default depends on the task type: 120s for
completionandchat_completion, and 30s for all other task types.External documentation
Body
Required
-
The chunking configuration object. Applies only to the
embedding,sparse_embeddingandtext_embeddingtask types. Not applicable to thererank,completion, orchat_completiontask types. -
The service type
-
Settings specific to the service
-
Task settings specific to the service and task type
PUT _inference/rerank/my-rerank-model
{
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
}resp = client.inference.put(
task_type="rerank",
inference_id="my-rerank-model",
inference_config={
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
},
)const response = await client.inference.put({
task_type: "rerank",
inference_id: "my-rerank-model",
inference_config: {
service: "cohere",
service_settings: {
model_id: "rerank-english-v3.0",
api_key: "{{COHERE_API_KEY}}",
},
},
});response = client.inference.put(
task_type: "rerank",
inference_id: "my-rerank-model",
body: {
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
}
)$resp = $client->inference()->put([
"task_type" => "rerank",
"inference_id" => "my-rerank-model",
"body" => [
"service" => "cohere",
"service_settings" => [
"model_id" => "rerank-english-v3.0",
"api_key" => "{{COHERE_API_KEY}}",
],
],
]);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"var response = await client.Inference
.PutAsync(new PutInferenceRequest()
{
InferenceId = "my-rerank-model",
TaskType = TaskType.Rerank,
InferenceConfig = new()
{
Service = "cohere",
ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
{
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
""")
}
});client.inference().put(p -> p
.inferenceId("my-rerank-model")
.taskType(TaskType.Rerank)
.inferenceConfig(i -> i
.service("cohere")
.serviceSettings(JsonData.fromJson("""
{"model_id":"rerank-english-v3.0","api_key":"{{COHERE_API_KEY}}"}
"""))
)
);
{
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
}