Create an Elasticsearch inference endpoint
- PUT /_inference/{task_type}/{elasticsearch_inference_id}
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.
- Cluster privileges:
manage_inference
Path Parameters
-
task_typeElasticsearchTaskTyperequired -
The type of the inference task that the model will perform.
-
Values:
reranksparse_embeddingtext_embedding -
elasticsearch_inference_idstring Idrequired -
The unique identifier of the inference endpoint. The must not match the
model_id.
Query Parameters
-
timeoutstring Duration · default: 30s Specifies the amount of time to wait for the inference endpoint to be created.
Request
-
chunking_settingsInferenceChunkingSettings -
The chunking configuration object.
- Reference
-
max_chunk_sizenumber · default: 250 -
The maximum size of a chunk in words. This value cannot be higher than
300or lower than20(forsentencestrategy) or10(forwordstrategy). -
overlapnumber · default: 100 -
The number of overlapping words for chunks. It is applicable only to a
wordchunking strategy. This value cannot be higher than half themax_chunk_sizevalue. -
sentence_overlapnumber · default: 1 -
The number of overlapping sentences for chunks. It is applicable only for a
sentencechunking strategy. It can be either1or0. -
strategystring · default: sentence -
The chunking strategy:
sentenceorword. -
serviceElasticsearchServiceTyperequired -
The type of service supported for the specified task type. In this case,
elasticsearch. -
Values:
elasticsearch -
service_settingsElasticsearchServiceSettingsrequired -
Settings used to install the inference model. These settings are specific to the
elasticsearchservice. -
adaptive_allocationsAdaptiveAllocations -
Adaptive allocations configuration details. If
enabledis 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 ofmax_number_of_allocationsif it's set. When the load is low, a model allocation is automatically removed, respecting the value ofmin_number_of_allocationsif it's set. Ifenabledis true, do not set the number of allocations manually. -
enabledboolean · default: false -
Turn on
adaptive_allocations. -
max_number_of_allocationsnumber -
The maximum number of allocations to scale to. If set, it must be greater than or equal to
min_number_of_allocations. -
min_number_of_allocationsnumber -
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.
-
deployment_idstring -
The deployment identifier for a trained model deployment. When
deployment_idis used themodel_idis optional. -
model_idstringrequired -
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-smallfor E5) or a text embedding model that was uploaded by using the Eland client. - Reference
-
num_allocationsnumber -
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.
-
num_threadsnumberrequired -
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_allocationsmust not exceed the number of available allocated processors per node. The value must be a power of 2. The maximum value is 32. -
task_settingsElasticsearchTaskSettings -
Settings to configure the inference task. These settings are specific to the task type you specified.
-
return_documentsboolean · default: true -
For a
reranktask, return the document instead of only the index.
Response
-
chunking_settingsInferenceChunkingSettings -
Chunking configuration object
-
max_chunk_sizenumber · default: 250 -
The maximum size of a chunk in words. This value cannot be higher than
300or lower than20(forsentencestrategy) or10(forwordstrategy). -
overlapnumber · default: 100 -
The number of overlapping words for chunks. It is applicable only to a
wordchunking strategy. This value cannot be higher than half themax_chunk_sizevalue. -
sentence_overlapnumber · default: 1 -
The number of overlapping sentences for chunks. It is applicable only for a
sentencechunking strategy. It can be either1or0. -
strategystring · default: sentence -
The chunking strategy:
sentenceorword. -
servicestringrequired -
The service type
-
service_settingsServiceSettingsrequired -
Settings specific to the service
-
task_settingsTaskSettings -
Task settings specific to the service and task type
-
inference_idstringrequired -
The inference Id
-
task_typeTaskTypeElasticsearchrequired -
The task type
-
Values:
sparse_embeddingtext_embeddingrerank