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.
{
"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"
}
}
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.
{
"service": "elasticsearch",
"service_settings": {
"model_id": ".rerank-v1",
"num_threads": 1,
"adaptive_allocations": {
"enabled": true,
"min_number_of_allocations": 1,
"max_number_of_allocations": 4
}
}
}
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.
{
"service": "elasticsearch",
"service_settings": {
"num_allocations": 1,
"num_threads": 1,
"model_id": ".multilingual-e5-small"
}
}
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.
{
"service": "elasticsearch",
"service_settings": {
"num_allocations": 1,
"num_threads": 1,
"model_id": "msmarco-MiniLM-L12-cos-v5"
}
}
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.
{
"service": "elasticsearch",
"service_settings": {
"adaptive_allocations": {
"enabled": true,
"min_number_of_allocations": 3,
"max_number_of_allocations": 10
},
"num_threads": 1,
"model_id": ".multilingual-e5-small"
}
}
Run `PUT _inference/sparse_embedding/use_existing_deployment` to use an already existing model deployment when creating an inference endpoint.
{
"service": "elasticsearch",
"service_settings": {
"deployment_id": ".elser_model_2"
}
}