---
type: api
title: Create an inference endpoint
description: **All methods and paths for this operation:**
url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch-serverless/operation/operation-inference-put
resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch-serverless/operation/operation-inference-put
products:
  - Elasticsearch Serverless
---

# Create an inference endpoint


Availability:

## Paths

- `PUT` `/_inference/{task_type}/{inference_id}`
- `PUT` `/_inference/{inference_id}`

- [Api key auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch-serverless/authentication#apikeyauth)

#### Path Parameters

- `task_type` (enum TaskType) — optional, leave it out to use `/_inference/{inference_id}`
  The task type. Refer to the integration list in the API description for the available task types.
  Values: `sparse_embedding`, `text_embedding`, `rerank`, `completion`, `chat_completion`, `embedding`
- `inference_id` (string Id)
  The inference Id

## 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 (`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`

## Query String Parameters

- `timeout` (string Duration)
  Specifies the amount of time to wait for the inference endpoint to be created.
The default depends on the task type: 120s for `completion` and `chat_completion`, and 30s for all other task types.

## Request Body

`application/json`

- `chunking_settings` ({} InferenceChunkingSettings) — optional
  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.
  - `max_chunk_size` (number · default: 250) — optional
    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.
  - `overlap` (number · default: 100) — optional
    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.
  - `sentence_overlap` (number · default: 1) — optional
    The number of overlapping sentences for chunks.
It is applicable only for a `sentence` chunking strategy.
It can be either `1` or `0`.
  - `separator_group` (string) — optional
    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.
  - `separators` ([] string) — optional
    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.
  - `strategy` (string · default: sentence) — optional
    The chunking strategy: `sentence`, `word`, `none` or `recursive`.

 * If `strategy` is set to `recursive`, you must also specify:

- `max_chunk_size`
- either `separators` or`separator_group`

Learn more about different chunking strategies in the linked documentation.
- `service` (string) — required
  The service type
- `service_settings` (ServiceSettings) — required
  Settings specific to the service
- `task_settings` (TaskSettings) — optional
  Task settings specific to the service and task type

## Response

`application/json`

- `chunking_settings` ({} InferenceChunkingSettings)
  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.
  - `max_chunk_size` (number · default: 250)
    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.
  - `overlap` (number · default: 100)
    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.
  - `sentence_overlap` (number · default: 1)
    The number of overlapping sentences for chunks.
It is applicable only for a `sentence` chunking strategy.
It can be either `1` or `0`.
  - `separator_group` (string)
    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.
  - `separators` ([] string)
    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.
  - `strategy` (string · default: sentence)
    The chunking strategy: `sentence`, `word`, `none` or `recursive`.

 * If `strategy` is set to `recursive`, you must also specify:

- `max_chunk_size`
- either `separators` or`separator_group`

Learn more about different chunking strategies in the linked documentation.
- `service` (string)
  The service type
- `service_settings` (ServiceSettings)
  Settings specific to the service
- `task_settings` (TaskSettings)
  Task settings specific to the service and task type
- `inference_id` (string)
  The inference Id
- `task_type` (enum TaskType)
  The task type
  Values: `sparse_embedding`, `text_embedding`, `rerank`, `completion`, `chat_completion`, `embedding`
## Code Examples

#### Console

```console
PUT _inference/rerank/my-rerank-model
{
 "service": "cohere",
 "service_settings": {
   "model_id": "rerank-english-v3.0",
   "api_key": "{{COHERE_API_KEY}}"
 }
}
```

#### JavaScript

```javascript
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}}",
    },
  },
});
```

#### Python

```python
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}}"
        }
    },
)
```

#### curl

```curl
curl -X PUT "$ELASTICSEARCH_URL/_inference/rerank/my-rerank-model" \
  -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}}"
    }
  }'
```

#### Java

```java
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}}"}
"""))
    )
);
```

#### C#

```c#
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}}"
            }
            """)
        }
    });
```

#### PHP

```php
$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}}",
        ],
    ],
]);
```

#### Ruby

```ruby
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}}"
    }
  }
)
```