---
type: api
title: Create a VoyageAI inference endpoint
description: Create an inference endpoint to perform an inference task with the `voyageai` service.
url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/operation/operation-inference-put-voyageai
resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/operation/operation-inference-put-voyageai
products:
  - Elasticsearch
---

# Create a VoyageAI inference endpoint

Availability: 8.19+

## Paths

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

- [Api key auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/authentication#apikeyauth)
- [Basic auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/authentication#basicauth)
- [Bearer auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/authentication#bearerauth)

#### Path Parameters

- `task_type` (enum VoyageAITaskType)
  The type of the inference task that the model will perform.
  Values: `text_embedding`, `rerank`
- `voyageai_inference_id` (string Id)
  The unique identifier of the 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.

## Required authorization

* Cluster privileges: `manage_inference`

## Query String Parameters

- `timeout` (string Duration · default: 30s)
  Specifies the amount of time to wait for the inference endpoint to be created.

## Request Body

`application/json`

- `chunking_settings` ({} InferenceChunkingSettings) — optional
  The chunking configuration object.
Applies only to the `text_embedding` task type.
Not applicable to the `rerank` task type.
  - `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` (enum VoyageAIServiceType) — required
  The type of service supported for the specified task type. In this case, `voyageai`.
  Values: `voyageai`
- `service_settings` ({} VoyageAIServiceSettings) — required
  Settings used to install the inference model. These settings are specific to the `voyageai` service.
  - `dimensions` (number) — optional
    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.
  - `model_id` (string) — required
    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.
  - `rate_limit` ({} RateLimitSetting) — optional
    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`.
    - `requests_per_minute` (number) — optional
      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`
* `fireworksai` service: `6000`
* `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`
  - `embedding_type` (number) — optional
    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.
- `task_settings` ({} VoyageAITaskSettings) — optional
  Settings to configure the inference task.
These settings are specific to the task type you specified.
  - `input_type` (string) — optional
    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.
  - `return_documents` (boolean · default: false) — optional
    Whether to return the source documents in the response.
Only for the `rerank` task type.
  - `top_k` (number) — optional
    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.
  - `truncation` (boolean · default: true) — optional
    Whether to truncate the input texts to fit within the context length.

## 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 TaskTypeVoyageAI)
  The task type
  Values: `text_embedding`, `rerank`
## Code Examples

#### Console

```console
PUT _inference/text_embedding/openai-embeddings
{
    "service": "voyageai",
    "service_settings": {
        "model_id": "voyage-3-large",
        "dimensions": 512
    }
}
```

#### JavaScript

```javascript
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "openai-embeddings",
  inference_config: {
    service: "voyageai",
    service_settings: {
      model_id: "voyage-3-large",
      dimensions: 512,
    },
  },
});
```

#### Python

```python
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="openai-embeddings",
    inference_config={
        "service": "voyageai",
        "service_settings": {
            "model_id": "voyage-3-large",
            "dimensions": 512
        }
    },
)
```

#### curl

```curl
curl -X PUT "$ELASTICSEARCH_URL/_inference/text_embedding/openai-embeddings" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "service": "voyageai",
    "service_settings": {
      "model_id": "voyage-3-large",
      "dimensions": 512
    }
  }'
```

#### Java

```java
client.inference().put(p -> p
    .inferenceId("openai-embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("voyageai")
        .serviceSettings(JsonData.fromJson("""
{"model_id":"voyage-3-large","dimensions":512}
"""))
    )
);
```

#### C#

```c#
var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "openai-embeddings",
        TaskType = TaskType.TextEmbedding,
        InferenceConfig = new()
        {
            Service = "voyageai",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "model_id": "voyage-3-large",
              "dimensions": 512
            }
            """)
        }
    });
```

#### PHP

```php
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "openai-embeddings",
    "body" => [
        "service" => "voyageai",
        "service_settings" => [
            "model_id" => "voyage-3-large",
            "dimensions" => 512,
        ],
    ],
]);
```

#### Ruby

```ruby
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "openai-embeddings",
  body: {
    "service": "voyageai",
    "service_settings": {
      "model_id": "voyage-3-large",
      "dimensions": 512
    }
  }
)
```