---
type: api
title: Create an JinaAI inference endpoint
description: Create an inference endpoint to perform an inference task with the `jinaai` service.
url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/operation/operation-inference-put-jinaai
resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/operation/operation-inference-put-jinaai
products:
  - Elasticsearch
---

# Create an JinaAI inference endpoint

Availability: 8.18+

## Paths

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

- [Api key auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/authentication#apikeyauth)
- [Basic auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/authentication#basicauth)
- [Bearer auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/authentication#bearerauth)

#### Path Parameters

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

## Description

Create an inference endpoint to perform an inference task with the `jinaai` service.

To review the available `rerank` models, refer to <https://jina.ai/reranker>.
To review the available `text_embedding` models, refer to the <https://jina.ai/embeddings/>.

## 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.
  - `max_chunk_size` (number · default: 250) — optional
    The maximum size of a chunk in words.
This value cannot be higher than `300` or lower than `20` (for `sentence` strategy) or `10` (for `word` strategy).
  - `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`.
  - `strategy` (string · default: sentence) — optional
    The chunking strategy: `sentence` or `word`.
- `service` (enum JinaAIServiceType) — required
  The type of service supported for the specified task type. In this case, `jinaai`.
  Values: `jinaai`
- `service_settings` ({} JinaAIServiceSettings) — required
  Settings used to install the inference model. These settings are specific to the `jinaai` service.
  - `api_key` (string) — required
    A valid API key of your JinaAI account.

IMPORTANT: You need to provide the API key only once, during the inference model creation.
The get inference endpoint API does not retrieve your API key.
  - `model_id` (string) — required
    The name of the model to use for the inference task.
  - `rate_limit` ({} RateLimitSetting) — optional
    This setting helps to minimize the number of rate limit errors returned from JinaAI.
By default, the `jinaai` service sets the number of requests allowed per minute to 2000 for all task types.
    - `requests_per_minute` (number) — optional
      The number of requests allowed per minute.
  - `similarity` (enum JinaAISimilarityType) — optional
    For a `text_embedding` task, the similarity measure. One of cosine, dot_product, l2_norm.
The default values varies with the embedding type.
For example, a float embedding type uses a `dot_product` similarity measure by default.
    Values: `cosine`, `dot_product`, `l2_norm`
  - `dimensions` (number) — optional
    For a `text_embedding` task, the number of dimensions the resulting output embeddings should have.
By default, the model's standard output dimension is used.
Refer to the Jina documentation for more information.
  - `embedding_type` (enum JinaAIElementType · default: float) — optional
    For a `text_embedding` task, the data type returned by the model.
Use `bit` for binary embeddings, which are encoded as bytes with signed int8 precision.
Use `binary` for binary embeddings, which are encoded as bytes with signed int8 precision (this is a synonym of `bit`).
Use `float` for the default float embeddings.
    Values: `binary`, `bit`, `float`
- `task_settings` ({} JinaAITaskSettings) — optional
  Settings to configure the inference task.
These settings are specific to the task type you specified.
  - `return_documents` (boolean) — optional
    For a `rerank` task, return the doc text within the results.
  - `task` (enum JinaAITextEmbeddingTask) — optional
    For a `text_embedding` task, the task passed to the model.
Valid values are:

* `classification`: Use it for embeddings passed through a text classifier.
* `clustering`: Use it for the embeddings run through a clustering algorithm.
* `ingest`: Use it for storing document embeddings in a vector database.
* `search`: Use it for storing embeddings of search queries run against a vector database to find relevant documents.
    Values: `classification`, `clustering`, `ingest`, `search`
  - `top_n` (number) — optional
    For a `rerank` task, the number of most relevant documents to return.
It defaults to the number of the documents.
If this inference endpoint is used in a `text_similarity_reranker` retriever query and `top_n` is set, it must be greater than or equal to `rank_window_size` in the query.

## Response

`application/json`

- `chunking_settings` ({} InferenceChunkingSettings)
  Chunking configuration object
  - `max_chunk_size` (number · default: 250)
    The maximum size of a chunk in words.
This value cannot be higher than `300` or lower than `20` (for `sentence` strategy) or `10` (for `word` strategy).
  - `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`.
  - `strategy` (string · default: sentence)
    The chunking strategy: `sentence` or `word`.
- `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 TaskTypeJinaAi)
  The task type
  Values: `text_embedding`, `rerank`
## Code Examples

#### Console

```console
PUT _inference/text_embedding/jinaai-embeddings
{
    "service": "jinaai",
    "service_settings": {
        "model_id": "jina-embeddings-v3",
        "api_key": "JinaAi-Api-key"
    }
}
```

#### JavaScript

```javascript
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "jinaai-embeddings",
  inference_config: {
    service: "jinaai",
    service_settings: {
      model_id: "jina-embeddings-v3",
      api_key: "JinaAi-Api-key",
    },
  },
});
```

#### Python

```python
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="jinaai-embeddings",
    inference_config={
        "service": "jinaai",
        "service_settings": {
            "model_id": "jina-embeddings-v3",
            "api_key": "JinaAi-Api-key"
        }
    },
)
```

#### curl

```curl
curl -X PUT "$ELASTICSEARCH_URL/_inference/text_embedding/jinaai-embeddings" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "service": "jinaai",
    "service_settings": {
      "model_id": "jina-embeddings-v3",
      "api_key": "JinaAi-Api-key"
    }
  }'
```

#### Java

```java
client.inference().put(p -> p
    .inferenceId("jinaai-embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("jinaai")
        .serviceSettings(JsonData.fromJson("{\"model_id\":\"jina-embeddings-v3\",\"api_key\":\"JinaAi-Api-key\"}"))
    )
);
```

#### C#

```c#
var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "jinaai-embeddings",
        TaskType = TaskType.TextEmbedding,
        InferenceConfig = new()
        {
            Service = "jinaai",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "model_id": "jina-embeddings-v3",
              "api_key": "JinaAi-Api-key"
            }
            """)
        }
    });
```

#### PHP

```php
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "jinaai-embeddings",
    "body" => [
        "service" => "jinaai",
        "service_settings" => [
            "model_id" => "jina-embeddings-v3",
            "api_key" => "JinaAi-Api-key",
        ],
    ],
]);
```

#### Ruby

```ruby
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "jinaai-embeddings",
  body: {
    "service": "jinaai",
    "service_settings": {
      "model_id": "jina-embeddings-v3",
      "api_key": "JinaAi-Api-key"
    }
  }
)
```