---
type: api
title: Perform dense embedding inference on the service
url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch-serverless/operation/operation-inference-embedding
resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch-serverless/operation/operation-inference-embedding
products:
  - Elasticsearch Serverless
---

# Perform dense embedding inference on the service

Availability:

## Paths

- `POST` `/_inference/embedding/{inference_id}`

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

#### Path Parameters

- `inference_id` (string Id)
  The inference Id

## Query String Parameters

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

## Request Body

`application/json`

- `input` (EmbeddingInput) — required
  Inference input.
Either a string, an array of strings, a `content` object, or an array of `content` objects.
`content` objects may contain a single item or an array of items. Models that support multiple items per `content`
object will return a single embedding for each `content` object, regardless of how many items it contains.
Support for multiple items in a single `content` object is available in Elasticsearch 9.5.0 and later.

string example:
```
"input": "Some text"
```
string array example:
```
"input": ["Some text", "Some more text"]
```
`content` object example:
```
"input": {
    "content": {
      "type": "image",
      "format": "base64",
      "value": "data:image/jpeg;base64,..."
    }
  }
```
`content` object array example:
```
"input": [
  {
    "content": {
      "type": "text",
      "format": "text",
      "value": "Some text to generate an embedding"
    }
  },
  {
    "content": {
      "type": "image",
      "format": "base64",
      "value": "data:image/jpeg;base64,..."
    }
  }
]
```
`content` object using the `url` format example (available in Elasticsearch 9.6.0 and later):
```
"input": {
    "content": {
      "type": "image",
      "format": "url",
      "value": "https://example.com/image.jpg"
    }
  }
```
Multiple items in one `content` object example (available in Elasticsearch 9.5.0 and later):
```
"input": [
  {
    "content": [
      {
        "type": "image",
        "format": "base64",
        "value": "data:image/jpeg;base64,..."
      },
      {
        "type": "text",
        "value": "Some text to create an embedding"
      }
    ]
  }
]
```
- `input_type` (string) — optional
  The input data type for the embedding model. Possible values include:
* `SEARCH`
* `INGEST`
* `CLASSIFICATION`
* `CLUSTERING`

Not all models support all values. Unsupported values will trigger a validation exception.
Accepted values depend on the configured inference service, refer to the relevant service-specific documentation for more info.

> info
> The `input_type` parameter specified on the root level of the request body will take precedence over the `input_type` parameter specified in `task_settings`.
- `task_settings` (TaskSettings) — optional
  Task settings for the individual inference request. These settings are specific to the <task_type> you specified and override the task settings specified when initializing the service.

## Response

`application/json`

- `embeddings_bytes` ([] DenseEmbeddingByteResult)
  - `embedding` (DenseByteVector)
- `embeddings_bits` ([] DenseEmbeddingByteResult)
  - `embedding` (DenseByteVector)
- `embeddings` ([] DenseEmbeddingResult)
  - `embedding` (DenseVector)
## Code Examples

#### Console

```console
POST _inference/embedding/my-multimodal-endpoint
{
  "input": [
      {
          "content": {
              "type": "image",
              "format": "base64",
              "value": "data:image/jpeg;base64,..."
          }
      },
      {
          "content": {
              "type": "text",
              "value": "Some text to create an embedding"
          }
      }
  ]
}
```

#### JavaScript

```javascript
const response = await client.inference.embedding({
  inference_id: "my-multimodal-endpoint",
  embedding: {
    input: [
      {
        content: {
          type: "image",
          format: "base64",
          value: "data:image/jpeg;base64,...",
        },
      },
      {
        content: {
          type: "text",
          value: "Some text to create an embedding",
        },
      },
    ],
  },
});
```

#### Python

```python
resp = client.inference.embedding(
    inference_id="my-multimodal-endpoint",
    embedding={
        "input": [
            {
                "content": {
                    "type": "image",
                    "format": "base64",
                    "value": "data:image/jpeg;base64,..."
                }
            },
            {
                "content": {
                    "type": "text",
                    "value": "Some text to create an embedding"
                }
            }
        ]
    },
)
```

#### curl

```curl
curl -X POST "$ELASTICSEARCH_URL/_inference/embedding/my-multimodal-endpoint" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      {
        "content": {
          "type": "image",
          "format": "base64",
          "value": "data:image/jpeg;base64,..."
        }
      },
      {
        "content": {
          "type": "text",
          "value": "Some text to create an embedding"
        }
      }
    ]
  }'
```

#### Java

```java
client.inference().embedding(e -> e
    .inferenceId("my-multimodal-endpoint")
    .embedding(em -> em
        .input(i -> i
            .content(List.of(EmbeddingContentObject.of(emb -> emb
                    .content(c -> c
                        .type(EmbeddingContentType.Image)
                        .format(EmbeddingContentFormat.Base64)
                        .value("data:image/jpeg;base64,...")
                    )
                ),EmbeddingContentObject.of(emb -> emb
                    .content(c -> c
                        .type(EmbeddingContentType.Text)
                        .value("Some text to create an embedding")
                    )
                )))
        )
    )
);
```

#### PHP

```php
$resp = $client->inference()->embedding([
    "inference_id" => "my-multimodal-endpoint",
    "body" => [
        "input" => array(
            [
                "content" => [
                    "type" => "image",
                    "format" => "base64",
                    "value" => "data:image/jpeg;base64,...",
                ],
            ],
            [
                "content" => [
                    "type" => "text",
                    "value" => "Some text to create an embedding",
                ],
            ],
        ),
    ],
]);
```

#### Ruby

```ruby
response = client.inference.embedding(
  inference_id: "my-multimodal-endpoint",
  body: {
    "input": [
      {
        "content": {
          "type": "image",
          "format": "base64",
          "value": "data:image/jpeg;base64,..."
        }
      },
      {
        "content": {
          "type": "text",
          "value": "Some text to create an embedding"
        }
      }
    ]
  }
)
```

## Response Examples

#### Multimodal embedding task

An abbreviated response from `POST _inference/embedding/my-multimodal-endpoint`.

```json
{
  "embeddings": [
    {
      "embedding": [
        -0.0189209,
        -0.04174805,
        0.00854492,
        0.01556396,
        0.01928711,
        -0.00616455,
        -0.00460815,
        0.01477051,
        -0.00656128,
        0.05419922
      ]
    },
    {
      "embedding": [
        -0.01379395,
        -0.02368164,
        0.01068115,
        0.0279541,
        0.01043701,
        -7.7057E-4,
        0.04150391,
        0.00836182,
        -0.01135254,
        0.0246582
      ]
    }
  ]
}
```

#### Text-only embedding task

An abbreviated response from `POST _inference/embedding/my-text-only-endpoint`.

```json
{
  "embeddings": [
    {
      "embedding": [
        0.00854492,
        -0.00616455,
        -0.0189209,
        0.01556396,
        -0.00460815,
        0.01477051,
        -0.04174805,
        0.01928711,
        -0.00656128,
        0.05419922
      ]
    },
    {
      "embedding": [
        -0.01135254,
        0.0279541,
        -0.02368164,
        0.01068115,
        0.01043701,
        0.04150391,
        0.00836182,
        -7.7057E-4,
        -0.01379395,
        0.0246582
      ]
    }
  ]
}
```