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

# Create a Llama inference endpoint

Availability:

## Paths

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

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

#### Path Parameters

- `task_type` (enum LlamaTaskType)
  The type of the inference task that the model will perform.
  Values: `text_embedding`, `completion`, `chat_completion`
- `llama_inference_id` (string Id)
  The unique identifier of the inference endpoint.

## Description

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

## 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 `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` (enum LlamaServiceType) — required
  The type of service supported for the specified task type. In this case, `llama`.
  Values: `llama`
- `service_settings` ({} LlamaServiceSettings) — required
  Settings used to install the inference model. These settings are specific to the `llama` service.
  - `url` (string) — required
    The URL endpoint of the Llama stack endpoint.
URL must contain:
* For `text_embedding` task - `/v1/inference/embeddings`.
* For `completion` and `chat_completion` tasks - `/v1/openai/v1/chat/completions`.
  - `model_id` (string) — required
    The name of the model to use for the inference task.
Refer to the Llama downloading models documentation for different ways of getting a list of available models and downloading them.
Service has been tested and confirmed to be working with the following models:
* For `text_embedding` task - `all-MiniLM-L6-v2`.
* For `completion` and `chat_completion` tasks - `llama3.2:3b`.
  - `max_input_tokens` (number) — optional
    For a `text_embedding` task, the maximum number of tokens per input before chunking occurs.
  - `similarity` (enum LlamaSimilarityType) — optional
    For a `text_embedding` task, the similarity measure. One of cosine, dot_product, l2_norm.
    Values: `cosine`, `dot_product`, `l2_norm`
  - `rate_limit` ({} RateLimitSetting) — optional
    This setting helps to minimize the number of rate limit errors returned from the Llama API.
By default, the `llama` service sets the number of requests allowed per minute to 3000.
    - `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`

## 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 TaskTypeLlama)
  The task type
  Values: `text_embedding`, `chat_completion`, `completion`
## Code Examples

#### Console

```console
PUT _inference/text_embedding/llama-text-embedding
{
  "service": "llama",
  "service_settings": {
    "url": "http://localhost:8321/v1/inference/embeddings",
    "dimensions": 384,
    "model_id": "all-MiniLM-L6-v2" 
  }
}
```

#### JavaScript

```javascript
const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "llama-text-embedding",
  inference_config: {
    service: "llama",
    service_settings: {
      url: "http://localhost:8321/v1/inference/embeddings",
      dimensions: 384,
      model_id: "all-MiniLM-L6-v2",
    },
  },
});
```

#### Python

```python
resp = client.inference.put(
    task_type="text_embedding",
    inference_id="llama-text-embedding",
    inference_config={
        "service": "llama",
        "service_settings": {
            "url": "http://localhost:8321/v1/inference/embeddings",
            "dimensions": 384,
            "model_id": "all-MiniLM-L6-v2"
        }
    },
)
```

#### curl

```curl
curl -X PUT "$ELASTICSEARCH_URL/_inference/text_embedding/llama-text-embedding" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "service": "llama",
    "service_settings": {
      "url": "http://localhost:8321/v1/inference/embeddings",
      "dimensions": 384,
      "model_id": "all-MiniLM-L6-v2"
    }
  }'
```

#### Java

```java
client.inference().put(p -> p
    .inferenceId("llama-text-embedding")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("llama")
        .serviceSettings(JsonData.fromJson("""
{"url":"http://localhost:8321/v1/inference/embeddings","dimensions":384,"model_id":"all-MiniLM-L6-v2"}
"""))
    )
);
```

#### C#

```c#
var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "llama-text-embedding",
        TaskType = TaskType.TextEmbedding,
        InferenceConfig = new()
        {
            Service = "llama",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "url": "http://localhost:8321/v1/inference/embeddings",
              "dimensions": 384,
              "model_id": "all-MiniLM-L6-v2"
            }
            """)
        }
    });
```

#### PHP

```php
$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "llama-text-embedding",
    "body" => [
        "service" => "llama",
        "service_settings" => [
            "url" => "http://localhost:8321/v1/inference/embeddings",
            "dimensions" => 384,
            "model_id" => "all-MiniLM-L6-v2",
        ],
    ],
]);
```

#### Ruby

```ruby
response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "llama-text-embedding",
  body: {
    "service": "llama",
    "service_settings": {
      "url": "http://localhost:8321/v1/inference/embeddings",
      "dimensions": 384,
      "model_id": "all-MiniLM-L6-v2"
    }
  }
)
```