--- type: api title: Create a Groq inference endpoint description: Create an inference endpoint to perform an inference task with the `groq` service. url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/operation/operation-inference-put-groq resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/operation/operation-inference-put-groq products: - Elasticsearch --- # Create a Groq inference endpoint Availability: 9.3+ ## Paths - `PUT` `/_inference/{task_type}/{groq_inference_id}` - [Api key auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/authentication#apikeyauth) - [Basic auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/authentication#basicauth) - [Bearer auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v9/authentication#bearerauth) #### Path Parameters - `task_type` (enum GroqTaskType) The type of the inference task that the model will perform. Values: `chat_completion` - `groq_inference_id` (string Id) The unique identifier of the inference endpoint. ## Description Create an inference endpoint to perform an inference task with the `groq` 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` - `service` (enum GroqServiceType) — required The type of service supported for the specified task type. In this case, `groq`. Values: `groq` - `service_settings` ({} GroqServiceSettings) — required Settings used to install the inference model. These settings are specific to the `groq` service. - `model_id` (string) — required The name of the model to use for the inference task. Refer to the Groq model documentation for the list of supported models and versions. Service has been tested and confirmed to be working for `completion` and `chat_completion` tasks with the following models: * `llama-3.3-70b-versatile` - `api_key` (string) — optional A valid API key for accessing Groq API. 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. - `rate_limit` ({} RateLimitSetting) — optional This setting helps to minimize the number of rate limit errors returned from the Groq API. By default, the `groq` service sets the number of requests allowed per minute to 1000. Refer to Groq documentation for more details. - `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 TaskTypeGroq) The task type Values: `chat_completion` ## Code Examples #### Console ```console PUT _inference/chat_completion/groq-chat-completion { "service": "groq", "service_settings": { "api_key": "groq-api-key", "model_id": "llama-3.3-70b-versatile" } } ``` #### JavaScript ```javascript const response = await client.inference.put({ task_type: "chat_completion", inference_id: "groq-chat-completion", inference_config: { service: "groq", service_settings: { api_key: "groq-api-key", model_id: "llama-3.3-70b-versatile", }, }, }); ``` #### Python ```python resp = client.inference.put( task_type="chat_completion", inference_id="groq-chat-completion", inference_config={ "service": "groq", "service_settings": { "api_key": "groq-api-key", "model_id": "llama-3.3-70b-versatile" } }, ) ``` #### curl ```curl curl -X PUT "$ELASTICSEARCH_URL/_inference/chat_completion/groq-chat-completion" \ -H "Authorization: ApiKey $ELASTIC_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "service": "groq", "service_settings": { "api_key": "groq-api-key", "model_id": "llama-3.3-70b-versatile" } }' ``` #### Java ```java client.inference().put(p -> p .inferenceId("groq-chat-completion") .taskType(TaskType.ChatCompletion) .inferenceConfig(i -> i .service("groq") .serviceSettings(JsonData.fromJson(""" {"api_key":"groq-api-key","model_id":"llama-3.3-70b-versatile"} """)) ) ); ``` #### C# ```c# var response = await client.Inference .PutAsync(new PutInferenceRequest() { InferenceId = "groq-chat-completion", TaskType = TaskType.ChatCompletion, InferenceConfig = new() { Service = "groq", ServiceSettings = JsonSerializer.Deserialize(""" { "api_key": "groq-api-key", "model_id": "llama-3.3-70b-versatile" } """) } }); ``` #### PHP ```php $resp = $client->inference()->put([ "task_type" => "chat_completion", "inference_id" => "groq-chat-completion", "body" => [ "service" => "groq", "service_settings" => [ "api_key" => "groq-api-key", "model_id" => "llama-3.3-70b-versatile", ], ], ]); ``` #### Ruby ```ruby response = client.inference.put( task_type: "chat_completion", inference_id: "groq-chat-completion", body: { "service": "groq", "service_settings": { "api_key": "groq-api-key", "model_id": "llama-3.3-70b-versatile" } } ) ```