--- type: api title: Create an Amazon Bedrock inference endpoint description: Create an inference endpoint to perform an inference task with the `amazonbedrock` service. url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/operation/operation-inference-put-amazonbedrock resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/operation/operation-inference-put-amazonbedrock products: - Elasticsearch --- # Create an Amazon Bedrock inference endpoint Availability: 8.12+ ## Paths - `PUT` `/_inference/{task_type}/{amazonbedrock_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 AmazonBedrockTaskType) The type of the inference task that the model will perform. Values: `completion`, `text_embedding` - `amazonbedrock_inference_id` (string Id) The unique identifier of the inference endpoint. ## Description Create an inference endpoint to perform an inference task with the `amazonbedrock` service. >info > You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys. ## 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 AmazonBedrockServiceType) — required The type of service supported for the specified task type. In this case, `amazonbedrock`. Values: `amazonbedrock` - `service_settings` ({} AmazonBedrockServiceSettings) — required Settings used to install the inference model. These settings are specific to the `amazonbedrock` service. - `access_key` (string) — required A valid AWS access key that has permissions to use Amazon Bedrock and access to models for inference requests. - `model` (string) — required The base model ID or an ARN to a custom model based on a foundational model. The base model IDs can be found in the Amazon Bedrock documentation. Note that the model ID must be available for the provider chosen and your IAM user must have access to the model. - `provider` (string) — optional The model provider for your deployment. Note that some providers may support only certain task types. Supported providers include: * `amazontitan` - available for `text_embedding` and `completion` task types * `anthropic` - available for `completion` task type only * `ai21labs` - available for `completion` task type only * `cohere` - available for `text_embedding` and `completion` task types * `meta` - available for `completion` task type only * `mistral` - available for `completion` task type only - `region` (string) — required The region that your model or ARN is deployed in. The list of available regions per model can be found in the Amazon Bedrock documentation. - `rate_limit` ({} RateLimitSetting) — optional This setting helps to minimize the number of rate limit errors returned from Amazon Bedrock. By default, the `amazonbedrock` service sets the number of requests allowed per minute to 240. - `requests_per_minute` (number) — optional The number of requests allowed per minute. - `secret_key` (string) — required A valid AWS secret key that is paired with the `access_key`. For informationg about creating and managing access and secret keys, refer to the AWS documentation. - `task_settings` ({} AmazonBedrockTaskSettings) — optional Settings to configure the inference task. These settings are specific to the task type you specified. - `max_new_tokens` (number · default: 64) — optional For a `completion` task, it sets the maximum number for the output tokens to be generated. - `temperature` (number) — optional For a `completion` task, it is a number between 0.0 and 1.0 that controls the apparent creativity of the results. At temperature 0.0 the model is most deterministic, at temperature 1.0 most random. It should not be used if `top_p` or `top_k` is specified. - `top_k` (number) — optional For a `completion` task, it limits samples to the top-K most likely words, balancing coherence and variability. It is only available for anthropic, cohere, and mistral providers. It is an alternative to `temperature`; it should not be used if `temperature` is specified. - `top_p` (number) — optional For a `completion` task, it is a number in the range of 0.0 to 1.0, to eliminate low-probability tokens. Top-p uses nucleus sampling to select top tokens whose sum of likelihoods does not exceed a certain value, ensuring both variety and coherence. It is an alternative to `temperature`; it should not be used if `temperature` is specified. ## 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 TaskTypeAmazonBedrock) The task type Values: `text_embedding`, `completion` ## Code Examples #### Console ```console PUT _inference/text_embedding/amazon_bedrock_embeddings { "service": "amazonbedrock", "service_settings": { "access_key": "AWS-access-key", "secret_key": "AWS-secret-key", "region": "us-east-1", "provider": "amazontitan", "model": "amazon.titan-embed-text-v2:0" } } ``` #### JavaScript ```javascript const response = await client.inference.put({ task_type: "text_embedding", inference_id: "amazon_bedrock_embeddings", inference_config: { service: "amazonbedrock", service_settings: { access_key: "AWS-access-key", secret_key: "AWS-secret-key", region: "us-east-1", provider: "amazontitan", model: "amazon.titan-embed-text-v2:0", }, }, }); ``` #### Python ```python resp = client.inference.put( task_type="text_embedding", inference_id="amazon_bedrock_embeddings", inference_config={ "service": "amazonbedrock", "service_settings": { "access_key": "AWS-access-key", "secret_key": "AWS-secret-key", "region": "us-east-1", "provider": "amazontitan", "model": "amazon.titan-embed-text-v2:0" } }, ) ``` #### curl ```curl curl -X PUT "$ELASTICSEARCH_URL/_inference/text_embedding/amazon_bedrock_embeddings" \ -H "Authorization: ApiKey $ELASTIC_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "service": "amazonbedrock", "service_settings": { "access_key": "AWS-access-key", "secret_key": "AWS-secret-key", "region": "us-east-1", "provider": "amazontitan", "model": "amazon.titan-embed-text-v2:0" } }' ``` #### Java ```java client.inference().put(p -> p .inferenceId("amazon_bedrock_embeddings") .taskType(TaskType.TextEmbedding) .inferenceConfig(i -> i .service("amazonbedrock") .serviceSettings(JsonData.fromJson("{\"access_key\":\"AWS-access-key\",\"secret_key\":\"AWS-secret-key\",\"region\":\"us-east-1\",\"provider\":\"amazontitan\",\"model\":\"amazon.titan-embed-text-v2:0\"}")) ) ); ``` #### C# ```c# var response = await client.Inference .PutAsync(new PutInferenceRequest() { InferenceId = "amazon_bedrock_embeddings", TaskType = TaskType.TextEmbedding, InferenceConfig = new() { Service = "amazonbedrock", ServiceSettings = JsonSerializer.Deserialize(""" { "access_key": "AWS-access-key", "secret_key": "AWS-secret-key", "region": "us-east-1", "provider": "amazontitan", "model": "amazon.titan-embed-text-v2:0" } """) } }); ``` #### PHP ```php $resp = $client->inference()->put([ "task_type" => "text_embedding", "inference_id" => "amazon_bedrock_embeddings", "body" => [ "service" => "amazonbedrock", "service_settings" => [ "access_key" => "AWS-access-key", "secret_key" => "AWS-secret-key", "region" => "us-east-1", "provider" => "amazontitan", "model" => "amazon.titan-embed-text-v2:0", ], ], ]); ``` #### Ruby ```ruby response = client.inference.put( task_type: "text_embedding", inference_id: "amazon_bedrock_embeddings", body: { "service": "amazonbedrock", "service_settings": { "access_key": "AWS-access-key", "secret_key": "AWS-secret-key", "region": "us-east-1", "provider": "amazontitan", "model": "amazon.titan-embed-text-v2:0" } } ) ```