Amazon OpenSearch Service ML connectors for third-party platforms - Amazon OpenSearch Service
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Amazon OpenSearch Service ML connectors for third-party platforms

In this tutorial, we cover how to create a connector from OpenSearch Service to Cohere. For more information about connectors, see Supported connectors.

When you use an Amazon OpenSearch Service machine learning (ML) connector with an external remote model, you need to store your specific authorization credentials in Amazon Secrets Manager. This could be an API key, or a username and password combination. This means you also need to create an IAM role that allows OpenSearch Service access to read from Secrets Manager.


To create a connector for Cohere or any external provider with OpenSearch Service, you must have an IAM role that grants OpenSearch Service access to Amazon Secrets Manager, where you store your credentials. You must also store your credentials in Secrets Manager.

Create an IAM role

Set up an IAM role to delegate Secrets Manager permissions to OpenSearch Service. You can also use the existing SecretManagerReadWrite role. To create a new role, see Creating an IAM role (console) in the IAM User Guide. If you do create a new role instead of using an Amazon managed role, replace opensearch-secretmanager-role in this tutorial with the name of your own role.

  1. Attach the following managed IAM policy to your new role to allow OpenSearch Service to access to your Secrets Manager values. To attach a policy to a role, see Adding IAM Identity Permissions.

    { "Version": "2012-10-17", "Statement": [ { "Action": [ "secretsmanager:GetSecretValue" ], "Effect": "Allow", "Resource": "*" } ] }
  2. Follow the instructions in Modifying a role trust policy to edit the trust relationship of the role. You must specify OpenSearch Service in the Principal statement:

    { "Version": "2012-10-17", "Statement": [ { "Action": [ "sts:AssumeRole" ], "Effect": "Allow", "Principal": { "Service": [ "" ] } } ] }

    We recommend that you use the aws:SourceAccount and aws:SourceArn condition keys to limit access to specific domain. The SourceAccount is the Amazon Web Services account ID that belongs to the owner of the domain, and the SourceArn is the ARN of the domain. For example, you can add the following condition block to the trust policy:

    "Condition": { "StringEquals": { "aws:SourceAccount": "account-id" }, "ArnLike": { "aws:SourceArn": "arn:aws:es:region:account-id:domain/domain-name" } }

Configure permissions

In order to create the connector, you need permission to pass the IAM role to OpenSearch Service. You also need access to the es:ESHttpPost action. To grant both of these permissions, attach the following policy to the IAM role whose credentials are being used to sign the request:

{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "iam:PassRole", "Resource": "arn:aws:iam::account-id:role/opensearch-secretmanager-role" }, { "Effect": "Allow", "Action": "es:ESHttpPost", "Resource": "arn:aws:es:region:account-id:domain/domain-name/*" } ] }

If your user or role doesn't have iam:PassRole permissions to pass your role, you might encounter an authorization error when you try to register a repository in the next step.

Set up Amazon Secrets Manager

To store your authorization credentials in Secrets Manager, see Create an Amazon Secrets Manager secret in the Amazon Secrets Manager User Guide.

After Secrets Manager accepts your key-value pair as a secret, you receive an ARN with the format: arn:aws:secretsmanager:us-west-2:123456789012:secret:MySecret-a1b2c3. Keep a record of this ARN, as you use it and your key when you create a connector in the next step.

Map the ML role in OpenSearch Dashboards (if using fine-grained access control)

Fine-grained access control introduces an additional step when setting up a connector. Even if you use HTTP basic authentication for all other purposes, you need to map the ml_full_access role to your IAM role that has iam:PassRole permissions to pass opensearch-sagemaker-role.

  1. Navigate to the OpenSearch Dashboards plugin for your OpenSearch Service domain. You can find the Dashboards endpoint on your domain dashboard on the OpenSearch Service console.

  2. From the main menu choose Security, Roles, and select the ml_full_access role.

  3. Choose Mapped users, Manage mapping.

  4. Under Backend roles, add the ARN of the role that has permissions to pass opensearch-sagemaker-role.

  5. Select Map and confirm the user or role shows up under Mapped users.

Create an OpenSearch Service connector

To create a connector, send a POST request to the OpenSearch Service domain endpoint. You can use curl, the sample Python client, Postman, or another method to send a signed request. Note that you can't use a POST request in the Kibana console. The request takes the following format:

POST domain-endpoint/_plugins/_ml/connectors/_create { "name": "Cohere Connector: embedding", "description": "The connector to cohere embedding model", "version": 1, "protocol": "http", "credential": { "secretArn": "arn:aws:secretsmanager:region:account-id:secret:cohere-key-id", "roleArn": "arn:aws:iam::account-id:role/opensearch-secretmanager-role" }, "actions": [ { "action_type": "predict", "method": "POST", "url": "", "headers": { "Authorization": "Bearer ${credential.secretArn.cohere-key-used-in-secrets-manager}" }, "request_body": "{ \"texts\": ${parameters.texts}, \"truncate\": \"END\" }" } ] }

The request body for this request is different than that of an open-source connector request in two ways. Inside the credential field, you pass the ARN for the IAM role that permits OpenSearch Service to read from Secrets Manager, along with the ARN for the what secret. In the headers field, you refer to the secret using the secret key and the fact its coming from an ARN.

If your domain resides within a virtual private cloud (VPC), your computer must be connected to the VPC for the request to successfully create the AI connetor. Accessing a VPC varies by network configuration, but usually involves connecting to a VPN or corporate network. To check that you can reach your OpenSearch Service domain, navigate to in a web browser and verify that you receive the default JSON response.

Sample Python client

The Python client is simpler to automate than a HTTP request and has better reusability. To create the AI connector with the Python client, save the following sample code to a Python file. The client requires the Amazon SDK for Python (Boto3), requests, and requests-aws4auth packages.

import boto3 import requests from requests_aws4auth import AWS4Auth host = 'domain-endpoint/' region = 'region' service = 'es' credentials = boto3.Session().get_credentials() awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, region, service, session_token=credentials.token) path = '_plugins/_ml/connectors/_create' url = host + path payload = { "name": "Cohere Connector: embedding", "description": "The connector to cohere embedding model", "version": 1, "protocol": "http", "credential": { "secretArn": "arn:aws:secretsmanager:region:account-id:secret:cohere-key-id", "roleArn": "arn:aws:iam::account-id:role/opensearch-secretmanager-role" }, "actions": [ { "action_type": "predict", "method": "POST", "url": "", "headers": { "Authorization": "Bearer ${credential.secretArn.cohere-key-used-in-secrets-manager}" }, "request_body": "{ \"texts\": ${parameters.texts}, \"truncate\": \"END\" }" } ] } headers = {"Content-Type": "application/json"} r =, auth=awsauth, json=payload, headers=headers) print(r.status_code) print(r.text)