Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

OpenSearch Service flow framework templates

Amazon OpenSearch Service flow framework templates allow you to automate complex OpenSearch Service setup and preprocessing tasks by providing templates for common use cases. For example, you can use flow framework templates to automate machine learning setup tasks. Amazon OpenSearch Service flow framework templates provide a compact description of the setup process in a JSON or YAML document. These templates describe automated workflow configurations for conversational chat or query generation, AI connectors, tools, agents, and other components that prepare OpenSearch Service for backend use for generative models.

Amazon OpenSearch Service flow framework templates can be customized to meet your specific needs. To see an example of a custom flow framework template, see flow-framework. For OpenSearch Service provided templates, see workflow-templates. For comprehensive documentation, including detailed steps, an API reference, and a reference of all available settings, see automating configuration in the open source OpenSearch documentation.

Creating ML connectors in OpenSearch Service

Amazon OpenSearch Service flow framework templates allow you to configure and install ML connectors by utilizing the create connector API offered in ml-commons. You can use ML connectors to connect OpenSearch Service to other Amazon services or third party platforms. For more information on this, see Creating connectors for third-party ML platforms. The Amazon OpenSearch Service flow framework API allows you to automate OpenSearch Service setup and preprocessing tasks and can be used to create ML connectors.

Before you can create a connector in OpenSearch Service, you must do the following:

For more information on how to setup ML connectors for Amazon services, see Amazon OpenSearch Service ML connectors for Amazon services. To learn more about using OpenSearch Service ML connectors with third-party platforms, see Amazon OpenSearch Service ML connectors for third-party platforms.

Creating a connector through a flow-framework service

To create a flow-framework template with connector, you will need to send a POST request to your OpenSearch Service domain endpoint. You can use cURL, a sample Python client, Postman, or another method to send a signed request. The POST request takes the following format:

POST /_plugins/_flow_framework/workflow 
{
  "name": "Deploy Claude Model",
  "description": "Deploy a model using a connector to Claude",
  "use_case": "PROVISION",
  "version": {
    "template": "1.0.0",
    "compatibility": [
      "2.12.0",
      "3.0.0"
    ]
  },
  "workflows": {
    "provision": {
      "nodes": [
        {
          "id": "create_claude_connector",
          "type": "create_connector",
          "user_inputs": {
            "name": "Claude Instant Runtime Connector",
            "version": "1",
            "protocol": "aws_sigv4",
            "description": "The connector to BedRock service for Claude model",
            "actions": [
              {
                "headers": {
                  "x-amz-content-sha256": "required",
                  "content-type": "application/json"
                },
                "method": "POST",
                "request_body": "{ \"prompt\":\"${parameters.prompt}\", \"max_tokens_to_sample\":${parameters.max_tokens_to_sample}, \"temperature\":${parameters.temperature},  \"anthropic_version\":\"${parameters.anthropic_version}\" }",
                "action_type": "predict",
                "url": "https://bedrock-runtime.us-west-2.amazonaws.com/model/anthropic.claude-instant-v1/invoke"
              }
            ],
            "credential": {
                "roleArn": "arn:aws:iam::account-id:role/opensearch-secretmanager-role" 
             },
            "parameters": {
              "endpoint": "bedrock-runtime.us-west-2.amazonaws.com",
              "content_type": "application/json",
              "auth": "Sig_V4",
              "max_tokens_to_sample": "8000",
              "service_name": "bedrock",
              "temperature": "0.0001",
              "response_filter": "$.completion",
              "region": "us-west-2",
              "anthropic_version": "bedrock-2023-05-31"
            }
          }
        }
      ]
    }
  }
}

If your domain resides within a virtual private cloud (Amazon VPC), you must be connected to the Amazon VPC for the request to successfully create the AI connector. Accessing an Amazon 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 https://your-vpc-domain.region.es.amazonaws.com 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:HTTP for Humans, and requests-aws4auth 1.2.3 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/_flow_framework/workflow'
url = host + path

payload = {
  "name": "Deploy Claude Model",
  "description": "Deploy a model using a connector to Claude",
  "use_case": "PROVISION",
  "version": {
    "template": "1.0.0",
    "compatibility": [
      "2.12.0",
      "3.0.0"
    ]
  },
  "workflows": {
    "provision": {
      "nodes": [
        {
          "id": "create_claude_connector",
          "type": "create_connector",
          "user_inputs": {
            "name": "Claude Instant Runtime Connector",
            "version": "1",
            "protocol": "aws_sigv4",
            "description": "The connector to BedRock service for Claude model",
            "actions": [
              {
                "headers": {
                  "x-amz-content-sha256": "required",
                  "content-type": "application/json"
                },
                "method": "POST",
                "request_body": "{ \"prompt\":\"${parameters.prompt}\", \"max_tokens_to_sample\":${parameters.max_tokens_to_sample}, \"temperature\":${parameters.temperature},  \"anthropic_version\":\"${parameters.anthropic_version}\" }",
                "action_type": "predict",
                "url": "https://bedrock-runtime.us-west-2.amazonaws.com/model/anthropic.claude-instant-v1/invoke"
              }
            ],
            "credential": {
                "roleArn": "arn:aws:iam::account-id:role/opensearch-secretmanager-role" 
             },
            "parameters": {
              "endpoint": "bedrock-runtime.us-west-2.amazonaws.com",
              "content_type": "application/json",
              "auth": "Sig_V4",
              "max_tokens_to_sample": "8000",
              "service_name": "bedrock",
              "temperature": "0.0001",
              "response_filter": "$.completion",
              "region": "us-west-2",
              "anthropic_version": "bedrock-2023-05-31"
            }
          }
        }
      ]
    }
  }
}

headers = {"Content-Type": "application/json"}

r = requests.post(url, auth=awsauth, json=payload, headers=headers)
print(r.status_code)
print(r.text)

Pre-defined workflow templates

Amazon OpenSearch Service provides several workflow templates for some common machine learning (ML) use cases. Using a template simplifies complex setups and provides many default values for use cases like semantic or conversational search. You can specify a workflow template when you call the Create Workflow API.

Template Use Cases

This table provides an overview of the different templates available, a description of the templates, and the required parameters.

Default templates with pretrained models

Amazon OpenSearch Service offers two additonal default workflow templates not available in the opensource OpenSearch Service.

Configure permissions

If you create a new domain with version 2.13 or later, permissions are already in place. If you enable flow framework on a preexisting OpenSearch Service domain with version 2.11 or earlier that you then upgrade to version 2.13 or later, you must define the flow_framework_manager role. Non-admin users must be mapped to this role in order to manage warm indexes on domains using fine-grained access control. To manually create the flow_framework_manager role, perform the following steps: