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default Connectors false false 61 Connecting to remote models

Creating connectors for third-party ML platforms

Connectors facilitate access to remote models hosted on third-party platforms.

You can provision connectors in two ways:

  1. Create a standalone connector: A standalone connector can be reused and shared by multiple remote models but requires access to both the model and connector in OpenSearch and the third-party platform, such as OpenAI or Amazon SageMaker, that the connector is accessing. Standalone connectors are saved in a connector index.

  2. Create a remote model with an internal connector: An internal connector can only be used with the remote model in which it was created. To access an internal connector, you only need access to the model itself because the connection is established inside the model. Internal connectors are saved in the model index.

Supported connectors

As of OpenSearch 2.9, connectors have been tested for the following ML services, though it is possible to create connectors for other platforms not listed here:

  • Amazon SageMaker allows you to host and manage the lifecycle of text embedding models, powering semantic search queries in OpenSearch. When connected, Amazon SageMaker hosts your models and OpenSearch is used to query inferences. This benefits Amazon SageMaker users who value its functionality, such as model monitoring, serverless hosting, and workflow automation for continuous training and deployment.
  • OpenAI ChatGPT enables you to invoke an OpenAI chat model from inside an OpenSearch cluster.
  • Cohere allows you to use data from OpenSearch to power the Cohere large language models.
  • The Bedrock Titan Embeddings model can drive semantic search and retrieval-augmented generation in OpenSearch.

All connectors consist of a JSON blueprint created by machine learning (ML) developers. The blueprint allows administrators and data scientists to make connections between OpenSearch and an AI service or model-serving technology.

You can find blueprints for each connector in the ML Commons repository.

For more information about blueprint parameters, see Connector blueprints.

Admins are only required to enter their credential settings, such as "openAI_key", for the service they are connecting to. All other parameters are defined within the blueprint. {: .note}

Standalone connector

To create a standalone connector, send a request to the connectors/_create endpoint and provide all of the parameters described in Connector blueprints:

POST /_plugins/_ml/connectors/_create
{
    "name": "OpenAI Chat Connector",
    "description": "The connector to public OpenAI model service for GPT 3.5",
    "version": 1,
    "protocol": "http",
    "parameters": {
        "endpoint": "api.openai.com",
        "model": "gpt-3.5-turbo"
    },
    "credential": {
        "openAI_key": "..."
    },
    "actions": [
        {
            "action_type": "predict",
            "method": "POST",
            "url": "https://${parameters.endpoint}/v1/chat/completions",
            "headers": {
                "Authorization": "Bearer ${credential.openAI_key}"
            },
            "request_body": "{ \"model\": \"${parameters.model}\", \"messages\": ${parameters.messages} }"
        }
    ]
}

{% include copy-curl.html %}

Internal connector

To create an internal connector, provide all of the parameters described in Connector blueprints within the connector object of a request to the models/_register endpoint:

POST /_plugins/_ml/models/_register
{
    "name": "openAI-GPT-3.5: internal connector",
    "function_name": "remote",
    "model_group_id": "lEFGL4kB4ubqQRzegPo2",
    "description": "test model",
    "connector": {
        "name": "OpenAI Connector",
        "description": "The connector to public OpenAI model service for GPT 3.5",
        "version": 1,
        "protocol": "http",
        "parameters": {
            "endpoint": "api.openai.com",
            "max_tokens": 7,
            "temperature": 0,
            "model": "text-davinci-003"
        },
        "credential": {
            "openAI_key": "..."
        },
        "actions": [
            {
                "action_type": "predict",
                "method": "POST",
                "url": "https://${parameters.endpoint}/v1/completions",
                "headers": {
                    "Authorization": "Bearer ${credential.openAI_key}"
                },
                "request_body": "{ \"model\": \"${parameters.model}\", \"prompt\": \"${parameters.prompt}\", \"max_tokens\": ${parameters.max_tokens}, \"temperature\": ${parameters.temperature} }"
            }
        ]
    }
}

{% include copy-curl.html %}

OpenAI chat connector

The following example creates a standalone OpenAI chat connector. The same options can be used for an internal connector under the connector parameter:

POST /_plugins/_ml/connectors/_create
{
    "name": "OpenAI Chat Connector",
    "description": "The connector to public OpenAI model service for GPT 3.5",
    "version": 1,
    "protocol": "http",
    "parameters": {
        "endpoint": "api.openai.com",
        "model": "gpt-3.5-turbo"
    },
    "credential": {
        "openAI_key": "..."
    },
    "actions": [
        {
            "action_type": "predict",
            "method": "POST",
            "url": "https://${parameters.endpoint}/v1/chat/completions",
            "headers": {
                "Authorization": "Bearer ${credential.openAI_key}"
            },
            "request_body": "{ \"model\": \"${parameters.model}\", \"messages\": ${parameters.messages} }"
        }
    ]
}

After creating the connector, you can retrieve the task_id and connector_id to register and deploy the model and then use the Predict API, similarly to a standalone connector.

Amazon SageMaker connector

The following example creates a standalone Amazon SageMaker connector. The same options can be used for an internal connector under the connector parameter:

POST /_plugins/_ml/connectors/_create
{
    "name": "sagemaker: embedding",
    "description": "Test connector for Sagemaker embedding model",
    "version": 1,
    "protocol": "aws_sigv4",
    "credential": {
        "access_key": "...",
        "secret_key": "...",
        "session_token": "..."
    },
    "parameters": {
        "region": "us-west-2",
        "service_name": "sagemaker"
    },
    "actions": [
        {
            "action_type": "predict",
            "method": "POST",
            "headers": {
                "content-type": "application/json"
            },
            "url": "https://runtime.sagemaker.${parameters.region}.amazonaws.com/endpoints/lmi-model-2023-06-24-01-35-32-275/invocations",
            "request_body": "[\"${parameters.inputs}\"]"
        }
    ]
}

The credential parameter contains the following options reserved for aws_sigv4 authentication:

  • access_key: Required. Provides the access key for the AWS instance.
  • secret_key: Required. Provides the secret key for the AWS instance.
  • session_token: Optional. Provides a temporary set of credentials for the AWS instance.

The parameters section requires the following options when using aws_sigv4 authentication:

  • region: The AWS Region in which the AWS instance is located.
  • service_name: The name of the AWS service for the connector.

Cohere connector

The following example request creates a standalone Cohere connector:

POST /_plugins/_ml/connectors/_create
{
  "name": "<YOUR CONNECTOR NAME>",
  "description": "<YOUR CONNECTOR DESCRIPTION>",
  "version": "<YOUR CONNECTOR VERSION>",
  "protocol": "http",
  "credential": {
    "cohere_key": "<YOUR Cohere API KEY HERE>"
  },
  "parameters": {
    "model": "embed-english-v2.0",
    "truncate": "END"
  },
  "actions": [
    {
      "action_type": "predict",
      "method": "POST",
      "url": "https://api.cohere.ai/v1/embed",
      "headers": {
        "Authorization": "Bearer ${credential.cohere_key}"
      },
      "request_body": "{ \"texts\": ${parameters.texts}, \"truncate\": \"${parameters.truncate}\", \"model\": \"${parameters.model}\" }", 
      "pre_process_function": "connector.pre_process.cohere.embedding",
      "post_process_function": "connector.post_process.cohere.embedding"
    }
  ]
}

{% include copy-curl.html %}

Amazon Bedrock connector

The following example request creates a standalone Amazon Bedrock connector:

POST /_plugins/_ml/connectors/_create
{
  "name": "Amazon Bedrock Connector: embedding",
  "description": "The connector to the Bedrock Titan embedding model",
  "version": 1,
  "protocol": "aws_sigv4",
  "parameters": {
    "region": "<YOUR AWS REGION>",
    "service_name": "bedrock"
  },
  "credential": {
    "access_key": "<YOUR AWS ACCESS KEY>",
    "secret_key": "<YOUR AWS SECRET KEY>",
    "session_token": "<YOUR AWS SECURITY TOKEN>"
  },
  "actions": [
    {
      "action_type": "predict",
      "method": "POST",
      "url": "https://bedrock-runtime.us-east-1.amazonaws.com/model/amazon.titan-embed-text-v1/invoke",
      "headers": {
        "content-type": "application/json",
        "x-amz-content-sha256": "required"
      },
      "request_body": "{ \"inputText\": \"${parameters.inputText}\" }",
      "pre_process_function": "\n    StringBuilder builder = new StringBuilder();\n    builder.append(\"\\\"\");\n    String first = params.text_docs[0];\n    builder.append(first);\n    builder.append(\"\\\"\");\n    def parameters = \"{\" +\"\\\"inputText\\\":\" + builder + \"}\";\n    return  \"{\" +\"\\\"parameters\\\":\" + parameters + \"}\";",
      "post_process_function": "\n      def name = \"sentence_embedding\";\n      def dataType = \"FLOAT32\";\n      if (params.embedding == null || params.embedding.length == 0) {\n        return params.message;\n      }\n      def shape = [params.embedding.length];\n      def json = \"{\" +\n                 \"\\\"name\\\":\\\"\" + name + \"\\\",\" +\n                 \"\\\"data_type\\\":\\\"\" + dataType + \"\\\",\" +\n                 \"\\\"shape\\\":\" + shape + \",\" +\n                 \"\\\"data\\\":\" + params.embedding +\n                 \"}\";\n      return json;\n    "
    }
  ]
}

{% include copy-curl.html %}

Next steps