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default Connector blueprints false 65 Connecting to remote models Integrating ML models
ml-commons-plugin/extensibility/blueprints/

Connector blueprints

Introduced 2.9 {: .label .label-purple }

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.

The following example shows a blueprint of an Amazon SageMaker connector:

POST /_plugins/_ml/connectors/_create
{
  "name": "<YOUR CONNECTOR NAME>",
  "description": "<YOUR CONNECTOR DESCRIPTION>",
  "version": "<YOUR CONNECTOR VERSION>",
  "protocol": "aws_sigv4",
  "credential": {
    "access_key": "<YOUR AWS ACCESS KEY>",
    "secret_key": "<YOUR AWS SECRET KEY>",
    "session_token": "<YOUR AWS SECURITY TOKEN>"
  },
  "parameters": {
    "region": "<YOUR AWS REGION>",
    "service_name": "sagemaker"
  },
  "actions": [
    {
      "action_type": "predict",
      "method": "POST",
      "headers": {
        "content-type": "application/json"
      },
      "url": "<YOUR SAGEMAKER MODEL ENDPOINT URL>",
      "request_body": "<YOUR REQUEST BODY. Example: ${parameters.inputs}>"
    }
  ]
}

{% include copy-curl.html %}

Example blueprints

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

Configuration options

The following configuration options are required in order to build a connector blueprint. These settings can be used for both standalone and internal connectors.

Field Data type Description
name String The name of the connector.
description String A description of the connector.
version Integer The version of the connector.
protocol String The protocol for the connection. For AWS services such as Amazon SageMaker and Amazon Bedrock, use aws_sigv4. For all other services, use http.
parameters JSON object The default connector parameters, including endpoint and model. Any parameters indicated in this field can be overridden by parameters specified in a predict request.
credential JSON object Defines any credential variables required in order to connect to your chosen endpoint. ML Commons uses AES/GCM/NoPadding symmetric encryption to encrypt your credentials. When the connection to the cluster first starts, OpenSearch creates a random 32-byte encryption key that persists in OpenSearch's system index. Therefore, you do not need to manually set the encryption key.
actions JSON array Defines what actions can run within the connector. If you're an administrator creating a connection, add the blueprint for your desired connection.
backend_roles JSON array A list of OpenSearch backend roles. For more information about setting up backend roles, see Assigning backend roles to users.
access_mode String Sets the access mode for the model, either public, restricted, or private. Default is private. For more information about access_mode, see Model groups.
add_all_backend_roles Boolean When set to true, adds all backend_roles to the access list, which only a user with admin permissions can adjust. When set to false, non-admins can add backend_roles.

The action parameter supports the following options.

Field Data type Description
action_type String Required. Sets the ML Commons API operation to use upon connection. As of OpenSearch 2.9, only predict is supported.
method String Required. Defines the HTTP method for the API call. Supports POST and GET.
url String Required. Sets the connection endpoint at which the action occurs. This must match the regex expression for the connection used when adding trusted endpoints.
headers JSON object Sets the headers used inside the request or response body. Default is ContentType: application/json. If your third-party ML tool requires access control, define the required credential parameters in the headers parameter.
request_body String Required. Sets the parameters contained inside the request body of the action. The parameters must include \"inputText\, which specifies how users of the connector should construct the request payload for the action_type.
pre_process_function String Optional. A built-in or custom Painless script used to preprocess the input data. OpenSearch provides the following built-in preprocess functions that you can call directly:
- connector.pre_process.cohere.embedding for Cohere embedding models
- connector.pre_process.openai.embedding for OpenAI embedding models
- connector.pre_process.default.embedding, which you can use to preprocess documents in neural search requests so that they are in the format that ML Commons can process with the default preprocessor (OpenSearch 2.11 or later). For more information, see built-in functions.
post_process_function String Optional. A built-in or custom Painless script used to post-process the model output data. OpenSearch provides the following built-in post-process functions that you can call directly:
- connector.pre_process.cohere.embedding for Cohere text embedding models
- connector.pre_process.openai.embedding for OpenAI text embedding models
- connector.post_process.default.embedding, which you can use to post-process documents in the model response so that they are in the format that neural search expects (OpenSearch 2.11 or later). For more information, see built-in functions.

Built-in pre- and post-processing functions

Call the built-in pre- and post-processing functions instead of writing a custom Painless script when connecting to the following text embedding models or your own text embedding models deployed on a remote server (for example, Amazon SageMaker):

OpenSearch provides the following pre- and post-processing functions:

  • OpenAI: connector.pre_process.openai.embedding and connector.post_process.openai.embedding
  • Cohere: connector.pre_process.cohere.embedding and connector.post_process.cohere.embedding
  • Default (for neural search): connector.pre_process.default.embedding and connector.post_process.default.embedding

Default pre- and post-processing functions

When you perform vector search using neural search, the neural search request is routed first to ML Commons and then to the model. If the model is one of the pretrained models provided by OpenSearch, it can parse the ML Commons request and return the response in the format that ML Commons expects. However, for a remote model, the expected format may be different from the ML Commons format. The default pre- and post-processing functions translate between the format that the model expects and the format that neural search expects.

Example request

The following example request creates a SageMaker text embedding connector and calls the default post-processing function:

POST /_plugins/_ml/connectors/_create
{
  "name": "Sagemaker text embedding connector",
  "description": "The connector to Sagemaker",
  "version": 1,
  "protocol": "aws_sigv4",
  "credential": {
    "access_key": "<YOUR SAGEMAKER ACCESS KEY>",
    "secret_key": "<YOUR SAGEMAKER SECRET KEY>",
    "session_token": "<YOUR AWS SECURITY TOKEN>"
  },
  "parameters": {
    "region": "ap-northeast-1",
    "service_name": "sagemaker"
  },
  "actions": [
    {
      "action_type": "predict",
      "method": "POST",
      "url": "sagemaker.ap-northeast-1.amazonaws.com/endpoints/",
      "headers": {
        "content-type": "application/json"
      },
      "post_process_function": "connector.post_process.default.embedding",
      "request_body": "${parameters.input}"
    }
  ]
}

{% include copy-curl.html %}

The request_body template must be ${parameters.input}. {: .important}

Preprocessing function

The connector.pre_process.default.embedding default preprocessing function parses the neural search request and transforms it into the format that the model expects as input.

The ML Commons Predict API provides parameters in the following format:

{
  "parameters": {
    "input": ["hello", "world"]
  }
}

The default preprocessing function sends the input field contents to the model. Thus, the model input format must be a list of strings, for example:

["hello", "world"]

Post-processing function

The connector.post_process.default.embedding default post-processing function parses the model response and transforms it into the format that neural search expects as input.

The remote text embedding model output must be a two-dimensional float array, each element of which represents an embedding of a string from the input list. For example, the following two-dimensional array corresponds to the embedding of the list ["hello", "world"]:

[
  [
    -0.048237994,
    -0.07612697,
    ...
  ],
  [
    0.32621247,
    0.02328475,
    ...
  ]
]

Custom pre- and post-processing functions

You can write your own pre- and post-processing functions specifically for your model format. For example, the following Amazon Bedrock connector definition contains custom pre- and post-processing functions for the Amazon Bedrock Titan embedding model:

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 step

For examples of creating various connectors, see Connectors.