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Add docs on skip_validating_missing_parameters in ml-commons connector (
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opensearch-project#8118)

* add doc: skip_validating_missing_parameters

Signed-off-by: yuye-aws <[email protected]>

* Doc review

Signed-off-by: Fanit Kolchina <[email protected]>

* update: make both description consistent

Signed-off-by: yuye-aws <[email protected]>

* Apply suggestions from code review

Co-authored-by: Nathan Bower <[email protected]>
Signed-off-by: kolchfa-aws <[email protected]>

---------

Signed-off-by: yuye-aws <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Signed-off-by: kolchfa-aws <[email protected]>
Co-authored-by: Fanit Kolchina <[email protected]>
Co-authored-by: kolchfa-aws <[email protected]>
Co-authored-by: Nathan Bower <[email protected]>
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23 changes: 13 additions & 10 deletions _ml-commons-plugin/api/connector-apis/update-connector.md
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Expand Up @@ -29,17 +29,20 @@ PUT /_plugins/_ml/connectors/<connector_id>

The following table lists the updatable fields. For more information about all connector fields, see [Blueprint configuration parameters]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints#configuration-parameters).

| 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 included in this field can be overridden by parameters specified in a predict request. |
| Field | Data type | Description |
| :--- |:------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `name` | String | The name of the connector. |
| `description` | String | A description of the connector. |
| `version` | Integer | The connector version. |
| `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 included 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 which actions can run within the connector. If you're an administrator creating a connection, add the [blueprint]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/) 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]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#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]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#model-groups). |
| `actions` | JSON array | Defines which actions can run within the connector. If you're an administrator creating a connection, add the [blueprint]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/) 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]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#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]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#model-groups). |
| `parameters.skip_validating_missing_parameters` | Boolean | When set to `true`, this option allows you to send a request using a connector without validating any missing parameters. Default is `false`. |



#### Example request

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32 changes: 16 additions & 16 deletions _ml-commons-plugin/remote-models/blueprints.md
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Expand Up @@ -55,19 +55,20 @@ As an ML developer, you can build connector blueprints for other platforms. Usin

## Configuration parameters

| Field | Data type | Is required | Description |
|:---|:---|:---|:---|
| `name` | String | Yes | The name of the connector. |
| `description` | String | Yes | A description of the connector. |
| `version` | Integer | Yes | The version of the connector. |
| `protocol` | String | Yes | 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 | Yes | 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 | Yes | Defines any credential variables required 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 | Yes | Defines what actions can run within the connector. If you're an administrator creating a connection, add the [blueprint]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/) for your desired connection. |
| `backend_roles` | JSON array | Yes | A list of OpenSearch backend roles. For more information about setting up backend roles, see [Assigning backend roles to users]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#assigning-backend-roles-to-users). |
| `access_mode` | String | Yes | Sets the access mode for the model, either `public`, `restricted`, or `private`. Default is `private`. For more information about `access_mode`, see [Model groups]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#model-groups). |
| `add_all_backend_roles` | Boolean | Yes | 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`. |
| `client_config` | JSON object | No | The client configuration object, which provides settings that control the behavior of the client connections used by the connector. These settings allow you to manage connection limits and timeouts, ensuring efficient and reliable communication. |
| Field | Data type | Is required | Description |
|:-------------------------------------------------|:---|:------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `name` | String | Yes | The name of the connector. |
| `description` | String | Yes | A description of the connector. |
| `version` | Integer | Yes | The connector version. |
| `protocol` | String | Yes | 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 | Yes | The default connector parameters, including `endpoint`, `model`, and `skip_validating_missing_parameters`. Any parameters indicated in this field can be overridden by parameters specified in a predict request. |
| `credential` | JSON object | Yes | Defines any credential variables required for connecting to your chosen endpoint. ML Commons uses **AES/GCM/NoPadding** symmetric encryption to encrypt your credentials. When the cluster connection is initiated, 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 | Yes | Defines the actions that can run within the connector. If you're an administrator creating a connection, add the [blueprint]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/blueprints/) for your desired connection. |
| `backend_roles` | JSON array | Yes | A list of OpenSearch backend roles. For more information about setting up backend roles, see [Assigning backend roles to users]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#assigning-backend-roles-to-users). |
| `access_mode` | String | Yes | Sets the access mode for the model, either `public`, `restricted`, or `private`. Default is `private`. For more information about `access_mode`, see [Model groups]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#model-groups). |
| `add_all_backend_roles` | Boolean | Yes | 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`. |
| `client_config` | JSON object | No | The client configuration object, which provides settings that control the behavior of the client connections used by the connector. These settings allow you to manage connection limits and timeouts, ensuring efficient and reliable communication. |
| `parameters.skip_validating_missing_parameters` | Boolean | No | When set to `true`, this option allows you to send a request using a connector without validating any missing parameters. Default is `false`. |


The `actions` parameter supports the following options.
Expand All @@ -76,12 +77,11 @@ The `actions` parameter supports the following options.
|:---|:---|:---|
| `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]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/index#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. |
| `url` | String | Required. Specifies the connection endpoint at which the action occurs. This must match the regex expression for the connection used when [adding trusted endpoints]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/index#adding-trusted-endpoints).|
| `request_body` | String | Required. Sets the parameters contained in 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:<br> - `connector.pre_process.cohere.embedding` for [Cohere](https://cohere.com/) embedding models<br> - `connector.pre_process.openai.embedding` for [OpenAI](https://platform.openai.com/docs/guides/embeddings) embedding models <br> - `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](#built-in-pre--and-post-processing-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:<br> - `connector.pre_process.cohere.embedding` for [Cohere text embedding models](https://docs.cohere.com/reference/embed)<br> - `connector.pre_process.openai.embedding` for [OpenAI text embedding models](https://platform.openai.com/docs/api-reference/embeddings) <br> - `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). |

| `headers` | JSON object | Specifies the headers used in 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. |

The `client_config` parameter supports the following options.

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