Updates based on feedback

Signed-off-by: Andrew Chappell <acha5066@gmail.com>
opensearch-project · Dec 13, 2023 · ee83900 · ee83900
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -10,7 +10,7 @@ Inspired from [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 - Added `includePortInHostHeader` option to `ClientBuilder::fromConfig` ([#118](https://github.com/opensearch-project/opensearch-php/pull/118))
 - Added the `RefreshSearchAnalyzers` endpoint ([[#152](https://github.com/opensearch-project/opensearch-php/issues/152))
 - Added support for `format` parameter to specify the sql response format ([#161](https://github.com/opensearch-project/opensearch-php/pull/161))
-- Implemented the Model, Model Group and Connector apis ([#170](https://github.com/opensearch-project/opensearch-php/pull/170))
+- Added ml commons model, model group and connector APIs ([#170](https://github.com/opensearch-project/opensearch-php/pull/170))
 
 ### Changed
 

diff --git a/USER_GUIDE.md b/USER_GUIDE.md
@@ -24,7 +24,7 @@ class MyOpenSearchClass
 
     public function __construct()
     {
-        //simple Setup
+        // Simple Setup
         $this->client = OpenSearch\ClientBuilder::fromConfig([
             'hosts' => [
                 'https://localhost:9200'
@@ -501,69 +501,7 @@ $client = \OpenSearch\ClientBuilder::fromConfig($config);
 
 ...
 ```
-## Machine Learning Example Usage
 
-This example assumes you are using the AWS managed OpenSearch
-service. See [The ML Commons documentation for more examples and further information.](https://github.com/opensearch-project/ml-commons/blob/main/docs/remote_inference_blueprints/openai_connector_embedding_blueprint.md)
+## Advanced Features
 
-It walks through the process of setting up a model to generate
-vector embeddings from OpenAI.
-```php
-<?php
-
-# Register a model group.
-$modelGroupResponse = $client->ml()->registerModelGroup([
-  'body' => [
-    'name' => 'openai_model_group',
-    'description' => 'Group containing models for OpenAI',
-  ],
-]);
-
-# Create the connector.
-$connectorResponse = $client->ml()->createConnector([
-  'body' => [
-    'name' => "Open AI Embedding Connector",
-    'description' => "Creates a connector to Open AI's embedding endpoint",
-    'version' => 1,
-    'protocol' => 'http',
-    'parameters' => ['model' => 'text-embedding-ada-002'],
-    'credential' => [
-      "secretArn" => '<Your Secret ARN from AWS Secrets Manager>',
-      "roleArn" => '<Your IAM role ARN>',
-    ]
-    'actions' => [
-      [
-        'action_type' => 'predict',
-        'method' => 'POST',
-        'url' => 'https://api.openai.com/v1/embeddings',
-        'headers' => [
-          'Authorization': 'Bearer ${credential.secretArn.<Your Open AI Secret in Secrets Manager>}'
-        ],
-        'request_body' => "{ \"input\": \${parameters.input}, \"model\": \"\${parameters.model}\" }",
-        'pre_process_function' => "connector.pre_process.openai.embedding",
-        'post_process_function' => "connector.post_process.openai.embedding",
-      ],
-    ],
-  ],
-]);
-
-# Register the model.
-$registerModelResponse = $client->ml()->registerModel([
-  'body' => [
-    'name' => 'OpenAI embedding model',
-    'function_name' => 'remote',
-    'model_group_id' => $modelGroupResponse['model_group_id'],
-    'description' => 'Model for retrieving vector embeddings from OpenAI',
-    'connector_id' => $connectorResponse['connector_id'],
-  ],
-]);
-
-# Monitor the state of the register model task.
-$taskResponse = $client->ml()->getTask(['id' => $registerModelResponse['task_id']]);
-
-assert($taskResponse['state'] === 'COMPLETED');
-
-# Finally deploy the model. You will now be able to generate vector
-# embeddings from OpenSearch (via OpenAI).
-$client->ml()->deployModel(['id' => $taskResponse['model_id']]);
-```
+* [ML Commons](guides/ml-commons.md)
diff --git a/guides/ml-commons.md b/guides/ml-commons.md
@@ -0,0 +1,71 @@
+# Machine Learning Example Usage
+
+Walks through the process of setting up a model to generate
+vector embeddings from OpenAI on AWS managed Opensearch.
+
+### Prerequisites
+
+* This example assumes you are using the AWS managed OpenSearch
+  service. See [The ml commons documentation](https://github.com/opensearch-project/ml-commons/blob/main/docs/remote_inference_blueprints/openai_connector_embedding_blueprint.md) for more examples and further information.
+* You will need an API key from OpenAI. [Sign up](https://platform.openai.com/signup)
+* The API key must be stored in [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/)
+
+```php
+<?php
+
+# Register a model group.
+$modelGroupResponse = $client->ml()->registerModelGroup([
+  'body' => [
+    'name' => 'openai_model_group',
+    'description' => 'Group containing models for OpenAI',
+  ],
+]);
+
+# Create the connector.
+$connectorResponse = $client->ml()->createConnector([
+  'body' => [
+    'name' => "Open AI Embedding Connector",
+    'description' => "Creates a connector to Open AI's embedding endpoint",
+    'version' => 1,
+    'protocol' => 'http',
+    'parameters' => ['model' => 'text-embedding-ada-002'],
+    'credential' => [
+      "secretArn" => '<Your Secret ARN from AWS Secrets Manager>',
+      "roleArn" => '<Your IAM role ARN>',
+    ]
+    'actions' => [
+      [
+        'action_type' => 'predict',
+        'method' => 'POST',
+        'url' => 'https://api.openai.com/v1/embeddings',
+        'headers' => [
+          'Authorization': 'Bearer ${credential.secretArn.<Your Open AI Secret in Secrets Manager>}'
+        ],
+        'request_body' => "{ \"input\": \${parameters.input}, \"model\": \"\${parameters.model}\" }",
+        'pre_process_function' => "connector.pre_process.openai.embedding",
+        'post_process_function' => "connector.post_process.openai.embedding",
+      ],
+    ],
+  ],
+]);
+
+# Register the model.
+$registerModelResponse = $client->ml()->registerModel([
+  'body' => [
+    'name' => 'OpenAI embedding model',
+    'function_name' => 'remote',
+    'model_group_id' => $modelGroupResponse['model_group_id'],
+    'description' => 'Model for retrieving vector embeddings from OpenAI',
+    'connector_id' => $connectorResponse['connector_id'],
+  ],
+]);
+
+# Monitor the state of the register model task.
+$taskResponse = $client->ml()->getTask(['id' => $registerModelResponse['task_id']]);
+
+assert($taskResponse['state'] === 'COMPLETED');
+
+# Finally deploy the model. You will now be able to generate vector
+# embeddings from OpenSearch (via OpenAI).
+$client->ml()->deployModel(['id' => $taskResponse['model_id']]);
+```