add standard blueprint for azure embedding ada2 (#3725)

Signed-off-by: Shengbo Ma <shengboma@gmail.com>

Author: shengbo-ma

docs/remote_inference_blueprints/standard_blueprints/azure_openai_connector_embedding_standard_blueprint.md

@@ -0,0 +1,172 @@
+# Azure OpenAI connector standard blueprint example for embedding model
+
+This blueprint demonstrates how to deploy a `text-embedding-ada-002` using the Azure OpenAI connector without pre and post processing functions. This is recommended for version after OS 2.14.0 for models to use the ML inference processor to handle input/output mapping. Note that if using a model that requires pre and post processing functions, you must provide the functions in the blueprint. Please refer to legacy blueprint: [Azure OpenAI connector blueprint example for embedding model](../azure_openai_connector_embedding_blueprint.md)
+
+## 1. Add Azure OpenAI endpoint to trusted URLs:
+
+```json
+PUT /_cluster/settings
+{
+    "persistent": {
+        "plugins.ml_commons.trusted_connector_endpoints_regex": [
+            "^https://.*\\.openai\\.azure\\.com/.*$"
+        ]
+    }
+}
+```
+
+## 2. Create connector for Azure OpenAI embedding model:
+
+Refer to [Azure OpenAI Service REST API reference - Embedding](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings).
+
+If you are using self-managed OpenSearch, you should supply OpenAI API key:
+
+```jsonc
+POST /_plugins/_ml/connectors/_create
+{
+  "name": "<YOUR CONNECTOR NAME>",
+  "description": "<YOUR CONNECTOR DESCRIPTION>",
+  "version": "<YOUR CONNECTOR VERSION>",
+  "protocol": "http",
+  "parameters": {
+    "endpoint": "<YOUR RESOURCE NAME>.openai.azure.com/",
+    "deploy-name": "<YOUR DEPLOYMENT NAME>",
+    "model": "text-embedding-ada-002",
+    "api-version": "<YOUR API VERSION>"
+  },
+  "credential": {
+    "openAI_key": "<YOUR API KEY>"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "https://${parameters.endpoint}/openai/deployments/${parameters.deploy-name}/embeddings?api-version=${parameters.api-version}",
+      "headers": {
+        "api-key": "${credential.openAI_key}"
+      },
+      "request_body": "{ \"input\": ${parameters.input}, \"input_type\":  \"array\"}" // support array of strings
+    }
+  ]
+}
+```
+
+> [!NOTE]
+> If you need to the input type to be a string instead of array of strings, you can modify the request body to:
+> ```json
+> "request_body": "{ \"input\": \"${parameters.input}\" }"
+> ```
+> See [Azure OpenAI API Reference - Request Body - input_type](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#request-body-1)
+
+Sample response:
+```json
+{
+  "connector_id": "OyB0josB2yd36FqHy3lO"
+}
+```
+
+## 3. Create model group:
+
+```json
+POST /_plugins/_ml/model_groups/_register
+{
+    "name": "remote_model_group",
+    "description": "This is an example description"
+}
+```
+
+Sample response:
+```json
+{
+  "model_group_id": "TWR0josByE8GuSOJ629m",
+  "status": "CREATED"
+}
+```
+
+## 4. Register model to model group & deploy model:
+
+```json
+POST /_plugins/_ml/models/_register
+{
+  "name": "OpenAI embedding model",
+  "function_name": "remote",
+  "model_group_id": "TWR0josByE8GuSOJ629m",
+  "description": "test model",
+  "connector_id": "OyB0josB2yd36FqHy3lO"
+}
+```
+
+
+Sample response:
+```json
+{
+  "task_id": "PCB1josB2yd36FqHAXk9",
+  "status": "CREATED"
+}
+```
+Get model id from task
+```json
+GET /_plugins/_ml/tasks/PCB1josB2yd36FqHAXk9
+```
+Deploy model, in this demo the model id is `PSB1josB2yd36FqHAnl1`
+```json
+POST /_plugins/_ml/models/PSB1josB2yd36FqHAnl1/_deploy
+```
+
+## 5. Test model inference
+
+```json
+POST /_plugins/_ml/models/PSB1josB2yd36FqHAnl1/_predict
+{
+  "parameters": {
+    "input": ["What is the meaning of life?", "42"]
+  }
+}
+```
+
+Response:
+```json
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "response",
+          "dataAsMap": {
+            "object": "list",
+            "data": [
+              {
+                "object": "embedding",
+                "index": 0,
+                "embedding": [
+                  0.004411249,
+                  -0.029655455,
+                  -0.008198498,
+                  ...
+                ]
+              },
+                            {
+                "object": "embedding",
+                "index": 1,
+                "embedding": [
+                  -0.020884188,
+                  -0.012239939,
+                  0.031366087,
+                  ...
+                ]
+              }
+            ],
+            "model": "text-embedding-ada-002",
+            "usage": {
+              "prompt_tokens": 7,
+              "total_tokens": 7
+            }
+          }
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+} 
+```
+