add: connector blueprint for Azure OpenAI embedding

docs/remote_inference_blueprints/azure_openai_connector_embedding_blueprint.md

@@ -0,0 +1,146 @@
+# Azure OpenAI connector blueprint example for embedding model
+
+## 1. Add Azure OpenAI endpoint to trusted URLs:
+
+```json
+PUT /_cluster/settings
+{
+    "persistent": {
+        "plugins.ml_commons.trusted_connector_endpoints_regex": [
+            "^https://.*\\.openai\\.azure\\.com/.*$"
+        ]
+    }
+}
+```
+
+## 2. Create connector for Azure OpenAI embedding model:
+
+Refer to [Azure OpenAI Service REST API reference - Embedding](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings).
+
+If you are using self-managed Opensearch, you should supply OpenAI API key:
+
+
+```json
+POST /_plugins/_ml/connectors/_create
+{
+  "name": "<YOUR CONNECTOR NAME>",
+  "description": "<YOUR CONNECTOR DESCRIPTION>",
+  "version": "<YOUR CONNECTOR VERSION>",
+  "protocol": "http",
+  "parameters": {
+    "endpoint": "<YOUR RESOURCE NAME>.openai.azure.com/",
+    "deploy-name": "<YOUR DEPLOYMENT NAME>",
+    "model": "text-embedding-ada-002",
+    "api-version": "<YOUR API VERSION>"
+  },
+  "credential": {
+    "openAI_key": "YOUR API KEY"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "https://${parameters.endpoint}/openai/deployments/${parameters.deploy-name}/embeddings?api-version=${parameters.api-version}",
+      "headers": {
+        "api-key": "${credential.openAI_key}"
+      },
+      "request_body": "{ \"input\": ${parameters.input}}",
+      "pre_process_function": "connector.pre_process.openai.embedding",
+      "post_process_function": "connector.post_process.openai.embedding"
+    }
+  ]
+}
+```
+
+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?" ]
+  }
+}
+```
+
+Response:
+```json
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1536
+          ],
+          "data": [
+            -0.0043460787,
+            -0.029653417,
+            -0.008173223,
+            ...
+          ]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+