add tutorial doc for semantic search with OpenAI embedding model

docs/tutorials/aws/semantic_search_with_openai_embedding_model.md

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+# Topic
+
+This doc introduces how to build semantic search in Amazon managed OpenSearch with [OpenAI embedding model](https://platform.openai.com/docs/guides/embeddings).
+If you are not using Amazon OpenSearch, you can refer to [openai_connector_embedding_blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/openai_connector_embedding_blueprint.md) and [OpenSearch semantic search](https://opensearch.org/docs/latest/search-plugins/semantic-search/).
+
+Note: You should replace the placeholders with prefix `your_` with your own value
+
+# Steps
+
+## 0. Create OpenSearch cluster
+
+Go to AWS OpenSearch console UI and create OpenSearch domain.
+
+Copy the domain ARN which will be used in later steps.
+
+## 1. Create secret
+Store your OpenAI API key in Secret Manager.
+
+Use default value if not mentioned.
+
+1. Choose "Other type of secret" type.
+2. Create a "my_openai_key" key pais with your OpenAI API key as value.
+3. On next page, input `my_test_openai_secret` as secret name
+
+Copy the secret ARN which will be used in later steps.
+
+## 2. Create IAM role
+To use the secret created in Step1, we need to create an IAM role with read secret permission.
+This IAM role will be configured in connector. Connector will use this role to read secret.
+
+Go to IAM console, create IAM role `my_openai_secret_role` with:
+
+- Custom trust policy:
+```
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Effect": "Allow",
+            "Principal": {
+                "Service": "es.amazonaws.com"
+            },
+            "Action": "sts:AssumeRole"
+        }
+    ]
+}
+```
+- Permission
+```
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Action": [
+                "secretsmanager:GetSecretValue"
+            ],
+            "Effect": "Allow",
+            "Resource": "your_secret_arn_created_in_step1"
+        }
+    ]
+}
+```
+
+Copy the role ARN which will be used in later steps.
+
+## 3. Configure IAM role in OpenSearch
+
+### 3.1 Create IAM role for Signing create connector request
+
+Generate a new IAM role specifically for signing your create connector request.
+
+
+Create IAM role `my_create_openai_connector_role` with 
+- Custom trust policy. Note: `your_iam_user_arn` is the IAM user which will run `aws sts assume-role` in step 4.1
+```
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Effect": "Allow",
+            "Principal": {
+                "AWS": "your_iam_user_arn"
+            },
+            "Action": "sts:AssumeRole"
+        }
+    ]
+}
+```
+- permission
+```
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Effect": "Allow",
+            "Action": "iam:PassRole",
+            "Resource": "your_iam_role_arn_created_in_step2"
+        },
+        {
+            "Effect": "Allow",
+            "Action": "es:ESHttpPost",
+            "Resource": "your_opensearch_domain_arn_created_in_step0"
+        }
+    ]
+}
+```
+
+Copy this role ARN which will be used in later steps.
+
+### 3.2 Map backend role
+
+1. Log in to your OpenSearch Dashboard and navigate to the "Security" page, which you can find in the left-hand menu.
+2. Then click "Roles" on security page (you can find it on left-hand), then find "ml_full_access" role and click it. 
+3. On "ml_full_access" role detail page, click "Mapped users", then click "Manage mapping". Paste IAM role ARN created in Step 3.1 to backend roles part.
+Click "Map", then the IAM role configured successfully in your OpenSearch cluster.
+
+![Alt text](images/semantic_search/mapping_iam_role_arn.png)
+
+## 4. Create Connector
+
+Find more details on [connector](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/connectors/)
+
+
+### 4.1 Get temporary credential of the role created in step 3.1:
+```
+aws sts assume-role --role-arn your_iam_role_arn_created_in_step3.1 --role-session-name your_session_name
+```
+
+Configure the temporary credential in `~/.aws/credentials` like this
+
+```
+[default]
+AWS_ACCESS_KEY_ID=your_access_key_of_role_created_in_step3.1
+AWS_SECRET_ACCESS_KEY=your_secret_key_of_role_created_in_step3.1
+AWS_SESSION_TOKEN=your_session_token_of_role_created_in_step3.1
+```
+
+### 4.2 Create connector
+
+Run this python code with the temporary credential configured in `~/.aws/credentials`
+
+```
+import boto3
+import requests 
+from requests_aws4auth import AWS4Auth
+
+host = 'your_amazon_opensearch_domain_endpoint_created_in_step0'
+region = 'your_amazon_opensearch_domain_region'
+service = 'es'
+
+credentials = boto3.Session().get_credentials()
+awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, region, service, session_token=credentials.token)
+
+path = '/_plugins/_ml/connectors/_create'
+url = host + path
+
+payload = {
+  "name": "OpenAI embedding model connector",
+  "description": "Connector for OpenAI embedding model",
+  "version": "1.0",
+  "protocol": "http",
+  "credential": {
+    "secretArn": "your_secret_arn_created_in_step1",
+    "roleArn": "your_iam_role_arn_created_in_step2"
+  },
+  "parameters": {
+    "model": "text-embedding-ada-002"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "https://api.openai.com/v1/embeddings",
+      "headers": {
+        "Authorization": "Bearer ${credential.secretArn.my_openai_key}"
+      },
+      "request_body": "{ \"input\": ${parameters.input}, \"model\": \"${parameters.model}\" }",
+      "pre_process_function": "connector.pre_process.openai.embedding",
+      "post_process_function": "connector.post_process.openai.embedding"
+    }
+  ]
+}
+
+headers = {"Content-Type": "application/json"}
+
+r = requests.post(url, auth=awsauth, json=payload, headers=headers)
+print(r.text)
+```
+The script will output connector id.
+
+sample output
+```
+{"connector_id":"OBUSRI0BTaDH9c7tUxfU"}
+```
+Copy connector id which will be used in later steps.
+## 5. Create Model and test
+
+Login your OpenSearch Dashboard, open DevTools, then run these
+
+1. Create model group
+```
+POST /_plugins/_ml/model_groups/_register
+{
+    "name": "OpenAI_embedding_model",
+    "description": "Test model group for OpenAI embedding model"
+}
+```
+Sample output
+```
+{
+  "model_group_id": "ORUSRI0BTaDH9c7t9heA",
+  "status": "CREATED"
+}
+```
+
+2. Register model
+
+```
+POST /_plugins/_ml/models/_register
+{
+  "name": "OpenAI embedding model",
+  "function_name": "remote",
+  "description": "test embedding model",
+  "model_group_id": "ORUSRI0BTaDH9c7t9heA",
+  "connector_id": "OBUSRI0BTaDH9c7tUxfU"
+}
+```
+Sample output
+```
+{
+  "task_id": "OhUTRI0BTaDH9c7tLhcv",
+  "status": "CREATED",
+  "model_id": "OxUTRI0BTaDH9c7tLhdE"
+}
+```
+
+3. Deploy model
+```
+POST /_plugins/_ml/models/OxUTRI0BTaDH9c7tLhdE/_deploy
+```
+Sample output
+```
+{
+  "task_id": "PkoTRI0BOhavBOmfkCmF",
+  "task_type": "DEPLOY_MODEL",
+  "status": "COMPLETED"
+}
+```
+4. Predict
+```
+POST /_plugins/_ml/models/OxUTRI0BTaDH9c7tLhdE/_predict
+{
+  "parameters": {
+    "input": ["hello world", "how are you"]
+  }
+}
+```
+Sample response
+```
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1536
+          ],
+          "data": [
+            -0.014907048,
+            0.0013432145,
+            -0.01851529,
+            ...]
+        },
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1536
+          ],
+          "data": [
+            -0.014011521,
+            -0.0067330617,
+            -0.011700075,
+            ...]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+
+## 6. Semantic search
+
+### 6.1 create ingest pipeline
+Find more details: [ingest pipeline](https://opensearch.org/docs/latest/ingest-pipelines/)
+
+```
+PUT /_ingest/pipeline/my_openai_embedding_pipeline
+{
+    "description": "text embedding pentest",
+    "processors": [
+        {
+            "text_embedding": {
+                "model_id": "your_embedding_model_id_created_in_step5",
+                "field_map": {
+                    "text": "text_knn"
+                }
+            }
+        }
+    ]
+}
+```
+### 6.2 create k-NN index
+Find more details: [k-NN index](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/)
+
+You should customize your k-NN index for better performance.
+```
+PUT my_index
+{
+  "settings": {
+    "index": {
+      "knn.space_type": "cosinesimil",
+      "default_pipeline": "my_openai_embedding_pipeline",
+      "knn": "true"
+    }
+  },
+  "mappings": {
+    "properties": {
+      "text_knn": {
+        "type": "knn_vector",
+        "dimension": 1536
+      }
+    }
+  }
+}
+```
+### 6.3 ingest test data
+```
+POST /my_index/_doc/1000001
+{
+    "text": "hello world."
+}
+```
+### 6.4 search
+Find more details: [neural search](https://opensearch.org/docs/latest/search-plugins/neural-search/).
+```
+POST /my_index/_search
+{
+  "query": {
+    "neural": {
+      "text_knn": {
+        "query_text": "hello",
+        "model_id": "your_embedding_model_id_created_in_step5",
+        "k": 100
+      }
+    }
+  },
+  "size": "1",
+  "_source": ["text"]
+}
+```