diff --git a/docs/remote_inference_blueprints/sagemaker_connector_blueprint.md b/docs/remote_inference_blueprints/sagemaker_connector_blueprint.md
index d3e76d802b..4a90977deb 100644
--- a/docs/remote_inference_blueprints/sagemaker_connector_blueprint.md
+++ b/docs/remote_inference_blueprints/sagemaker_connector_blueprint.md
@@ -1,5 +1,28 @@
 ### Sagemaker connector blueprint example for embedding:
 
+Read more details on https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/blueprints/
+
+Make sure your Sagemaker model input follow such format, so the [default pre-process function](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/blueprints/#preprocessing-function) can work
+```
+["hello world", "how are you"]
+```
+and output follow such format, so the [default post-process function](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/blueprints/#post-processing-function) can work
+```
+[
+  [
+    -0.048237994,
+    -0.07612697,
+    ...
+  ],
+  [
+    0.32621247,
+    0.02328475,
+    ...
+  ]
+]
+```
+
+Then, you can create Sagemaker embedding model with default pre/post process function:
 ```json
 POST /_plugins/_ml/connectors/_create
 {
@@ -23,8 +46,10 @@ POST /_plugins/_ml/connectors/_create
       "headers": {
         "content-type": "application/json"
       },
-      "url": "<PLEASE ADD YOUR Sagemaker MODEL ENDPOINT URL>",
-      "request_body": "<PLEASE ADD YOUR REQUEST BODY. Example: ${parameters.inputs}>"
+      "url": "<PLEASE ADD YOUR Sagemaker MODEL INFERENCE ENDPOINT URL>",
+      "request_body": "${parameters.inputs}",
+      "pre_process_function": "connector.pre_process.default.embedding",
+      "post_process_function": "connector.post_process.default.embedding"
     }
   ]
 }
diff --git a/docs/tutorials/aws/images/semantic_search/mapping_iam_role_arn.png b/docs/tutorials/aws/images/semantic_search/mapping_iam_role_arn.png
new file mode 100644
index 0000000000..047752bbc5
Binary files /dev/null and b/docs/tutorials/aws/images/semantic_search/mapping_iam_role_arn.png differ
diff --git a/docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md b/docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md
new file mode 100644
index 0000000000..5e2085141b
--- /dev/null
+++ b/docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md
@@ -0,0 +1,348 @@
+# Topic
+
+> The easiest way for setting up embedding model on your Amazon OpenSearch cluster is using [AWS CloudFormation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/cfn-template.html)
+
+> This tutorial explains detail steps if you want to configure everything manually. 
+
+This doc introduces how to build semantic search in Amazon managed OpenSearch with [Bedrock Titan embedding model](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
+If you are not using Amazon OpenSearch, you can refer to [bedrock_connector_titan_embedding_blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/bedrock_connector_titan_embedding_blueprint.md).
+
+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 IAM role to invoke Bedrock model
+To invoke Bedrock model, we need to create an IAM role with proper permission.
+This IAM role will be configured in connector. Connector will use this role to invoke Bedrock model.
+
+Go to IAM console, create IAM role `my_invoke_bedrock_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": [
+                "bedrock:InvokeModel"
+            ],
+            "Effect": "Allow",
+            "Resource": "arn:aws:bedrock:*::foundation-model/amazon.titan-embed-text-v1"
+        }
+    ]
+}
+```
+
+Copy the role ARN which will be used in later steps.
+
+## 2. Configure IAM role in OpenSearch
+
+### 2.1 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_bedrock_connector_role` with 
+- Custom trust policy. Note: `your_iam_user_arn` is the IAM user which will run `aws sts assume-role` in step 3.1
+```
+{
+    "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_step1"
+        },
+        {
+            "Effect": "Allow",
+            "Action": "es:ESHttpPost",
+            "Resource": "your_opensearch_domain_arn_created_in_step0"
+        }
+    ]
+}
+```
+
+Copy this role ARN which will be used in later steps.
+
+### 2.2 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 2.1 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)
+
+## 3. Create Connector
+
+Find more details on [connector](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/connectors/)
+
+
+### 3.1 Get temporary credential of the role created in step 2.1:
+```
+aws sts assume-role --role-arn your_iam_role_arn_created_in_step2.1 --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_step2.1
+AWS_SECRET_ACCESS_KEY=your_secret_key_of_role_created_in_step2.1
+AWS_SESSION_TOKEN=your_session_token_of_role_created_in_step2.1
+```
+
+### 3.2 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": "Amazon Bedrock Connector: titan embedding v1",
+  "description": "The connector to bedrock Titan embedding model",
+  "version": 1,
+  "protocol": "aws_sigv4",
+  "parameters": {
+    "region": "your_bedrock_model_region",
+    "service_name": "bedrock"
+  },
+  "credential": {
+    "roleArn": "your_iam_role_arn_created_in_step1"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "https://bedrock-runtime.your_bedrock_model_region.amazonaws.com/model/amazon.titan-embed-text-v1/invoke",
+      "headers": {
+        "content-type": "application/json",
+        "x-amz-content-sha256": "required"
+      },
+      "request_body": "{ \"inputText\": \"${parameters.inputText}\" }",
+      "pre_process_function": "\n    StringBuilder builder = new StringBuilder();\n    builder.append(\"\\\"\");\n    String first = params.text_docs[0];\n    builder.append(first);\n    builder.append(\"\\\"\");\n    def parameters = \"{\" +\"\\\"inputText\\\":\" + builder + \"}\";\n    return  \"{\" +\"\\\"parameters\\\":\" + parameters + \"}\";",
+      "post_process_function": "\n      def name = \"sentence_embedding\";\n      def dataType = \"FLOAT32\";\n      if (params.embedding == null || params.embedding.length == 0) {\n        return params.message;\n      }\n      def shape = [params.embedding.length];\n      def json = \"{\" +\n                 \"\\\"name\\\":\\\"\" + name + \"\\\",\" +\n                 \"\\\"data_type\\\":\\\"\" + dataType + \"\\\",\" +\n                 \"\\\"shape\\\":\" + shape + \",\" +\n                 \"\\\"data\\\":\" + params.embedding +\n                 \"}\";\n      return json;\n    "
+    }
+  ]
+}
+
+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":"N0qpQY0BOhavBOmfOCnw"}
+```
+
+Copy connector id which will be used in later steps.
+
+## 4. Create Model and test
+
+Login your OpenSearch Dashboard, open DevTools, then run these
+
+1. Create model group
+```
+POST /_plugins/_ml/model_groups/_register
+{
+    "name": "Bedrock_embedding_model",
+    "description": "Test model group for bedrock embedding model"
+}
+```
+Sample output
+```
+{
+  "model_group_id": "LxWiQY0BTaDH9c7t9xeE",
+  "status": "CREATED"
+}
+```
+
+2. Register model
+
+```
+POST /_plugins/_ml/models/_register
+{
+  "name": "bedrock titan embedding model v1",
+  "function_name": "remote",
+  "description": "test embedding model",
+  "model_group_id": "LxWiQY0BTaDH9c7t9xeE",
+  "connector_id": "N0qpQY0BOhavBOmfOCnw"
+}
+```
+Sample output
+```
+{
+  "task_id": "O0q3QY0BOhavBOmf1SmL",
+  "status": "CREATED",
+  "model_id": "PEq3QY0BOhavBOmf1Sml"
+}
+```
+
+3. Deploy model
+```
+POST /_plugins/_ml/models/PEq3QY0BOhavBOmf1Sml/_deploy
+```
+Sample output
+```
+{
+  "task_id": "PUq4QY0BOhavBOmfBCkQ",
+  "task_type": "DEPLOY_MODEL",
+  "status": "COMPLETED"
+}
+```
+4. Predict
+```
+POST /_plugins/_ml/models/PEq3QY0BOhavBOmf1Sml/_predict
+{
+  "parameters": {
+    "inputText": "hello world"
+  }
+}
+```
+Sample response
+```
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1536
+          ],
+          "data": [
+            0.7265625,
+            -0.0703125,
+            0.34765625,
+            ...]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+
+## 5. Semantic search
+
+### 5.1 create ingest pipeline
+Find more details: [ingest pipeline](https://opensearch.org/docs/latest/ingest-pipelines/)
+
+```
+PUT /_ingest/pipeline/my_bedrock_embedding_pipeline
+{
+    "description": "text embedding pentest",
+    "processors": [
+        {
+            "text_embedding": {
+                "model_id": "your_bedrock_embedding_model_id_created_in_step4",
+                "field_map": {
+                    "text": "text_knn"
+                }
+            }
+        }
+    ]
+}
+```
+### 5.2 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_bedrock_embedding_pipeline",
+      "knn": "true"
+    }
+  },
+  "mappings": {
+    "properties": {
+      "text_knn": {
+        "type": "knn_vector",
+        "dimension": 1536
+      }
+    }
+  }
+}
+```
+### 5.3 ingest test data
+```
+POST /my_index/_doc/1000001
+{
+    "text": "hello world."
+}
+```
+### 5.4 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_step4",
+        "k": 100
+      }
+    }
+  },
+  "size": "1",
+  "_source": ["text"]
+}
+```
\ No newline at end of file
diff --git a/docs/tutorials/aws/semantic_search_with_cohere_embedding_model.md b/docs/tutorials/aws/semantic_search_with_cohere_embedding_model.md
new file mode 100644
index 0000000000..d42f433ee5
--- /dev/null
+++ b/docs/tutorials/aws/semantic_search_with_cohere_embedding_model.md
@@ -0,0 +1,373 @@
+# Topic
+
+> The easiest way for setting up embedding model on your Amazon OpenSearch cluster is using [AWS CloudFormation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/cfn-template.html)
+
+> This tutorial explains detail steps if you want to configure everything manually. You can also connect to other service with similar way.
+
+This doc introduces how to build semantic search in Amazon managed OpenSearch with [Cohere embedding model](https://docs.cohere.com/reference/embed).
+If you are not using Amazon OpenSearch, you can refer to [cohere_v3_connector_embedding_blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/cohere_v3_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
+
+Cohere embedding model also available on Bedrock. You can use same steps of [semantic_search_with_cohere_embedding_model.md](https://github.com/opensearch-project/ml-commons/tree/2.x/docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md) to use Bedrock Cohere embedding model.
+
+# 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 Cohere API key in Secret Manager.
+
+Use default value if not mentioned.
+
+1. Choose "Other type of secret" type.
+2. Create a "my_cohere_key" key pais with your Cohere API key as value.
+3. On next page, input `my_test_cohere_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_cohere_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_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": "cohere-embed-v3",
+  "description": "The connector to public Cohere model service for embed",
+  "version": "1",
+  "protocol": "http",
+  "credential": {
+    "secretArn": "your_secret_arn_created_in_step1",
+    "roleArn": "your_iam_role_arn_created_in_step2"
+  },
+  "parameters": {
+    "model": "embed-english-v3.0",
+    "input_type":"search_document",
+    "truncate": "END"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "https://api.cohere.ai/v1/embed",
+      "headers": {
+        "Authorization": "Bearer ${credential.secretArn.my_cohere_key}",
+        "Request-Source": "unspecified:opensearch"
+      },
+      "request_body": "{ \"texts\": ${parameters.texts}, \"truncate\": \"${parameters.truncate}\", \"model\": \"${parameters.model}\", \"input_type\": \"${parameters.input_type}\" }",
+      "pre_process_function": "connector.pre_process.cohere.embedding",
+      "post_process_function": "connector.post_process.cohere.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":"qp2QP40BWbTmLN9Fpo40"}
+```
+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": "Cohere_embedding_model",
+    "description": "Test model group for cohere embedding model"
+}
+```
+Sample output
+```
+{
+  "model_group_id": "KEqTP40BOhavBOmfXikp",
+  "status": "CREATED"
+}
+```
+
+2. Register model
+
+```
+POST /_plugins/_ml/models/_register
+{
+  "name": "cohere embedding model v3",
+  "function_name": "remote",
+  "description": "test embedding model",
+  "model_group_id": "KEqTP40BOhavBOmfXikp",
+  "connector_id": "qp2QP40BWbTmLN9Fpo40"
+}
+```
+Sample output
+```
+{
+  "task_id": "q52VP40BWbTmLN9F9I5S",
+  "status": "CREATED",
+  "model_id": "MErAP40BOhavBOmfQCkf"
+}
+```
+
+3. Deploy model
+```
+POST /_plugins/_ml/models/MErAP40BOhavBOmfQCkf/_deploy
+```
+Sample output
+```
+{
+  "task_id": "KUqWP40BOhavBOmf4Clx",
+  "task_type": "DEPLOY_MODEL",
+  "status": "COMPLETED"
+}
+```
+4. Predict
+```
+POST /_plugins/_ml/models/MErAP40BOhavBOmfQCkf/_predict
+{
+  "parameters": {
+    "texts": ["hello world", "how are you"]
+  }
+}
+```
+Sample response
+```
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1024
+          ],
+          "data": [
+            -0.029510498,
+            -0.023223877,
+            -0.059631348,
+            ...]
+        },
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1024
+          ],
+          "data": [
+            0.02279663,
+            0.014976501,
+            -0.04058838,]
+        }
+      ],
+      "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_cohere_embedding_pipeline
+{
+    "description": "text embedding pentest",
+    "processors": [
+        {
+            "text_embedding": {
+                "model_id": "your_cohere_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_cohere_embedding_pipeline",
+      "knn": "true"
+    }
+  },
+  "mappings": {
+    "properties": {
+      "text_knn": {
+        "type": "knn_vector",
+        "dimension": 1024
+      }
+    }
+  }
+}
+```
+### 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"]
+}
+```
\ No newline at end of file
diff --git a/docs/tutorials/aws/semantic_search_with_sagemaker_embedding_model.md b/docs/tutorials/aws/semantic_search_with_sagemaker_embedding_model.md
new file mode 100644
index 0000000000..3f86e2b087
--- /dev/null
+++ b/docs/tutorials/aws/semantic_search_with_sagemaker_embedding_model.md
@@ -0,0 +1,385 @@
+# Topic
+
+> The easiest way for setting up embedding model on your Amazon OpenSearch cluster is using [AWS CloudFormation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/cfn-template.html)
+
+> This tutorial explains detail steps if you want to configure everything manually. 
+
+This doc introduces how to build semantic search in Amazon managed OpenSearch with embedding model running on [Sagemaker](https://aws.amazon.com/sagemaker/).
+If you are not using Amazon OpenSearch, you can refer to [sagemaker_connector_blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/sagemaker_connector_blueprint.md).
+
+Note: You should replace the placeholders with prefix `your_` with your own value
+
+This doc doesn't cover how to deploy model to Sagemaker, read [Sagemaker doc](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html) for more details.
+Make sure your sageMaker model inputs follow the format as an array of Strings, so the [default pre-process function](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/blueprints/#preprocessing-function) can work
+```
+["hello world", "how are you"]
+```
+and output follow such format as an array of array, with each array corresponds to the result of an input String, so the [default post-process function](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/blueprints/#post-processing-function) can work
+```
+[
+  [
+    -0.048237994,
+    -0.07612697,
+    ...
+  ],
+  [
+    0.32621247,
+    0.02328475,
+    ...
+  ]
+]
+```
+If your Sagemaker model input/output is different, you need to write your own pre/post process function.
+# 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 IAM role to invoke Sagemaker model
+To invoke Sagemaker model, we need to create an IAM role with proper permission.
+This IAM role will be configured in connector. Connector will use this role to invoke Sagemaker model.
+
+Go to IAM console, create IAM role `my_invoke_sagemaker_model_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": [
+        {
+            "Effect": "Allow",
+            "Action": [
+                "sagemaker:InvokeEndpoint"
+            ],
+            "Resource": [
+                "your_sagemaker_model_inference_endpoint_arn"
+            ]
+        }
+    ]
+}
+```
+
+Copy the role ARN which will be used in later steps.
+
+## 2. Configure IAM role in OpenSearch
+
+### 2.1 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_sagemaker_connector_role` with 
+- Custom trust policy. Note: `your_iam_user_arn` is the IAM user which will run `aws sts assume-role` in step 3.1
+```
+{
+    "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_step1"
+        },
+        {
+            "Effect": "Allow",
+            "Action": "es:ESHttpPost",
+            "Resource": "your_opensearch_domain_arn_created_in_step0"
+        }
+    ]
+}
+```
+
+Copy this role ARN which will be used in later steps.
+
+### 2.2 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 2.1 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)
+
+## 3. Create Connector
+
+Find more details on [connector](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/connectors/)
+
+
+### 3.1 Get temporary credential of the role created in step 2.1:
+```
+aws sts assume-role --role-arn your_iam_role_arn_created_in_step2.1 --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_step2.1
+AWS_SECRET_ACCESS_KEY=your_secret_key_of_role_created_in_step2.1
+AWS_SESSION_TOKEN=your_session_token_of_role_created_in_step2.1
+```
+
+### 3.2 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": "Sagemaker embedding model connector",
+  "description": "Connector for my Sagemaker embedding model",
+  "version": "1.0",
+  "protocol": "aws_sigv4",
+  "credential": {
+    "roleArn": "your_iam_role_arn_created_in_step1"
+  },
+  "parameters": {
+    "region": "your_sagemaker_model_region",
+    "service_name": "sagemaker"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "headers": {
+        "content-type": "application/json"
+      },
+      "url": "your_sagemaker_model_inference_endpoint",
+      "request_body": "${parameters.input}",
+      "pre_process_function": "connector.pre_process.default.embedding",
+      "post_process_function": "connector.post_process.default.embedding"
+    }
+  ]
+}
+
+headers = {"Content-Type": "application/json"}
+
+r = requests.post(url, auth=awsauth, json=payload, headers=headers)
+print(r.status_code)
+print(r.text)
+```
+The script will output connector id.
+
+sample output
+```
+{"connector_id":"tZ09Qo0BWbTmLN9FM44V"}
+```
+
+Copy connector id which will be used in later steps.
+
+## 4. Create Model and test
+
+Login your OpenSearch Dashboard, open DevTools, then run these
+
+1. Create model group
+```
+POST /_plugins/_ml/model_groups/_register
+{
+    "name": "Sagemaker_embedding_model",
+    "description": "Test model group for Sagemaker embedding model"
+}
+```
+Sample output
+```
+{
+  "model_group_id": "MhU3Qo0BTaDH9c7tKBfR",
+  "status": "CREATED"
+}
+```
+
+2. Register model
+
+```
+POST /_plugins/_ml/models/_register
+{
+  "name": "Sagemaker embedding model",
+  "function_name": "remote",
+  "description": "test embedding model",
+  "model_group_id": "MhU3Qo0BTaDH9c7tKBfR",
+  "connector_id": "tZ09Qo0BWbTmLN9FM44V"
+}
+```
+Sample output
+```
+{
+  "task_id": "NhU9Qo0BTaDH9c7t0xft",
+  "status": "CREATED",
+  "model_id": "NxU9Qo0BTaDH9c7t1Bca"
+}
+```
+
+3. Deploy model
+```
+POST /_plugins/_ml/models/NxU9Qo0BTaDH9c7t1Bca/_deploy
+```
+Sample output
+```
+{
+  "task_id": "MxU4Qo0BTaDH9c7tJxde",
+  "task_type": "DEPLOY_MODEL",
+  "status": "COMPLETED"
+}
+```
+4. Predict
+```
+POST /_plugins/_ml/models/NxU9Qo0BTaDH9c7t1Bca/_predict
+{
+  "parameters": {
+    "inputs": ["hello world", "how are you"]
+  }
+}
+```
+Sample response
+```
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            384
+          ],
+          "data": [
+            -0.034477264,
+            0.031023195,
+            0.0067349933,
+            ...]
+        },
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            384
+          ],
+          "data": [
+            -0.031369038,
+            0.037830487,
+            0.07630822,
+            ...]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+
+## 5. Semantic search
+
+### 5.1 create ingest pipeline
+Find more details: [ingest pipeline](https://opensearch.org/docs/latest/ingest-pipelines/)
+
+```
+PUT /_ingest/pipeline/my_sagemaker_embedding_pipeline
+{
+    "description": "text embedding pentest",
+    "processors": [
+        {
+            "text_embedding": {
+                "model_id": "your_sagemaker_embedding_model_id_created_in_step4",
+                "field_map": {
+                    "text": "text_knn"
+                }
+            }
+        }
+    ]
+}
+```
+### 5.2 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_sagemaker_embedding_pipeline",
+      "knn": "true"
+    }
+  },
+  "mappings": {
+    "properties": {
+      "text_knn": {
+        "type": "knn_vector",
+        "dimension": your_sagemake_model_embedding_dimension
+      }
+    }
+  }
+}
+```
+### 5.3 ingest test data
+```
+POST /my_index/_doc/1000001
+{
+    "text": "hello world."
+}
+```
+### 5.4 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_step4",
+        "k": 100
+      }
+    }
+  },
+  "size": "1",
+  "_source": ["text"]
+}
+```
\ No newline at end of file