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diff --git a/docs/tutorials/aws/semantic_search_with_CFN_template_for_Sagemaker.md b/docs/tutorials/aws/semantic_search_with_CFN_template_for_Sagemaker.md
new file mode 100644
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+++ b/docs/tutorials/aws/semantic_search_with_CFN_template_for_Sagemaker.md
@@ -0,0 +1,240 @@
+# Topic
+
+This doc describes how to build semantic search in Amazon-managed OpenSearch service with [AWS CloudFormation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/cfn-template.html) and SageMaker.
+If you are not using Amazon OpenSearch, refer to [sagemaker_connector_blueprint](https://github.com/opensearch-project/ml-commons/blob/main/docs/remote_inference_blueprints/sagemaker_connector_blueprint.md) and [OpenSearch semantic search](https://opensearch.org/docs/latest/search-plugins/semantic-search/).
+
+The CloudFormation integration automates the manual process described in the [semantic_search_with_sagemaker_embedding_model tutorial](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/tutorials/aws/semantic_search_with_sagemaker_embedding_model.md).
+
+The CloudFormation template creates an IAM role and then uses a Lambda function to create an AI connector and model.
+
+Make sure your SageMaker model inputs follow the format that the [default pre-processing function](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/blueprints/#preprocessing-function) requires. The model input must be an array of strings. 
+```
+["hello world", "how are you"]
+```
+Additionally, make sure the model output follows the format that the [default post-processing function](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/blueprints/#post-processing-function) requires. The model output must be an array of arrays, where each array corresponds to the embedding of an input string.
+```
+[
+  [
+    -0.048237994,
+    -0.07612697,
+    ...
+  ],
+  [
+    0.32621247,
+    0.02328475,
+    ...
+  ]
+]
+```
+
+If your model input/output is not the same as the required default, you can build your own pre/post-processing function using a [Painless script](https://opensearch.org/docs/latest/api-reference/script-apis/exec-script/).
+
+For example, the Amazon Bedrock Titan embedding model ([blueprint](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/remote_inference_blueprints/bedrock_connector_titan_embedding_blueprint.md#2-create-connector-for-amazon-bedrock)) input is 
+```
+{ "inputText": "your_input_text" }
+```
+The Neural Search plugin will sends the model input to ml-commons as follows:
+```
+{ "text_docs": [ "your_input_text1", "your_input_text2"] }
+```
+Thus, you need to build a pre-processing function to transform `text_docs` into `inputText`:
+```
+"pre_process_function": """
+    StringBuilder builder = new StringBuilder();
+    builder.append("\"");
+    String first = params.text_docs[0];// Get the first doc, ml-commons will iterate all docs
+    builder.append(first);
+    builder.append("\"");
+    def parameters = "{" +"\"inputText\":" + builder + "}"; // This is the Bedrock Titan embedding model input
+    return  "{" +"\"parameters\":" + parameters + "}";"""
+```
+
+The default Amazon Bedrock Titan embedding model output has the following format:
+```
+{
+  "embedding": <float_array>
+}
+```
+However, the Neural Search plugin expects the following format:
+```
+{
+  "name": "sentence_embedding",
+  "data_type": "FLOAT32",
+  "shape": [ <embedding_size> ],
+  "data": <float_array>
+}
+```
+Similarly, you need to build a post-processing function to transform the Bedrock Titan embedding model output into a format that the Neural Search plugin requires:
+
+```
+"post_process_function": """
+      def name = "sentence_embedding";
+      def dataType = "FLOAT32";
+      if (params.embedding == null || params.embedding.length == 0) {
+        return params.message;
+      }
+      def shape = [params.embedding.length];
+      def json = "{" +
+                 "\"name\":\"" + name + "\"," +
+                 "\"data_type\":\"" + dataType + "\"," +
+                 "\"shape\":" + shape + "," +
+                 "\"data\":" + params.embedding +
+                 "}";
+      return json;
+    """
+```
+
+Note: Replace the placeholders that start with the prefix `your_` with your own values.
+
+# Steps
+
+## 0. Create an OpenSearch cluster
+
+Go to the AWS OpenSearch console UI and create an OpenSearch domain.
+
+Note the domain ARN; you'll use it in the next step.
+
+## 1. Map backend role
+
+AWS OpenSearch Integration CloudFormation template will use a Lambda function to create an AI connector with an IAM role. You need to 
+map the IAM role to `ml_full_access` to grant it the required permissions. 
+Refer to [semantic_search_with_sagemaker_embedding_model#map-backend-role](https://github.com/opensearch-project/ml-commons/blob/2.x/docs/tutorials/aws/semantic_search_with_sagemaker_embedding_model.md#22-map-backend-role). 
+
+You can find the IAM role in the `Lambda Invoke OpenSearch ML Commons Role Name` field in the CloudFormation template (see the screenshot in step 2.1).
+
+The default IAM role is `LambdaInvokeOpenSearchMLCommonsRole`, so you need to map the `arn:aws:iam::your_aws_account_id:role/LambdaInvokeOpenSearchMLCommonsRole` backend role  to `ml_full_access`.
+
+For a quick start, you can also map all roles to `ml_full_access` using a wildcard `arn:aws:iam::your_aws_account_id:role/*`
+
+Because `all_access` has more permissions than `ml_full_access`, it's OK to map the backend role to `all_access`.
+
+
+## 2. Run CloudFormation template
+
+You can find CloudFormation template integration in the AWS OpenSearch console.
+
+![Alt text](images/semantic_search/semantic_search_remote_model_Integration_1.png)
+
+For all options below, you can find the OpenSearch AI connector and model IDs in the CloudFormation stack `Outputs` when it completes.
+
+If you see any failure, you can find the log in the SageMaker Console by searching for `Log Groups` with the CloudFormation stack name.
+
+### 2.1 Option 1: Deploy pretrained model to SageMaker
+
+You can deploy a pretrained Huggingface sentence-transformer embedding model from the [DJL](https://djl.ai/) model repo.
+
+Fill out the following fields as described. Keep the default values for all fields not mentioned below:
+
+1. You must fill your `Amazon OpenSearch Endpoint`.
+2. You can use the default setting of the `Sagemaker Configuration` field for a quick start. If necessary, you can change these values. For all supported SageMaker instance types, see [SageMaker documentation](https://aws.amazon.com/sagemaker/pricing/).
+3. You must leave the `SageMaker Endpoint Url` empty. If you input a URL in this field, you will not deploy the model to SageMaker to create a new inference endpoint.
+4. You can leave the `Custom Image` field empty. The default is `djl-inference:0.22.1-cpu-full`. For all available images, see [this document](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-images.html).
+5. You must leave the `Custom Model Data Url` empty.
+6. The default value of `Custom Model Environment` is `djl://ai.djl.huggingface.pytorch/sentence-transformers/all-MiniLM-L6-v2`. For all supported models see the [Appendix](#appendix).
+
+![Alt text](images/semantic_search/semantic_search_remote_model_Integration_2.png)
+
+
+### 2.2 Option 2: Create model with your existing Sagemaker inference endpoint
+
+If you already have a SageMaker inference endpoint, you can create a remote model directly using this endpoint.
+
+Fill out the following fields as described. Keep the default values for all fields not mentioned below:
+1. You must fill your `Amazon OpenSearch Endpoint`.
+2. You must fill your `SageMaker Endpoint Url`.
+3. You must leave `Custom Image`, `Custom Model Data Url`, and `Custom Model Environment` empty.
+
+![Alt text](images/semantic_search/semantic_search_remote_model_Integration_3.png)
+
+
+# Appendix
+## Huggingface sentence-transformer embedding models available in DJL model repo
+```
+djl://ai.djl.huggingface.pytorch/sentence-transformers/LaBSE/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/all-MiniLM-L12-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/all-MiniLM-L12-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/all-MiniLM-L6-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/all-MiniLM-L6-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/all-distilroberta-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/all-mpnet-base-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/all-mpnet-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/all-roberta-large-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/allenai-specter/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-base-nli-cls-token/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-base-nli-max-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-base-nli-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-base-nli-stsb-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-base-wikipedia-sections-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-large-nli-cls-token/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-large-nli-max-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-large-nli-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/bert-large-nli-stsb-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/clip-ViT-B-32-multilingual-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/distilbert-base-nli-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/distilbert-base-nli-stsb-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/distilbert-base-nli-stsb-quora-ranking/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/distiluse-base-multilingual-cased-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/facebook-dpr-ctx_encoder-multiset-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/facebook-dpr-question_encoder-multiset-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/facebook-dpr-question_encoder-single-nq-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-MiniLM-L-12-v3/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-MiniLM-L-6-v3/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-MiniLM-L12-cos-v5/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-MiniLM-L6-cos-v5/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-bert-base-dot-v5/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-bert-co-condensor/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-base-dot-prod-v3/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-base-tas-b/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-base-v3/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-base-v4/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-cos-v5/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-dot-v5/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-distilroberta-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-roberta-base-ance-firstp/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-roberta-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/msmarco-roberta-base-v3/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/multi-qa-MiniLM-L6-dot-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/multi-qa-distilbert-cos-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/multi-qa-distilbert-dot-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/nli-bert-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/nli-bert-large-max-pooling/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/nli-distilbert-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/nli-distilroberta-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/nli-roberta-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/nli-roberta-large/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/nq-distilbert-base-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-MiniLM-L12-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-MiniLM-L3-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-MiniLM-L6-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-TinyBERT-L6-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-albert-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-albert-small-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-distilroberta-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-multilingual-mpnet-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/paraphrase-xlm-r-multilingual-v1/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/quora-distilbert-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/quora-distilbert-multilingual/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/roberta-base-nli-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/roberta-base-nli-stsb-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/roberta-large-nli-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/roberta-large-nli-stsb-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/stsb-bert-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/stsb-bert-large/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/stsb-distilbert-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/stsb-distilroberta-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/stsb-roberta-base-v2/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/stsb-roberta-base/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/stsb-roberta-large/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/stsb-xlm-r-multilingual/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/use-cmlm-multilingual/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens/
+djl://ai.djl.huggingface.pytorch/sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1/
+```
\ No newline at end of file
diff --git a/docs/tutorials/aws/semantic_search_with_bedrock_cohere_embedding_model.md b/docs/tutorials/aws/semantic_search_with_bedrock_cohere_embedding_model.md
new file mode 100644
index 0000000000..03353115ae
--- /dev/null
+++ b/docs/tutorials/aws/semantic_search_with_bedrock_cohere_embedding_model.md
@@ -0,0 +1,356 @@
+# 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. 
+
+> Bedrock has [quota limit](https://docs.aws.amazon.com/bedrock/latest/userguide/quotas.html). You can purchase [Provisioned Throughput](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html) to increase quota limit.
+
+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_cohere_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/cohere.embed-english-v3"
+        }
+    ]
+}
+```
+
+If you need to support multi-language, you can use multilingual model: `cohere.embed-multilingual-v3`
+
+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_cohere_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`
+
+Read [Cohere blueprint](https://github.com/opensearch-project/ml-commons/blob/main/docs/remote_inference_blueprints/cohere_connector_embedding_blueprint.md) for more details.
+
+```
+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 Cohere Connector: embedding v3",
+  "description": "The connector to Bedrock Cohere embedding model",
+  "version": 1,
+  "protocol": "aws_sigv4",
+  "parameters": {
+    "region": "your_bedrock_model_region",
+    "service_name": "bedrock",
+    "input_type":"search_document",
+    "truncate": "END"
+  },
+  "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/cohere.embed-english-v3/invoke",
+      "headers": {
+        "content-type": "application/json",
+        "x-amz-content-sha256": "required"
+      },
+      "request_body": "{ \"texts\": ${parameters.texts}, \"truncate\": \"${parameters.truncate}\", \"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":"1p0u8o0BWbTmLN9F2Y7m"}
+```
+
+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": "050q8o0BWbTmLN9Foo4f",
+  "status": "CREATED"
+}
+```
+
+2. Register model
+
+```
+POST /_plugins/_ml/models/_register
+{
+  "name": "Bedrock Cohere embedding model v3",
+  "function_name": "remote",
+  "description": "test embedding model",
+  "model_group_id": "050q8o0BWbTmLN9Foo4f",
+  "connector_id": "0p0p8o0BWbTmLN9F-o4G"
+}
+```
+Sample output
+```
+{
+  "task_id": "TRUr8o0BTaDH9c7tSRfx",
+  "status": "CREATED",
+  "model_id": "VRUu8o0BTaDH9c7t9xet"
+}
+```
+
+3. Deploy model
+```
+POST /_plugins/_ml/models/VRUu8o0BTaDH9c7t9xet/_deploy
+```
+Sample output
+```
+{
+  "task_id": "1J0r8o0BWbTmLN9FjY6I",
+  "task_type": "DEPLOY_MODEL",
+  "status": "COMPLETED"
+}
+```
+4. Predict
+```
+POST /_plugins/_ml/models/VRUu8o0BTaDH9c7t9xet/_predict
+{
+  "parameters": {
+    "texts": ["hello world"]
+  }
+}
+```
+Sample response
+```
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1024
+          ],
+          "data": [
+            -0.02973938,
+            -0.023651123,
+            -0.06021118,
+            ...]
+        }
+      ],
+      "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_cohere_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_cohere_embedding_pipeline",
+      "knn": "true"
+    }
+  },
+  "mappings": {
+    "properties": {
+      "text_knn": {
+        "type": "knn_vector",
+        "dimension": 1024
+      }
+    }
+  }
+}
+```
+### 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_bedrock_titan_embedding_model.md b/docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md
index 5e2085141b..40d7b27eb5 100644
--- a/docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md
+++ b/docs/tutorials/aws/semantic_search_with_bedrock_titan_embedding_model.md
@@ -3,6 +3,8 @@
 > 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. 
+ 
+> Bedrock has [quota limit](https://docs.aws.amazon.com/bedrock/latest/userguide/quotas.html). You can purchase [Provisioned Throughput](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html) to increase quota limit.
 
 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).