diff --git a/docs/tutorials/rerank/rerank_pipeline_with_CrossEncoder_model_deployed_on_Sagemaker.md b/docs/tutorials/rerank/rerank_pipeline_with_CrossEncoder_model_deployed_on_Sagemaker.md
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+# Topic
+
+[Reranking pipeline](https://opensearch.org/docs/latest/search-plugins/search-relevance/reranking-search-results/) is a feature released in OpenSearch 2.12.
+It can rerank search results, providing a relevance score for each document in the search results with respect to the search query.
+The relevance score is calculated by a cross-encoder model. 
+
+This tutorial explains how to use the [Huggingface cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) model in a reranking pipeline. 
+
+Note: Replace the placeholders that start with `your_` with your own values.
+
+# Steps
+
+## 0. Deploy Model on Sagemaker
+Use this code to deploy model on Sagemaker.
+```python
+import sagemaker
+import boto3
+from sagemaker.huggingface import HuggingFaceModel
+sess = sagemaker.Session()
+role = sagemaker.get_execution_role()
+
+hub = {
+    'HF_MODEL_ID':'cross-encoder/ms-marco-MiniLM-L-6-v2',
+    'HF_TASK':'text-classification'
+}
+huggingface_model = HuggingFaceModel(
+    transformers_version='4.37.0',
+    pytorch_version='2.1.0',
+    py_version='py310',
+    env=hub,
+    role=role, 
+)
+predictor = huggingface_model.deploy(
+    initial_instance_count=1, # number of instances
+    instance_type='ml.m5.xlarge' # ec2 instance type
+)
+```
+Find the model inference endpoint and note it. We will use it to create connector in next step
+
+## 1. Create Connector and Model
+
+If you are using self-managed Opensearch, you should supply AWS credentials:
+```json
+POST /_plugins/_ml/connectors/_create
+{
+  "name": "Sagemakre cross-encoder model",
+  "description": "Test connector for Sagemaker cross-encoder model",
+  "version": 1,
+  "protocol": "aws_sigv4",
+  "credential": {
+    "access_key": "your_access_key",
+    "secret_key": "your_secret_key",
+    "session_token": "your_session_token"
+  },
+  "parameters": {
+    "region": "your_sagemkaer_model_region_like_us-west-2",
+    "service_name": "sagemaker"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "your_sagemaker_model_inference_endpoint_created_in_last_step",
+      "headers": {
+        "content-type": "application/json"
+      },
+      "request_body": "{ \"inputs\": ${parameters.inputs} }",
+      "pre_process_function": "\n    String escape(def input) { \n       if (input.contains(\"\\\\\")) {\n        input = input.replace(\"\\\\\", \"\\\\\\\\\");\n      }\n      if (input.contains(\"\\\"\")) {\n        input = input.replace(\"\\\"\", \"\\\\\\\"\");\n      }\n      if (input.contains('\r')) {\n        input = input = input.replace('\r', '\\\\r');\n      }\n      if (input.contains(\"\\\\t\")) {\n        input = input.replace(\"\\\\t\", \"\\\\\\\\\\\\t\");\n      }\n      if (input.contains('\n')) {\n        input = input.replace('\n', '\\\\n');\n      }\n      if (input.contains('\b')) {\n        input = input.replace('\b', '\\\\b');\n      }\n      if (input.contains('\f')) {\n        input = input.replace('\f', '\\\\f');\n      }\n      return input;\n    }\n\n   String query = params.query_text;\n   StringBuilder builder = new StringBuilder('[');\n    \n    for (int i=0; i<params.text_docs.length; i ++) {\n      builder.append('\"');\n      builder.append(escape(query));\n      builder.append(' . ');\n      builder.append(escape(params.text_docs[i]));\n      builder.append('\"');\n      if (i<params.text_docs.length - 1) {\n        builder.append(',');\n      }\n    }\n    builder.append(']');\n    \n    def parameters = '{ \"inputs\": ' + builder + ' }';\n    return  '{\"parameters\": ' + parameters + '}';\n     ",
+      "post_process_function": "\n      \n      def dataType = \"FLOAT32\";\n      \n      \n      if (params.result == null)\n      {\n          return 'no result generated';\n          //return params.response;\n      }\n      def outputs = params.result;\n      \n      \n      def resultBuilder = new StringBuilder('[ ');\n      for (int i=0; i<outputs.length; i++) {\n        resultBuilder.append(' {\"name\": \"similarity\", \"data_type\": \"FLOAT32\", \"shape\": [1],');\n        //resultBuilder.append('{\"name\": \"similarity\"}');\n        \n        resultBuilder.append('\"data\": [');\n        resultBuilder.append(outputs[i].score);\n        resultBuilder.append(']}');\n        if (i<outputs.length - 1) {\n          resultBuilder.append(',');\n        }\n      }\n      resultBuilder.append(']');\n      \n      return resultBuilder.toString();\n    "
+    }
+  ]
+}
+```
+
+If using the AWS Opensearch Service, you can provide an IAM role arn that allows access to the Sagemaker model inference endpoint. Refer to this [AWS doc](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/ml-amazon-connector.html) and [tutorial](../aws/semantic_search_with_sagemaker_embedding_model.md)
+```json
+POST /_plugins/_ml/connectors/_create
+{
+  "name": "Sagemakre cross-encoder model",
+  "description": "Test connector for Sagemaker cross-encoder model",
+  "version": 1,
+  "protocol": "aws_sigv4",
+  "credential": {
+    "roleArn": "your_role_arn_which_allows_access_to_sagemaker_model_inference_endpoint"
+  },
+  "parameters": {
+    "region": "your_sagemkaer_model_region_like_us-west-2",
+    "service_name": "sagemaker"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "your_sagemaker_model_inference_endpoint_created_in_last_step",
+      "headers": {
+        "content-type": "application/json"
+      },
+      "request_body": "{ \"inputs\": ${parameters.inputs} }",
+      "pre_process_function": "\n    String escape(def input) { \n       if (input.contains(\"\\\\\")) {\n        input = input.replace(\"\\\\\", \"\\\\\\\\\");\n      }\n      if (input.contains(\"\\\"\")) {\n        input = input.replace(\"\\\"\", \"\\\\\\\"\");\n      }\n      if (input.contains('\r')) {\n        input = input = input.replace('\r', '\\\\r');\n      }\n      if (input.contains(\"\\\\t\")) {\n        input = input.replace(\"\\\\t\", \"\\\\\\\\\\\\t\");\n      }\n      if (input.contains('\n')) {\n        input = input.replace('\n', '\\\\n');\n      }\n      if (input.contains('\b')) {\n        input = input.replace('\b', '\\\\b');\n      }\n      if (input.contains('\f')) {\n        input = input.replace('\f', '\\\\f');\n      }\n      return input;\n    }\n\n   String query = params.query_text;\n   StringBuilder builder = new StringBuilder('[');\n    \n    for (int i=0; i<params.text_docs.length; i ++) {\n      builder.append('\"');\n      builder.append(escape(query));\n      builder.append(' . ');\n      builder.append(escape(params.text_docs[i]));\n      builder.append('\"');\n      if (i<params.text_docs.length - 1) {\n        builder.append(',');\n      }\n    }\n    builder.append(']');\n    \n    def parameters = '{ \"inputs\": ' + builder + ' }';\n    return  '{\"parameters\": ' + parameters + '}';\n     ",
+      "post_process_function": "\n      \n      def dataType = \"FLOAT32\";\n      \n      \n      if (params.result == null)\n      {\n          return 'no result generated';\n          //return params.response;\n      }\n      def outputs = params.result;\n      \n      \n      def resultBuilder = new StringBuilder('[ ');\n      for (int i=0; i<outputs.length; i++) {\n        resultBuilder.append(' {\"name\": \"similarity\", \"data_type\": \"FLOAT32\", \"shape\": [1],');\n        //resultBuilder.append('{\"name\": \"similarity\"}');\n        \n        resultBuilder.append('\"data\": [');\n        resultBuilder.append(outputs[i].score);\n        resultBuilder.append(']}');\n        if (i<outputs.length - 1) {\n          resultBuilder.append(',');\n        }\n      }\n      resultBuilder.append(']');\n      \n      return resultBuilder.toString();\n    "
+    }
+  ]
+}
+```
+
+Use the connector ID from the response to create a model:
+```json
+POST /_plugins/_ml/models/_register?deploy=true
+{
+    "name": "cohere rerank model",
+    "function_name": "remote",
+    "description": "test rerank model",
+    "connector_id": "your_connector_id"
+}
+```
+Note the model ID in the response; you will use it in the following steps.
+
+Test the model using the Predict API:
+```json
+POST _plugins/_ml/models/your_model_id/_predict
+{
+  "parameters":  {
+    "inputs": ["I kike you . I hate you", "I kike you . I love you"]
+  }
+}
+```
+
+Each item in the inputs array comprises the 'query text' and a 'text doc', separated by a ` . `
+
+The API can also be tested similarly to a [local cross-encoder model](https://opensearch.org/docs/latest/ml-commons-plugin/pretrained-models/#cross-encoder-models).
+The connector `pre_process_function` transforms the input into the format required by `inputs` parameter shown above. 
+```json
+POST _plugins/_ml/_predict/text_similarity/your_model_id
+{
+  "query_text": "I kike you",
+  "text_docs": ["I hate you", "I love you"]
+}
+```
+
+By default, the Sagemaker model output is in the following format:
+```json
+[
+  {
+    "label": "LABEL_0",
+    "score": 0.00964462198317051
+  },
+  {
+    "label": "LABEL_0",
+    "score": 0.01644575409591198
+  }
+]
+```
+The connector `pre_process_function` transforms the model's output into a format that the [Reranker processor](https://opensearch.org/docs/latest/search-plugins/search-pipelines/rerank-processor/) can interpret. This adapted format is as follows:
+```json
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "similarity",
+          "data_type": "FLOAT32",
+          "shape": [
+            1
+          ],
+          "data": [
+            0.002032809890806675
+          ]
+        },
+        {
+          "name": "similarity",
+          "data_type": "FLOAT32",
+          "shape": [
+            1
+          ],
+          "data": [
+            0.0026099851820617914
+          ]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+
+Explanation of the response:
+1. The response contains 2 `similarity` outputs. For each `similarity` output, the `data` array contains a relevance score between each document and the query.
+2. The `similarity` outputs are provided in the order of the input documents; the first result of similarity pertains to the first document.
+
+
+## 2. Reranking pipeline
+### 2.1 Ingest test data
+```json
+POST _bulk
+{ "index": { "_index": "my-test-data" } }
+{ "passage_text" : "Carson City is the capital city of the American state of Nevada." }
+{ "index": { "_index": "my-test-data" } }
+{ "passage_text" : "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan." }
+{ "index": { "_index": "my-test-data" } }
+{ "passage_text" : "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district." }
+{ "index": { "_index": "my-test-data" } }
+{ "passage_text" : "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states." }
+
+```
+### 2.2 Create reranking pipeline
+```json
+PUT /_search/pipeline/rerank_pipeline_sagemaker
+{
+    "description": "Pipeline for reranking with Sagemaker cross-encoder model",
+    "response_processors": [
+        {
+            "rerank": {
+                "ml_opensearch": {
+                    "model_id": "your_model_id_created_in_step1"
+                },
+                "context": {
+                    "document_fields": ["passage_text"]
+                }
+            }
+        }
+    ]
+}
+```
+Note: if you provide multiple filed names in `document_fields`, it will concat the value of all fields then do rerank.
+### 2.2 Test reranking
+
+You can tune `size` if you want to return less result. For example, set `"size": 2` if you want to return top 2 documents.
+
+```json
+GET my-test-data/_search?search_pipeline=rerank_pipeline_sagemaker
+{
+  "query": {
+    "match_all": {}
+  },
+  "size": 4,
+  "ext": {
+    "rerank": {
+      "query_context": {
+         "query_text": "What is the capital of the United States?"
+      }
+    }
+  }
+}
+```
+Response:
+```json
+{
+  "took": 3,
+  "timed_out": false,
+  "_shards": {
+    "total": 1,
+    "successful": 1,
+    "skipped": 0,
+    "failed": 0
+  },
+  "hits": {
+    "total": {
+      "value": 4,
+      "relation": "eq"
+    },
+    "max_score": 0.99424136,
+    "hits": [
+      {
+        "_index": "my-test-data",
+        "_id": "tYLLdZABHToP7ahNFqmx",
+        "_score": 0.99424136,
+        "_source": {
+          "passage_text": "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.",
+          "title": "title3"
+        }
+      },
+      {
+        "_index": "my-test-data",
+        "_id": "toLLdZABHToP7ahNFqmx",
+        "_score": 0.69457644,
+        "_source": {
+          "passage_text": "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states.",
+          "title": "title4"
+        }
+      },
+      {
+        "_index": "my-test-data",
+        "_id": "s4LLdZABHToP7ahNFqmx",
+        "_score": 0.41946858,
+        "_source": {
+          "passage_text": "Carson City is the capital city of the American state of Nevada.",
+          "title": "title1"
+        }
+      },
+      {
+        "_index": "my-test-data",
+        "_id": "tILLdZABHToP7ahNFqmx",
+        "_score": 0.2727688,
+        "_source": {
+          "passage_text": "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
+          "title": "title2"
+        }
+      }
+    ]
+  },
+  "profile": {
+    "shards": []
+  }
+}
+```
+Test without reranking pipeline:
+```
+GET my-test-data/_search
+{
+  "query": {
+    "match_all": {}
+  },
+  "ext": {
+    "rerank": {
+      "query_context": {
+         "query_text": "What is the capital of the United States?"
+      }
+    }
+  }
+}
+```
+The first document in the response is `Carson City is the capital city of the American state of Nevada`, which is incorrect.
+```json
+{
+  "took": 2,
+  "timed_out": false,
+  "_shards": {
+    "total": 1,
+    "successful": 1,
+    "skipped": 0,
+    "failed": 0
+  },
+  "hits": {
+    "total": {
+      "value": 4,
+      "relation": "eq"
+    },
+    "max_score": 1.0,
+    "hits": [
+      {
+        "_index": "my-test-data",
+        "_id": "s4LLdZABHToP7ahNFqmx",
+        "_score": 1.0,
+        "_source": {
+          "passage_text": "Carson City is the capital city of the American state of Nevada.",
+          "title": "title1"
+        }
+      },
+      {
+        "_index": "my-test-data",
+        "_id": "tILLdZABHToP7ahNFqmx",
+        "_score": 1.0,
+        "_source": {
+          "passage_text": "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
+          "title": "title2"
+        }
+      },
+      {
+        "_index": "my-test-data",
+        "_id": "tYLLdZABHToP7ahNFqmx",
+        "_score": 1.0,
+        "_source": {
+          "passage_text": "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.",
+          "title": "title3"
+        }
+      },
+      {
+        "_index": "my-test-data",
+        "_id": "toLLdZABHToP7ahNFqmx",
+        "_score": 1.0,
+        "_source": {
+          "passage_text": "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states.",
+          "title": "title4"
+        }
+      }
+    ]
+  }
+}
+```
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