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+# Bedrock connector blueprint example for Cohere Rerank model
+
+This blueprint shows how to create a connector for the [Cohere Rerank v3.5](https://docs.aws.amazon.com/bedrock/latest/userguide/rerank.html) model on Amazon Bedrock. The Cohere Rerank model calculates relevance scores for documents with respect to a query, enabling you to reorder search results for improved accuracy in RAG pipelines and enterprise search.
+
+For a complete end-to-end tutorial including search pipeline setup, see [Reranking search results using Cohere Rerank on Amazon Bedrock](https://opensearch.org/docs/latest/tutorials/reranking/reranking-cohere-bedrock/).
+
+## 1. Add connector endpoint to trusted URLs:
+
+Note: no need to do this after 2.11.0
+
+```json
+PUT /_cluster/settings
+{
+    "persistent": {
+        "plugins.ml_commons.trusted_connector_endpoints_regex": [
+            "^https://bedrock-runtime\\..*[a-z0-9-]\\.amazonaws\\.com/.*$"
+        ]
+    }
+}
+```
+
+## 2. Create connector for Amazon Bedrock:
+
+### 2.1 Self-managed OpenSearch with AWS credentials
+
+If you are using self-managed OpenSearch, you should supply AWS credentials:
+
+```json
+POST /_plugins/_ml/connectors/_create
+{
+    "name": "Amazon Bedrock Connector: Cohere Rerank",
+    "description": "Connector for Cohere Rerank v3.5 model on Amazon Bedrock",
+    "version": 1,
+    "protocol": "aws_sigv4",
+    "credential": {
+        "access_key": "<PLEASE ADD YOUR AWS ACCESS KEY HERE>",
+        "secret_key": "<PLEASE ADD YOUR AWS SECRET KEY HERE>",
+        "session_token": "<PLEASE ADD YOUR AWS SECURITY TOKEN HERE>"
+    },
+    "parameters": {
+        "service_name": "bedrock",
+        "endpoint": "bedrock-runtime",
+        "region": "<PLEASE ADD YOUR AWS REGION HERE>",
+        "model_name": "cohere.rerank-v3-5:0"
+    },
+    "actions": [
+        {
+            "action_type": "predict",
+            "method": "POST",
+            "headers": {
+                "x-amz-content-sha256": "required",
+                "content-type": "application/json"
+            },
+            "url": "https://${parameters.endpoint}.${parameters.region}.amazonaws.com/model/${parameters.model_name}/invoke",
+            "request_body": "{ \"documents\": ${parameters.documents}, \"query\": \"${parameters.query}\", \"api_version\": 2 }",
+            "pre_process_function": "connector.pre_process.cohere.rerank",
+            "post_process_function": "connector.post_process.cohere.rerank"
+        }
+    ]
+}
+```
+
+> **Security note:** Do not commit real AWS credentials to version control. For production deployments, consider using IAM roles (section 2.2) or [AWS Secrets Manager](https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html) to manage credentials securely.
+
+### 2.2 AWS OpenSearch Service with IAM role
+
+If using the AWS OpenSearch Service, you can provide an IAM role ARN that allows access to the Amazon Bedrock service.
+Refer to this [AWS doc](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/ml-amazon-connector.html).
+
+```json
+POST /_plugins/_ml/connectors/_create
+{
+    "name": "Amazon Bedrock Connector: Cohere Rerank",
+    "description": "Connector for Cohere Rerank v3.5 model on Amazon Bedrock",
+    "version": 1,
+    "protocol": "aws_sigv4",
+    "credential": {
+        "roleArn": "<PLEASE ADD YOUR AWS ROLE ARN HERE>"
+    },
+    "parameters": {
+        "service_name": "bedrock",
+        "endpoint": "bedrock-runtime",
+        "region": "<PLEASE ADD YOUR AWS REGION HERE>",
+        "model_name": "cohere.rerank-v3-5:0"
+    },
+    "actions": [
+        {
+            "action_type": "predict",
+            "method": "POST",
+            "headers": {
+                "x-amz-content-sha256": "required",
+                "content-type": "application/json"
+            },
+            "url": "https://${parameters.endpoint}.${parameters.region}.amazonaws.com/model/${parameters.model_name}/invoke",
+            "request_body": "{ \"documents\": ${parameters.documents}, \"query\": \"${parameters.query}\", \"api_version\": 2 }",
+            "pre_process_function": "connector.pre_process.cohere.rerank",
+            "post_process_function": "connector.post_process.cohere.rerank"
+        }
+    ]
+}
+```
+
+Sample response:
+```json
+{
+    "connector_id": "aB1cD2eF3gH4iJ5kL6mN"
+}
+```
+
+### 2.3 Using custom Painless scripts (for OpenSearch versions before built-in function support)
+
+If your OpenSearch version does not support the built-in `connector.pre_process.cohere.rerank` and `connector.post_process.cohere.rerank` functions, you can use custom Painless scripts instead:
+
+```json
+POST /_plugins/_ml/connectors/_create
+{
+    "name": "Amazon Bedrock Connector: Cohere Rerank",
+    "description": "Connector for Cohere Rerank v3.5 model on Amazon Bedrock",
+    "version": 1,
+    "protocol": "aws_sigv4",
+    "credential": {
+        "access_key": "<PLEASE ADD YOUR AWS ACCESS KEY HERE>",
+        "secret_key": "<PLEASE ADD YOUR AWS SECRET KEY HERE>",
+        "session_token": "<PLEASE ADD YOUR AWS SECURITY TOKEN HERE>"
+    },
+    "parameters": {
+        "service_name": "bedrock",
+        "endpoint": "bedrock-runtime",
+        "region": "<PLEASE ADD YOUR AWS REGION HERE>",
+        "model_name": "cohere.rerank-v3-5:0"
+    },
+    "actions": [
+        {
+            "action_type": "predict",
+            "method": "POST",
+            "headers": {
+                "x-amz-content-sha256": "required",
+                "content-type": "application/json"
+            },
+            "url": "https://${parameters.endpoint}.${parameters.region}.amazonaws.com/model/${parameters.model_name}/invoke",
+            "request_body": "{ \"documents\": ${parameters.documents}, \"query\": \"${parameters.query}\", \"api_version\": 2 }",
+            "pre_process_function": "\n    def query_text = params.query_text;\n    def text_docs = params.text_docs;\n    def textDocsBuilder = new StringBuilder('[');\n    for (int i=0; i<text_docs.length; i++) {\n      textDocsBuilder.append('\"');\n      textDocsBuilder.append(text_docs[i].replace('\\\\', '\\\\\\\\').replace('\"', '\\\\\"'));\n      textDocsBuilder.append('\"');\n      if (i<text_docs.length - 1) {\n        textDocsBuilder.append(',');\n      }\n    }\n    textDocsBuilder.append(']');\n    def escapedQuery = query_text.replace('\\\\', '\\\\\\\\').replace('\"', '\\\\\"');\n    def parameters = '{ \"query\": \"' + escapedQuery + '\",  \"documents\": ' + textDocsBuilder.toString() + ' }';\n    return  '{\"parameters\": ' + parameters + '}';\n  ",
+            "post_process_function": "\n    if (params.results == null || params.results.length == 0) {\n      throw new IllegalArgumentException(\"Post process function input is empty.\");\n    }\n    def outputs = params.results;\n    def relevance_scores = new Double[outputs.length];\n    for (int i=0; i<outputs.length; i++) {\n      def index = new BigDecimal(outputs[i].index.toString()).intValue();\n      relevance_scores[index] = outputs[i].relevance_score;\n    }\n    def resultBuilder = new StringBuilder('[');\n    for (int i=0; i<relevance_scores.length; i++) {\n      resultBuilder.append(' {\"name\": \"similarity\", \"data_type\": \"FLOAT32\", \"shape\": [1],');\n      resultBuilder.append('\"data\": [');\n      resultBuilder.append(relevance_scores[i]);\n      resultBuilder.append(']}');\n      if (i<outputs.length - 1) {\n        resultBuilder.append(',');\n      }\n    }\n    resultBuilder.append(']');\n    return resultBuilder.toString();\n  "
+        }
+    ]
+}
+```
+
+## 3. Create model group:
+
+```json
+POST /_plugins/_ml/model_groups/_register
+{
+    "name": "remote_model_group_rerank",
+    "description": "Model group for reranking models"
+}
+```
+
+Sample response:
+```json
+{
+    "model_group_id": "rqR9PIsBQRofe4CScErR",
+    "status": "CREATED"
+}
+```
+
+## 4. Register model to model group & deploy model:
+
+```json
+POST /_plugins/_ml/models/_register?deploy=true
+{
+    "name": "Cohere Rerank v3.5",
+    "function_name": "remote",
+    "model_group_id": "rqR9PIsBQRofe4CScErR",
+    "description": "Cohere Rerank v3.5 model for reranking search results",
+    "connector_id": "aB1cD2eF3gH4iJ5kL6mN"
+}
+```
+
+Sample response:
+```json
+{
+    "task_id": "r6R9PIsBQRofe4CSlUoG",
+    "status": "CREATED",
+    "model_id": "sKR9PIsBQRofe4CSlUov"
+}
+```
+
+## 5. Test model inference
+
+You can test the model using the Predict API:
+
+```json
+POST /_plugins/_ml/models/sKR9PIsBQRofe4CSlUov/_predict
+{
+    "parameters": {
+        "query": "What is the capital city of America?",
+        "documents": [
+            "Carson City is the capital city of the American state of Nevada.",
+            "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
+            "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.",
+            "Capital punishment (the death penalty) has existed in the United States since before the United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."
+        ]
+    }
+}
+```
+
+Alternatively, you can test the model using the text similarity API:
+
+```json
+POST /_plugins/_ml/_predict/text_similarity/sKR9PIsBQRofe4CSlUov
+{
+    "query_text": "What is the capital city of America?",
+    "text_docs": [
+        "Carson City is the capital city of the American state of Nevada.",
+        "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
+        "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.",
+        "Capital punishment (the death penalty) has existed in the United States since before the United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."
+    ]
+}
+```
+
+Sample response:
+```json
+{
+    "inference_results": [
+        {
+            "output": [
+                {
+                    "name": "similarity",
+                    "data_type": "FLOAT32",
+                    "shape": [1],
+                    "data": [0.32418242]
+                },
+                {
+                    "name": "similarity",
+                    "data_type": "FLOAT32",
+                    "shape": [1],
+                    "data": [0.07456104]
+                },
+                {
+                    "name": "similarity",
+                    "data_type": "FLOAT32",
+                    "shape": [1],
+                    "data": [0.7190094]
+                },
+                {
+                    "name": "similarity",
+                    "data_type": "FLOAT32",
+                    "shape": [1],
+                    "data": [0.06124987]
+                }
+            ],
+            "status_code": 200
+        }
+    ]
+}
+```
+
+The `post_process_function` reorders the results by document index (not by relevance score), so the scores correspond to the original document order. The highest score (`0.7190094`) belongs to the third document ("Washington, D.C. ..."), which correctly answers the query.
+
+## 6. Use with reranking search pipeline
+
+You can use the deployed rerank model in a [reranking pipeline](https://opensearch.org/docs/latest/search-plugins/search-pipelines/rerank-processor/) to rerank search results:
+
+```json
+PUT /_search/pipeline/rerank_pipeline_bedrock
+{
+    "description": "Pipeline for reranking with Bedrock Cohere Rerank model",
+    "response_processors": [
+        {
+            "rerank": {
+                "ml_opensearch": {
+                    "model_id": "sKR9PIsBQRofe4CSlUov"
+                },
+                "context": {
+                    "document_fields": ["passage_text"]
+                }
+            }
+        }
+    ]
+}
+```
+
+Test the reranking pipeline with a search query:
+
+```json
+POST /my-index/_search?search_pipeline=rerank_pipeline_bedrock
+{
+    "query": {
+        "match": {
+            "passage_text": "What is the capital city of America?"
+        }
+    },
+    "ext": {
+        "rerank": {
+            "query_context": {
+                "query_text": "What is the capital city of America?"
+            }
+        }
+    }
+}
+```
+
+To avoid writing the query twice, you can use `query_text_path` instead of `query_text`:
+
+```json
+POST /my-index/_search?search_pipeline=rerank_pipeline_bedrock
+{
+    "query": {
+        "match": {
+            "passage_text": "What is the capital city of America?"
+        }
+    },
+    "ext": {
+        "rerank": {
+            "query_context": {
+                "query_text_path": "query.match.passage_text.query"
+            }
+        }
+    }
+}
+```
+
+## Notes
+
+1. **Model ID**: The Cohere Rerank v3.5 model ID on Bedrock is `cohere.rerank-v3-5:0`. Check [AWS documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/rerank-supported.html) for the latest available model versions.
+2. **Region availability**: Cohere Rerank on Bedrock is available in select regions. Verify availability in your target region before creating the connector.
+3. **Built-in functions**: The `connector.pre_process.cohere.rerank` and `connector.post_process.cohere.rerank` functions are available starting from OpenSearch 2.19. For earlier versions, use the custom Painless scripts shown in section 2.3.
+4. **IAM permissions**: Ensure your IAM role or credentials have `bedrock:InvokeModel` permission for the Cohere Rerank model.
+5. **Use case**: This connector is particularly useful for RAG pipelines where you want to rerank retrieved documents before passing them to a generative model, improving response quality while reducing token usage.
+6. **Document fields**: If you provide multiple field names in `document_fields`, the values of all fields are concatenated before reranking is performed.