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+# Connector blueprint examples for semantic search using Cohere Embed v4 model on Amazon 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/.*$"
+        ]
+    }
+}
+```
+
+## 2. Create connector for Amazon Bedrock:
+
+If you are using self-managed Opensearch, you should supply AWS credentials:
+
+```json
+POST /_plugins/_ml/connectors/_create
+{
+  "name": "Amazon Bedrock Connector: Cohere Embed v4",
+  "description": "Test connector for Amazon Bedrock Cohere Embed v4",
+  "version": 1,
+  "protocol": "aws_sigv4",
+  "credential": {
+    "access_key": "<PLEASE ADD YOUR AWS ACCESS KEY HERE>",
+    "secret_key": "<PLEASE ADD YOUR AWS SECRET KEY HERE>",
+    "session_token": "<PLEASE ADD YOUR AWS SECURITY TOKEN HERE>"
+  },
+  "parameters": {
+    "region": "<PLEASE ADD YOUR AWS REGION HERE>",
+    "service_name": "bedrock",
+    "truncate": "<NONE|START|END>",
+    "input_type": "<search_document|search_query|classification|clustering>",
+    "model": "cohere.embed-v4:0",
+    "embedding_types": [<"float"|"int8"|"binary">],
+    "output_dimension": <256 | 512 | 1024 | 1536>
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "headers": {
+          "x-amz-content-sha256": "required",
+          "content-type": "application/json"
+      },
+      "url": "https://bedrock-runtime.${parameters.region}.amazonaws.com/model/${parameters.model}/invoke",
+      "request_body": """
+        { 
+          "texts": ${parameters.texts},
+          "truncate": "${parameters.truncate}",
+          "input_type": "${parameters.input_type}",
+          "embedding_types": ${parameters.embedding_types},
+          "output_dimension": ${parameters.output_dimension}
+        }
+      """,
+      "pre_process_function": "connector.pre_process.cohere.embedding",
+      "post_process_function": """
+        def name = "sentence_embedding";
+        def data_type = "FLOAT32";
+        def result;
+        if (params.embeddings.int8 != null) {
+          data_type = "INT8";
+          result = params.embeddings.int8;
+        }
+        else if (params.embeddings.uint8 != null) {
+          data_type = "UINT8";
+          result = params.embeddings.uint8;
+        }
+        if (params.embeddings.binary != null) {
+          data_type = "BINARY";
+          result = params.embeddings.binary;
+        } 
+        else if (params.embeddings.ubinary != null) {
+          data_type = "UBINARY";
+          result = params.embeddings.ubinary;
+        }
+        else if (params.embeddings.float != null) {
+          data_type = "FLOAT32";
+          result = params.embeddings.float;
+        }
+        if (result == null) {
+          return "Invalid embedding result";
+        }
+        def embedding_list = new StringBuilder("[");
+        for (int m=0; m<result.length; m++) {
+          def embedding_size = result[m].length;
+          def embedding = new StringBuilder("[");
+          def shape = [embedding_size];
+          for (int i=0; i<embedding_size; i++) {
+            def val;
+            if ("FLOAT32".equals(data_type)) {
+              val = result[m][i].floatValue();
+            }
+            else if ("INT8".equals(data_type) || "UINT8".equals(data_type)) {
+              val = result[m][i].intValue();
+            }
+            else if ("BINARY".equals(data_type) || "UBINARY".equals(data_type)) {
+              val = result[m][i].intValue();
+            }
+            embedding.append(val);
+            if (i < embedding_size - 1) {
+              embedding.append(",");
+            }
+          }
+          embedding.append("]");
+          // workaround for compatible with neural-search
+          def dummy_data_type = 'FLOAT32';
+          def json = '{' + '"name":"' + name + '",' + '"data_type":"' + dummy_data_type + '",' + '"shape":' + shape + ',' + '"data":' + embedding + '}';
+          embedding_list.append(json);
+          if (m < result.length - 1) {
+            embedding_list.append(",");
+          }
+        }
+        embedding_list.append("]");
+        return embedding_list.toString();
+      """
+    }
+  ]
+}
+```
+
+If using the AWS Opensearch Service, you can provide an IAM role arn that allows access to the 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 Embed v4",
+  "description": "Test connector for Amazon Bedrock Cohere Embed v4",
+  "version": 1,
+  "protocol": "aws_sigv4",
+  "credential": {
+    "roleArn": "<PLEASE ADD YOUR AWS ROLE ARN HERE>"
+  },
+  "parameters": {
+    "region": "<PLEASE ADD YOUR AWS REGION HERE>",
+    "service_name": "bedrock",
+    "truncate": "<NONE|START|END>",
+    "input_type": "<search_document|search_query|classification|clustering>",
+    "model": "cohere.embed-v4:0",
+    "embedding_types": [<"float"|"int8"|"binary">],
+    "output_dimension": <256 | 512 | 1024 | 1536>
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "headers": {
+          "x-amz-content-sha256": "required",
+          "content-type": "application/json"
+      },
+      "url": "https://bedrock-runtime.${parameters.region}.amazonaws.com/model/${parameters.model}/invoke",
+      "request_body": """
+        { 
+          "texts": ${parameters.texts},
+          "truncate": "${parameters.truncate}",
+          "input_type": "${parameters.input_type}",
+          "embedding_types": ${parameters.embedding_types},
+          "output_dimension": ${parameters.output_dimension}
+        }
+      """,
+      "pre_process_function": "connector.pre_process.cohere.embedding",
+      "post_process_function": """
+        def name = "sentence_embedding";
+        def data_type = "FLOAT32";
+        def result;
+        if (params.embeddings.int8 != null) {
+          data_type = "INT8";
+          result = params.embeddings.int8;
+        }
+        else if (params.embeddings.uint8 != null) {
+          data_type = "UINT8";
+          result = params.embeddings.uint8;
+        }
+        if (params.embeddings.binary != null) {
+          data_type = "BINARY";
+          result = params.embeddings.binary;
+        } 
+        else if (params.embeddings.ubinary != null) {
+          data_type = "UBINARY";
+          result = params.embeddings.ubinary;
+        }
+        else if (params.embeddings.float != null) {
+          data_type = "FLOAT32";
+          result = params.embeddings.float;
+        }
+        if (result == null) {
+          return "Invalid embedding result";
+        }
+        def embedding_list = new StringBuilder("[");
+        for (int m=0; m<result.length; m++) {
+          def embedding_size = result[m].length;
+          def embedding = new StringBuilder("[");
+          def shape = [embedding_size];
+          for (int i=0; i<embedding_size; i++) {
+            def val;
+            if ("FLOAT32".equals(data_type)) {
+              val = result[m][i].floatValue();
+            }
+            else if ("INT8".equals(data_type) || "UINT8".equals(data_type)) {
+              val = result[m][i].intValue();
+            }
+            else if ("BINARY".equals(data_type) || "UBINARY".equals(data_type)) {
+              val = result[m][i].intValue();
+            }
+            embedding.append(val);
+            if (i < embedding_size - 1) {
+              embedding.append(",");
+            }
+          }
+          embedding.append("]");
+          // workaround for compatible with neural-search
+          def dummy_data_type = 'FLOAT32';
+          def json = '{' + '"name":"' + name + '",' + '"data_type":"' + dummy_data_type + '",' + '"shape":' + shape + ',' + '"data":' + embedding + '}';
+          embedding_list.append(json);
+          if (m < result.length - 1) {
+            embedding_list.append(",");
+          }
+        }
+        embedding_list.append("]");
+        return embedding_list.toString();
+      """
+    }
+  ]
+}
+```
+
+Sample response:
+```json
+{
+  "connector_id": "ISj-wZABNrAVdFa9cLju"
+}
+```
+For more information of the model inference parameters in the connector, please refer to this [AWS doc](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-embed.html)
+
+## 3. Create model group:
+
+```json
+POST /_plugins/_ml/model_groups/_register
+{
+    "name": "remote_model_group_cohere",
+    "description": "model group for cohere models"
+}
+```
+
+Sample response:
+```json
+{
+  "model_group_id": "IMobmY8B8aiZvtEZeO_i",
+  "status": "CREATED"
+}
+```
+
+## 4. Register model to model group & deploy model:
+
+```json
+POST /_plugins/_ml/models/_register?deploy=true
+{
+    "name": "cohere.embed-v4",
+    "function_name": "remote",
+    "model_group_id": "IMobmY8B8aiZvtEZeO_i",
+    "description": "cohere embed v4 model",
+    "connector_id": "ISj-wZABNrAVdFa9cLju"
+}
+```
+
+Sample response:
+```json
+{
+  "task_id": "rMormY8B8aiZvtEZIO_j",
+  "status": "CREATED",
+  "model_id": "KSj-wZABNrAVdFa937iS"
+}
+```
+
+## 5. Test model inference
+
+```json
+POST /_plugins/_ml/models/KSj-wZABNrAVdFa937iS/_predict
+{
+  "parameters": {
+    "texts" : ["上海", "This is a test"]
+  }
+}
+```
+or 
+```json
+POST /_plugins/_ml/_predict/text_embedding/KSj-wZABNrAVdFa937iS
+{
+    "text_docs":["上海", "This is a test"],
+    "return_number": true,
+    "target_response": ["sentence_embedding"]
+}
+```
+
+Sample response:
+```json
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1536
+          ],
+          "data": [
+            0.012939453,
+            -0.0017471313,
+            0.0056152344,
+            -0.0059814453,
+            ...
+          ]
+        },
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1536
+          ],
+          "data": [
+            -0.000957489,
+            0.023803711,
+            0.001045227,
+            -0.01373291,
+            ...
+          ]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```