add bedrock titan embedding model blueprint

docs/remote_inference_blueprints/bedrock_connector_blueprint.md → docs/remote_inference_blueprints/bedrock_connector_anthropic_claude_blueprint.md

@@ -47,7 +47,8 @@ POST /_plugins/_ml/connectors/_create
 }
 ```
 
-If using the AWS Opensearch Service, you can provide an IAM role arn that allows access to the bedrock service:
+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
@@ -123,7 +124,7 @@ GET /_plugins/_ml/tasks/SnDo-ooB0wiuGR4SfMNS
 POST /_plugins/_ml/models/S3Do-ooB0wiuGR4SfcNv/_deploy
 ```
 
-6. Test model inference
+5. Test model inference
 
 ```json
 POST /_plugins/_ml/models/S3Do-ooB0wiuGR4SfcNv/_predict

docs/remote_inference_blueprints/bedrock_connector_titan_embedding_blueprint.md

@@ -0,0 +1,177 @@
+### Bedrock connector blueprint example
+
+1. Add connector endpoint to trusted URLs:
+
+```json
+PUT /_cluster/settings
+{
+    "persistent": {
+        "plugins.ml_commons.trusted_connector_endpoints_regex": [
+            "^https://bedrock-runtime\\..*[a-z0-9-]\\.amazonaws\\.com/.*$"
+        ]
+    }
+}
+```
+
+2. Create connector for Amazon Bedrock:
+
+If you are using self-managed Opensearch, you should supply AWS credentials:
+
+```json
+POST /_plugins/_ml/connectors/_create
+{
+  "name": "Amazon Bedrock Connector: embedding",
+  "description": "The connector to bedrock Titan embedding model",
+  "version": 1,
+  "protocol": "aws_sigv4",
+  "parameters": {
+    "region": "<PLEASE ADD YOUR AWS REGION HERE>",
+    "service_name": "bedrock"
+  },
+  "credential": {
+    "access_key": "<PLEASE ADD YOUR AWS ACCESS KEY HERE>",
+    "secret_key": "<PLEASE ADD YOUR AWS SECRET KEY HERE>",
+    "session_token": "<PLEASE ADD YOUR AWS SECURITY TOKEN HERE>"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "https://bedrock-runtime.us-east-1.amazonaws.com/model/amazon.titan-embed-text-v1/invoke",
+      "headers": {
+        "content-type": "application/json",
+        "x-amz-content-sha256": "required"
+      },
+      "request_body": "{ \"inputText\": \"${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    "
+    }
+  ]
+}
+```
+
+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: embedding",
+  "description": "The connector to bedrock Titan embedding model",
+  "version": 1,
+  "protocol": "aws_sigv4",
+  "parameters": {
+    "region": "<PLEASE ADD YOUR AWS REGION HERE>",
+    "service_name": "bedrock"
+  },
+  "credential": {
+    "roleArn": "<PLEASE ADD YOUR AWS ROLE ARN HERE>"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "https://bedrock-runtime.us-east-1.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    "
+    }
+  ]
+}
+```
+
+Response:
+```json
+{
+  "connector_id": "nzh9PIsBnGXNcxYpPEcv"
+}
+```
+
+3. Create model group:
+
+```json
+POST /_plugins/_ml/model_groups/_register
+{
+    "name": "remote_model_group",
+    "description": "This is an example description"
+}
+```
+
+Response:
+```json
+{
+  "model_group_id": "rqR9PIsBQRofe4CScErR",
+  "status": "CREATED"
+}
+```
+
+4. Register model to model group & deploy model:
+
+```json
+POST /_plugins/_ml/models/_register
+{
+  "name": "Bedrock embedding model",
+  "function_name": "remote",
+  "model_group_id": "rqR9PIsBQRofe4CScErR",
+  "description": "test model",
+  "connector_id": "nzh9PIsBnGXNcxYpPEcv"
+}
+```
+
+Response:
+```json
+{
+  "task_id": "r6R9PIsBQRofe4CSlUoG",
+  "status": "CREATED"
+}
+```
+Get model id from task
+```json
+GET /_plugins/_ml/tasks/r6R9PIsBQRofe4CSlUoG
+```
+Deploy model, in this demo the model id is `sKR9PIsBQRofe4CSlUov`
+```json
+POST /_plugins/_ml/models/sKR9PIsBQRofe4CSlUov/_deploy
+```
+
+5. Test model inference
+
+```json
+POST /_plugins/_ml/models/sKR9PIsBQRofe4CSlUov/_predict
+{
+  "parameters": {
+    "inputs": "What is the meaning of life?"
+  }
+}
+```
+
+Response:
+```json
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1536
+          ],
+          "data": [
+            0.5390625,
+            -0.46679688,
+            -0.125,
+            ...
+          ]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+