support of amazon nova model

Author: liustve

aws-opentelemetry-distro/src/amazon/opentelemetry/distro/patches/_bedrock_patches.py

@@ -268,6 +268,8 @@ def extract_attributes(self, attributes: _AttributeMapT):
 
                     if "amazon.titan" in model_id:
                         self._extract_titan_attributes(attributes, request_body)
+                    if "amazon.nova" in model_id:
+                        self._extract_nova_attributes(attributes, request_body)
                     elif "anthropic.claude" in model_id:
                         self._extract_claude_attributes(attributes, request_body)
                     elif "meta.llama" in model_id:
@@ -288,6 +290,12 @@ def _extract_titan_attributes(self, attributes, request_body):
         self._set_if_not_none(attributes, GEN_AI_REQUEST_TOP_P, config.get("topP"))
         self._set_if_not_none(attributes, GEN_AI_REQUEST_MAX_TOKENS, config.get("maxTokenCount"))
 
+    def _extract_nova_attributes(self, attributes, request_body):
+        config = request_body.get("inferenceConfig", {})
+        self._set_if_not_none(attributes, GEN_AI_REQUEST_TEMPERATURE, config.get("temperature"))
+        self._set_if_not_none(attributes, GEN_AI_REQUEST_TOP_P, config.get("top_p"))
+        self._set_if_not_none(attributes, GEN_AI_REQUEST_MAX_TOKENS, config.get("max_new_tokens"))
+        
     def _extract_claude_attributes(self, attributes, request_body):
         self._set_if_not_none(attributes, GEN_AI_REQUEST_MAX_TOKENS, request_body.get("max_tokens"))
         self._set_if_not_none(attributes, GEN_AI_REQUEST_TEMPERATURE, request_body.get("temperature"))
@@ -342,6 +350,8 @@ def on_success(self, span: Span, result: Dict[str, Any]):
                 response_body = json.loads(telemetry_content.decode("utf-8"))
                 if "amazon.titan" in model_id:
                     self._handle_amazon_titan_response(span, response_body)
+                if "amazon.nova" in model_id:
+                    self._handle_amazon_nova_response(span, response_body)
                 elif "anthropic.claude" in model_id:
                     self._handle_anthropic_claude_response(span, response_body)
                 elif "meta.llama" in model_id:
@@ -374,6 +384,14 @@ def _handle_amazon_titan_response(self, span: Span, response_body: Dict[str, Any
                     span.set_attribute(GEN_AI_USAGE_OUTPUT_TOKENS, result["tokenCount"])
                 if "completionReason" in result:
                     span.set_attribute(GEN_AI_RESPONSE_FINISH_REASONS, [result["completionReason"]])
+    
+    def _handle_amazon_nova_response(self, span: Span, response_body: Dict[str, Any]):
+        if "inputTokenCount" in response_body:
+            span.set_attribute(GEN_AI_USAGE_INPUT_TOKENS, response_body["inputTokenCount"])
+        if "outputTokenCount" in response_body:
+            span.set_attribute(GEN_AI_USAGE_OUTPUT_TOKENS, response_body["outputTokenCount"])
+        if "stopReason" in response_body:
+            span.set_attribute(GEN_AI_RESPONSE_FINISH_REASONS, [response_body["stopReason"]])
 
     # pylint: disable=no-self-use
     def _handle_anthropic_claude_response(self, span: Span, response_body: Dict[str, Any]):

sample-applications/simple-client-server/server_automatic_s3client.py

@@ -1,20 +1,29 @@
 # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
 import boto3
-from flask import Flask, request
-
-# Let's use Amazon S3
-s3 = boto3.resource("s3")
+import json
+from flask import Flask
 
+client = boto3.client(service_name="bedrock-runtime")
 app = Flask(__name__)
 
-
 @app.route("/server_request")
 def server_request():
-    print(request.args.get("param"))
-    for bucket in s3.buckets.all():
-        print(bucket.name)
-    return "served"
+    messages = [
+    {"role": "user", "content": [{"text": "Write a short poem"}]},
+ ]
+
+    model_response = client.converse(
+        modelId="us.amazon.nova-lite-v1:0", 
+        messages=messages
+    )
+
+    print("\n[Full Response]")
+    print(json.dumps(model_response, indent=2))
+
+    print("\n[Response Content Text]")
+    print(model_response["output"]["message"]["content"][0]["text"])
+    
 
 
 if __name__ == "__main__":