diff --git a/python/example_code/bedrock-runtime/models/anthropic_claude/converse_stream_pdf.py b/python/example_code/bedrock-runtime/models/anthropic_claude/converse_stream_pdf.py
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+++ b/python/example_code/bedrock-runtime/models/anthropic_claude/converse_stream_pdf.py
@@ -0,0 +1,79 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Use the Conversation API to send a text message along with PDF as input to Anthropic Claude
+# and print the response stream.
+
+import boto3
+from botocore.config import Config
+
+config = Config(
+       connect_timeout=1000,
+    read_timeout=1000,
+)
+# Create a Bedrock Runtime client in the AWS Region you want to use.
+session = boto3.session.Session(region_name='us-east-1')
+bedrock_runtime = session.client(service_name = 'bedrock-runtime', 
+                                 config=config)
+pdf_path = input("Enter the path to the PDF file: ")
+prompt = """
+Please analyze this PDF document and provide the following information:
+
+1. Document Title
+2. Main topics covered
+3. Key findings or conclusions
+4. Important dates or numbers mentioned
+5. Summary in 3-4 sentences
+
+Format your response in a clear, structured way.
+"""
+
+# Set the model ID
+
+#SONNET_V2_MODEL_ID = "anthropic.claude-3-5-sonnet-20241022-v2:0"
+SONNET_V2_MODEL_ID = "us.anthropic.claude-3-5-sonnet-20241022-v2:0"   
+def optimize_reel_prompt(user_prompt,ref_image):
+    # open PDF
+    with open(ref_image, "rb") as f:
+        image = f.read()
+
+    system = [
+        {
+            "text": "You are an expert in summarizing PDF docs."
+        }
+    ]
+    # payload of PDF as input
+    messages = [
+        {
+            "role": "user",
+            "content": [
+             {
+                "document": {
+                    "format": "pdf",
+                    "name": "DocumentPDFmessages",
+                    "source": {
+                        "bytes": image
+                    }
+                }
+            },
+            {"text": user_prompt}
+            ],
+        }
+    ]
+    # Configure the inference parameters.
+    inf_params = {"maxTokens": 800, "topP": 0.9, "temperature": 0.5}
+    model_response = bedrock_runtime.converse_stream(
+        modelId=SONNET_V2_MODEL_ID, messages=messages, system=system, inferenceConfig=inf_params
+    )
+    text = ""
+    stream = model_response.get("stream")
+    if stream:
+        for event in stream:
+            if "contentBlockDelta" in event:
+                text += event["contentBlockDelta"]["delta"]["text"]
+                print(event["contentBlockDelta"]["delta"]["text"], end="")
+    return text
+
+if __name__ == "__main__":
+    txt = optimize_reel_prompt(prompt,pdf_path)
+    print(txt)
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