Python: Adding converse_stream_pdf.py sample to show how does converse stream API process PDF file. Bedrock runtime (awsdocs#7292)

* add converse_stream_pdf.py under bedrock_runtime...anthropic_claude

Author: xueyao-bai-mike

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)