Processing model responses and error handling

SkyCast Assistant also powers personalized weather alerts. As part of this initiative, they've asked to generate a reply to a user who wants to change the frequency of their newsletter. Use Claude to generate the response, then parse it so the final message fits into the app’s compact notification view.

The json library, bedrock client, and request_body have been pre-imported.

Questo esercizio fa parte del corso

Introduction to Amazon Bedrock

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Istruzioni dell'esercizio

Check if the 'output' key exists in the data dictionary.
Extract 'text' from the content in the message output.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

response = bedrock.invoke_model(modelId='amazon.nova-lite-v1:0', body=request_body)
data = json.loads(response['body'].read().decode())

# Check for the output key
if '____' in ____:
    response = data['output']['message']['content'][0]['____'][:100]
    print(response)
else:
    print('Key not found')

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Questo esercizio fa parte del corso

Introduction to Amazon Bedrock

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4.8+

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Learn the fundamentals of Amazon Bedrock, AWS's fully managed foundation model service. Start with basic setup and authentication, explore available foundation models like Claude and Titan, and master essential API interactions. This chapter covers setting up a development environment, choosing appropriate models for a use case, and implementing proper API request handling, including streaming responses and error management.

Exercise 1: Accessing Amazon Bedrock Exercise 2: Initializing the Bedrock client Exercise 3: Retrieving Bedrock model details Exercise 4: Selecting a foundation model Exercise 5: Using the correct model in Bedrock Exercise 6: Generating text in Bedrock

In this chapter, you'll progress through three key lessons. Starting with the basics of text generation and response handling, you'll then advance to sophisticated prompt engineering techniques like few-shot learning and structured outputs. The final lesson covers model parameter optimization, teaching learners to fine-tune settings like temperature and token limits for optimal results. Each lesson builds upon the previous, moving from fundamental API interactions to advanced parameter control for specific use cases.

Exercise 1: Generating text with language models Exercise 2: Enhancing prompts with role assignment Exercise 3: Processing model responses and error handling

Esercizio in corso

Exercise 4: Foundation model report categorization Exercise 5: Applying prompt engineering techniques Exercise 6: Implement few-shot learning for geometry Exercise 7: Creating structured output prompts Exercise 8: Working with different model parameters Exercise 9: Adjusting creativity with temperature control Exercise 10: Controlling output with top_p and max_tokens Exercise 11: Parameters for product descriptions

In this chapter, you’ll build robust and responsible applications with Amazon Bedrock, moving beyond basic API interactions. Starting with conversational AI, you’ll master state management and context handling to create coherent chat experiences. You’ll then explore advanced prompting techniques, learning how to generate diverse content types while maintaining consistent style and tone. The chapter covers critical production aspects like error handling, rate limiting, and efficient response processing. Finally, you will implement ethical guardrails and safety measures, ensuring your AI applications are both powerful and responsible.

Exercise 1: Ethical considerations and guardrails Exercise 2: Product review moderation Exercise 3: Creating age-appropriate content filters Exercise 4: Managing AI conversations Exercise 5: Building a conversation-manager class Exercise 6: Making a conversation memory-aware Exercise 7: Advanced prompting strategies Exercise 8: Implementing chain of thought prompting Exercise 9: Styled content generation with Bedrock Exercise 10: Handling API responses and errors Exercise 11: Implementing rate limit handling Exercise 12: Implementing model fallbacks Exercise 13: Congratulations!