Recuperare i dettagli di un modello Bedrock

Proseguendo il lavoro con la startup e-commerce, il team di sviluppo vuole capire le capacità specifiche dei diversi modelli di AI prima di sceglierne uno per il loro chatbot di assistenza clienti. La tech lead chiede:

"Per aiutarmi a scegliere un modello, c’è un modo per ottenere informazioni più dettagliate su ciascuno di essi?"

Mostra come recuperare i dettagli di un modello per aiutare il team a prendere una decisione informata. Le librerie boto3 e json sono già state importate.

Questo esercizio fa parte del corso

Introduzione ad Amazon Bedrock

Visualizza il corso

Istruzioni dell'esercizio

Usa il metodo per ottenere i dettagli per l'ID del modello fornito.
Stampa il nome del modello.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

bedrock = boto3.client('bedrock', region_name='us-east-1')

model_id = 'anthropic.claude-3-5-sonnet-20240620-v1:0'

# Retrieve model details
response = bedrock.____(modelIdentifier=model_id)

model_info = response['modelDetails']

# Print model's name
print(model_info['____'])

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione ad Amazon Bedrock

IntermediárioNível de habilidade

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Inizia il corso gratis

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: Accedere ad Amazon Bedrock Exercise 2: Inizializzare il client Bedrock Exercise 3: Recuperare i dettagli di un modello Bedrock

Esercizio in corso

Exercise 4: Selezionare un foundation model Exercise 5: Usare il modello corretto in Bedrock Exercise 6: Generare testo 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 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!