Memproses respons model dan penanganan error

SkyCast Assistant juga mendukung peringatan cuaca yang dipersonalisasi. Sebagai bagian dari inisiatif ini, mereka meminta untuk membuat balasan kepada pengguna yang ingin mengubah frekuensi buletin mereka. Gunakan Claude untuk menghasilkan respons, lalu uraikan sehingga pesan akhir cocok dengan tampilan notifikasi ringkas aplikasi.

Pustaka json, klien bedrock, dan request_body telah diimpor sebelumnya.

Latihan ini adalah bagian dari kursus

Pengantar Amazon Bedrock

Petunjuk latihan

Periksa apakah kunci 'output' ada dalam kamus data.
Ekstrak 'text' dari konten di keluaran pesan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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')

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Amazon Bedrock

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: 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: Menghasilkan teks dengan model bahasa Exercise 2: Menyempurnakan prompt dengan penetapan peran Exercise 3: Memproses respons model dan penanganan error

Latihan Saat Ini

Exercise 4: Kategorisasi laporan model dasar Exercise 5: Menerapkan teknik prompt engineering Exercise 6: Menerapkan few-shot learning untuk geometri Exercise 7: Membuat prompt keluaran terstruktur Exercise 8: Bekerja dengan berbagai parameter model Exercise 9: Menyesuaikan kreativitas dengan kontrol suhu Exercise 10: Controlling output with top_p and max_tokens Exercise 11: Parameter untuk deskripsi produk

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!