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Learn Mistral

You're reading from   Learn Mistral Elevating Mistral systems through embeddings, agents, RAG, AWS Bedrock, and Vertex AI

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Product type Paperback
Published in Oct 2025
Publisher Packt
ISBN-13 9781835888643
Length 528 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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Pavlo Cherkashin Pavlo Cherkashin
Author Profile Icon Pavlo Cherkashin
Pavlo Cherkashin
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Toc

Table of Contents (14) Chapters Close

Preface 1. Strengths, Limitations, and Use Cases of Language Models FREE CHAPTER 2. Setting Up Your Own Chat 3. Managing the Model 4. Mastering Embeddings 5. Agents: From Automation to Intelligence 6. Unpacking RAG Workflows 7. Coding with Mistral 8. Building Smarter Defenses with Mistral 9. Take-Home RAG Challenges 10. Mistral on AWS Bedrock 11. Harnessing Mistral’s Power via Google Cloud Vertex AI 12. Other Books You May Enjoy
13. Index

API-driven model interactions

Cloud-based AI services rely on a client-server model, where applications (clients) send requests to AWS Bedrock (the server), which processes them and returns responses. This structured approach enables scalable AI interactions, allowing developers to integrate powerful models without managing complex infrastructure. Instead of manually handling HTTP requests and responses, AWS provides an official software development kit (SDK) to simplify communication—boto3.

Boto3 is the official AWS SDK for Python, designed to enable seamless interactions with AWS services, including Bedrock. It is open source, released under the Apache 2.0 license, meaning it is free to use and modify. The library abstracts the complexities of authentication, request formatting, and response handling, allowing developers to work with AWS services using familiar Python syntax. Using boto3, applications can interact with Bedrock by listing available foundation models, sending...

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