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Generative AI on Google Cloud with LangChain

You're reading from   Generative AI on Google Cloud with LangChain Design scalable generative AI solutions with Python, LangChain, and Vertex AI on Google Cloud

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Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781835889329
Length 306 pages
Edition 1st Edition
Concepts
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Authors (3):
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Leonid Kuligin Leonid Kuligin
Author Profile Icon Leonid Kuligin
Leonid Kuligin
LinkedIn
Leonid Kuligin is a staff AI engineer at Google Cloud, working on generative AI and classical machine learning solutions (such as demand forecasting or optimization problems). Leonid is one of the key maintainers of Google Cloud integrations on LangChain, and a visiting lecturer at CDTM (TUM and LMU). Prior to Google, Leonid gained more than 20 years of experience in building B2C and B2B applications based on complex machine learning and data processing solutions such as search, maps, and investment management in German, Russian, and US technological, financial, and retail companies.
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See other products by Leonid Kuligin
Jorge Zaldívar Jorge Zaldívar
Author Profile Icon Jorge Zaldívar
Jorge Zaldívar
LinkedIn
Jorge Zaldivar is an AI Engineer at Google and also a contributor to LangChain's integrations with Google. He has a decade of experience building complex Machine Learning applications and products applied to the energy and financial industries.
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See other products by Jorge Zaldívar
Maximilian Tschochohei Maximilian Tschochohei
Author Profile Icon Maximilian Tschochohei
Maximilian Tschochohei
LinkedIn
Maximilian Tschochohei leads AI engineering at Google Cloud Consulting EMEA. He implements LangChain applications together with Google Cloud customers. Before Google, he worked in strategy and technology consulting with Boston Consulting Group.
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Toc

Table of Contents (22) Chapters Close

Preface 1. Part 1: Intro to LangChain and Generative AI on Google Cloud FREE CHAPTER
2. Chapter 1: Using LangChain with Google Cloud 3. Chapter 2: Foundational Models on Google Cloud 4. Part 2: Hallucinations and Grounding Responses
5. Chapter 3: Grounding Responses 6. Chapter 4: Vector Search on Google Cloud 7. Chapter 5: Ingesting Documents 8. Chapter 6: Multimodality 9. Part 3: Common Generative AI Architectures
10. Chapter 7: Working with Long Context 11. Chapter 8: Building Chatbots 12. Chapter 9: Tools and Function Calling 13. Chapter 10: Agents 14. Chapter 11: Agentic Workflows 15. Part 4: Designing Generative AI Applications
16. Chapter 12: Evaluating GenAI Applications 17. Chapter 13: Generative AI System Design 18. Index 19. Other Books You May Enjoy Appendix 1: Overview of Generative AI 1. Appendix 2: Google Cloud Foundations

References

  1. Setup checklist – Google Cloud documentation

    https://cloud.google.com/docs/enterprise/setup-checklist

  2. Organization and identity best practices – Google Cloud documentation

    https://cloud.google.com/architecture/identity/best-practices-for-planning

  3. PoLP – Wikipedia

    https://en.wikipedia.org/wiki/Principle_of_least_privilege

  4. RBAC – Wikipedia

    https://en.wikipedia.org/wiki/Role-based_access_control

  5. IAM – Google Cloud documentation

    https://cloud.google.com/iam/docs/overview

  6. Billing – Google Cloud documentation.

    https://cloud.google.com/billing/docs

  7. Deciding a resource hierarchy – Google Cloud documentation

    https://cloud.google.com/architecture/landing-zones/decide-resource-hierarchy

  8. Google Cloud Observability – Google Cloud documentation

    https://cloud.google.com/products/operations

  9. Security Command Center – Google Cloud documentation

    https://cloud.google.com/security-command-center...

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Authors (3)

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Profile icon Leonid Kuligin
Leonid Kuligin
LinkedIn icon Github icon
Leonid Kuligin is a staff AI engineer at Google Cloud, working on generative AI and classical machine learning solutions (such as demand forecasting or optimization problems). Leonid is one of the key maintainers of Google Cloud integrations on LangChain, and a visiting lecturer at CDTM (TUM and LMU). Prior to Google, Leonid gained more than 20 years of experience in building B2C and B2B applications based on complex machine learning and data processing solutions such as search, maps, and investment management in German, Russian, and US technological, financial, and retail companies.
Read more
See other products by Leonid Kuligin
Profile icon Jorge Zaldívar
Jorge Zaldívar
LinkedIn icon
Jorge Zaldivar is an AI Engineer at Google and also a contributor to LangChain's integrations with Google. He has a decade of experience building complex Machine Learning applications and products applied to the energy and financial industries.
Read more
See other products by Jorge Zaldívar
Profile icon Maximilian Tschochohei
Maximilian Tschochohei
LinkedIn icon
Maximilian Tschochohei leads AI engineering at Google Cloud Consulting EMEA. He implements LangChain applications together with Google Cloud customers. Before Google, he worked in strategy and technology consulting with Boston Consulting Group.
Read more
See other products by Maximilian Tschochohei
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