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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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Author (1):
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Leonid Kuligin Leonid Kuligin
Author Profile Icon Leonid Kuligin
Leonid Kuligin
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Table of Contents (22) Chapters Close

Preface 1. Part 1: Intro to LangChain and Generative AI on Google Cloud
2. Chapter 1: Using LangChain with Google Cloud FREE CHAPTER 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

Workflow state management

A cognitive architecture can be thought of as a connected graph, with designated entry and exit points. Each node within the graph represents a specific task, ranging from simple post-processing to a complex, self-contained agent. The edges of the graph represent the transitions between two different tasks.

The application begins with an initial state, which is fed into the entry point of the graph. Each node processes and potentially modifies this state as it travels through the graph. Finally, upon reaching the exit point, the final state is returned as the output. In most applications, the state is represented as a list of messages, but it can be any data structure based on the application’s specific needs.

To create dynamic behavior that responds to the application’s state, we can use conditional edges. These structures guide the flow to different nodes based on the application’s current state when it reaches that point in the...

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