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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

Using callbacks

Callbacks can be used for logging, token counting, and so on. You can combine multiple callbacks. You can define your own callback or use an existing one. Creating your own callback is very easy: You inherit from the langchain_core.callbacks.BaseCallbackHandler class and define your logic on specific events by implementing methods such as on_llm_new_token, on_llm_new_start, and so on. Take a look at BaseCallbackHandler’s source code for a full list of such events!

If you need token counting, you can use a predefined VertexAICallbackHandler. You can pass it either when you instantiate your LLM (and in that case, it will count all tokens consumed by any requests), or you can pass it through your chain and count only tokens consumed by this execution:

from langchain_google_vertexai.callbacks import (
    VertexAICallbackHandler)
handler = VertexAICallbackHandler()
config = {
    'callbacks' : [handler]
}
result...
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