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Generative AI Application Integration Patterns

You're reading from   Generative AI Application Integration Patterns Integrate large language models into your applications

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835887608
Length 218 pages
Edition 1st Edition
Languages
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Authors (2):
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Luis Lopez Soria Luis Lopez Soria
Author Profile Icon Luis Lopez Soria
Luis Lopez Soria
Juan Pablo Bustos Juan Pablo Bustos
Author Profile Icon Juan Pablo Bustos
Juan Pablo Bustos
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction to Generative AI Patterns 2. Identifying Generative AI Use Cases FREE CHAPTER 3. Designing Patterns for Interacting with Generative AI 4. Generative AI Batch and Real-Time Integration Patterns 5. Integration Pattern: Batch Metadata Extraction 6. Integration Pattern: Batch Summarization 7. Integration Pattern: Real-Time Intent Classification 8. Integration Pattern: Real-Time Retrieval Augmented Generation 9. Operationalizing Generative AI Integration Patterns 10. Embedding Responsible AI into Your GenAI Applications 11. Other Books You May Enjoy
12. Index

Inference

After constructing the prompt with the retrieved context and the user’s query, the next step is to submit the formatted prompt directly to Vertex AI’s API endpoint to be processed by Gemini 1.5 Flash. This is where the actual generation of the response takes place. In the following code snippet, the generate() function is responsible for sending the prompt to the Gemini 1.5 Flash model and obtaining the generated response:

#This is the section where we submit the full prompt and 
#context to the LLM
result = generate(prompt)

The generate() function encapsulates the configuration and settings required for the generation process. It includes two main components: generation_config and safety_settings.

The generation_config dictionary specifies the parameters that control the behavior of the language model during the generation process. In this example, the following settings are provided:

generation_config = {
   "max_output_tokens"...
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