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Designing RAG-Capable Generative AI Applications on Google Cloud

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This document outlines a reference architecture for designing infrastructure to run generative AI applications with retrieval-augmented generation (RAG) on Google Cloud. It details the components involved, including data ingestion, serving, and quality evaluation subsystems, and highlights the use of various Google Cloud products such as Vertex AI, Cloud Run, and BigQuery. The document is aimed at developers and cloud architects with a foundational understanding of AI and machine learning.
  • main points
  • unique insights
  • practical applications
  • key topics
  • key insights
  • learning outcomes

  • • main points

    • 1
      Comprehensive breakdown of RAG architecture components
    • 2
      Clear diagrams illustrating system interactions
    • 3
      Practical use cases demonstrating real-world applications

  • • unique insights

    • 1
      Integration of various Google Cloud products for optimized performance
    • 2
      Detailed steps for data ingestion and processing workflows

  • • practical applications

    • The article provides a practical framework for developers to implement RAG-capable generative AI applications, enhancing their understanding of cloud architecture and AI integration.

  • • key topics

    • 1
      RAG architecture components
    • 2
      Google Cloud product integration
    • 3
      Quality evaluation in AI applications

  • • key insights

    • 1
      In-depth exploration of RAG capabilities
    • 2
      Use of real-world examples to illustrate concepts
    • 3
      Focus on security, reliability, and cost optimization in cloud architecture

  • • learning outcomes

    • 1
      Understand the components of a RAG-capable generative AI application
    • 2
      Learn how to integrate various Google Cloud products for AI applications
    • 3
      Gain insights into real-world applications and use cases of RAG
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“ Introduction to RAG-capable Generative AI

Retrieval-augmented generation (RAG) enhances the capabilities of generative AI applications by integrating external data into the response generation process. This document serves as a guide for developers and cloud architects to design RAG-capable applications using Google Cloud.

“ Overview of the Architecture

The architecture for a RAG-capable generative AI application on Google Cloud consists of interconnected components that facilitate data ingestion, processing, and response generation. Key components include the data ingestion subsystem, serving subsystem, and quality evaluation subsystem.

“ Data Ingestion Subsystem

The data ingestion subsystem is responsible for preparing and processing external data to enable RAG capabilities. It ingests data from various sources, including files and databases, and prepares it for further processing using tools like Document AI and Vertex AI.

“ Serving Subsystem

The serving subsystem manages the interaction between users and the generative AI application. It converts user requests into embeddings, performs semantic searches, and generates contextualized prompts for the LLM inference stack, ensuring relevant responses.

“ Quality Evaluation Subsystem

This subsystem evaluates the quality of responses generated by the serving subsystem. It uses Cloud Run jobs to assess responses based on predefined metrics, storing evaluation results for future analysis.

“ Google Cloud Products Used

The architecture leverages several Google Cloud products, including Vertex AI for model training and deployment, Cloud Run for serverless computing, BigQuery for data analytics, and AlloyDB for PostgreSQL for data management.

“ Use Cases for RAG Applications

RAG-capable generative AI applications can be utilized in various domains, such as personalized product recommendations, clinical assistance systems for healthcare, and efficient legal research, enhancing the relevance and accuracy of generated outputs.

“ Design Considerations

When developing a RAG-capable architecture, consider factors such as security, compliance, reliability, and performance to meet specific application requirements.

“ Security and Compliance

Implement security measures across Google Cloud products to ensure data protection and compliance with regulations. This includes using encryption, access controls, and audit logging.

“ Cost Optimization Strategies

To manage costs effectively, start with minimal resource allocations for Cloud Run jobs and optimize based on performance requirements. Monitor usage and adjust resources as necessary.

 Original link: https://cloud.google.com/architecture/rag-capable-gen-ai-app-using-vertex-ai

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