Simplify gen AI by optimizing RAG deployments on AWS with Intel & OPEA (AIM232)

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Introduction to Opa

  • Opa is an open-source framework that aims to simplify the deployment of generative AI applications.
  • Enterprises often face challenges when trying to get value from generative AI applications, such as managing multiple moving parts and integrating different components.
  • Opa provides a collaborative, open-source solution to address these challenges.

Opa Architecture

  • Opa is built on a microservices architecture, where each component (e.g., retriever, ranking, embeddings, LLM) can be easily interchanged.
  • This flexibility helps avoid vendor lock-in and accelerates time-to-market for enterprises.
  • Opa provides over 20 different generative AI use case examples, such as chat Q&A, visual Q&A, and video Q&A.
  • The Opa repository contains blueprints and configurations for deploying these examples, using various open-source components contributed by partners.

Intel and AWS Integration

  • Intel has contributed optimized microservices for the Opa platform, leveraging Intel Xeon and Gaudi accelerators.
  • Intel has also contributed to the underlying open-source components, such as text embedding, LLM inference, and vector databases.
  • AWS and Intel have a long-standing partnership, with Intel enabling instances and accelerators on the AWS platform.
  • This partnership extends to the Opa project, where AWS manages services (e.g., SageMaker, OpenSearch) can be integrated with the Opa blueprints.
  • The presentation showcases a multi-cloud deployment scenario, where the LLM inference is hosted on the Denvër DataWorks platform (powered by Intel Gaudi accelerators) and integrated with the Opa application running on AWS.

OpenSearch Integration

  • OpenSearch is an open-source search and analytics engine, forked from Elasticsearch, and managed by the OpenSearch software foundation.
  • OpenSearch provides capabilities for structured and unstructured search, analytics, and vector/generative AI support.
  • Opa integrates with OpenSearch as a vector store and search engine, leveraging its lexical, semantic, and hybrid search capabilities.
  • AWS provides a managed OpenSearch service, which Opa can utilize, as well as self-managed and serverless deployment options.
  • The presentation covers details on how Opa's data preparation and retriever components interact with OpenSearch.

Demo

  • The presenters showcased a live demonstration of the Opa chat Q&A example, deployed on AWS.
  • The demo highlighted the flexibility of the Opa framework, allowing users to easily integrate external knowledge sources and customize the application.
  • The underlying Opa components, such as the retriever, reranker, and LLM, were briefly explained.

Conclusion

  • Opa is a collaborative, open-source project that aims to simplify the deployment of generative AI applications.
  • It provides a flexible, microservices-based architecture with various partner-contributed components.
  • Opa integrates with AWS services and the OpenSearch platform, leveraging the strengths of these technologies.
  • The presenters encouraged attendees to explore the Opa project and participate in the community.

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