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Building an end-to-end Retrieval- Augmented Generation (RAG) workflow
- The Retrieval-Augmented Generation (RAG) workflow addresses gaps in traditional Large Language Models by integrating current and new information.
- Building a robust generative AI infrastructure like RAG requires considerations for technology stack, data, scalability, ethics, and security.
- Open source software options are available for building generative AI infrastructure, accelerating development, reducing costs, and meeting enterprise needs.
- RAG workflow involves components such as databases, knowledge bases, retrieval systems, model embeddings, and inference engines, among others.
- Key services in the RAG workflow include text splitters, data processing, embedding models, retrieval and ranking, LLM inference, and guardrail validation.
- Charmed OpenSearch is a critical component in the RAG workflow, providing data processing and encryption features for efficient deployment.
- KServe, within the Kubeflow ecosystem, serves machine learning models for various purposes including model deployment and LLM inference.
- The deployment guide for building an end-to-end RAG workflow with Charmed OpenSearch and KServe covers prerequisites, deployment steps, and accessing the guide.
- Canonical offers workshops and services for building enterprise RAG systems, providing expertise on securing code, data, and models in production.
- Enterprise-ready AI infrastructure and open source tools from Canonical can help kickstart RAG projects, ensuring security and best practices.
- Secure your AI stack with confidence using Confidential AI to protect code, data, and machine learning models in production environments.
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