GenAI Distributed SQL

How to Build a RAG Workflow for Agentic AI without Code

How to Build a RAG Workflow for Agentic AI without Code

Thanks to the pgvector PostgreSQL extension, YugabyteDB functions as a highly performant vector database. Its scalability allows it to store and search billions of vectors. This blog starts by detailing why a vector database is essential to agentic AI and how YugabyteDB meets key requirements for global organizations, including data sovereignty and zero-downtime. In the second part, we demonstrate how you can easily leverage this technology to provide AI with your organization’s information using n8n, a no-code orchestration tool.

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Don’t Miss DSS Day Atlanta: Live on Nov 10th

Don’t Miss DSS Day Atlanta: Live on Nov 10th

If you’re planning to attend KubeCon, you won’t want to miss Distributed SQL Summit (DSS) Day Atlanta – a one-day deep dive into the future of distributed databases, cloud-native applications, and AI-driven data! This blog shares why DSS 2025 is our most exciting event yet, and why you should register to discover the progress and possibilities of using distributed SQL for building and deploying advanced AI applications.

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Generative AI for Databases: Choosing the Right AI and Putting it to Work

Generative AI for Databases: Choosing the Right AI and Putting it to Work

Generative AI is transforming how we build and operate data‑driven applications. Whether you are a developer eager to embed semantic search into your app, a business leader exploring AI‑powered analytics, or a DBA looking to automate routine tasks, the right database foundation is critical. This new guide will help you choose the best GenAI option for your database needs.

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Hello RAG! Using YugabyteDB to power a RAG Pipeline

Hello RAG! Using YugabyteDB to power a RAG Pipeline

RAG architectures hold the key to moving beyond static, pre-trained models by anchoring responses in live, curated data. In this blog, we explore how YugabyteDB, can power the retrieval layer of a RAG pipeline—offering scale, resilience, and low-latency access to semantically rich data. Whether you’re building smart chatbots, enterprise search, or generative AI assistants, this setup can improve performance, operational efficiency, and cost-effective scaling.

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