Modern organizations use a variety of databases to manage information. As AI and machine learning models continue to grow in popularity, a new category has emerged: vector databases. This blog provides a clear definition of vector databases and answers key questions about how they work, how they differ from regular databases, and when you should use them for your AI and ML projects.
In this post, we walk through how to build a RAG pipeline using YugabyteDB’s new vector capabilities to enable smarter and more context-aware support automation. Discover how to ingest internal documents, vectorize them, store them efficiently in YugabyteDB, and finally how to use an LLM like GPT-4 to answer internal questions—rooted in your own support stack data.
The AI app-building landscape is evolving rapidly – you’ll soon be able to quickly build robust AI applications that scale from thousands to billions of vectors using familiar PostgreSQL and powerful vector search capabilities, without architectural changes. In this blog, we examine the current and future AI landscape and share the benefits of the new and enhanced AI functionality introduced in the latest YugabyteDB release.
The YugabyteDB MCP Server brings your AI stack and your operational data into the same conversation. Easily accessible on AWS Marketplace, teams can now skip time-consuming procurement processes and deploy the YugabyteDB MCP Server in just a few minutes. Find out more.
Discover how to build Agentic AI workflows using tools like n8n, YugabyteDB, and LangChain. By seamlessly integrating YugabyteDB’s vector embedding capabilities, OpenAI models, and n8n workflow orchestration, businesses can develop robust and scalable AI solutions that deliver real value.
The YugabyteDB MCP Server is a new, lightweight, Python-based server that allows LLMs like Anthropic’s Claude to interact directly with your YugabyteDB database. In this blog we demonstrate how MCP allows an AI application to access, query, analyze, and interpret data in your YugabyteDB database, using only natural language prompts.
As vector search becomes foundational to modern AI workloads, databases must rethink how their architecture handles high-dimensional vector data at scale. This blog reveals how YugabyteDB integrates a distributed vector indexing engine powered by USearch to deliver a fast, scalable, and resilient vector search natively with a Postgres-compatible SQL interface.
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.
Yugabyte’s next-generation agentic AI application, Performance Advisor for YugabyteDB Aeon, uses AI-first observability to deliver an agentic architecture and enable automated anomaly detection and optimization.
YugabyteDB’s new extensible indexing framework is designed to support the seamless integration of state-of-the-art vector indexing libraries and algorithms, augmenting the capabilities offered by pgvector.
New Agentic AI for YugabyteDB Metadata brings the power of natural language processing to your database management workflow. This blog examines how Agentic AI for YugabyteDB Metadata transforms how organizations interact with their distributed SQL database by providing intelligent, real-time insights through natural language queries.
