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.
Combine the power of cloud-native distributed database YugabyteDB with real-time, scalable data lake intelligence from watsonx.data. This blog explores how YugabyteDB can be seamlessly integrated with watsonx.data (IBM’s Presto-based data lake) using the Postgres driver, and details critical business use cases.
Large language models (LLMs) have evolved beyond generating text responses to interpreting text prompts. Discover how you can use a multimodal LLM to search your AI applications with PostgreSQL pgvector. This blog examines the architecture, database schema, and code required to build an application that searches recipes by both text and image.
Developers can now store, index and query vectors in PostgreSQL using pgvector, eliminating the need for a dedicated vector database. This blog deep dives into pgvector, and explains how to store text embeddings as vectors, execute similarity search, choose between indexes, and scale with distributed SQL.
In this blog, we examine how to create a fully autonomous AI travel agent, capable of finding listings and managing bookings using LangChain, OpenAI, PostgreSQL and pgvector, with YugabyteDB as the underlying database. Learn how to build your own AI agent backed by PostgreSQL using components available in today’s ecosystem.
