How to Build a RAG Workflow for Agentic AI without Code

YugabyteDB, known for its PostgreSQL compatibility, scalability, and resilience, serves as a highly effective vector database essential for agentic AI, particularly in Retrieval Augmented Generation (RAG) workflows. RAG enables AI agents to access private, organizational information by processing unstructured data into vectors, which are stored in a database for semantic search, allowing AI models to provide context-rich responses. This approach improves AI's ability to perform tasks typically handled by knowledge workers, like retrieving specific information from documents. The guide also illustrates the use of n8n, a no-code orchestration tool, to create a workflow that leverages YugabyteDB for RAG applications, demonstrating how AI agents can efficiently access and utilize unstructured data. With geo-placement features, YugabyteDB supports data sovereignty and location-specific context, providing scalability and resilience for enterprise-level applications.