a desnk with a 15-step pipeline graphic for orchestrating enterprise AI brilliance with agentic RAG systems allowing to dock data sources like pipes and enabling user specific datasources within the enterprise.
Revolutionize enterprise AI with agentic RAGs. This guide explores a 15-step pipeline and offers insights for enterprise AI implementation.
Cover image for the article 'Guide: Exposing Enterprise Data with Java and Spring for AI Indexing (for NLWeb)' featuring the Java and Spring logos prominently displayed alongside AI and NLWeb branding elements. The design includes a graph database visualization with interconnected nodes, symbolizing knowledge graphs and semantic data. A modern, professional aesthetic with a blue and white color scheme highlights the integration of Schema.org datatypes, JSON-LD, and OrientDB for enterprise data solutions. The background incorporates subtle binary code patterns, emphasizing AI-driven indexing and the semantic web for NLWeb’s conversational interfaces.
Discover how to expose enterprise data for AI indexing with Java and Spring using the jsonld-schemaorg-javatypes library for NLWeb. Learn to leverage Schema.org, JSON-LD, and OrientDB for semantic search, knowledge graphs, and interoperability, with sustainable Fair Code licensing.
Analysis and predictions of occupancy in public transport are essential in order to use vehicles intelligently […]
ZooKeeper is an open-source Apache project that provides a centralized service for providing configuration over large clusters in distributed systems
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