The research focuses on incorporating Retrieval-Augmented Generation (RAG) methods into Oracle APEX to enhance the context, semantics, and accurateness of responses given by AI assistants in enterprise applications. We developed a fully integrated, low-latency RAG system tailored for Oracle’s low-code framework by embedding dense semantic search through FAISS vector stores and hybrid BM25 keyword filter with transformer embedding retrieval pipelines. The system integrates effortlessly with GPT-style language models through RESTful APIs, drawing upon domain-specific corpora within Oracle databases to enrich the generative processes and perform retrieval-augmented generation. Cross-functional domain experiments, including multi-turn interactions in HR, IT support, and finance, demonstrated remarkable improvements overall, including a 21% increase in BLEU scores, 25% in ROUGE-L, and 34% in user satisfaction as opposed to non-RAG configurations. Context Relevance Scores (CRS) were particularly high for multi-turn technical queries, underscoring the critical impact of retrieval accuracy for grounding generative outputs. The hybrid retriever also demonstrated strong performance in minimizing token overhead while maintaining contextual precision. These results illustrate how Oracle APEX can scale as a secure host environment for sophisticated AI-driven feedback systems and how the RAG architecture presented in this work acts as a generic enhancement blueprint to task-oriented dialogue systems in low-code enterprise applications.
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