Build an enterprise RAG system with OGX

Naive RAG systems often retrieve chunks that are mathematically similar to a query but contextually irrelevant. This tutorial demonstrates how to evolve a basic RAG chatbot into an enterprise-grade system using the OGX (Open GenAI Stack) framework on Red Hat OpenShift AI. The approach layers three techniques: metadata filtering to narrow the search space using structured attributes like category, department, or access level; hybrid search combining dense vector search and sparse BM25 keyword search via Reciprocal Rank Fusion or Weighted Average Fusion; and neural reranking using cross-encoder models that process query-document pairs together for more accurate relevance scoring. A hands-on demo uses the AG News dataset, Milvus as the vector store, Llama 3.2 for generation, Granite for embeddings, and Qwen3 as the reranker, walking through both ingestion and retrieval pipelines with OGX APIs.