Enterprise Knowledge Management with RAG

This post details a production-grade Enterprise RAG architecture that leverages Confluent for event streaming as the backbone. It contrasts static knowledge bases with real-time RAG, highlighting architectural differences in ingestion, context synchronization, and scalability. The deep dive covers knowledge sources, event streaming ingestion via CDC, real-time processing with Flink for chunking and PII redaction, streaming embedding generation, real-time vector store upserts, retrieval with RBAC filtering, LLM generation, and an observability layer for data lineage. The real-time data flow is explained step-by-step, emphasizing how Confluent enables document ingestion, granular embedding updates, and cross-system context synchronization. Core capabilities like continuous embedding updates and cross-system context synchronization are elaborated with architectural enablers and real-world examples.