Enterprise RAG (Retrieval-Augmented Generation) architecture connects LLMs to real-time proprietary corporate data using event streaming instead of batch processing. The architecture uses Change Data Capture (CDC) via Confluent/Kafka to instantly capture document updates, Apache Flink for in-flight processing (chunking, PII redaction, metadata tagging), streaming embedding generation, and vector stores (Pinecone, Milvus, Qdrant) with real-time upserts. Key benefits over batch-based RAG include sub-second context propagation, elimination of stale embeddings, cross-system knowledge unification, RBAC-enforced retrieval, and full audit lineage for compliance. Design principles include decoupled ingestion pipelines, exactly-once processing guarantees, schema enforcement via Schema Registry, and zero-trust retrieval policies.
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Why Digital-Native Companies Outgrow Static Knowledge BasesDeep Architecture Overview: Enterprise RAG SystemThe Real-Time RAG Data FlowWhy Streaming Matters for Enterprise RAGCore Capabilities Enabled by Real-Time Enterprise RAGDesign Principles for Production-Grade RAG SystemsReal-Time RAG vs Traditional Enterprise SearchGovernance, Compliance, and Security in Enterprise RAGBusiness Impact for Digital-Native CompaniesIs Enterprise RAG Right for Your Organization?FAQsSort: