RAG Architecture Diagram Retrieval Augmented Generation

RAG Architecture Diagram Retrieval Augmented Generation is a comprehensive visual blueprint that illustrates the complete system design for implementing Retrieval-Augmented Generation frameworks, combining external knowledge retrieval mechanisms with Large Language Model generation capabilities. The diagram systematically maps the bidirectional data flow between retrieval and generation components, depicting how documents are preprocessed, vectorized through embedding models, stored in vector databases, and subsequently retrieved through semantic similarity matching. Core architectural elements include the knowledge base ingestion pipeline, embedding transformation layer, vector storage infrastructure, query processing engine, context augmentation module, and the generative model interface. The visualization demonstrates how user queries trigger parallel processes: semantic search through the retrieval system to identify relevant context passages, and prompt construction that combines the original query with retrieved information before feeding into the LLM. This architectural representation serves as the foundational design pattern for enterprise RAG implementations, clearly delineating component responsibilities, data transformation stages, API interfaces, and integration points that enable organizations to ground AI responses in authoritative, domain-specific knowledge while maintaining system scalability and performance optimization.