Not every Retrieval-Augmented Generation (RAG) system should use the same vector store. As an AI Application Engineer and Senior Freelance Programmer, I architect three distinct tiers of RAG depending on your data topology and privacy model:
1. PostgreSQL `pgvector` + BM25 Full-Text Search
Best for multi-tenant B2B SaaS where vector embeddings must stay transactional and enforce Row-Level Security (RLS) alongside relational tables (Production RAG Architecture with pgvector).
2. FalkorDB / Neo4j Property Knowledge Graphs (GraphRAG)
Best for autonomous AI agents that require multi-hop relational reasoning and long-term episodic memory (The Dual-Memory Architecture with FalkorDB).
3. In-Browser SQLite WASM + ONNX Runtime Web
Best for local-first, privacy-preserving PWAs with zero cloud vector database cost (Offline RAG Inside a Browser Web Worker).