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).

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About the Author — Hire Freelance Programmer Aldian Fazrihady

Aldian Fazrihady is a Top-Rated Senior Freelance Programmer, Freelance Full Stack Developer, and AI Application Engineer with 15+ years of production software engineering experience and Google Cloud Professional Cloud Architect & Data Engineer certifications. He specializes in building custom LLM & autonomous AI agents, RAG pipelines, Next.js/React PWAs, Python (Django/FastAPI), Go microservices, and serverless cloud infrastructure on Google Cloud Run and AWS.

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