RAG stacks are moving from buzzword to practice in three flavors: embedded, graph-based, and RAM-busting vector indexes. NextJS-RAG demonstrates an SQLite-based approach for side projects, avoiding heavy libs and an external Postgres setup [1]. Neo4j Aura powers context-aware GraphRAG agents in minutes, while relational databases push beyond RAM with vector indexes [2]. Planetscale explains how vector indexes scale relational databases beyond RAM [3].
• NextJS-RAG on SQLite keeps the RAG loop lean for side projects; no external Postgres dependency, and it runs in a lightweight Node.js context [1]. • Neo4j Aura enables context-aware GraphRAG agents in minutes, tapping into graph-native context to guide results [2]. • Planetscale highlights how vector indexes can scale beyond RAM for relational databases, letting you search across bigger datasets [3].
Bottom line: embedded speed wins for small, self-contained apps; graph-aware context shines when relationships matter; vector indexes push relational stores past memory limits for big, search-heavy workloads [1][2][3].
References
NextJS-RAG – opinionated SQLite RAG
SQLite based RAG plugin; critiques heavy libs; avoids external Postgres; aims at small projects in NodeJS.
View sourceNeo4j Aura Agent: Create Your Own GraphRAG Agent in Minutes
Describes building a context-aware GraphRAG agent with Neo4j Aura in minutes
View sourceLarger Than RAM Vector Indexes for Relational Databases
Discusses extending indexing beyond RAM using vector indexes in relational databases, performance tradeoffs and practical implications.
View source