Mini RAG: Vectorized Retrieval-Augmented Generation Stack
Vectorized Retrieval-Augmented Generation Stack
¶ 01Context & Design Rationale
“A reliable system is designed not around hypothetical optimal conditions, but around deterministic resilience under resource pressure.”
End-to-end RAG application built with Next.js. The ingest phase chunks pasted text via LangChain, embeds it with Google Gemini, and stores vectors in Pinecone. At query time, the top-K candidates are retrieved from Pinecone, reranked by Cohere for precision, and passed to Gemini to synthesise a cited answer. Answers include inline citations linking back to the source chunks in the knowledge base. Engineered with strict architectural boundaries, deterministic memory models, and zero extraneous runtime dependencies.
¶ 02Architectural Pipeline
SUBSYSTEM RUNTIMEINGESTION PHASE
Protocol Parser
Zero-Copy Frame
→
TRANSFORMATION
Core Logic Unit
Deterministic Arena
→
PERSISTENCE
Storage / State Layer
Atomic Append