Vertex AI RAG Engine Advanced RAG with Terraform: Chunking, Hybrid Search, and Reranking 🧠

Basic chunking gets you a demo. Hybrid search, reranking with the Vertex AI Ranking API, metadata filtering, and tuned retrieval configs turn a RAG Engine corpus into a production system. All wired through Terraform and the Python SDK. In RAG Post 1 , we deployed a Vertex AI RAG Engine corpus with basic fixed-size chunking. It works, but retrieval quality is mediocre. Your users ask nuanced questions and get incomplete or irrelevant answers back. The fix isn't a better generation model. It's better retrieval. RAG Engine supports chunking tuning, hybrid search with configurable alpha weighting, reranking via the Vertex AI Ranking API, metadata filtering, and vector distance thresholds. The infrastructure layer (Terraform) and the operational layer (Python SDK) each handle different parts. This post covers the production patterns that make the difference. 🎯 🧱 Chunking: The Biggest Lever You Control RAG Engine uses fixed-size token chunking configured at file import time. Unlike AWS Bedro