GCP Vertex AI RAG Pipeline Uses Terraform to Power Internal Docs Q&A

A developer has published a guide for building an internal knowledge Q&A system on Google Cloud Platform using Vertex AI RAG Engine, a managed retrieval-augmented generation service. The pipeline ingests company documents such as PDFs, FAQs, and policy files stored in a GCS bucket, then handles chunking, embedding, and vector storage automatically. Terraform is used to provision stable infrastructure components including APIs, IAM roles, and the RAG database tier, while the Python SDK manages the frequently changing corpus and file operations. A key design choice ties the RAG corpus name to the embedding model variable, making embedding upgrades straightforward through a Terraform configuration change. The architecture is intentionally model-agnostic, allowing teams to swap the text generation model with a single-line update.