Vertex AI RAG Engine with Terraform: Your First RAG Pipeline on GCP 🔍

Vertex AI RAG Engine handles chunking, embedding, and retrieval with a managed vector database. Terraform provisions the infrastructure - GCS bucket, service account, engine config - while the Python SDK manages corpus and file operations. You have a Vertex AI endpoint answering general questions. Ask it about your company's internal docs and it hallucinates with confidence. RAG fixes this by grounding model responses in your actual data. Vertex AI RAG Engine is GCP's fully managed RAG service. You create a corpus (the index), import files from Cloud Storage or Google Drive, and the engine handles chunking, embedding with a model like text-embedding-005 , and vector storage in a managed Spanner-based database. At query time, it retrieves relevant chunks and feeds them as context to Gemini. 🏗️ Architecture Overview ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐ │ GCS Bucket │────>│ RAG Engine │────>│ RagManagedDb │ │ (Documents) │ │ (Corpus) │ │ (Managed Spanner │ │ │ │ │ │ Vect