The same internal-docs Q&A problem every company has, solved on Vertex AI RAG Engine. Terraform provisions the infrastructure, the SDK manages the corpus, and the design keeps your generation model swappable and your embedding upgrades painless.
Same problem as every company: product docs, FAQs, and policy PDFs pile up, support agents can't find the right answer fast enough, and the same questions land in the same Slack channel every week. This post builds the fix - internal knowledge Q&A - on Vertex AI RAG Engine, GCP's managed RAG service. RAG Engine handles chunking, embedding, and retrieval internally with a managed vector database.
Terraform provisions the infrastructure that changes rarely: APIs, the GCS bucket, IAM, and the RAG Engine's database tier. The Python SDK manages the corpus and file operations - the things that change often. The design keeps your generation model swappable with a one-line change, and gives you an honest, workable pattern for upgrading embedding models too. 🎯
Docs (PDF/HTML/TXT) → GCS bucket
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