GCP: Building a RAG Pipeline with AlloyDB AI and Vertex AI | TO THE NEW Blog
Summary
Build a Retrieval-Augmented Generation (RAG) pipeline by keeping retrieval and embedding logic inside AlloyDB and calling Vertex AI models via SQL. Ingest raw data into AlloyDB, generate embeddings with a GENERATED column using the embedding() function, and use a ScaNN index for high-performance similarity search. Call Gemini (Vertex AI) directly from the database with google_ml.predict_row to generate grounded answers without external middleware. The approach eliminates ETL to separate vector stores, enables SQL-first development for faster implementation, and scales to millions of vectors with sub-millisecond retrieval.
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