How to Build a production-ready RAG AI agent

Author: Google Cloud Tech - Bewertung: 7x - Views:3

GCP credit →https://goo.gle/handson-ep2-lab2

Codelab & source code → https://goo.gle/scholar

Try Google ADK → https://goo.gle/4bPEHej



In this episode, Ayo and Annie go from structured data to a fully deployed, data-aware RAG agent, and we cover a LOT of ground. Starting where they left off from last episode (BigQuery + BQML.GENERATE_TEXT), the duo now wire up the full backend for an AI agent: a vector database, an embedding pipeline, a RAG retrieval system, and a production ready Cloud Run deployment.



🛠️ *What we build:*

* Cloud SQL for PostgreSQL with pgvector for semantic search

* A containerized Apache Beam pipeline on Dataflow to batch-process text and generate Gemini embeddings

* A RAG retrieval layer that lets the agent query vectorized knowledge

* An ADK based agent that answers questions using that knowledge

* A Cloud Run deployment with proper security and scalability settings



This is hands-on, infrastructure-meets AI content. you'll leave with a real, working pattern you can adapt for your own projects.



Chapters:

0:00 - Intro

1:41 - (RAG) Retrieval Augmented Generation and chunking

4:40 - Data project overview

4:52 - Similarity search

6:40 - RAG in BigQuery

11:56 - [BQML] ML Generate in Big Query

19:46 - OLAP & OLTP

24:21 - AI in CloudSQL

28:38 - Index using HNSW

31:29 - Scaling with data pipeline

36:46 - Apache Beam

53:02 - RAG agent With CloudSQL

1:09:52 - Flight the BOSS with A2A



More resources:

AI in CloudSQL→ https://goo.gle/4uRlm5v

Apache Beam → https://goo.gle/3O6OJzY

ADK Sample → https://goo.gle/4rQKWVn



Watch more Hand on AI → https://goo.gle/HowToWithGemini

🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech



#Gemini #GoogleCloud



Speakers: Ayo Adedeji, Annie Wang

Products Mentioned: Agent Development Kit, Dataflow