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
