Build an AI Agent knowledge base using SQL (BigQuery + Gemini)

GCP credit → https://goo.gle/handson-ep2-lab1 Codelab & source code → https://goo.gle/scholar ML in BigQuery → https://goo.gle/3O5squw Did you know you can call a Gemini model directly from a SQL query in BigQuery? In this hands-on codelab, Ayo and Annie do exactly that, and use it to solve a real problem: converting messy, unstructured text into clean, structured data at scale. This is Episode 1 of our multi-part series where we build a fully functional, data-aware AI agent on Google Cloud. 🛠️ What we cover: Loading raw text files from Cloud Storage as BigQuery external tables Using BQML.GENERATE_TEXT to send prompts to Gemini inside SQL Parsing and structuring LLM output using JSON functions in BigQuery Building a clean, queryable dataset ready for downstream AI pipelines This pattern is incredibly powerful for any team sitting on a mountain of unstructured documents, and wanting to make them queryable without a heavy ETL pipeline. Chapters: 0:00 - Intro 1:44 - Claim GCP credit 2:40 - Data project overview 4:31 - Project set up 15:00 - ELT extraction loading transform intro 18:09 - Loading data 26:24 - BigQuery external table 33:52 [BQML] ML Generate In BigQuery 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: Gemini, BigQuery