Gemini & Agent Platform Notebooks: Your Fast Track to Gen AI on Google Cloud

You’ve seen the demos. Heard the buzz. Now you want to build something with Gemini on Google Cloud—but where do you actually start? The official generative-ai repo on GitHub just dropped a fresh batch of notebooks that skip the fluff and get you coding with Gemini and Agent Platform, step by step.

These aren’t your typical “hello world” examples. They’re production-minded, notebook-based guides that show you exactly how to call the Gemini API, build agents, and hook them into real Google Cloud services like Vertex AI, BigQuery, and Cloud Functions.

What It Does

The repository: GoogleCloudPlatform/generative-ai is a curated collection of Jupyter notebooks and sample code. It covers:

• Gemini API basics: Text generation, streaming, multimodal inputs (images + text), and function calling.
• Agent Platform notebooks: Build conversational agents that can search your own data, execute SQL queries, or trigger cloud workflows.
• End-to-end pipelines: Combine Gemini with LangChain or LlamaIndex for RAG apps, summarization, and data extraction.

Think of it as the “cookbook” for generative AI on GCP—recipes are tested, ingredients are listed (dependencies, setup cells), and you get a working dish at the end.

Why It’s Cool

What sets this repo apart from generic “AI tutorial” dumps:

1. First-party code from Google Cloud’s own team. These notebooks are maintained by the product engineers. When a new Gemini model version ships (like gemini-1.5-pro), the notebooks update quickly. You’re not relying on third-party tweaks.

2. Real use cases, not toy data. One notebook connects Gemini to BigQuery to let you ask natural-language questions about your sales data. Another uses Agent Platform to build a customer support bot that can actually query databases and return structured results. This is the kind of thing you’d deploy to production.

3. Clear setup cells. Every notebook starts with a !pip install ... block and a client = genai.Client() setup. No hidden environment variables or broken imports. You can copy-paste into Colab or Vertex AI Workbench and run within 5 minutes.

4. Multimodal examples. The gemini-api folder includes notebooks for image captioning, document parsing (PDFs), and even video summarization. This is where Gemini shines over pure text models.

How to Try It

You need a Google Cloud project with billing enabled (Vertex AI API must be active). If you don’t have one, Google Cloud gives you $300 in free credits—plenty for testing these notebooks.

1. Clone the repo:

   git clone https://github.com/GoogleCloudPlatform/generative-ai.git
   cd generative-ai
   

2. Open the notebook you want (e.g., gemini-api/quickstart.ipynb) in Colab, Jupyter, or Vertex AI Workbench.

3. Follow the setup cell: install the google-cloud-aiplatform library, authenticate (or set up a service account), and set your project ID.

4. Run the cells. Most notebooks have a mix of explanation markdown and executable Python. You can tweak prompts, change model parameters, or swap in your own data.

If you prefer a quick start without cloning, each notebook folder has a “Open in Colab” badge at the top of its README.

Final Thoughts

If you’re a developer who learns best by running code, this repo is worth a bookmark. It’s not just documentation; it’s working code that you can fork, modify, and even deploy as a quick prototype. The Agent Platform notebooks in particular are a hidden gem—they show you how to move from “I asked a chatbot a question” to “the bot queried my database and updated a ticket.”

Start with the gemini-api/ folder if you’re new, then jump to agent-platform/ when you want to build something that actually does work. And don’t be shy about opening issues or PRs—the maintainers are active.

Happy building.

— @githubprojects