Gemini 3.6 Flash
The Gemini API Cookbook is a collection of Jupyter notebooks and examples for learning and building with Google's Gemini API. It provides hands-on tutorials for various features like image generation, video editing, and agents, aimed at developers who want to integrate Gemini into their applications.
✨ Key features
- Step-by-step quickstarts for Gemini API features
- Practical examples combining multiple API capabilities
- Guides for image, video, and music generation
- Tutorials for Live API, grounding, and batch processing
- Official SDKs for Python, Go, Node.js, Java, C#
🎯 Use cases
- Build conversational video editing apps with Omni Flash
- Generate and edit images with Nano-Banana models
- Create custom agents using the Agents API
- Transcribe audio with word-level timestamps and diarization
- Ground responses with Google Search or Maps
📦 Installation
🧰 Requirements: Requires a Google account and an API key from Google AI Studio. No specific OS or runtime mentioned.
The README does not provide installation steps. It is a collection of notebooks and examples, so you can clone the repository and open the notebooks in Jupyter or Colab.
🚀 Usage
# Example from Get_started.ipynb (simplified)
import google.generativeai as genai
genai.configure(api_key='YOUR_API_KEY')
model = genai.GenerativeModel('gemini-3.8-flash')
response = model.generate_content('Hello')
print(response.text)
❓ FAQ
What do I need to get started?
You need a Google account and an API key from Google AI Studio.
What models are covered?
The cookbook covers Gemini 3.8 Flash, 3.5 Flash-Lite, Omni Flash, Nano-Banana 2, Lyria 3.5, and others.
Are there official SDKs?
Yes, official SDKs are available for Python, Go, Node.js, Java, and C#.
Where can I get help?
You can ask questions on the Google AI Developer Forum.
📊 Repository
🤖 Overview, features, install steps and FAQ were generated from the project's README on Sep 4, 2026. Always check the original source before running commands.