Gemini Samples: DeepDive into Google's AI Models

Unlock the Power of Google DeepMind's Gemini with gemini-samples

The gemini-samples repository, maintained by Philipp Schmid, is an exceptional hub for developers eager to explore and implement Google DeepMind's cutting-edge Gemini models. This public GitHub repository serves as a treasure trove of practical samples, insightful snippets, and comprehensive guides, showcasing a wide array of experiments and implementations.

What You'll Find Inside:

The repository is meticulously organized into various categories, offering a diverse range of functionalities and applications:

• Guides: Delve into detailed guides on implementing crucial AI features such as Function Calling, building ReAct Agents with LangGraph, understanding Agentic Patterns, and integrating long-term memory with Gemini. These guides are essential for grasping the foundational concepts and advanced techniques.
• Examples: Experiment with practical applications like using Gemini models with the OpenAI SDK, integrating Google Search for real-time information, generating structured JSON outputs with Pydantic, and leveraging meta-prompts for dynamic schema generation. You'll also find examples for audio transcription, analyzing YouTube videos, generating images with Gemini 2.0 Flash, and integrating with popular frameworks like LangChain and CrewAI.
• Scripts: Discover useful scripts for interacting with Gemini in browser environments, creating basic agents using Model Context Protocol (MCP), and optimizing prompts for specific Gemini versions.
• JavaScript Examples: Explore native image output generation directly with Gemini 2.0 Flash.

Key Features and Benefits:

• Practical Implementation: The repository focuses on providing ready-to-use code that can be directly applied or easily adapted to your projects.
• Broad Model Coverage: It showcases a variety of Gemini models and their capabilities, including advanced features like batch API usage and robust memory integration.
• Community Driven: With a high number of stars and forks, gemini-samples reflects a vibrant community of developers actively engaging with and contributing to the project.
• Learning Resource: For anyone looking to deepen their understanding of large language models and generative AI, this repository is an invaluable learning resource.

Getting Started:

Getting started is straightforward:

1. Clone the Repository: git clone https://github.com/philschmid/gemini-samples.git
2. Set Up Environment Variables: Create a .env file in the root directory and add your GEMINI_API_KEY.
3. Explore and Experiment: Browse the examples, guides, and scripts folders to find relevant code, run the examples, and adapt them to your needs.

Whether you are a seasoned AI developer or just beginning your journey into the world of large language models, the gemini-samples repository offers a wealth of resources to accelerate your learning and development with Google DeepMind's powerful AI models.