1 week ago - Docs · We recommend using the Interactions API for all new projects. It is optimized for agentic workflows, state management, and the latest models. Learn more in the Interactions API Overview. The fastest path from prompt to production with Gemini, Veo, Nano Banana, and more. from google import genai client = genai.Client() interaction = client.interactions.create( model="gemini-3.5-flash", input="Explain how AI works in a few words" ) print(interaction.output_text) import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); const interaction = await ai.interactions.create({

Documentation and resources for building and implementing generative AI applications with Google Cloud tools and productss.

from google import genai # Only run this block for Vertex AI API client = genai.Client( vertexai=True, project='your-project-id', location='us-central1' )

The Google Gen AI SDK provides a unified interface to Gemini models through both the Gemini Developer API and the Gemini API on Gemini Enterprise Agent Platform.

April 28, 2026 - When building with the Gemini API, we recommend using the Google GenAI SDK. These are the official, production-ready libraries that we develop and maintain for the most popular languages. They are in General Availability and used in all our official documentation and examples.

If you are not redirected automatically, click here to go to the release version docs

Google Generative AI High level API client library and tools.

1 week ago - Documentation: https://googleapis.github.io/js-genai/ The Google Gen AI JavaScript SDK is designed for TypeScript and JavaScript developers to build applications powered by Gemini.

Notebooks and other content used directly on ai.google.dev.

Learn how to use Google's Generative AI models through the Gemini Developer API, including text generation, embeddings, image generation, video generation, and text-to-speech.

2 weeks ago - This page provides information on downloading and installing the latest libraries for the Gemini API. If you're new to the Gemini API, get started with the API quickstart · We've recently launched a new set of libraries that provide a more consistent and streamlined experience for accessing ...

Find your Google Cloud project ID. ... The location to send API requests to (for example, us-central1). Can be obtained from environment variables. Applies to the Vertex AI API only. ... Config settings that control network behavior of the client. This is typically used when running test code. ... Http options to use for the client. These options will be applied to all requests made by the client. Example usage: client = genai...

5 days ago - This beginner's guide introduces you to the core technologies of generative AI and explains how they fit together to power chatbots and applications. Generative AI (also known as genAI or gen AI) is a field of machine learning (ML) that develops and uses ML models for generating new content...

2 weeks ago - Index and search across uploaded documents and media files. ... Ground responses in real-world geospatial and location data. ... Browser automation and screen interaction. Function calling lets you connect the model to your code. You declare a function's name and parameters, the model decides when to call it and returns structured arguments, and you execute it locally and send the result back. import json from google import genai client = genai.Client() weather_tool = { "type": "function", "name": "get_current_temperature", "description": "Gets the current temperature for a given location.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city name, e.g.

Learn how to use generative AI content on your website in a way that complies with Google's policies.

1 month ago - To make a model more deterministic, lowering the temperature parameter reduces randomness, with values near 0 minimizing variability. Capabilities and parameter defaults for each model is shown in the Vertex AI docs and Gemini API docs respectively. Note that all API methods support Pydantic types and dictionaries, which you can access from google.genai.types.

import google.generativeai as genai import os genai.configure(api_key=os.environ['API_KEY']) model = genai.GenerativeModel(model_name='gemini-1.5-flash') response = model.generate_content('Teach me about how an LLM works') print(response.text)

from llama_index.llms.google_genai import GoogleGenAI ... Here's a cute dog for you! We can also edit the image! ... content="Please edit the image to make the dog a mini-schnauzer, but keep the same overall pose, framing, background, and art style.", ... Here's your mini-schnauzer! Note for AI agents: this documentation is built for programmatic access.

2 weeks ago - To make a model more deterministic, lowering the temperature parameter reduces randomness, with values near 0 minimizing variability. Capabilities and parameter defaults for each model is shown in the Vertex AI docs and Gemini API docs respectively. Note that all API methods support Pydantic types and dictionaries, which you can access from google.genai.types.