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

1 week ago - 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)

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

from google import genai from google.genai.types import HttpOptions client = genai.Client(http_options=HttpOptions(api_version="v1")) response = client.models.generate_content( model="gemini-3.5-flash", contents="How does AI work?", ) print(response.text) # Example response: # Okay, let's break down how AI works.

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.

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.

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.

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

1 month ago - By default we use httpx for both sync and async client implementations. In order to have faster performance, you may install google-genai[aiohttp]. In Gen AI SDK we configure trust_env=True to match with the default behavior of httpx.

The ML Kit GenAI APIs are built on top of AICore, an Android system service that enables on-device execution of GenAI foundation models to facilitate features such as enhanced app functionality and improved user privacy by processing data locally.

Google Generative AI High level API client library and tools.

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.

These reference pages contain auto-generated API documentation for each module, focusing on the "what" rather than the "how" or "why" (i.e. no end-to-end tutorials or conceptual overviews). ... Gemini chat models (primary interface). ... Gemini embedding models. ... Utility to create a context cache for reusing large content across requests. ... Custom exception class for errors associated with the Google GenAI API.

April 28, 2026 - import requests import pathlib from google import genai from google.genai import types client = genai.Client() # Check which models support caching. for m in client.models.list(): for action in m.supported_actions: if action == "createCachedContent": print(m.name) break # Download file response = requests.get( 'https://storage.googleapis.com/generativeai-downloads/data/a11.txt') pathlib.Path('a11.txt').write_text(response.text) # Upload file document = client.files.upload(file='a11.txt') # Create cache model='gemini-3.5-flash' apollo_cache = client.caches.create( model=model, config={ 'contents': [document], 'system_instruction': 'You are an expert at analyzing transcripts.', }, ) # Generate response response = client.models.generate_content( model=model, contents='Find a lighthearted moment from this transcript', config=types.GenerateContentConfig( cached_content=apollo_cache.name, ) )

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 ...

Official Documentation: ai.google.dev/gemini-api/docs · Always use the Google GenAI SDK (google-genai), which is the unified standard library for all Gemini API requests (AI Studio/Gemini Developer API and Vertex AI) as of 2025.