4 weeks ago - The following example shows how to declare a function and pass it as a tool. Then you will receive a function call part in the response. from google.genai import types function = types.FunctionDeclaration( name='get_current_weather', description='Get the current weather in a given location', parameters_json_schema={ 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'The city and state, e.g.

The following example shows how to declare a function and pass it as a tool. Then you will receive a function call part in the response. from google.genai import types function = types.FunctionDeclaration( name='get_current_weather', description='Get the current weather in a given location', parameters_json_schema={ 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'The city and state, e.g.

4 weeks ago - from google import genai # TODO(developer): Update below line API_KEY = "YOUR_API_KEY" client = genai.Client(vertexai=True, api_key=API_KEY) response = client.models.generate_content( model="gemini-3.5-flash", contents="Explain bubble sort to me.", ) print(response.text) # Example response: # Bubble Sort is a simple sorting algorithm that repeatedly steps through the list

2 weeks ago - The following example shows how to declare a function and pass it as a tool. Then you will receive a function call part in the response. from google.genai import types function = types.FunctionDeclaration( name='get_current_weather', description='Get the current weather in a given location', parameters_json_schema={ 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'The city and state, e.g.

May 21, 2026 - Weights & Biases, developer tools for machine learning

April 6, 2025 - A basic text generation example would look like this: ... from google import genai # Initialize the client (assuming API key is set as environment variable) client = genai.Client() # Generate a short poem response = client.generate_content(...

Example: fileSearchStores/file-search-store-123 · file – A path to the file or an IOBase object to be uploaded. If it’s an IOBase object, it must be opened in blocking (the default) mode and binary mode. In other words, do not use non-blocking mode or text mode. The given stream must be seekable, that is, it must be able to call seek() on ‘path’. config – Optional parameters to set display_name, mime_type and others. class genai.file_search_stores.FileSearchStores(api_client_)¶

With Gemini 2.0, we took the chance to create a single unified SDK for all developers who want to use Google's GenAI models (Gemini, Veo, Imagen, etc). As part of that process, we took all of the feedback from this SDK and what developers like about other SDKs in the ecosystem to create the Google Gen AI SDK. The full migration guide from the old SDK to new SDK is available in the Gemini API docs. The Gemini API docs are fully updated to show examples of the new Google Gen AI SDK.

Always use client = genai.Client() to create a client object. Set GEMINI_API_KEY (or GOOGLE_API_KEY) environment variable, which will be picked up automatically.

Let's see an example below with the sample Audio file above where we'll load it in using our from_url method. Note that we support local files and base64 strings too with the from_path · from instructor.processing.multimodal import Audio from pydantic import BaseModel import instructor from google.genai import Client class AudioDescription(BaseModel): transcript: str summary: str speakers: list[str] key_points: list[str] url = "https://raw.githubusercontent.com/instructor-ai/instructor/main/tests/assets/gettysburg.wav" client = instructor.from_provider("google/gemini-2.5-flash") response = cl

The built_in_tool parameter also accepts code execution tools that enable the model to write and execute Python code to solve problems, perform calculations, and analyze data. This is particularly useful for mathematical computations, data analysis, and generating visualizations. from llama_index.llms.google_genai import GoogleGenAI

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) ...

April 28, 2026 - The Google GenAI SDK is available for the Python, JavaScript/TypeScript, Go and Java languages.

August 17, 2025 - You will create an instance of the client for each interaction with the Google generative AI models. You will instantiate the client with different methods depending on the API you are using. For the Gemini Developer API, you can instantiate the client by passing along your API key: client = genai.Client(api_key='YOUR_GEMINI_API_KEY')

2 weeks ago - from google import genai client = genai.Client() interaction = client.interactions.create( agent="antigravity-preview-05-2026", input="Write a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt.

Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. - python-genai/google/genai at main · googleapis/python-genai

import types from google.genai import types as genai_types prompt = types.Prompt( prompt_data=types.PromptData( contents=[genai_types.Content(parts=[genai_types.Part(text="Hello, {name}! How are you?")])], system_instruction=genai_types.Content(parts=[genai_types.Part(text="Please answer in a short sentence.")]), variables=[ {"name": genai_types.Part(text="Alice")}, ], model="gemini-2.5-flash", ), )

March 11, 2025 - # Provide project details from google import genai from IPython.display import Markdown, display from google.colab import userdata PROJECT_ID = userdata.get('PROJECT_ID') LOCATION = userdata.get('LOCATION') # Create a client instance using Vertex AI in Google Cloud client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION) response = client.models.generate_content( model='gemini-2.0-flash', contents='Create a short bedtime story for a 3 year old using unicorns and rainbows' ) display(Markdown(response.text))

Processors: Simple, composable Python classes that transform content streams using native asyncio. Streaming: Asynchronous streaming capabilities built-in by default, without added plumbing complexity. At the ecosystem's core lies the Processor, which encapsulates a unit of work. Through a "dual-interface" pattern, it handles the complexity of asynchronous, multimodal data streaming while exposing a simple API to developers: from typing import AsyncIterable from genai_processors import content_api from genai_processors import processor class EchoProcessor(processor.Processor): # The PRODUCER interface (for the processor author): # Takes a robust ProcessorStream as input, and yields part types.