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.

1 month 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.

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).

April 8, 2026 - Please provide an image file.") exit() model = genai.GenerativeModel('gemini-pro-vision') prompt_parts = [ img, # The image "Describe this image in detail and suggest a creative caption.", # Text prompt ] response = model.generate_content(prompt_parts) print("--- Multimodal (Image + Text) Example ---") print(response.text) print("\n") Note: For the multimodal example, you need the Pillow library (pip install Pillow). Also, make sure cat.jpg is a valid image file in your script's directory. Example 4: Adjusting Generation Parameters & Safety Settings You can control the generation process (e.g., creativity, token limits) and apply safety settings. import google.generativeai as genai import os genai.configure(api_key=os.environ.get("GOOGLE_API_KEY")) model = genai.GenerativeModel('gemini-pro') prompt = "Write a short, creative story about a robot who discovers emotions."

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

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

Build Modular, Asynchronous, and Composable AI Pipelines for Generative AI. GenAI Processors is a lightweight Python library that enables efficient, parallel content processing.

August 17, 2025 - If you want to use the SDK asynchronously, run pip install google-genai[aiohttp].

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

2 weeks ago - import { GoogleGenAI } from ... agent: "antigravity-preview-05-2026", input: "Write a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt....

Google AI Python SDK · pip install google-generativeai · Use genai.GenerativeModel to access the API: import google.generativeai as genai import os genai.configure(api_key=os.environ['API_KEY']) model = genai.GenerativeModel(model_name='g...

## Installation ```sh pip install google-genai ``` <small>With `uv`:</small> ```sh uv pip install google-genai ``` ## Imports ```python from google import genai from google.genai import types ``` ## Create a client Please run one of the following code blocks to create a client for different ...

April 6, 2025 - The installation of the Google ... prompt. Once the terminal is open, the following command should be executed: pip install google-genai.2 It is generally recommended ......

July 11, 2024 - First, you need to install the necessary Python packages. Open your terminal or command prompt and run the following commands: pip install google-generativeai pip install python-dotenv

Installation: Incorrect: pip install google-generativeai · Incorrect: pip install google-ai-generativelanguage · Correct: pip install google-genai · APIs and Usage: Incorrect: import google.generativeai as genai-> Correct: from google import ...

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

April 28, 2026 - #pip install https://github.com/google-gemini/generative-ai-python@imagen import google.generativeai as genai imagen = genai.ImageGenerationModel( "imagen-3.0-generate-001") gen_images = imagen.generate_images( prompt="Robot holding a red skateboard", number_of_images=1, safety_filter_level="block_low_and_above", person_generation="allow_adult", aspect_ratio="3:4", ) After ·

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", ), )

Google Gen AI Python SDK provides an interface for developers to integrate Google’s generative models into their Python applications. It supports the Gemini Developer API and Vertex AI APIs. ... Please run one of the following code blocks to create a client for different services (Gemini Developer API or Vertex AI). from google import genai # Only run this block for Gemini Developer API client = genai.Client(api_key='GEMINI_API_KEY')