2 weeks ago - For example, to set the API version to v1 for Vertex AI: from google import genai from google.genai import types client = genai.Client( enterprise=True, project='your-project-id', location='global', http_options=types.HttpOptions(api_version='v1') ...

For tuned models, the model name starts with ‘tunedModels/’, for example: ‘tunedModels/1234567890123456789’ · Some models support multimodal input and output. Built-in MCP support is an experimental feature. ... from google.genai import types from google import genai client = genai.Client( vertexai=True, project='my-project-id', location='us-central1' ) response = client.models.generate_content( model='gemini-2.0-flash', contents='''What is a good name for a flower shop that specializes in selling bouquets of dried flowers?''' ) print(response.text) # **Elegant & Classic:** # * The Dr

To set the API version use http_options. For example, to set the API version to v1 for Vertex AI: from google import genai from google.genai import types client = genai.Client( vertexai=True, project='your-project-id', location='us-central1', http_options=types.HttpOptions(api_version='v1') )

April 28, 2026 - For utility and uniformity, all definitions within the types module are pydantic classes. from google import genai from google.genai import types client = genai.Client() response = client.models.generate_content( model='gemini-3.5-flash', ...

1 month ago - San Francisco, CA', } }, 'required': ['location'], }, ) tool = types.Tool(function_declarations=[function]) response = client.models.generate_content( model='gemini-3.5-flash', contents='What is the weather like in Boston?', config=types.GenerateContentConfig(tools=[tool]), ) print(response.function_calls[0]) After you receive the function call part from the model, you can invoke the function and get the function response. And then you can pass the function response to the model. The following example shows how to do it for a simple function invocation. from google.genai import types user_prom

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?", ) ...

You can also set temperature or max_output_tokens within types.GenerateContentConfig Avoid setting max_output_tokens, topP, topK unless explicitly requested by the user. Avoid setting safety configurations unless explicitly requested by the user. If explicitly asked for by the user, here is a sample API: from google import genai from google.genai import types from PIL import Image client = genai.Client() img = Image.open('/path/to/img') response = client.models.generate_content( model='gemini-2.0-flash', contents=['Do these look store-bought or homemade?', img], config=types.GenerateContentConfig( safety_settings=[ types.SafetySetting( category=types.HarmCategory.HARM_CATEGORY_HATE_SPEECH, threshold=types.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, ), ] ) ) print(response.text)

1 week ago - import {GoogleGenAI, FunctionCallingConfigMode, FunctionDeclaration, Type} from '@google/genai'; const GEMINI_API_KEY = process.env.GEMINI_API_KEY; async function main() { const controlLightDeclaration: FunctionDeclaration = { name: 'controlLight', parametersJsonSchema: { type: 'object', properties:{ brightness: { type:'number', }, colorTemperature: { type:'string', }, }, required: ['brightness', 'colorTemperature'], }, }; const ai = new GoogleGenAI({apiKey: GEMINI_API_KEY}); const response = await ai.models.generateContent({ model: 'gemini-2.5-flash', contents: 'Dim the lights so the room feels cozy and warm.', config: { toolConfig: { functionCallingConfig: { // Force it to call any function mode: FunctionCallingConfigMode.ANY, allowedFunctionNames: ['controlLight'], } }, tools: [{functionDeclarations: [controlLightDeclaration]}] } }); console.log(response.functionCalls); } main();

Let's see an example below with the sample image 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 and the from_base64 class methods. from instructor.processing.multimodal import Image from pydantic import BaseModel, Field import instructor from google.genai import Client class ImageDescription(BaseModel): objects: list[str] = Field(..., description="The objects in the image") scene: str = Field(..., description="The scene of the image") colors: list[str] = Field(..., description="The colors in the image") client

from google import genai from google.genai import types as genai_types # Create a Client in the Gen AI SDK genai_client = genai.Client(vertexai=True, project="your-project", location="your-location") # Call generate_content() with the prompt ...

March 11, 2025 - from google import genai from google.genai import types 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, ...

1 week ago - The following examples show how to query a Foundation Model API pay-per-token endpoint using the Google Gemini API. Python · REST API · Python · from google import genai from google.genai import types import os DATABRICKS_TOKEN = os.environ.get('DATABRICKS_TOKEN') client = genai.Client( api_key="databricks", http_options=types.HttpOptions( base_url="https://example.staging.cloud.databricks.com/serving-endpoints/gemini", headers={ "Authorization": f"Bearer {DATABRICKS_TOKEN}", }, ), ) response = client.models.generate_content( model="databricks-gemini-2-5-pro", contents=[ types.Content( role="user", parts=[types.Part(text="What is a mixture of experts model?")], ), ], config=types.GenerateContentConfig( max_output_tokens=256, ), ) print(response.text) Bash ·

July 24, 2025 - With the new SDK, the library does ... function, right? 😉). from google.genai import types def get_current_weather(location: str) -> str: """Returns the current weather....

August 17, 2025 - The genai module creates a client used for API interaction, while the types module has data structures and classes that serve as helpers used to build requests and configure request parameters. 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')

February 17, 2025 - To do this, use types.Part.from_bytes and add it to the contents argument when calling generate_content(): from google.genai import types with open('sample.mp3', 'rb') as f: image_bytes = f.read() response = client.models.generate_content( ...

May 27, 2025 - The sdk-samples/live_client_content.ts file provides a clear example: // Snippet from live_client_content.ts, simplified import { GoogleGenAI, LiveServerMessage, Modality } from '@google/genai'; // ... (ai initialization and helper functions ...

May 20, 2026 - from google import genai from google.genai import types client = genai.Client() document = client.files.upload(file=media / "a11.txt") model_name = "gemini-3.5-flash" cache = client.caches.create( model=model_name, config=types.CreateCachedContentConfig( contents=[document], system_instruction="You are an expert analyzing transcripts.", ), ) print(cache) response = client.models.generate_content( model=model_name, contents="Please summarize this transcript", config=types.GenerateContentConfig(cached_content=cache.name), ) print(response.text)

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

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) See the python quickstart for more details. caching module · protos module: This module provides low level access to the ProtoBuffer "Message" classes used by the API. types module: A collection of type definitions used throughout the library.

January 6, 2026 - If you frequently use a specific type, like GenerationConfig, and you want to make its usage super clear and concise, you can do this: from google.generativeai.types import GenerationConfig as GenAICfg # Later in your code: config = ...