The SDK always converts the inputs to the contents argument into list[types.Content]. The following shows some common ways to provide your inputs. This is the canonical way to provide contents, SDK will not do any conversion. from google.genai import types contents = types.Content( role='user', parts=[types.Part.from_text(text='Why is the sky blue?')] )

1 month ago - Pydantic model types are available in the types module. The client.models module exposes model inferencing and model getters. See the 'Create a client' section above to initialize a client. response = client.models.generate_content( model='gemini-3.5-flash', contents='Why is the sky blue?' ) print(response.text) from google.genai import types response = client.models.generate_content( model='gemini-3.1-flash-image', contents='A cartoon infographic for flying sneakers', config=types.GenerateContentConfig( response_modalities=["IMAGE"], image_config=types.ImageConfig( aspect_ratio="9:16", ), ), ) for part in response.parts: if part.inline_data: generated_image = part.as_image() generated_image.show()

from google import genai from google.genai import types # Define a function that the model can call (to access external information) def get_current_weather(city: str) -> str: """Returns the current weather in a given city.

import google.genai from google.genai import types import os if os.environ.get('GOOGLE_GENAI_USE_ENTERPRISE'): MODEL_NAME = 'gemini-2.0-flash-live-preview-04-09' else: MODEL_NAME = 'gemini-live-2.5-flash-preview'; client = genai.Client() async with client.aio.live.connect( model=MODEL_NAME, config={"response_modalities": ["TEXT"]} ) as session: await session.send_client_content( turns=types.Content( role='user', parts=[types.Part(text="Hello world!")])) async for msg in session.receive(): if msg.text: print(msg.text)

April 28, 2026 - The genai.NewClient function created a client, but generative model operations were typically called on a separate GenerativeModel instance obtained from this client. Other services might have been accessed via distinct packages or patterns. import ( "github.com/google/generative-ai-go/genai" "github.com/google/generative-ai-go/genai/fileman" // For files "google.golang.org/api/option" ) client, err := genai.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY")) fileClient, err := fileman.NewClient(ctx, option.WithAPIKey("GEMINI_API_KEY")) // Get a model instance, then call methods on it model := client.GenerativeModel("gemini-3.5-flash") resp, err := model.GenerateContent(...) cs := model.StartChat() // Call methods on separate client objects for other services uploadedFile, err := fileClient.UploadFile(...)

2 weeks ago - Where a types.UserContent is a subclass of types.Content, the role field in types.UserContent is fixed to be user. from google.genai import types contents = types.Part.from_function_call( name='get_weather_by_location', args={'location': 'Boston'} )

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.

This SDK is now deprecated, use the new unified Google GenAI SDK. - deprecated-generative-ai-python/google/generativeai/types/generation_types.py at main · google-gemini/deprecated-generative-ai-python

We recommend using our Code Generation instructions [`codegen_instructions.md`](https://raw.githubusercontent.com/googleapis/python-genai/refs/heads/main/codegen_instructions.md) when generating Google Gen AI SDK code to guide your model towards using the more recent SDK features. Copy and paste the instructions into your development environment to provide the model with the necessary context. ## 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 services ([Gemini Developer API](https://ai.google.dev/gemini-api/docs) or [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/overview)).

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 = instructor.from_provider("google/gemini-2.5-flash") url = "https://raw.githubusercontent.com/instructor-ai/instructor/main/tests/assets/image.jpg" # Multiple ways to load an image: response = client.create( respon

May 5, 2026 - package <your package name>; import com.google.common.collect.ImmutableList; import com.google.genai.Client; import com.google.genai.types.Content; import com.google.genai.types.GenerateContentResponse; import com.google.genai.types.Part; public class GenerateContentWithImageInput { public static void main(String[] args) { // Instantiate the client using Gemini Enterprise Agent Platform API. The client gets the project and // location from the environment variables `GOOGLE_CLOUD_PROJECT` and // `GOOGLE_CLOUD_LOCATION`. Client client = Client.builder().enterprise(true).build(); // Construct a m

May 2, 2025 - The embedding generated from the input content. Embedding *ContentEmbedding } EmbedContentResponse is the response to an `EmbedContentRequest`. type EmbeddingBatch struct { // contains filtered or unexported fields } An EmbeddingBatch holds a collection of embedding requests. ... package main import ( "context" "fmt" "log" "os" "github.com/google/generative-ai-go/genai" "google.golang.org/api/option" ) func main() { ctx := context.Background() client, err := genai.NewClient(ctx, option.WithAPIKey(os.Getenv("GEMINI_API_KEY"))) if err != nil { log.Fatal(err) } defer client.Close() em := client.EmbeddingModel("embedding-001") b := em.NewBatch().

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.

January 9, 2025 - Fix may not be included in next release.type: feature request‘Nice-to-have’ improvement, new feature or different behavior or design.‘Nice-to-have’ improvement, new feature or different behavior or design. ... import os from google import genai from google.genai import types from google.genai.types import ( GoogleSearch, ) client = genai.Client(api_key=os.environ["GEMINI_API_KEY"]) def search(query: str) -> str: return query # mytools = [{"google_search": GoogleSearch()}] mytools = [search] generation_config = types.GenerateContentConfig( temperature=1, top_p=0.95, top_k=40, max_output_tokens=8192, tools=mytools, ) params = { "model": "gemini-2.0-flash-exp", "config": generation_config, "contents": "Search something about hongkong", } for chunk in client.models.generate_content_stream(**params): print(chunk.text)

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"cell_type": "code", "execution_count": null, "metadata": { "id": "qgdSpVmDbdQ9" }, "outputs": [], "source": [ "import datetime\n", "\n", "from google import genai\n", "from google.genai.types import (\n", " CreateBatchJobConfig,\n", " CreateCachedContentConfig,\n", " EmbedContentConfig,\n", " FunctionDeclaration,\n", " GenerateContentConfig,\n", " HarmBlockThreshold,\n", " HarmCategory,\n", " Part,\n", " SafetySetting,\n",

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The Google Gen AI SDK for TypeScript and JavaScript is available on npm and GitHub: ... # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values # with appropriate values for your project. export GOOGLE_CLOUD_PROJECT=GOOGLE_C...

June 6, 2025 - import os import google.generativeai as genai import logging from google.generativeai.types import Content, Part # This line causes the ImportError logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') def run_test(): try: api_key = os.getenv("GEMINI_API_KEY") if not api_key: logging.error("GEMINI_API_KEY environment variable not set.

from google.genai import types · import os · · if os.environ.get('GOOGLE_GENAI_USE_ENTERPRISE'): MODEL_NAME = 'gemini-2.0-flash-live-preview-04-09' else: MODEL_NAME = 'gemini-live-2.5-flash-preview'; ·