SDK Google Gen AI

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-2.5-flash",     contents="How does AI work?", ) print(response.text) # Example response: # Okay, let's break down how AI works. It's a broad field, so I'll focus on the ... # # Here's a simplified overview: # ...

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-2.5-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

import ( 	"context" 	"fmt" 	"io"  	"google.golang.org/genai" )  // generateWithText shows how to generate text using a text prompt. func generateWithText(w io.Writer) error { 	ctx := context.Background()  	client, err := genai.NewClient(ctx, &genai.ClientConfig{ 		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"}, 	}) 	if err != nil { 		return fmt.Errorf("failed to create genai client: %w", err) 	}  	resp, err := client.Models.GenerateContent(ctx, 		"gemini-2.5-flash", 		genai.Text("How does AI work?"), 		nil, 	) 	if err != nil { 		return fmt.Errorf("failed to generate content: %w", err) 	}  	respText := resp.Text()  	fmt.Fprintln(w, respText) 	// Example response: 	// That's a great question! Understanding how AI works can feel like ... 	// ... 	// **1. The Foundation: Data and Algorithms** 	// ...  	return nil } 

/**  * @license  * Copyright 2025 Google LLC  * SPDX-License-Identifier: Apache-2.0  */ import {GoogleGenAI} from '@google/genai';  const GEMINI_API_KEY = process.env.GEMINI_API_KEY; const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT; const GOOGLE_CLOUD_LOCATION = process.env.GOOGLE_CLOUD_LOCATION; const GOOGLE_GENAI_USE_VERTEXAI = process.env.GOOGLE_GENAI_USE_VERTEXAI;  async function generateContentFromMLDev() {   const ai = new GoogleGenAI({vertexai: false, apiKey: GEMINI_API_KEY});   const response = await ai.models.generateContent({     model: 'gemini-2.0-flash',     contents: 'why is the sky blue?',   });   console.debug(response.text); }  async function generateContentFromVertexAI() {   const ai = new GoogleGenAI({     vertexai: true,     project: GOOGLE_CLOUD_PROJECT,     location: GOOGLE_CLOUD_LOCATION,   });   const response = await ai.models.generateContent({     model: 'gemini-2.0-flash',     contents: 'why is the sky blue?',   });   console.debug(response.text); }  async function main() {   if (GOOGLE_GENAI_USE_VERTEXAI) {     await generateContentFromVertexAI().catch((e) =>       console.error('got error', e),     );   } else {     await generateContentFromMLDev().catch((e) =>       console.error('got error', e),     );   } }  main(); 

/*  * Copyright 2025 Google LLC  *  * Licensed under the Apache License, Version 2.0 (the "License");  * you may not use this file except in compliance with the License.  * You may obtain a copy of the License at  *  *      https://www.apache.org/licenses/LICENSE-2.0  *  * Unless required by applicable law or agreed to in writing, software  * distributed under the License is distributed on an "AS IS" BASIS,  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  * See the License for the specific language governing permissions and  * limitations under the License.  */  /**  * Usage:  *  * <p>1a. If you are using Vertex AI, setup ADC to get credentials:  * https://cloud.google.com/docs/authentication/provide-credentials-adc#google-idp  *  * <p>Then set Project, Location, and USE_VERTEXAI flag as environment variables:  *  * <p>export GOOGLE_CLOUD_PROJECT=YOUR_PROJECT  *  * <p>export GOOGLE_CLOUD_LOCATION=YOUR_LOCATION  *  * <p>export GOOGLE_GENAI_USE_VERTEXAI=true  *  * <p>1b. If you are using Gemini Developer API, set an API key environment variable. You can find a  * list of available API keys here: https://aistudio.google.com/app/apikey  *  * <p>export GOOGLE_API_KEY=YOUR_API_KEY  *  * <p>2. Compile the java package and run the sample code.  *  * <p>mvn clean compile exec:java -Dexec.mainClass="com.google.genai.examples.GenerateContent"  * -Dexec.args="YOUR_MODEL_ID"  */ package com.google.genai.examples;  import com.google.genai.Client; import com.google.genai.types.GenerateContentResponse;  /** An example of using the Unified Gen AI Java SDK to generate content. */ public final class GenerateContent {   public static void main(String[] args) {     final String modelId;     if (args.length != 0) {       modelId = args[0];     } else {       modelId = Constants.GEMINI_MODEL_NAME;     }      // Instantiate the client. The client by default uses the Gemini Developer API. It gets the API     // key from the environment variable `GOOGLE_API_KEY`. Vertex AI API can be used by setting the     // environment variables `GOOGLE_CLOUD_LOCATION` and `GOOGLE_CLOUD_PROJECT`, as well as setting     // `GOOGLE_GENAI_USE_VERTEXAI` to "true".     //     // Note: Some services are only available in a specific API backend (Gemini or Vertex), you will     // get a `UnsupportedOperationException` if you try to use a service that is not available in     // the backend you are using.     Client client = new Client();      if (client.vertexAI()) {       System.out.println("Using Vertex AI");     } else {       System.out.println("Using Gemini Developer API");     }      GenerateContentResponse response =         client.models.generateContent(modelId, "What is your name?", null);      // Gets the text string from the response by the quick accessor method `text()`.     System.out.println("Unary response: " + response.text());      // Gets the http headers from the response.     response         .sdkHttpResponse()         .ifPresent(             httpResponse ->                 System.out.println("Response headers: " + httpResponse.headers().orElse(null)));   }    private GenerateContent() {} }