1 week ago - Learn more in the Interactions API Overview. The fastest path from prompt to production with Gemini, Veo, Nano Banana, and more. from google import genai client = genai.Client() interaction = client.interactions.create( model="gemini-3.5-flash", input="Explain how AI works in a few words" ) print(interaction.output_text) import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({}); const interaction = await ai.interactions.create({ model: "gemini-3.5-flash", input: "Explain how AI works in a few words", }); console.log(interaction.output_text); curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \ -H "x-goog-api-key: $GEMINI_API_KEY" \ -H 'Content-Type: application/json' \ -d '{ "model": "gemini-3.5-flash", "input": "Explain how AI works in a few words" }' Start building ·

from google import genai # Only run this block for Vertex AI API client = genai.Client( vertexai=True, project='your-project-id', location='us-central1' )

1 month ago - client.tunings contains tuning job APIs and supports supervised fine tuning through tune. Only supported in Vertex AI. See the 'Create a client' section above to initialize a client. Vertex AI supports tuning from GCS source or from a Vertex AI Multimodal Dataset · from google.genai import types model = 'gemini-3.5-flash' training_dataset = types.TuningDataset( # or gcs_uri=my_vertex_multimodal_dataset gcs_uri='gs://your-gcs-bucket/your-tuning-data.jsonl', )

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.

2 weeks ago - The API automatically searches, processes results, and returns citations. from google import genai client = genai.Client() interaction = client.interactions.create( model="gemini-3.5-flash", input="Who won the euro 2024?", tools=[{"type": ...

If you are not redirected automatically, click here to go to the release version docs

This API reference describes the unary, streaming, and real-time APIs you can use to interact with the Gemini models. You can use the REST APIs in any environment that supports HTTP requests. Refer to the Get started guide for how to get started with your first API call.

1 month ago - The Gemini Live API uses WebSockets for real-time communication. The google-genai SDK provides a high-level asynchronous interface for managing these connections. ... Session: A persistent connection to the model.

View code samples for popular use cases and deploy examples of generative AI applications that are secure, efficient, resilient, high-performing, and cost-effective.

The non-streaming API is more suitable for short responses or when processing results in batches. The GenAI feature-specific APIs (Summarization, Proofreading, Rewriting, and Image Description) have different device support than Prompt API, as described in the following sections.

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)

For the model parameter, supported formats for Gemini API include: - The Gemini model ID, for example: ‘gemini-2.0-flash’ - The model name starts with ‘models/’, for example: ... 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

1 week ago - The following example shows how to use Gemma Image inputs with the Gemini API: from google import genai client = genai.Client() my_file = client.files.upload(file="path/to/sample.jpg") response = client.models.generate_content( model="gemma...

5 days ago - Complete at least one of the following ... connect to the Developer Knowledge MCP server. Examples include Antigravity, Gemini CLI, or Gemini Code Assist in your IDE....

5 days ago - For example: from google import genai from google.genai.types import ( GenerateContentConfig, HttpOptions, Tool, ToolCodeExecution, ) client = genai.Client(http_options=HttpOptions(api_version="v1")) model_id = "gemini-3.5-flash" code_execution_tool ...

Next, add the required dependency for the SDK to your build configuration or import it directly using @google/genai. Then, you can initialize the library with your API key and make your first API call. ... Explore more advanced use cases for the Google Gen AI SDK for JavaScript and TypeScript with the sample app on GitHub. This example app demonstrates several key use cases in more detail: generating text, photo reasoning (using multimodal inputs), and generating videos using Veo.

May 5, 2026 - To get a response in JSON by passing in a response schema to the GenerateContent API. package <your package name>; import com.google.common.collect.ImmutableList; import com.google.common.collect.ImmutableMap; import com.google.genai.Client; import com.google.genai.types.GenerateContentConfig; import com.google.genai.types.GenerateContentResponse; import com.google.genai.types.Schema; import com.google.genai.types.Type; public class GenerateContentWithSchema { public static void main(String[] args) { Client client = new Client(); // Define the schema for the response, in Json format.

May 22, 2026 - This SDK supports use cases like: ... For example, with just a few lines of code, you can access Gemini's multimodal capabilities to generate text from text-and-image input. parts := []*genai.Part{ {Text: "What's this image about?"}, {InlineData: ...

2 weeks ago - client.tunings contains tuning job APIs and supports supervised fine tuning through tune. Only supported in Vertex AI. See the 'Create a client' section above to initialize a client. Vertex AI supports tuning from GCS source or from a Vertex AI Multimodal Dataset · from google.genai import types model = 'gemini-3.5-flash' training_dataset = types.TuningDataset( # or gcs_uri=my_vertex_multimodal_dataset gcs_uri='gs://your-gcs-bucket/your-tuning-data.jsonl', )