Access Google's most advanced AI models with Gemini API keys. Build intelligent applications with powerful language, vision, and multimodal capabilities.
Follow these steps to create, secure, and use your Gemini API keys for building AI-powered applications.
Create a new project in Google Cloud Console or select an existing project where you want to enable the Gemini API.
Navigate to the Google Cloud Console, create a new project, and ensure billing is enabled for the project to use paid Gemini API services.
Use descriptive project names that indicate the purpose of your Gemini API integration for better organization.
Activate the Gemini API for your Google Cloud project through the API Library in the Cloud Console.
Search for "Generative Language API" in the API Library and enable it for your project. This grants your project access to Gemini models.
The Gemini API might be listed under "Generative Language API" in the Google Cloud Console API Library.
Generate an API key in the Credentials section of Google Cloud Console to authenticate your API requests.
Navigate to APIs & Services > Credentials, click "Create Credentials," and select "API Key." Copy and securely store your generated API key.
Never commit API keys to version control. Use environment variables or secure secret management systems.
Apply restrictions to your API key to enhance security and control how the key can be used.
Set application restrictions (HTTP referrers, IP addresses, Android apps, or iOS apps) and API restrictions (limit to Gemini API only).
Always restrict API keys to specific APIs and applications to minimize potential abuse if the key is compromised.
Install the appropriate Google AI Python client library or other language SDKs to simplify API integration.
Use pip to install the Google Generative AI Python package: pip install google-generativeai for Python development.
Gemini API supports multiple programming languages including Python, JavaScript, Java, and Go through client libraries.
Test your setup by making a simple API call to the Gemini model with your API key.
Use the client library to initialize the model with your API key and send a prompt to verify everything is working correctly.
Start with simple text generation requests before moving to more complex multimodal or chat applications.
Explore the powerful features available through the Gemini API for building next-generation AI applications.
Leverage state-of-the-art natural language processing for text generation, summarization, translation, and content analysis with high accuracy.
Process and understand multiple types of input including text, images, and eventually audio for comprehensive AI interactions.
Build sophisticated chatbots and conversational agents with context awareness, memory, and natural dialogue capabilities.
Adapt Gemini models to your specific use case with fine-tuning options and parameter adjustments for optimal performance.
Access Google's optimized infrastructure with low latency responses and high throughput for production applications.
Built-in safety features and content filtering to ensure responsible AI usage and compliance with content policies.
Protect your Gemini API keys with enterprise-grade security measures to prevent unauthorized access and potential abuse.
Implement comprehensive security protocols to safeguard your API credentials and ensure responsible usage of AI capabilities.
Always apply API restrictions to limit key usage to specific Gemini APIs and set application restrictions based on your deployment environment.
Implement a key rotation policy to regularly generate new API keys and retire old ones, reducing the impact of potential key exposure.
Never store API keys in client-side code or version control systems. Use environment variables, secret managers, or secure configuration services.
Quick start code examples for integrating Gemini API into your applications using popular programming languages.
Use the Google Generative AI Python client library to interact with Gemini models in your Python applications.
import google.generativeai as genai # Configure the API key genai.configure(api_key="YOUR_API_KEY") # Initialize the model model = genai.GenerativeModel('gemini-pro') # Generate content response = model.generate_content("Explain how AI works in simple terms") print(response.text)
Install the client library with: pip install google-generativeai
Integrate Gemini API into your web applications using the Google AI JavaScript SDK for client-side or server-side usage.
import { GoogleGenerativeAI } from "@google/generative-ai"; // Initialize the SDK const genAI = new GoogleGenerativeAI("YOUR_API_KEY"); // Get the generative model const model = genAI.getGenerativeModel({ model: "gemini-pro" }); // Generate content async function generateContent() { const result = await model.generateContent("Write a poem about technology"); const response = await result.response; console.log(response.text()); } generateContent();
Install with npm: npm install @google/generative-ai
Flexible pricing options for Gemini API usage based on your application needs and scale requirements.