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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'google/gemini-2.0-flash',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="google/gemini-2.0-flash",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
Product Detail
💡 Introducing Gemini 2.0 Flash: Accelerated AI Performance
Experience the power of Gemini 2.0 Flash, meticulously engineered for unparalleled efficiency. This advanced model delivers accelerated processing capabilities, ensuring swift execution across a diverse range of applications. Its core architecture masterfully balances both speed and accuracy, making it the optimal choice for scenarios demanding time-sensitive responses and critical performance.
✅ Key Features of Gemini 2.0 Flash
- 🚀 Optimized Performance: Engineered for rapid task execution and high throughput, ensuring maximum efficiency.
- 🎯 Versatile Applications: Seamlessly adaptable for a broad spectrum of tasks, including demanding real-time data processing and complex computations.
- 📊 Scalability: Robust design capable of efficiently handling varying workloads, from small-scale projects to enterprise-level demands.
- 🔎 Native Tool Use: Integrates advanced capabilities including Google Search, code execution, and sophisticated function calling for enhanced AI interactions.
💪 Intended Use Cases
- ⏱ Real-Time Applications: Ideal for scenarios requiring immediate responses like virtual assistants, multimedia projects, coding applications, and advanced agentic AI experiences.
- 📈 Data Analysis: Greatly accelerates the processing of large, complex datasets, providing critical insights faster and more efficiently.
- 💻 Interactive Tools: Elevates user experience in applications that demand fast and seamless interactions and dynamic responses.
🔧 Technical Specifications
Architecture: A cutting-edge Transformer-based architecture, meticulously optimized for multimodal inputs. Features integrated real-time streaming layers facilitated by the Multimodal Live API.
Training Data: Trained on vast multimodal datasets, encompassing extensive text, diverse images, and specialized synthetic data crucial for robust coding benchmarks.
Data Source & Size: Combines billions of tokens with terabytes of multimedia data, aggregated from a multitude of diverse domains.
💻 How to Use Gemini 2.0 Flash
Code Samples: The model is readily accessible on the AI/ML API Platform as "Gemini 2.0 Flash". Explore example implementations to get started quickly.
// Example Python code snippet for using Gemini 2.0 Flash via API
// (Note: This is a representation; actual code varies by SDK/API version)
import google.generativeai as genai
# Configure your API key
genai.configure(api_key="YOUR_API_KEY")
# Initialize the model
model = genai.GenerativeModel('gemini-2.0-flash')
# Example: Generate content from text input
try:
response = model.generate_content("Describe the benefits of edge computing in smart cities.")
print("Generated Text:", response.text)
except Exception as e:
print(f"Error generating content: {e}")
# For multimodal inputs (e.g., image + text) and function calling,
# refer to the comprehensive official API documentation for detailed examples.
API Documentation: For comprehensive details, advanced usage patterns, and further technical insights, refer to the Official AI/ML API Documentation.
📙 Ethical AI Guidelines
Google DeepMind places strong emphasis on ethical considerations in all AI development. We promote transparency regarding the model's capabilities and inherent limitations. Users are strongly encouraged to practice responsible usage to actively prevent any potential misuse or the creation of harmful content.
📜 Licensing Information
Gemini 2.0 Flash is made available under a commercial license. This license grants comprehensive research and commercial usage rights, while strictly ensuring full compliance with ethical standards concerning creator rights and intellectual property.
❓ Frequently Asked Questions (FAQ)
Q1: What defines Gemini 2.0 Flash's performance?
A1: Gemini 2.0 Flash is characterized by its accelerated processing speed, robust scalability, and native integration with tools like Google Search, all optimized for efficient, time-sensitive AI applications.
Q2: What are the main applications for Gemini 2.0 Flash?
A2: It is exceptionally suited for real-time applications such as virtual assistants, multimedia projects, coding applications, agentic AI experiences, and rapid analysis of large datasets.
Q3: How does Gemini 2.0 Flash process multimodal inputs?
A3: Built on an advanced Transformer-based architecture, it is specifically optimized for multimodal inputs and features integrated real-time streaming layers via the Multimodal Live API.
Q4: Can Gemini 2.0 Flash be used for commercial projects?
A4: Absolutely. It is available under a commercial license that grants permission for both research and commercial usage, ensuring compliance with ethical and creator rights standards.
Q5: Where can developers access more information and documentation?
A5: Comprehensive documentation, including detailed API references and code samples, is available on the Official AI/ML API Documentation Portal.
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