Google AI Studio API - Free Gemini API Service

import google.generativeai as genai

# Configure API key
genai.configure(api_key="YOUR_API_KEY")

# Create model instance (using latest stable model)
model = genai.GenerativeModel('gemini-2.5-flash')

# Send request
response = model.generate_content("Hello, please introduce yourself")
print(response.text)

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=YOUR_API_KEY \
  -H 'Content-Type: application/json' \
  -d '{
    "contents": [{
      "parts":[{"text": "Hello, please introduce yourself"}]
    }]
  }'

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel('gemini-2.5-flash')

# Simple conversation
response = model.generate_content("Explain what machine learning is")
print(response.text)

# Request with parameters
response = model.generate_content(
    "Write a poem about autumn",
    generation_config={
        "temperature": 0.9,
        "top_p": 0.95,
        "max_output_tokens": 1024,
    }
)
print(response.text)

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=YOUR_API_KEY \
  -H 'Content-Type: application/json' \
  -d '{
    "contents": [{
      "parts":[{"text": "Explain what machine learning is"}]
    }],
    "generationConfig": {
      "temperature": 0.7,
      "maxOutputTokens": 1024
    }
  }'

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel('gemini-2.5-flash')

# Create chat session
chat = model.start_chat(history=[])

# First round
response = chat.send_message("Hello, I want to learn Python")
print(response.text)

# Second round (model remembers context)
response = chat.send_message("Where should I start?")
print(response.text)

# View chat history
print(chat.history)

import google.generativeai as genai
from PIL import Image

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel('gemini-2.5-flash')

# Load image
image = Image.open('example.jpg')

# Send image and question
response = model.generate_content([
    "What's in this image? Please describe in detail.",
    image
])
print(response.text)

import google.generativeai as genai
from PIL import Image
import requests
from io import BytesIO

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel('gemini-2.5-flash')

# Load image from URL
response = requests.get('https://example.com/image.jpg')
image = Image.open(BytesIO(response.content))

# Analyze image
response = model.generate_content(["Analyze this image", image])
print(response.text)

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel('gemini-2.5-flash')

# Streaming output
response = model.generate_content(
    "Write an article about artificial intelligence",
    stream=True
)

for chunk in response:
    print(chunk.text, end='')

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")

# Define function
def get_weather(city: str):
    """Get weather for specified city"""
    # Should call real weather API here
    return f"Weather in {city}: Sunny, 25°C"

# Define function description
tools = [{
    "function_declarations": [{
        "name": "get_weather",
        "description": "Get weather information for specified city",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "City name"
                }
            },
            "required": ["city"]
        }
    }]
}]

model = genai.GenerativeModel(
    'gemini-2.5-flash',
    tools=tools
)

# Send request
chat = model.start_chat()
response = chat.send_message("What's the weather in Shanghai today?")

# Check if function call is needed
if response.candidates[0].content.parts[0].function_call:
    function_call = response.candidates[0].content.parts[0].function_call
    
    # Call function
    if function_call.name == "get_weather":
        result = get_weather(function_call.args["city"])
        
        # Return result to model
        response = chat.send_message({
            "function_response": {
                "name": "get_weather",
                "response": {"result": result}
            }
        })
        print(response.text)

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")

# Enable Google Search Grounding
model = genai.GenerativeModel('gemini-2.5-flash')

# ⚠️ Note: Grounding feature may be separately charged, check latest pricing
response = model.generate_content(
    "What are the latest AI technology trends?",
    tools=[{"google_search_retrieval": {}}]
)

print(response.text)

# View citation sources
for candidate in response.candidates:
    if hasattr(candidate, 'grounding_metadata'):
        print("\nCitation Sources:")
        for attribution in candidate.grounding_metadata.grounding_attributions:
            print(f"- {attribution.source_id.grounding_passage.url}")

pip install google-generativeai

npm install @google/generative-ai

go get github.com/google/generative-ai-go

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_GOOGLE_API_KEY",
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
)

response = client.chat.completions.create(
    model="gemini-2.5-flash",
    messages=[
        {"role": "user", "content": "Hello"}
    ]
)

print(response.choices[0].message.content)

# Check API key
import google.generativeai as genai
genai.configure(api_key="YOUR_API_KEY")

# List available models to verify
for model in genai.list_models():
    print(model.name)

import time
from google.api_core.exceptions import ResourceExhausted

def call_with_backoff(prompt, max_retries=3):
    for i in range(max_retries):
        try:
            return model.generate_content(prompt)
        except ResourceExhausted:
            if i < max_retries - 1:
                wait_time = 2 ** i  # Exponential backoff
                print(f"Rate limited, waiting {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                raise

from google.api_core import timeout

# Set timeout
timeout_config = timeout.ExponentialTimeout(initial=30, maximum=120)
response = model.generate_content(prompt, timeout=timeout_config)

from functools import lru_cache

@lru_cache(maxsize=100)
def cached_api_call(prompt):
    return model.generate_content(prompt).text

import asyncio
from google.generativeai import GenerativeModel

async def batch_generate(prompts):
    model = GenerativeModel('gemini-2.5-flash')
    tasks = [model.generate_content_async(p) for p in prompts]
    return await asyncio.gather(*tasks)

# Usage
prompts = ["Question 1", "Question 2", "Question 3"]
responses = asyncio.run(batch_generate(prompts))

def process_large_text(text, chunk_size=10000):
    chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
    results = []
    
    for chunk in chunks:
        response = model.generate_content(f"Summarize: {chunk}")
        results.append(response.text)
    
    return " ".join(results)