Gemini, Google’s latest AI model, is competing with OpenAI and Anthropic in changing the way we interact with technology. With its advanced capabilities, Gemini can understand and respond to complex queries, generate creative text formats, and even analyze images and videos.
Key Features:
Multimodal Capabilities: Process and understand various forms of media, including text, images, and audio.
Enhanced Reasoning and Problem-Solving: Tackle complex problems and provide insightful solutions.
Improved Language Understanding and Generation: Generate high-quality text, translate languages, and write different kinds of creative content.
Advanced Function Calling and Code Execution: Interact with external APIs and databases, as well as generate and debug code.
Leveraging the Gemini API
To utilize Gemini’s power, you can leverage its API. Here’s a basic breakdown:
Obtain an API Key: Sign up for the Gemini API and get your API key.
Craft a Prompt: Formulate a clear and concise prompt to guide the model’s response.
Make an API Call: Send the prompt to the API endpoint.
Process the Response: Parse the JSON response to extract the generated output.
Integrate into Your Application: Incorporate the generated output into your application.
Code Example: Text Generation
Python
import google.generativeai as genai
# Set your API key
genai.configure(api_key="YOUR_API_KEY")
# Create a model instance
model = genai.GenerativeModel("gemini-1.5-flash")
prompt = "Write a poem about a lonely robot exploring a distant planet."
# Prompt the model to generate text
response = model.generate_content(prompt)
print(response.text)- Gemini 1.5 Pro: This model offers a 1 million token context window, allowing it to handle significantly longer inputs and outputs.
- Python Code Execution: Gemini can execute and edit Python code directly within the API, enabling developers to create more sophisticated applications.
import os
import google.generativeai as genai
genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
//If you are running this in Google Collab or any other notebook environment you may have different code to the get the API KEY
//For Google Collab use -
//from google.colab import userdata
//genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))
result = model.generate_content("What is the sum of the first 50 prime numbers?"
"Generate and run code for the calculation, and make sure you get all 50.")
print(response.text)To find the sum of the first 50 prime numbers, I need to first generate the first 50 prime numbers and then sum them. I will use a function to efficiently generate primes.
``` python
def is_prime(n):
"""Checks if a number is prime."""
if n <= 1:
return False
for i in range(2, int(n**0.5) + 1):
if n % i == 0:
return False
return True
def generate_primes(num_primes):
"""Generates a list of the first num_primes prime numbers."""
primes = []
num = 2
while len(primes) < num_primes:
if is_prime(num):
primes.append(num)
num += 1
return primes
first_50_primes = generate_primes(50)
print(f"The first 50 prime numbers are: {first_50_primes}")
sum_of_primes = sum(first_50_primes)
print(f"The sum of the first 50 prime numbers is: {sum_of_primes}")
```
```
The first 50 prime numbers are: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229]
The sum of the first 50 prime numbers is: 5117
```
The code generates the first 50 prime numbers and then calculates their sum. The output shows both the list of primes and their sum, which is 5117.
