Miscellaneous

Concurrent requests and generation

This example demonstrates how to generate text using concurrent.futures to make parallel requests to the Gemini API, with a focus on cat-related prompts.

Import the necessary libraries

import concurrent.futures
from google import genai
import os


client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

A function to generate a fun fact about cats.

def generate_cat_fact(model):

Generates a fun fact about cats.

response = client.models.generate_content(
        model=model,
        contents="Tell me a fun fact about cats.",
    )
    return response.text

A function to generate a short story about a cat.

def generate_cat_story(model):

Generates a short story about a cat.

response = client.models.generate_content(
        model=model,
        contents="Write a ultra-short story about a cat who goes on an adventure.",
    )
    return response.text

The model to use for the requests.

model = "gemini-2.0-flash-lite"

Use ThreadPoolExecutor to run the requests concurrently. We submit the tasks to the executor and then get the results.

with concurrent.futures.ThreadPoolExecutor() as executor:
    fact_future = executor.submit(generate_cat_fact, model)
    story_future = executor.submit(generate_cat_story, model)

    cat_fact = fact_future.result()
    cat_story = story_future.result()

print("Cat Fact:\n", cat_fact)
print("\nCat Story:\n", cat_story)

Running the Example

First, install the Google Generative AI library

$ pip install google-genai

Then, run the async program with Python

$ python async_cat_generation.py
Cat Fact:
 Cats can jump up to six times their height!

Further Information

Gemini docs link 1