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What is Asyncio? Getting Started with Asynchronous Python and Using Asyncio in an AI Application with an LLM

ByRicardo

In many AI functions as we speak, efficiency is a giant deal. You could have seen that whereas working with Large Language Models (LLMs), a whole lot of time is spent ready—ready for an API response, ready for a number of calls to complete, or ready for I/O operations.

That’s the place asyncio comes in. Surprisingly, many builders use LLMs with out realizing they’ll pace up their apps with asynchronous programming.

This information will stroll you thru:

  • What is asyncio?
  • Getting began with asynchronous Python
  • Using asyncio in an AI utility with an LLM

What is Asyncio?

Python’s asyncio library permits writing concurrent code utilizing the async/await syntax, permitting a number of I/O-bound duties to run effectively inside a single thread. At its core, asyncio works with awaitable objects—normally coroutines—that an occasion loop schedules and executes with out blocking.

In less complicated phrases, synchronous code runs duties one after one other, like standing in a single grocery line, whereas asynchronous code runs duties concurrently, like utilizing a number of self-checkout machines. This is particularly helpful for API calls (e.g., OpenAI, Anthropic, Hugging Face), the place more often than not is spent ready for responses, enabling a lot quicker execution.

Getting Started with asynchronous Python

Example: Running Tasks With and Without asyncio

In this instance, we ran a easy operate thrice in a synchronous manner. The output reveals that every name to say_hello() prints “Hello…”, waits 2 seconds, then prints “…World!”. Since the calls occur one after one other, the wait time provides up — 2 seconds × 3 calls = 6 seconds complete. Check out the FULL CODES here.

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import time

def say_hello():
    print("Hello...")
    time.sleep(2)  # simulate ready (like an API name)
    print("...World!")

def fundamental():
    say_hello()
    say_hello()
    say_hello()

if __name__ == "__main__":
    begin = time.time()
    fundamental()
    print(f"Finished in {time.time() - begin:.2f} seconds")

The under code reveals that every one three calls to the say_hello() operate began nearly on the similar time. Each prints “Hello…” instantly, then waits 2 seconds concurrently earlier than printing “…World!”.

Because these duties ran in parallel moderately than one after one other, the full time is roughly the longest single wait time (~2 seconds) as an alternative of the sum of all waits (6 seconds in the synchronous model). This demonstrates the efficiency benefit of asyncio for I/O-bound duties. Check out the FULL CODES here.

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import nest_asyncio, asyncio
nest_asyncio.apply()
import time

async def say_hello():
    print("Hello...")
    await asyncio.sleep(2)  # simulate ready (like an API name)
    print("...World!")

async def fundamental():
    # Run duties concurrently
    await asyncio.collect(
        say_hello(),
        say_hello(),
        say_hello()
    )

if __name__ == "__main__":
    begin = time.time()
    asyncio.run(fundamental())
    print(f"Finished in {time.time() - begin:.2f} seconds")

Example: Download Simulation

Imagine you have to obtain a number of information. Each obtain takes time, however throughout that wait, your program can work on different downloads as an alternative of sitting idle.

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import asyncio
import random
import time

async def download_file(file_id: int):
    print(f"Start downloading file {file_id}")
    download_time = random.uniform(1, 3)  # simulate variable obtain time
    await asyncio.sleep(download_time)    # non-blocking wait
    print(f"Finished downloading file {file_id} in {download_time:.2f} seconds")
    return f"File {file_id} content material"

async def fundamental():
    information = [1, 2, 3, 4, 5]

    start_time = time.time()
    
    # Run downloads concurrently
    outcomes = await asyncio.collect(*(download_file(f) for f in information))
    
    end_time = time.time()
    print("nAll downloads accomplished.")
    print(f"Total time taken: {end_time - start_time:.2f} seconds")
    print("Results:", outcomes)

if __name__ == "__main__":
    asyncio.run(fundamental())
  • All downloads began nearly on the similar time, as proven by the “Start downloading file X” traces showing instantly one after one other.
  • Each file took a special period of time to “obtain” (simulated with asyncio.sleep()), so that they completed at totally different instances — file 3 completed first in 1.42 seconds, and file 1 final in 2.67 seconds.
  • Since all downloads had been operating concurrently, the full time taken was roughly equal to the longest single obtain time (2.68 seconds), not the sum of all instances.

This demonstrates the facility of asyncio — when duties contain ready, they are often carried out in parallel, tremendously enhancing effectivity.

Using asyncio in an AI utility with an LLM

Now that we perceive how asyncio works, let’s apply it to a real-world AI instance. Large Language Models (LLMs) akin to OpenAI’s GPT fashions typically contain a number of API calls that every take time to finish. If we run these calls one after one other, we waste beneficial time ready for responses.

In this part, we’ll examine operating a number of prompts with and with out asyncio utilizing the OpenAI consumer. We’ll use 15 brief prompts to obviously show the efficiency distinction. Check out the FULL CODES here.

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!pip set up openai
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import asyncio
from openai import AsyncOpenAI


import os
from getpass import getpass
os.environ['OPENAI_API_KEY'] = getpass('Enter OpenAI API Key: ')
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import time
from openai import OpenAI

# Create sync consumer
consumer = OpenAI()

def ask_llm(immediate: str):
    response = consumer.chat.completions.create(
        mannequin="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.decisions[0].message.content material

def fundamental():
    prompts = [
    "Briefly explain quantum computing.",
    "Write a 3-line haiku about AI.",
    "List 3 startup ideas in agri-tech.",
    "Summarize Inception in 2 sentences.",
    "Explain blockchain in 2 sentences.",
    "Write a 3-line story about a robot.",
    "List 5 ways AI helps healthcare.",
    "Explain Higgs boson in simple terms.",
    "Describe neural networks in 2 sentences.",
    "List 5 blog post ideas on renewable energy.",
    "Give a short metaphor for time.",
    "List 3 emerging trends in ML.",
    "Write a short limerick about programming.",
    "Explain supervised vs unsupervised learning in one sentence.",
    "List 3 ways to reduce urban traffic."
]

    begin = time.time()
    outcomes = []
    for immediate in prompts:
        outcomes.append(ask_llm(immediate))
    finish = time.time()

    for i, res in enumerate(outcomes, 1):
        print(f"n--- Response {i} ---")
        print(res)

    print(f"n[Synchronous] Finished in {finish - begin:.2f} seconds")

if __name__ == "__main__":
    fundamental()

The synchronous model processed all 15 prompts one after one other, so the full time is the sum of every request’s length. Since every request took time to finish, the general runtime was for much longer — 49.76 seconds in this case. Check out the FULL CODES here.

Copy Code
from openai import AsyncOpenAI

# Create async consumer
consumer = AsyncOpenAI()

async def ask_llm(immediate: str):
    response = await consumer.chat.completions.create(
        mannequin="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.decisions[0].message.content material

async def fundamental():
    prompts = [
    "Briefly explain quantum computing.",
    "Write a 3-line haiku about AI.",
    "List 3 startup ideas in agri-tech.",
    "Summarize Inception in 2 sentences.",
    "Explain blockchain in 2 sentences.",
    "Write a 3-line story about a robot.",
    "List 5 ways AI helps healthcare.",
    "Explain Higgs boson in simple terms.",
    "Describe neural networks in 2 sentences.",
    "List 5 blog post ideas on renewable energy.",
    "Give a short metaphor for time.",
    "List 3 emerging trends in ML.",
    "Write a short limerick about programming.",
    "Explain supervised vs unsupervised learning in one sentence.",
    "List 3 ways to reduce urban traffic."
]

    begin = time.time()
    outcomes = await asyncio.collect(*(ask_llm(p) for p in prompts))
    finish = time.time()

    for i, res in enumerate(outcomes, 1):
        print(f"n--- Response {i} ---")
        print(res)

    print(f"n[Asynchronous] Finished in {finish - begin:.2f} seconds")

if __name__ == "__main__":
    asyncio.run(fundamental())

The asynchronous model processed all 15 prompts concurrently, beginning them nearly on the similar time as an alternative of one after the other. As a outcome, the full runtime was near the time of the slowest single request — 8.25 seconds as an alternative of including up all requests.

The massive distinction occurs as a result of, in synchronous execution, every API name blocks this system till it finishes, so instances add up. In asynchronous execution with asyncio, API calls run in parallel, permitting this system to deal with many duties whereas ready for responses, drastically decreasing complete execution time.

Why This Matters in AI Applications

In real-world AI functions, ready for every request to complete earlier than beginning the subsequent can shortly turn out to be a bottleneck, particularly when dealing with a number of queries or knowledge sources. This is significantly widespread in workflows akin to:

  • Generating content material for a number of customers concurrently — e.g., chatbots, advice engines, or multi-user dashboards.
  • Calling the LLM a number of instances in one workflow — akin to for summarization, refinement, classification, or multi-step reasoning.
  • Fetching knowledge from a number of APIs — for instance, combining LLM output with info from a vector database or exterior APIs.

Using asyncio in these circumstances brings vital advantages:

  • Improved efficiency — by making parallel API calls as an alternative of ready for every one sequentially, your system can deal with extra work in much less time.
  • Cost effectivity — quicker execution can cut back operational prices, and batching requests the place doable can additional optimize utilization of paid APIs.
  • Better person expertise — concurrency makes functions really feel extra responsive, which is essential for real-time techniques like AI assistants and chatbots.
  • Scalability — asynchronous patterns enable your utility to deal with many extra simultaneous requests with out proportionally rising useful resource consumption.

Check out the FULL CODES here. Feel free to take a look at our GitHub Page for Tutorials, Codes and Notebooks. Also, be happy to observe us on Twitter and don’t overlook to hitch our 100k+ ML SubReddit and Subscribe to our Newsletter.

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