Async | Mirascope
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Async

Asynchronous programming is a crucial concept when building applications with LLMs (Large Language Models) using Mirascope. This feature allows for efficient handling of I/O-bound operations (e.g., API calls), improving application responsiveness and scalability. Mirascope utilizes the asyncio library to implement asynchronous processing.

Best Practices
  • Use asyncio for I/O-bound tasks: Async is most beneficial for I/O-bound operations like API calls. It may not provide significant benefits for CPU-bound tasks.
  • Avoid blocking operations: Ensure that you're not using blocking operations within async functions, as this can negate the benefits of asynchronous programming.
  • Consider using connection pools: When making many async requests, consider using connection pools to manage and reuse connections efficiently.
  • Be mindful of rate limits: While async allows for concurrent requests, be aware of API rate limits and implement appropriate throttling if necessary.
  • Use appropriate timeouts: Implement timeouts for async operations to prevent hanging in case of network issues or unresponsive services.
  • Test thoroughly: Async code can introduce subtle bugs. Ensure comprehensive testing of your async implementations.
  • Leverage async context managers: Use async context managers (async with) for managing resources that require setup and cleanup in async contexts.

Key Terms

  • async: Keyword used to define a function as asynchronous
  • await: Keyword used to wait for the completion of an asynchronous operation
  • asyncio: Python library that supports asynchronous programming

Basic Usage and Syntax

If you haven't already, we recommend first reading the section on Calls

To use async in Mirascope, simply define the function as async and use the await keyword when calling it. Here's a basic example:

import asyncio

from mirascope import llm


@llm.call(provider="openai", model="gpt-4o-mini")
async def recommend_book(genre: str) -> str: 
    return f"Recommend a {genre} book"


async def main():
    response = await recommend_book("fantasy") 
    print(response.content)


asyncio.run(main())

In this example we:

  1. Define recommend_book as an asynchronous function.
  2. Create a main function that calls recommend_book and awaits it.
  3. Use asyncio.run(main()) to start the asynchronous event loop and run the main function.

Parallel Async Calls

One of the main benefits of asynchronous programming is the ability to run multiple operations concurrently. Here's an example of making parallel async calls:

import asyncio

from mirascope import llm


@llm.call(provider="openai", model="gpt-4o-mini")
async def recommend_book(genre: str) -> str: 
    return f"Recommend a {genre} book"


async def main():
    genres = ["fantasy", "scifi", "mystery"]
    tasks = [recommend_book(genre) for genre in genres] 
    results = await asyncio.gather(*tasks) 

    for genre, response in zip(genres, results):
        print(f"({genre}):\n{response.content}\n")


asyncio.run(main())

We are using asyncio.gather to run and await multiple asynchronous tasks concurrently, printing the results for each task one all are completed.

Async Streaming

If you haven't already, we recommend first reading the section on Streams

Streaming with async works similarly to synchronous streaming, but you use async for instead of a regular for loop:

import asyncio

from mirascope import llm


@llm.call(provider="openai", model="gpt-4o-mini", stream=True) 
async def recommend_book(genre: str) -> str: 
    return f"Recommend a {genre} book"


async def main():
    stream = await recommend_book("fantasy") 
    async for chunk, _ in stream: 
        print(chunk.content, end="", flush=True)


asyncio.run(main())

Async Tools

If you haven't already, we recommend first reading the section on Tools

When using tools asynchronously, you can make the call method of a tool async:

import asyncio

from mirascope import BaseTool, llm


class FormatBook(BaseTool):
    title: str
    author: str

    async def call(self) -> str: 
        # Simulating an async API call
        await asyncio.sleep(1)
        return f"{self.title} by {self.author}"


@llm.call(provider="openai", model="gpt-4o-mini", tools=[FormatBook]) 
async def recommend_book(genre: str) -> str: 
    return f"Recommend a {genre} book"


async def main():
    response = await recommend_book("fantasy")
    if tool := response.tool:
        if isinstance(tool, FormatBook): 
            output = await tool.call() 
            print(output)
    else:
        print(response.content)


asyncio.run(main())

It's important to note that in this example we use isinstance(tool, FormatBook) to ensure the call method can be awaited safely. This also gives us proper type hints and editor support.

Custom Client

When using custom clients with async calls, it's crucial to use the asynchronous version of the client. You can provide the async client either through the decorator or dynamic configuration:

Decorator Parameter

Dynamic Configuration

Synchronous vs Asynchronous Clients

Make sure to use the appropriate asynchronous client class (e.g., AsyncOpenAI instead of OpenAI) when working with async functions. Using a synchronous client in an async context can lead to blocking operations that defeat the purpose of async programming.

Next Steps

By leveraging these async features in Mirascope, you can build more efficient and responsive applications, especially when working with multiple LLM calls or other I/O-bound operations.

This section concludes the core functionality Mirascope supports. If you haven't already, we recommend taking a look at any previous sections you've missed to learn about what you can do with Mirascope.

You can also check out the section on Provider-Specific Features to learn about how to use features that only certain providers support, such as OpenAI's structured outputs.