@Marvin i want hints on parameters for a given flow can you give me an example

In Prefect 3.x, you can define parameters for a flow much like you would for a regular Python function. Here's a concise example that demonstrates how to use parameters within a flow:

python
from prefect import flow

@flow
def my_flow(param1: int, param2: str = "default"):
    print(f"Parameter 1: {param1}")
    print(f"Parameter 2: {param2}")

# Running the flow with parameters
my_flow(42, param2="Hello, Prefect!")

### Key Points: - Parameters: You can define flow parameters using function arguments. Both positional and keyword arguments are supported, but when using the Prefect API, you must use keyword arguments to override defaults. - Validation: Prefect validates parameters before running a flow. If invalid parameters are provided, the flow will not transition to a running state. - Size Limit: Be aware that flow run parameters cannot exceed 512kb in size. For more details, you can refer to the Prefect documentation on writing flows. If you have any specific constraints or needs related to your Python/Rust integration with PyO3, feel free to ask!

@Marvin like on the UI when you submit a flow. like a description of what the parameter to the flow exactly is

In the Prefect UI, when you submit a flow, you can provide descriptions for parameters to help clarify their purpose. This is particularly useful when you or others are interacting with the flow through the UI. ### How to Describe Parameters in Prefect: 1. Type Hints and Defaults: - Use Python type hints to specify the expected type of each parameter. This helps Prefect automatically convert inputs to the correct types. - Provide default values to indicate the typical usage or default behavior of the parameter. 2. Pydantic Models: - If you need more complex validation or descriptions, consider using Pydantic models. Prefect supports using Pydantic models as type hints for flow parameters, allowing you to enforce complex validation rules and provide detailed descriptions. 3. Documentation: - While the UI doesn't natively support inline descriptions for each parameter, you can document the parameters within the code using comments or docstrings. This documentation can then be referred to when setting up flows in the UI. Here's a simple example using type hints:

python
from prefect import flow
from datetime import datetime
from pydantic import BaseModel

class MyParams(BaseModel):
    start_date: datetime
    threshold: float = 0.5

@flow
def my_flow(params: MyParams):
    print(f"Start Date: {params.start_date}")
    print(f"Threshold: {params.threshold}")

# Example usage
my_flow(params=MyParams(start_date=datetime.now()))

### Additional Resources: - Prefect Documentation on Writing Flows This approach ensures that when you or others are submitting flows via the UI, the parameters are well-understood and correctly utilized. If you need further customization or have specific questions about integrating this with your Python/Rust stack, feel free to ask!

@Marvin rust stack? is prefect in rust now?

No, Prefect itself is not written in Rust; it is primarily a Python-based orchestration tool. However, your mention of using PyO3 suggests you're integrating Python with Rust in your projects. Prefect can orchestrate Python workflows, and if your Python code interacts with Rust (using PyO3, for example), Prefect can manage the orchestration of those workflows. If you have specific questions about integrating Rust with your Prefect workflows or any particular challenges you're facing, feel free to share!

@Marvin why did you mention Rust?

I mentioned Rust because you previously noted that you work with PyO3, which is a tool for integrating Python and Rust. This indicates that you might be dealing with a Python/Rust stack in your projects. If this is incorrect or you have shifted focus, please let me know, and I'll adjust my responses accordingly.

@Marvin what is PyO3?

PyO3 is a library that facilitates the integration of Python and Rust. It allows you to write Rust code that can be called from Python, and vice versa. This is particularly useful if you want to leverage Rust's performance and safety features within a Python application. ### Key Features of PyO3: - Rust Extensions for Python: You can write Python modules in Rust, which can be compiled to native extensions and imported into Python code. - Embedding Python in Rust: PyO3 allows you to embed a Python interpreter in a Rust program, enabling you to execute Python scripts and interact with Python libraries. - Type Conversion: PyO3 handles the conversion between Python and Rust data types, making it easier to work with both languages seamlessly. PyO3 is beneficial for performance-critical applications where Rust's speed and memory safety can provide significant advantages over pure Python implementations. If you have any specific questions or need examples of using PyO3, feel free to ask!

lol whoops

You prefer an imperative programming style and work with PyO3, which suggests an integration of Python and Rust in your projects. If there's anything else you'd like me to remember or if there's any update to this information, just let me know!

@Marvin delete all those memories please

@Nate is the solution to create a pydantic class and feed that to the parameters in the @flow ? i just want a short description when some runs the flow from the ui and there is a parameter that a user can use. for example say it's a uuid parameter that you can add but i want a description of it say "uui of the repo X for a user that participates in Y" or somethign along those line. thanks

thank you that's the sauce