Ops

A Weave op is a versioned function that automatically logs all calls.

To create an op, decorate a python function with weave.op()

import weave

@weave.op()
def track_me(v):
    return v + 5

weave.init('intro-example')
track_me(15)

Calling an op will create a new op version if the code has changed from the last call, and log the inputs and outputs of the function.

note

Functions decorated with @weave.op() will behave normally (without code versioning and tracking), if you don't call weave.init('your-project-name') before calling them.

Ops can be served or deployed using the Weave toolbelt.

Customize display names

You can customize the op's display name by setting the name parameter in the @weave.op decorator:

@weave.op(name="custom_name")
def func():
    ...

Customize logged inputs and outputs

If you want to change the data that is logged to weave without modifying the original function (e.g. to hide sensitive data), you can pass postprocess_inputs and postprocess_output to the op decorator.

postprocess_inputs takes in a dict where the keys are the argument names and the values are the argument values, and returns a dict with the transformed inputs.

postprocess_output takes in any value which would normally be returned by the function and returns the transformed output.

from dataclasses import dataclass
from typing import Any
import weave

@dataclass
class CustomObject:
    x: int
    secret_password: str

def postprocess_inputs(inputs: dict[str, Any]) -> dict[str, Any]:
    return {k:v for k,v in inputs.items() if k != "hide_me"}

def postprocess_output(output: CustomObject) -> CustomObject:
    return CustomObject(x=output.x, secret_password="REDACTED")

@weave.op(
    postprocess_inputs=postprocess_inputs,
    postprocess_output=postprocess_output,
)
def func(a: int, hide_me: str) -> CustomObject:
    return CustomObject(x=a, secret_password=hide_me)

weave.init('hide-data-example') # 🐝
func(a=1, hide_me="password123")