autoflows 0.0.1

Automated Flyte workflows, powered by GPTs.

Autoflows

Automated Flyte Workflows using LLMs

The autoflows package allows you to run Flyte workflows that are powered by LLMs. These workflows use LLMs to determine which task to run in a suite of user-defined, trusted Flyte tasks.

Installation

pip install autoflows

Usage

First we can define some Flyte tasks as usual:

# example.py

from flytekit import task
from autoflows import autoflow


image_spec = ImageSpec(
    "auto-workflows",
    registry="ghcr.io/unionai-oss",
    requirements="requirements.txt",
    python_version="3.10",
)


@task(container_image=image_spec)
def add_numbers(x: float, y: float) -> FlyteFile: ...


@task(container_image=image_spec)
def concat_strings(strings: List[str]) -> FlyteFile: ...


@task(container_image=image_spec)
def train_classifier(data: List[dict], target_column: str) -> FlyteFile:
    ...

Then, in the same file, we define a FlyteRemote object that we want to use to run our autoflow.

# example.py

remote = FlyteRemote(
    config=Config(
        platform=PlatformConfig(
            endpoint="<my_endpoitn>",
            client_id="<my_client_id>",
        ),
    ),
    default_project="flytesnacks",
    default_domain="development",
)

Finally, we define the autoflow function:

# example.py

@autoflow(
    tasks=[add_numbers, concat_strings, train_classifier],
    model="gpt-3.5-turbo-1106",
    remote=remote,
    openai_secret_group="<OPENAI_API_SECRET_GROUP>",
    openai_secret_key="<OPENAI_API_SECRET_KEY>",
    client_secret_group="<CLIENT_SECRET_GROUP>",
    client_secret_key="<CLIENT_SECRET_KEY>",
    container_image=image_spec,
)
async def main(prompt: str, inputs: dict) -> FlyteFile:
    """You are a helpful bot that picks functions based on a prompt and a set of inputs.

    What tool should I use for completing the task '{prompt}' using the following inputs?
    {inputs}
    """

Running on Flyte or Union

Then, you can register the workflow along with all of the tasks:

pyflyte --config config.yaml register example.py

Where config.yaml is the Flyte configuration file pointing to your Flyte or Union cluster.

Finally, you can run the workflow, and let the autoflow function decide which task to run based on the prompt and inputs. For example, to add two numbers, you would do:

pyflyte --config config.yaml run example.py main \
    --prompt "Add these two numbers" \
    --inputs '{"x": 1, "y": 2}'

To concatenate two strings, you would do:

pyflyte run \
    test_auto_workflow.py auto_wf \
    --prompt "Combine these two strings together" \
    --inputs '{"strings": ["hello", " ", "world"]}'

And to train a classifier based on data:

pyflyte run \
    test_auto_workflow.py auto_wf \
    --prompt "Train a classifier on this small dataset" \
    --inputs "{\"target_column\": \"y\", \"training_data\": $(cat data.json)}"

Where data.json contains json objects that looks something like:

[
    {"x": 5, "y": 10},
    {"x": 3, "y": 5},
    {"x": 10, "y": 19},
]