New RunLLM response generated: >>>*# Question* Hi, We have the following workflow: * We have a

task

that preprocesses data and returns a

list

of

dict

using

json.dumps

as

FlyteFile

* Then we want to use

map_task

to process each dict in list that's in the FlyteFile

@task
def ingest_data() -> pd.DataFrame: 
	return pd.DataFrame(...some Data)

@task
def preprocess_data(data: pd.DataFrame) -> FlyteFile:
	rows = []
	for _ , row in data.iterrows(): 
		... some processing
		rows.append(row)
	out_path = Path(flytekit.current_context().working_directory) / "preprocessed.json"  
	with out_path.open(mode="w") as output_file:  
	    pickle.dumps(input_rows, output_file)
	return FlyteFile(path=str(out_path))


@task
def generate_ai_response(row: ?) -> ?:
	ai_response = do_some_ai_stuff()
	return ai_response


@workflow
def ai_workflow(): 
	data = ingest_data()
	preprocessed_data = preprocess_data(data)
	predicted_responses: ? = map_task(generate_ai_response)(  
	    row=preprocessed_data  
	)

We don't understand how to

map_task

over a

FlyteFile

or if that's even possible. Can we deserialise the

preprocessed_data

inside the

workflow

prior to

map_task

? Or how else would we do this? Thank you! # Answer To use

map_task

over a

FlyteFile

, you need to deserialize the file content into a list of dictionaries before passing it to the

map_task

. You can achieve this by reading the

FlyteFile

in a task and returning the deserialized list. Here's a step-by-step approach: 1. Deserialize FlyteFile: Create a task to read and deserialize the

FlyteFile

into a list of dictionaries. 2. Map Task: Use

map_task

on the deserialized list. Here's an example:

from flytekit import task, workflow, map_task
from flytekit.types.file import FlyteFile
import pandas as pd
import json
from pathlib import Path

@task
def ingest_data() -> pd.DataFrame:
    return pd.DataFrame(...some Data)

@task
def preprocess_data(data: pd.DataFrame) -> FlyteFile:
    rows = []
    for _, row in data.iterrows():
        ... some processing
        rows.append(row.to_dict())
    out_path = Path(flytekit.current_context().working_directory) / "preprocessed.json"
    with out_path.open(mode="w") as output_file:
        json.dump(rows, output_file)
    return FlyteFile(path=str(out_path))

@task
def deserialize_file(file: FlyteFile) -> list:
    with file.open("r") as f:
        return json.load(f)

@task
def generate_ai_response(row: dict) -> str:
    ai_response = do_some_ai_stuff(row)
    return ai_response

@workflow
def ai_workflow():
    data = ingest_data()
    preprocessed_data = preprocess_data(data)
    deserialized_data = deserialize_file(preprocessed_data)
    predicted_responses = map_task(generate_ai_response)(row=deserialized_data)