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# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License
import asyncio
import os
import pandas as pd
from graphrag.index import run_pipeline, run_pipeline_with_config
from graphrag.index.config import PipelineWorkflowReference
# Our fake dataset
dataset = pd.DataFrame([
{"type": "A", "col1": 2, "col2": 4},
{"type": "A", "col1": 5, "col2": 10},
{"type": "A", "col1": 15, "col2": 26},
{"type": "B", "col1": 6, "col2": 15},
])
async def run_with_config():
"""Run a pipeline with a config file"""
# load pipeline.yml in this directory
config_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "./pipeline.yml"
)
tables = []
async for table in run_pipeline_with_config(
config_or_path=config_path, dataset=dataset
):
tables.append(table)
pipeline_result = tables[-1]
if pipeline_result.result is not None:
# Should look something like this, which should be identical to the python example:
# type aggregated_output
# 0 A 448
# 1 B 90
print(pipeline_result.result)
else:
print("No results!")
async def run_python():
workflows: list[PipelineWorkflowReference] = [
PipelineWorkflowReference(
name="aggregate_workflow",
steps=[
{
"verb": "aggregate", # https://github.com/microsoft/datashaper/blob/main/python/datashaper/datashaper/engine/verbs/aggregate.py
"args": {
"groupby": "type",
"column": "col_multiplied",
"to": "aggregated_output",
"operation": "sum",
},
"input": {
"source": "workflow:derive_workflow", # reference the derive_workflow, cause this one requires that one to run first
# Notice, these are out of order, the indexing engine will figure out the right order to run them in
},
}
],
),
PipelineWorkflowReference(
name="derive_workflow",
steps=[
{
# built-in verb
"verb": "derive", # https://github.com/microsoft/datashaper/blob/main/python/datashaper/datashaper/engine/verbs/derive.py
"args": {
"column1": "col1", # from above
"column2": "col2", # from above
"to": "col_multiplied", # new column name
"operator": "*", # multiply the two columns,
},
# Since we're trying to act on the default input, we don't need explicitly to specify an input
}
],
),
]
# Grab the last result from the pipeline, should be our aggregate_workflow since it should be the last one to run
tables = []
async for table in run_pipeline(dataset=dataset, workflows=workflows):
tables.append(table)
pipeline_result = tables[-1]
if pipeline_result.result is not None:
# Should look something like this:
# type aggregated_output
# 0 A 448
# 1 B 90
# This is because we first in "derive_workflow" we multiply col1 and col2 together, then in "aggregate_workflow" we sum them up by type
print(pipeline_result.result)
else:
print("No results!")
if __name__ == "__main__":
asyncio.run(run_python())
asyncio.run(run_with_config())
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