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# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License
import asyncio
import os
import pandas as pd
from examples.custom_set_of_available_workflows.custom_workflow_definitions import (
custom_workflows,
)
from graphrag.index import run_pipeline, run_pipeline_with_config
from graphrag.index.config import PipelineWorkflowReference
sample_data_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "../_sample_data/"
)
# our fake dataset
dataset = pd.DataFrame([{"col1": 2, "col2": 4}, {"col1": 5, "col2": 10}])
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"
)
# Grab the last result from the pipeline, should be our entity extraction
tables = []
async for table in run_pipeline_with_config(
config_or_path=config_path,
dataset=dataset,
additional_workflows=custom_workflows,
):
tables.append(table)
pipeline_result = tables[-1]
if pipeline_result.result is not None:
# Should look something like this:
# col1 col2 col_1_multiplied
# 0 2 4 8
# 1 5 10 50
print(pipeline_result.result)
else:
print("No results!")
async def run_python():
"""Run a pipeline using the python API"""
# Define the actual workflows to be run, this is identical to the python api
# but we're defining the workflows to be run via python instead of via a config file
workflows: list[PipelineWorkflowReference] = [
# run my_workflow against the dataset, notice we're only using the "my_workflow" workflow
# and not the "my_unused_workflow" workflow
PipelineWorkflowReference(
name="my_workflow", # should match the name of the workflow in the custom_workflows dict above
config={ # pass in a config
# set the derive_output_column to be "col_1_multiplied", this will be passed to the workflow definition above
"derive_output_column": "col_1_multiplied"
},
),
]
# Grab the last result from the pipeline, should be our entity extraction
tables = []
async for table in run_pipeline(
workflows, dataset=dataset, additional_workflows=custom_workflows
):
tables.append(table)
pipeline_result = tables[-1]
if pipeline_result.result is not None:
# Should look something like this:
# col1 col2 col_1_multiplied
# 0 2 4 8
# 1 5 10 50
print(pipeline_result.result)
else:
print("No results!")
if __name__ == "__main__":
asyncio.run(run_python())
asyncio.run(run_with_config())
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