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# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License
import asyncio
import os
from graphrag.index import run_pipeline, run_pipeline_with_config
from graphrag.index.config import PipelineCSVInputConfig, PipelineWorkflowReference
from graphrag.index.input import load_input
sample_data_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "../../_sample_data/"
)
shared_dataset = asyncio.run(
load_input(
PipelineCSVInputConfig(
file_pattern=".*\\.csv$",
base_dir=sample_data_dir,
source_column="author",
text_column="message",
timestamp_column="date(yyyyMMddHHmmss)",
timestamp_format="%Y%m%d%H%M%S",
title_column="message",
),
)
)
async def run_with_config():
"""Run a pipeline with a config file"""
# We're cheap, and this is an example, lets just do 10
dataset = shared_dataset.head(10)
# 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
):
tables.append(table)
pipeline_result = tables[-1]
# Print the entities. This will be a row for each text unit, each with a list of entities,
# This should look pretty close to the python version, but since we're using an LLM
# it will be a little different depending on how it feels about the text
if pipeline_result.result is not None:
print(pipeline_result.result["entities"].to_list())
else:
print("No results!")
async def run_python():
if (
"EXAMPLE_OPENAI_API_KEY" not in os.environ
and "OPENAI_API_KEY" not in os.environ
):
msg = "Please set EXAMPLE_OPENAI_API_KEY or OPENAI_API_KEY environment variable to run this example"
raise Exception(msg)
# We're cheap, and this is an example, lets just do 10
dataset = shared_dataset.head(10)
workflows: list[PipelineWorkflowReference] = [
PipelineWorkflowReference(
name="entity_extraction",
config={
"entity_extract": {
"strategy": {
"type": "graph_intelligence",
"llm": {
"type": "openai_chat",
"api_key": os.environ.get(
"EXAMPLE_OPENAI_API_KEY",
os.environ.get("OPENAI_API_KEY", None),
),
"model": os.environ.get(
"EXAMPLE_OPENAI_MODEL", "gpt-3.5-turbo"
),
"max_tokens": os.environ.get(
"EXAMPLE_OPENAI_MAX_TOKENS", 2500
),
"temperature": os.environ.get(
"EXAMPLE_OPENAI_TEMPERATURE", 0
),
},
}
}
},
)
]
# Grab the last result from the pipeline, should be our entity extraction
tables = []
async for table in run_pipeline(dataset=dataset, workflows=workflows):
tables.append(table)
pipeline_result = tables[-1]
# Print the entities. This will be a row for each text unit, each with a list of entities
if pipeline_result.result is not None:
print(pipeline_result.result["entities"].to_list())
else:
print("No results!")
if __name__ == "__main__":
asyncio.run(run_python())
asyncio.run(run_with_config())
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