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import argparse
from hay.retriever import generate_docs
from hay.pipeline import rg_pipeline, rs_pipeline, rsg_pipeline
from app import application
d = 'data'
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--docs', dest='docs',
action = 'store_true'
)
parser.add_argument(
'--rgpipeline', dest='rgpipeline',
action = 'store_true'
)
parser.add_argument(
'--rspipeline', dest='rspipeline',
action='store_true'
)
parser.add_argument(
'--conv', dest='conv',
action='store_true'
)
parser.add_argument(
'--gradio', dest='gradio',
action='store_true'
)
args = parser.parse_args()
if args.docs:
'''
Use this argument to generate the docs and store in DOCUMENT format
'''
generate_docs(overlap=10, length=100, d=d)
if args.rgpipeline:
'''
Use this argument to run the base retriever generator pipeline
'''
question = "How to reduce emissions?"
rg_pipeline(question, d)
if args.rspipeline:
'''
Use this argument to run the retriever summarizer pipeline
'''
question = "Who are the main users in the two-sided market"
# question = "What are the decisions made in the two-sided market? And who makes this decision? "
# question = "What are the main effects in the two-sided market?"
# question = "What are the main topics in these papers?"
answer = rs_pipeline(question, d)
print(answer)
if args.conv:
'''
Use this argument to run the pipeline using conversational agent class
'''
op = rsg_pipeline()
print(op)
if args.gradio:
'''
Use this argument to run the application
'''
application()
return None
if __name__ == '__main__':
main() |