48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
from fastapi import FastAPI
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from config import __CONFIG__
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import mysql_connector
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from fastgpt_uploader import upload2fastgpt
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from semanticscholar import search_paper
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app = FastAPI()
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def query(query:str):
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res = []
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list = search_paper(query)
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for i in list:
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res.append({
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'id':i['paperId'],
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'datasetId':__CONFIG__['fastgpt_setId'],
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'collectionId':__CONFIG__['fastgpt_colId'],
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'sourceName':'Semantic Scholar',
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'sourceId?':'',
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'q':str(i['title']),
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'a':str(i['abstract']),
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'score':[
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{
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'type': 'rrf',
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'value':0.8
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}
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]
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})
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return res
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@app.get("/fastdoi")
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async def get_reference(questions):
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print('Search: '+questions)
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res = query(questions)
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new = []
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for i in res:
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if not mysql_connector.is_loaded(i['id']):
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new.append(i)
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if(upload2fastgpt(new)):
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for i in new:
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mysql_connector.new_load(i['id'])
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print('New: '+i['q'])
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return new
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if __name__ == '__main__':
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import uvicorn
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uvicorn.run(app, host="127.0.0.1", port=8964)
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mysql_connector.end_mysql() |