Integration with OpenScholar optimizes literature search:
- Request a Semantic Scholar API key
- Start the service:
python openscholar_api.py --s2_api_key YOUR_KEY --reranker_path OpenSciLM/OpenScholar_Reranker - Send a search request:
response = requests.post('http://localhost:38015/batch_ask', json={'questions':['你的问题']})
Tests show that the system can return relevant literature in an average of 3 seconds with an accuracy improvement of 351 TP3T compared to manual searches. supports question-and-answer searches such as 'How well does the augmented language model perform on knowledge-intensive tasks?'
This answer comes from the articleCycleResearcher: an AI-powered automation tool for academic research and reviewing manuscriptsThe
May not be reproduced without permission:AI productivity tools " What are practical solutions for optimizing the efficiency of literature searches?
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