RUCKBReasoning/SoAy

Codes for paper SoAy: A Service-oriented APIs Applying Framework of Large Language Models

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Experimental

This project helps researchers and academics efficiently find specific information within academic databases using natural language questions. It takes your complex research questions, like "How many times has the most cited paper by Yann LeCun at New York University been cited?", and provides precise answers by intelligently using underlying API services from platforms like AMiner. The primary users are researchers, students, and librarians who need to quickly extract detailed facts from large academic datasets.

No commits in the last 6 months.

Use this if you need to build or enhance a system that allows large language models to accurately answer detailed academic information queries by orchestrating multiple API calls to academic databases.

Not ideal if your primary goal is general conversational AI or if you are not working with structured API services for academic information retrieval.

academic-search research-assistants scientific-information-retrieval database-querying scholarly-analysis
No License Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 7 / 25
Maturity 8 / 25
Community 7 / 25

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27

Forks

2

Language

Python

License

Last pushed

Jul 14, 2025

Commits (30d)

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