ContextualAI/examples
Aug 2025 – Aug 2025
1 Month active
Languages Used
Python
Technical Skills
ArXiv APIContextual AICrewAIMulti-agent SystemsPython DevelopmentRAG (Retrieval-Augmented Generation)
Qile-Jiang
PROFILE
Qile-jiang
Developed an Automated Academic Research Assistant feature for the ContextualAI/examples repository, delivering an end-to-end research automation workflow. The solution leveraged Python and CrewAI to orchestrate a multi-agent system that integrates contextual AI tools with the ArXiv API for academic paper discovery, processing, and knowledge extraction. By establishing a queryable knowledge base, the system enables users to ask questions and receive answers grounded in processed academic literature. The implementation focused on Retrieval-Augmented Generation (RAG) techniques to enhance information retrieval and synthesis. This work demonstrated depth in multi-agent system design and practical integration of AI-driven research automation within a Python environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
176
Activity Months1
Your Network
19 peopleSame Organization
@contextual.ai6
Akash MahajanMember
dzorluMember
Jinash RouniyarMember
Jonathan FengMember
Lingxi @ ContextualMember
Sean SmithMember
Shared Repositories
13
Ahmad KhanMember
Akash MahajanMember
dzorluMember
Jinash RouniyarMember
Jonathan FengMember
Karan BirMember
Lingxi @ ContextualMember
Nicholas LuiMember
nitishgourishettyMember
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025In August 2025, delivered the Automated Academic Research Assistant feature in ContextualAI/examples, implementing a CrewAI-based multi-agent system that automates academic research from paper discovery to knowledge extraction using ArXiv and contextual AI tools, with a queryable knowledge base to answer questions.
1 Commits • 1 Features
Aug 1, 2025In August 2025, delivered the Automated Academic Research Assistant feature in ContextualAI/examples, implementing a CrewAI-based multi-agent system that automates academic research from paper discovery to knowledge extraction using ArXiv and contextual AI tools, with a queryable knowledge base to answer questions.
August 2025
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage100.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
ArXiv APIContextual AICrewAIMulti-agent SystemsPython DevelopmentRAG (Retrieval-Augmented Generation)
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline