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Qile-Jiang

PROFILE

Qile-jiang

Qile Jiang developed the Automated Academic Research Assistant feature for the ContextualAI/examples repository, delivering an end-to-end research automation workflow. Leveraging Python, CrewAI, and the ArXiv API, Qile designed a multi-agent system that automates academic paper discovery, processing, and knowledge extraction. The system coordinates contextual AI tools to build a queryable knowledge base from retrieved papers, enabling users to ask questions and receive answers grounded in academic literature. This work demonstrates depth in multi-agent orchestration and retrieval-augmented generation, addressing the challenge of automating complex research tasks. The feature was delivered in one month, reflecting focused engineering and technical execution.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

1Total
Bugs
0
Commits
1
Features
1
Lines of code
176
Activity Months1

Work History

August 2025

1 Commits • 1 Features

Aug 1, 2025

In 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.

Activity

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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

1 repo

Overview of all repositories you've contributed to across your timeline

ContextualAI/examples

Aug 2025 Aug 2025
1 Month active

Languages Used

Python

Technical Skills

ArXiv APIContextual AICrewAIMulti-agent SystemsPython DevelopmentRAG (Retrieval-Augmented Generation)

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