nikbearbrown/INFO_7390_Art_and_Science_of_Data
Apr 2025 – Apr 2025
1 Month active
Languages Used
MarkdownPythonShell
Technical Skills
API IntegrationChromaDBData EngineeringData ProcessingFull Stack DevelopmentMachine Learning
gowrimg28
PROFILE
Gowrimg28
Mahadimanegovardha Ganesan developed an end-to-end Literature Review Assistant for the INFO_7390_Art_and_Science_of_Data repository, focusing on Retrieval-Augmented Generation (RAG) with ChromaDB. Over the course of a month, Mahadimanegovardha designed and implemented a Streamlit-based user interface, enabling researchers to query and view AI-generated insights from scholarly literature. The solution included Python ingestion pipelines for arXiv papers, integrating data engineering and natural language processing to populate a vector database. By building a complete RAG pipeline, Mahadimanegovardha addressed the challenge of scalable literature search and summarization, delivering a robust tool for accelerating research and knowledge discovery without reported bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,950
Activity Months1
Your Network
133 people
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Implemented end-to-end Literature Review Assistant (RAG) with ChromaDB for the INFO_7390 project. Delivered a Streamlit-based UI, arXiv ingestion scripts, and a Retrieval-Augmented Generation (RAG) engine to produce AI-driven insights from scholarly literature. This enables researchers to search, summarize, and derive insights from papers at scale, accelerating literature reviews and knowledge discovery. Work is captured under commit 3502582b964231ec9ffd24f528f5bfe5d57cea9d. No major bugs reported this month. Technologies demonstrated include RAG, ChromaDB, Streamlit, Python ingestion pipelines, and AI-powered summarization.
1 Commits • 1 Features
Apr 1, 2025April 2025: Implemented end-to-end Literature Review Assistant (RAG) with ChromaDB for the INFO_7390 project. Delivered a Streamlit-based UI, arXiv ingestion scripts, and a Retrieval-Augmented Generation (RAG) engine to produce AI-driven insights from scholarly literature. This enables researchers to search, summarize, and derive insights from papers at scale, accelerating literature reviews and knowledge discovery. Work is captured under commit 3502582b964231ec9ffd24f528f5bfe5d57cea9d. No major bugs reported this month. Technologies demonstrated include RAG, ChromaDB, Streamlit, Python ingestion pipelines, and AI-powered summarization.
April 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
MarkdownPythonShell
Technical Skills
API IntegrationChromaDBData EngineeringData ProcessingFull Stack DevelopmentMachine LearningNatural Language ProcessingRetrieval-Augmented Generation (RAG)StreamlitVector Databases
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