unb-mds/2025-1-NoFluxoUNB
Apr 2025 – Apr 2026
7 Months active
Languages Used
CSVMarkdownPythonJavaScriptTypeScriptJSONPlain TextText
Technical Skills
Agile MethodologiesCSV ParsingCode OrganizationData CleaningData CollectionData Engineering
Gustavo Choueiri
PROFILE
Gustavo Choueiri
Over seven months, contributed to unb-mds/2025-1-NoFluxoUNB by building AI-powered course recommendation systems, automating academic data extraction, and enhancing both backend and frontend workflows. Developed robust data pipelines using Python and integrated AI agents via Flask and Streamlit, enabling automated class search, elective recommendations, and natural language queries. Applied web scraping and PDF parsing to maintain up-to-date course datasets, while implementing error handling and validation for reliability. Improved user experience through UI/UX updates in Svelte and TypeScript, and ensured compliance with licensing and privacy standards. Maintained a clean, testable codebase with strong documentation and CI/CD practices.
Overall Statistics
Feature vs Bugs
69%Features
Repository Contributions
102Total
Bugs
11
Commits
102
Features
25
Lines of code
1,358,104
Activity Months7
Your Network
15 peopleShared Repositories
15
Work History
April 2026
5 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for repository unb-mds/2025-1-NoFluxoUNB. Focused on delivering a core elective course recommendation workflow, tightening topic adherence in responses, validating academic history inputs, and standardizing flow diagram visuals. These efforts improved business value by enabling tailored elective recommendations, reducing irrelevant responses, and enhancing reliability and UX through stable input handling and consistent visuals. The month also established solid, commit-driven foundations for future scalability and maintenance.
5 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for repository unb-mds/2025-1-NoFluxoUNB. Focused on delivering a core elective course recommendation workflow, tightening topic adherence in responses, validating academic history inputs, and standardizing flow diagram visuals. These efforts improved business value by enabling tailored elective recommendations, reducing irrelevant responses, and enhancing reliability and UX through stable input handling and consistent visuals. The month also established solid, commit-driven foundations for future scalability and maintenance.
April 2026
March 2026
21 Commits • 7 Features
Mar 1, 2026March 2026 monthly summary for unb-mds/2025-1-NoFluxoUNB: Delivered AI-driven capabilities, security hardening, licensing/compliance updates, and UI/UX improvements enabling automation, safer deployments, and improved user experience. Demonstrated solid Git workflows and production readiness.
March 2026
21 Commits • 7 Features
Mar 1, 2026March 2026 monthly summary for unb-mds/2025-1-NoFluxoUNB: Delivered AI-driven capabilities, security hardening, licensing/compliance updates, and UI/UX improvements enabling automation, safer deployments, and improved user experience. Demonstrated solid Git workflows and production readiness.
February 2026
9 Commits • 1 Features
Feb 1, 2026February 2026 (2026-02) monthly summary for unb-mds/2025-1-NoFluxoUNB. Delivered an AI-powered course recommendations and educational resources retrieval system with a local vector database, Gemini API integration, and a MaritacaAI-based subject query agent. Implemented robust data ingestion via web scraping to keep course data up to date, including JSON/CSV exports, and enhanced query expansion and ranking to improve relevance of course suggestions. Added SABIÁ model-based testing to validate AI components. Updated documentation and dependencies to support AI integrations, and applied maintainability-focused fixes in scraping-related code. Impact: Enabled data-driven, up-to-date personalized recommendations for students, improved data reliability and maintainability, and established a scalable foundation for future AI-driven features. Key achievements: - Built AI-powered university course recommendation server with local vector DB and Gemini API integration. - Implemented advanced query expansion and ranking for course suggestions. - Integrated MaritacaAI-based subject query agent for natural-language access. - Implemented web scraping to ingest up-to-date course data with JSON/CSV exports; added differentiation between scrapings for maintainability. - Validated AI components with SABIÁ model tests; updated dependencies and documentation to support AI integrations. - Minor scraping-related fixes and comments improvements to enhance code quality and maintainability.
9 Commits • 1 Features
Feb 1, 2026February 2026 (2026-02) monthly summary for unb-mds/2025-1-NoFluxoUNB. Delivered an AI-powered course recommendations and educational resources retrieval system with a local vector database, Gemini API integration, and a MaritacaAI-based subject query agent. Implemented robust data ingestion via web scraping to keep course data up to date, including JSON/CSV exports, and enhanced query expansion and ranking to improve relevance of course suggestions. Added SABIÁ model-based testing to validate AI components. Updated documentation and dependencies to support AI integrations, and applied maintainability-focused fixes in scraping-related code. Impact: Enabled data-driven, up-to-date personalized recommendations for students, improved data reliability and maintainability, and established a scalable foundation for future AI-driven features. Key achievements: - Built AI-powered university course recommendation server with local vector DB and Gemini API integration. - Implemented advanced query expansion and ranking for course suggestions. - Integrated MaritacaAI-based subject query agent for natural-language access. - Implemented web scraping to ingest up-to-date course data with JSON/CSV exports; added differentiation between scrapings for maintainability. - Validated AI components with SABIÁ model tests; updated dependencies and documentation to support AI integrations. - Minor scraping-related fixes and comments improvements to enhance code quality and maintainability.
February 2026
July 2025
12 Commits • 3 Features
Jul 1, 2025July 2025 performance summary for unb-mds/2025-1-NoFluxoUNB: Achieved key data engineering and AI tooling improvements by delivering an end-to-end UNB course data scraping pipeline, migrating the AI agent to a robust Flask API, and cleaning up repository structure to boost maintainability and deployment readiness. These changes establish AI-ready data flows, a centralized and testable AI service, and a cleaner codebase, enabling faster AI model provisioning and reliable data pipelines.
July 2025
12 Commits • 3 Features
Jul 1, 2025July 2025 performance summary for unb-mds/2025-1-NoFluxoUNB: Achieved key data engineering and AI tooling improvements by delivering an end-to-end UNB course data scraping pipeline, migrating the AI agent to a robust Flask API, and cleaning up repository structure to boost maintainability and deployment readiness. These changes establish AI-ready data flows, a centralized and testable AI service, and a cleaner codebase, enabling faster AI model provisioning and reliable data pipelines.
June 2025
19 Commits • 7 Features
Jun 1, 2025June 2025 focused on delivering AI-assisted class discovery, reliable data extraction from the Ragflow pipeline, and a beginner-friendly analytics UI to scale student adoption. Key outcomes include launching and tuning AI prompts for the student-facing class search, strengthening RAGFlow result handling (parsing Disciplina, Unidade responsavel, and Ementa; de-duplication; top-4 results; robust error handling), and delivering user-facing analytics via a Streamlit app. Additional progress includes building reusable tooling with a Ragflow API client and a JSON ranking parser to accelerate future work, and providing AI-agent testing data to ensure robust JSON outputs. These efforts increased search accuracy, reduced manual data wrangling, and established a repeatable framework for data formatting and testing. Technologies demonstrated include Python, Streamlit, API client design, robust JSON parsing, error handling, and data normalization.
19 Commits • 7 Features
Jun 1, 2025June 2025 focused on delivering AI-assisted class discovery, reliable data extraction from the Ragflow pipeline, and a beginner-friendly analytics UI to scale student adoption. Key outcomes include launching and tuning AI prompts for the student-facing class search, strengthening RAGFlow result handling (parsing Disciplina, Unidade responsavel, and Ementa; de-duplication; top-4 results; robust error handling), and delivering user-facing analytics via a Streamlit app. Additional progress includes building reusable tooling with a Ragflow API client and a JSON ranking parser to accelerate future work, and providing AI-agent testing data to ensure robust JSON outputs. These efforts increased search accuracy, reduced manual data wrangling, and established a repeatable framework for data formatting and testing. Technologies demonstrated include Python, Streamlit, API client design, robust JSON parsing, error handling, and data normalization.
June 2025
May 2025
16 Commits • 2 Features
May 1, 2025May 2025 monthly summary for unb-mds/2025-1-NoFluxoUNB: Delivered automation enhancements for academic records processing with a focus on PDF transcript extraction and export, alongside targeted maintenance and cleanup to improve maintainability and workflow reliability. The work emphasizes business value through reliable data ingestion and faster downstream reporting.
May 2025
16 Commits • 2 Features
May 1, 2025May 2025 monthly summary for unb-mds/2025-1-NoFluxoUNB: Delivered automation enhancements for academic records processing with a focus on PDF transcript extraction and export, alongside targeted maintenance and cleanup to improve maintainability and workflow reliability. The work emphasizes business value through reliable data ingestion and faster downstream reporting.
April 2025
20 Commits • 4 Features
Apr 1, 2025April 2025 (2025-04) in unb-mds/2025-1-NoFluxoUNB focused on delivering foundational documentation, governance improvements, data ingestion enhancements, and codebase stability. Key work spanned four feature areas with a total of 20 commits. No major defects were reported; minor stability and readability improvements were applied through refactoring and cleanup. The work lays groundwork for faster onboarding, clearer cross-team coordination, and broader data coverage.
20 Commits • 4 Features
Apr 1, 2025April 2025 (2025-04) in unb-mds/2025-1-NoFluxoUNB focused on delivering foundational documentation, governance improvements, data ingestion enhancements, and codebase stability. Key work spanned four feature areas with a total of 20 commits. No major defects were reported; minor stability and readability improvements were applied through refactoring and cleanup. The work lays groundwork for faster onboarding, clearer cross-team coordination, and broader data coverage.
April 2025
Activity
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Quality Metrics
Correctness89.4%
Maintainability89.0%
Architecture86.0%
Performance84.0%
AI Usage32.8%
Skills & Technologies
Programming Languages
BashBatchCSSCSVDartHTMLJSONJavaScriptMarkdownPlain Text
Technical Skills
AI Agent IntegrationAI Assistant DevelopmentAI ConfigurationAI DevelopmentAI IntegrationAI Prompt EngineeringAI integrationAPI DevelopmentAPI IntegrationAPI developmentAPI managementAgile MethodologiesBackend DevelopmentCI/CDCORS Configuration
Repositories Contributed To
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