Monash-FIT3170/2025W2-QualAI
Mar 2025 – Sep 2025
2 Months active
Languages Used
MarkdownCSSHTMLJavaScriptPython
Technical Skills
DocumentationAI/ML IntegrationAPI DevelopmentBackend DevelopmentDatabase IntegrationFastAPI
Eamon-Som
PROFILE
Eamon-som
Contributed to the Monash-FIT3170/2025W2-QualAI repository by developing a transcription analysis enhancement that integrates transcriptions with a vector database, enabling scalable and context-aware AI analysis. Leveraged Python, FastAPI, and Qdrant to connect backend processes with new prompt templates supporting summarization, theme identification, outlier detection, and quote extraction. This feature allows the AI assistant to analyze transcribed data based on user-selected templates, providing more actionable insights. Additionally, maintained repository governance by updating contributor documentation to ensure accurate attribution. The work demonstrated a disciplined approach to both backend development and documentation, focusing on maintainability and future extensibility without introducing bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
296
Activity Months2
Your Network
7 peopleWork History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Monash-FIT3170/2025W2-QualAI: Delivered Transcription Analysis Enhancement with Vector Database and Prompt Templates. Integrated transcriptions with a vector database to enable scalable AI analysis and added new prompt templates for analysis modes (summarization, theme identification, outlier detection, quote extraction). This feature enables the AI assistant to process and analyze transcribed data based on user-selected templates, delivering more contextual and actionable insights. The work is anchored by commit 25cccc37655df7ed63415e3317f2e3befab65f53 (Feat/eamon/transcript vector database connection (#70)).
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Monash-FIT3170/2025W2-QualAI: Delivered Transcription Analysis Enhancement with Vector Database and Prompt Templates. Integrated transcriptions with a vector database to enable scalable AI analysis and added new prompt templates for analysis modes (summarization, theme identification, outlier detection, quote extraction). This feature enables the AI assistant to process and analyze transcribed data based on user-selected templates, delivering more contextual and actionable insights. The work is anchored by commit 25cccc37655df7ed63415e3317f2e3befab65f53 (Feat/eamon/transcript vector database connection (#70)).
September 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025In 2025-03, delivered a focused Contributor Documentation Update for Monash-FIT3170/2025W2-QualAI: added Eamon's name and email to the README contributors list to ensure accurate attribution and contributor records. This was implemented via a single, well-scoped commit (8a69d4473684bdc6d05aab1c7a9f0cfac24cb64a) with the message 'Add Eamon's details to README'. No major bugs were fixed this month; the work prioritized documentation maintenance, governance, and process cleanliness. Overall impact: improved attribution accuracy, onboarding readiness for new contributors, and stronger contributor governance with minimal risk. Technologies/skills demonstrated: Git version control, documentation discipline, contributor governance, and repository hygiene.
March 2025
1 Commits • 1 Features
Mar 1, 2025In 2025-03, delivered a focused Contributor Documentation Update for Monash-FIT3170/2025W2-QualAI: added Eamon's name and email to the README contributors list to ensure accurate attribution and contributor records. This was implemented via a single, well-scoped commit (8a69d4473684bdc6d05aab1c7a9f0cfac24cb64a) with the message 'Add Eamon's details to README'. No major bugs were fixed this month; the work prioritized documentation maintenance, governance, and process cleanliness. Overall impact: improved attribution accuracy, onboarding readiness for new contributors, and stronger contributor governance with minimal risk. Technologies/skills demonstrated: Git version control, documentation discipline, contributor governance, and repository hygiene.
Activity
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture95.0%
Performance90.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
CSSHTMLJavaScriptMarkdownPython
Technical Skills
AI/ML IntegrationAPI DevelopmentBackend DevelopmentDatabase IntegrationDocumentationFastAPIFrontend DevelopmentQdrantReactVector Databases
Repositories Contributed To
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