Monash-FIT3170/2025W1-QualAI
May 2025 – Aug 2025
2 Months active
Languages Used
DockerfilePythonYAMLunittest
Technical Skills
ContainerizationDatabase ManagementDevOpsDockerAPI IntegrationBackend Development
MeV2V2
PROFILE
Mev2v2
Jaemin Park contributed to the Monash-FIT3170/2025W1-QualAI repository by building a knowledge triple extraction feature that automates the conversion of raw text into structured (Subject, Predicate, Object) data for knowledge graphs. He refined prompt engineering and API integration to improve extraction accuracy, and enhanced test coverage using Python and unittest to ensure reliability. Jaemin also improved backend reliability by implementing a Neo4j startup health check in Docker Compose, tuning healthcheck intervals, and maintaining Dockerfiles for cleaner builds. His work addressed deployment reliability, reduced manual curation, and enabled more accurate reasoning in QA and search workflows through robust backend engineering.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
7Total
Bugs
1
Commits
7
Features
2
Lines of code
277
Activity Months2
Your Network
17 people
Work History
August 2025
4 Commits • 1 Features
Aug 1, 2025In August 2025, delivered end-to-end Knowledge Triple Extraction in DeepSeekClient for Monash-FIT3170/2025W1-QualAI, enabling automatic extraction of (Subject, Predicate, Object) triples from text and feeding a structured knowledge graph. Strengthened testing, refined prompts and API usage to improve extraction accuracy, and refactored Neo4j integration and test infrastructure for reliability. This work enhances data quality and supports faster, more accurate reasoning for QA and search workflows, while reducing manual curation and operational risk.
4 Commits • 1 Features
Aug 1, 2025In August 2025, delivered end-to-end Knowledge Triple Extraction in DeepSeekClient for Monash-FIT3170/2025W1-QualAI, enabling automatic extraction of (Subject, Predicate, Object) triples from text and feeding a structured knowledge graph. Strengthened testing, refined prompts and API usage to improve extraction accuracy, and refactored Neo4j integration and test infrastructure for reliability. This work enhances data quality and supports faster, more accurate reasoning for QA and search workflows, while reducing manual curation and operational risk.
August 2025
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for Monash-FIT3170/2025W1-QualAI focused on reliability improvements and build hygiene. Delivered a Neo4j startup health check in Docker Compose to ensure dependent services start only after Neo4j is ready, with tuned healthcheck intervals to speed startup. Completed Dockerfile maintenance including line endings cleanup and removal of a redundant COPY for requirements.txt. These changes reduce startup time, improve deployment reliability, and streamline builds.
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for Monash-FIT3170/2025W1-QualAI focused on reliability improvements and build hygiene. Delivered a Neo4j startup health check in Docker Compose to ensure dependent services start only after Neo4j is ready, with tuned healthcheck intervals to speed startup. Completed Dockerfile maintenance including line endings cleanup and removal of a redundant COPY for requirements.txt. These changes reduce startup time, improve deployment reliability, and streamline builds.
Activity
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Quality Metrics
Correctness77.2%
Maintainability77.2%
Architecture62.8%
Performance57.2%
AI Usage40.0%
Skills & Technologies
Programming Languages
DockerfilePythonYAMLunittest
Technical Skills
API IntegrationBackend DevelopmentContainerizationDatabase IntegrationDatabase InteractionDatabase ManagementDevOpsDockerLarge Language ModelsNatural Language ProcessingPrompt EngineeringTestingUnit Testing
Repositories Contributed To
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