wellcomecollection/docs
Sep 2025 – Sep 2025
1 Month active
Languages Used
MarkdownPlantUMLPythonYAML
Technical Skills
AI IntegrationAPI DesignData EngineeringDocumentationMachine LearningNatural Language Processing
Daniel Sim
PROFILE
Daniel Sim
Developed the Name Reconciliation Service (NARESE) within the wellcomecollection/docs repository, focusing on RFC-driven architecture and comprehensive documentation. The work introduced a hybrid embedding retrieval system combined with large language model reasoning, enabling scalable retrieval-augmented workflows for improved name reconciliation accuracy. Implemented a parameterized cutoff multiplier, initially set at 0.8, to balance recall and relevance, with clear rationale documented for future tuning. Leveraged Python and YAML for system design and data engineering, while using Markdown and PlantUML to produce detailed architecture diagrams and onboarding materials. The approach emphasized maintainability, traceability, and clarity for both current and future developers.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
2
Lines of code
1,142
Activity Months1
Your Network
11 peopleWork History
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 (2025-09) focused on designing and documenting the Name Reconciliation Service (NARESE) with RFC-driven architecture. Delivered hybrid embedding retrieval and LLM reasoning patterns, introduced a cutoff multiplier parameterization starting at 0.8 with rationale, and expanded documentation to clearly balance recall and relevance. This work lays the foundation for scalable retrieval-augmented workflows and improved name reconciliation accuracy while improving developer clarity and onboarding.
6 Commits • 2 Features
Sep 1, 2025September 2025 (2025-09) focused on designing and documenting the Name Reconciliation Service (NARESE) with RFC-driven architecture. Delivered hybrid embedding retrieval and LLM reasoning patterns, introduced a cutoff multiplier parameterization starting at 0.8 with rationale, and expanded documentation to clearly balance recall and relevance. This work lays the foundation for scalable retrieval-augmented workflows and improved name reconciliation accuracy while improving developer clarity and onboarding.
September 2025
Activity
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Quality Metrics
Correctness98.4%
Maintainability98.4%
Architecture98.4%
Performance93.4%
AI Usage38.4%
Skills & Technologies
Programming Languages
MarkdownPlantUMLPythonYAML
Technical Skills
AI IntegrationAPI DesignData EngineeringDocumentationMachine LearningNatural Language ProcessingRFC ProcessSystem DesignTechnical Writing
Repositories Contributed To
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