📅 2025-08-18 — Session: Enhanced Clustering Algorithms and GitHub Integration
🕒 11:30–13:35
🏷️ Labels: Clustering, HDBSCAN, Github, Data Transformation, Diagnostics
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session focused on improving clustering algorithms by addressing limitations in hierarchical clustering and integrating HDBSCAN. Additionally, it aimed to enhance GitHub repository ingestion processes.
Key Activities:
- Discussed the limitations of
scipy.cluster.hierarchy.fclusterregarding minimum cluster size, proposing alternative methods like post-processing and using HDBSCAN and DBSCAN. - Provided guidance on integrating HDBSCAN into a clustering wrapper to maintain dendrogram order and handle noise points.
- Developed a robust solution for handling
KeyError: 'url'exceptions in LlamaIndex’sGithubRepositoryReader, implementing a fallback mechanism for manual fetching from GitHub’s API. - Diagnosed issues in dendrogram clustering, emphasizing potential pitfalls and providing diagnostic checks.
- Designed a transformation layer for organizing book-ready chapters from sorted snippets.
- Outlined specifications for Annotator 1 to organize content into clusters and cards.
Achievements:
- Successfully integrated HDBSCAN with existing clustering methods.
- Implemented a robust error handling mechanism for GitHub repository ingestion.
- Diagnosed and proposed solutions for dendrogram clustering issues.
Pending Tasks:
- Further testing of the HDBSCAN integration and GitHub ingestion improvements.
- Implementation of the transformation layer and Annotator 1 specifications.