📅 2025-03-07 — Session: Developed Custom Embedder and Analyzed Political Clusters
🕒 16:05–17:55
🏷️ Labels: Embedder, Text Embeddings, Political Analysis, Clustering, FAISS
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance text embedding techniques by developing a custom Embedder class and to analyze political and philosophical document clusters for strategic insights.
Key Activities
- Developed a custom Embedder class integrating OpenAI and Hugging Face models for efficient text embeddings, including batch processing and caching.
- Fixed FAISS integration issues by converting embeddings to NumPy arrays for compatibility.
- Conducted a comprehensive analysis of document clustering, focusing on similarity matrices, dendrograms, and eigenvalue decomposition.
- Explored thematic clusters within political and philosophical notes, identifying patterns in group dynamics and leadership strategies.
- Analyzed dendrogram clusters to understand political power dynamics, narrative control, and strategic positioning.
Achievements
- Successfully implemented a custom Embedder class with enhanced capabilities.
- Resolved FAISS integration challenges, ensuring smooth data handling for similarity searches.
- Identified key themes and strategic insights from political document clusters, providing a foundation for future research and publication.
Pending Tasks
- Upload and analyze the similarity matrix data for further eigenvalue analysis.
- Clarify the definition and use of
groups_componentin clustering analysis.