π 2025-07-23 β Session: Optimized Text Embedding and Clustering Workflows
π 00:10β03:00
π·οΈ Labels: Data Processing, Clustering, Embedding, Python, Optimization
π Project: Dev
β Priority: MEDIUM
Session Goal
The session aimed to enhance various data processing workflows, focusing on error resolution, clustering techniques, and optimization of text embedding processes.
Key Activities
- Resolved βmodule tmap has no attribute LSHForestβ Error: Addressed package conflicts and provided integration instructions with LlamaIndex.
- Explored Dendrogram-Style Structures: Evaluated methods for dendrogram-style clustering using HDBSCAN and SciPy.
- Developed JSONL to Document Conversion Pipeline: Created a streamlined process for converting JSONL files to Markdown and organizing them based on cosine distance.
- Analyzed Hierarchical Linkage: Discussed strategies for improving dendrogram clarity through filtering and clustering.
- Managed Oversized Nodes in Embedding Pipeline: Implemented techniques to handle nodes exceeding token limits.
- Generated Markdown Files for Clustering: Developed methods for concatenating notes and creating clustered reports.
- Optimized Text Embedding with Caching: Introduced caching and hashing to optimize embedding processes.
- Debugged Filename Matching with glob(): Provided solutions for handling non-ASCII characters in filenames.
- Enhanced Persistence Layer for Embeddings: Improved data management using SQLite for node and daily embeddings.
- Separated Ingest and Analysis Workflows: Structured data processing workflows to improve efficiency.
- Transitioned from SQLite to ChromaDB: Evaluated the benefits of using ChromaDB for vector storage.
Achievements
- Successfully resolved multiple technical challenges and optimized data processing workflows.
- Improved efficiency in embedding processes and data management.
Pending Tasks
- Further evaluation of ChromaDB for long-term vector storage solutions.
- Continuous monitoring and refinement of the new processes implemented.