📅 2025-05-17 — Session: Developed Modular Data Processing Pipeline
🕒 21:30–22:20
🏷️ Labels: Clustering, Data Processing, Modularization, Chromadb, HDBSCAN
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to enhance the data processing pipeline for clustering and visualizing session data extracted from a Chroma database.
Key Activities:
- Identified potential sources for clustering scripts, focusing on documents with relevant code and concepts.
- Utilized Bash and Unix commands to list and locate Jupyter notebooks modified recently or on specific dates.
- Discussed the current state and proposed modular evolution of the session processing pipeline.
- Proposed a modular structure for Chroma data processing notebooks, detailing responsibilities and content.
- Developed a notebook for data extraction and preprocessing from ChromaDB, saving processed data as CSV.
- Evaluated file formats for saving embeddings, recommending more efficient alternatives like Parquet or NPY.
- Provided Python code for listing collection names in Chroma Persistent Client.
- Organized notebooks for clustering and feature engineering, detailing responsibilities and expected outputs.
- Implemented a Python workflow for daily data clustering using HDBSCAN and UMAP with pandas.
Achievements:
- Established a clear modular structure for data processing and clustering tasks.
- Enhanced the session processing pipeline with new notebooks and workflows.
- Improved data extraction and preprocessing capabilities with efficient file format recommendations.
Pending Tasks:
- Further refine and test the modular notebooks for data processing and clustering.
- Explore additional optimization techniques for embedding storage and retrieval.