Developed Modular Data Processing Pipeline

• Day: 2025-05-17
• Time: 21:30 to 22:20
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Clustering, Data Processing, Modularization, Chromadb, HDBSCAN

Description

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.

Evidence

• source_file=2025-05-17.sessions.jsonl, line_number=0, event_count=0, session_id=0f905caa7af8e756501abcaba665f2ed9514afffc1d96b726c1090fd0ad92041
• event_ids: []