📅 2025-08-16 — Session: Enhanced Logging and Debugging for RAG Process
🕒 21:30–22:30
🏷️ Labels: RAG, Python, Logging, Debugging, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to enhance the logging and debugging capabilities of a Python script involved in a Retrieval-Augmented Generation (RAG) process. This was aimed at improving traceability, error handling, and overall script performance.
Key Activities
- Implemented verbose logging in the
main()function to track execution stages and error handling. - Fixed argument parsing issues by correcting a broken help string and enhancing logging features.
- Enhanced the RAG.py script with unbuffered output and periodic stack trace dumps to diagnose silent hangs.
- Debugged Python module execution by ensuring the presence of the
if __name__ == '__main__':guard. - Resolved disk space issues for model downloads by modifying code and suggesting alternative cache management solutions.
- Updated the JSON loader function to improve document parsing with support for multiple content keys.
- Implemented and fixed the
TokenCapPostprocessorin LlamaIndex, addressing abstract class errors and Pydantic model issues. - Resolved duplicate argument errors in the LlamaIndex API, providing code examples for integration with Chroma.
Achievements
- Successfully enhanced logging and debugging capabilities in the RAG process scripts, improving error traceability and execution monitoring.
- Addressed and fixed critical errors in argument parsing, disk space management, and API usage.
Pending Tasks
- Further testing and validation of the implemented changes in a production environment are required to ensure stability and performance improvements.
- Explore additional enhancements for the automation run process to improve speed and clarity.