Optimization of RAG and File Processing Systems

📅 2025-02-10 — Session: Optimization of RAG and File Processing Systems

🕒 12:30–14:45
🏷️ Labels: RAG, Knowledge Management, Python, File Processing, Optimization
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to optimize Retrieval-Augmented Generation (RAG) systems and improve file processing efficiency in knowledge management systems.

Key Activities

1. Developed a plan for scaling RAG systems focusing on knowledge ingestion, embedding, storage, and retrieval processes.
2. Outlined strategies for knowledge management optimization, including preliminary indexing and vector pruning.
3. Reflected on managing retrieval in RAG systems, addressing challenges and best practices for scalability.
4. Converted DataFrame size columns from kilobytes to megabytes using Python.
5. Formulated strategies for managing large files in data systems, including categorization and automation.
6. Provided Bash command for listing large files, enhancing file management.
7. Compared implementations of process_file_metadata, recommending a more efficient version.
8. Updated a chunking function with new indexing logic for better file processing.
9. Resolved TypeErrors in Python scripts related to chunk processing and file indexing.
10. Modified scripts to prevent reprocessing of already chunked files, optimizing processing time.

Achievements

• Developed comprehensive plans and strategies for optimizing RAG and file processing systems.
• Implemented code improvements and error resolutions in Python scripts.

Pending Tasks

• Further testing and validation of the updated chunking and indexing logic in real-world scenarios.