Implemented CRAG system with OpenAI enhancements

📅 2025-02-03 — Session: Implemented CRAG system with OpenAI enhancements

🕒 17:15–18:15
🏷️ Labels: CRAG, Openai, Python, Optimization, RAG, FAISS
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to implement and optimize the CRAG system using OpenAI’s tools for retrieval and synthesis, improving efficiency and performance without relying on FAISS for low-level embedding management.

Key Activities

• Developed a Python script for querying and processing text chunks using OpenAI’s retrieval-augmented generation (RAG) system.
• Optimized retrieval processes by leveraging OpenAI embeddings, enhancing the efficiency of querying and response generation.
• Created a time_logger decorator and a TimeBlock context manager in Python for tracking and logging execution times within the CRAG class.
• Outlined an optimization plan for FAISS to store and load embeddings efficiently, avoiding recomputation.
• Addressed text corruption and encoding issues in RAG processing, integrating a normalization function for chunk loading and embedding.
• Modified query processing for OpenAI RAG to improve logging and performance tracking.
• Resolved an AttributeError in the CRAG class by correcting the initialization of the embedding_model attribute.
• Fixed a TypeError in FAISS retrieval by adjusting the search method.

Achievements

• Successfully implemented and optimized the CRAG system with OpenAI enhancements, improving retrieval and processing efficiency.
• Enhanced the CRAG class with robust logging and performance monitoring tools.

Pending Tasks

• Further testing and validation of the optimized retrieval system to ensure stability and performance under different conditions.