Enhanced CRAG Implementation and Optimization

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📅 2025-02-03 — Session: Enhanced CRAG Implementation and Optimization

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

Session Goal

The session aimed to enhance the CRAG system by integrating OpenAI for retrieval and synthesis, optimizing retrieval processes, and addressing various code issues for improved performance and reliability.

Key Activities

  • Implemented a new CRAG system using OpenAI, eliminating the need for low-level embedding management and FAISS.
  • Optimized retrieval processes with OpenAI embeddings to enhance efficiency.
  • Developed a time_logger decorator and a TimeBlock context manager for performance monitoring.
  • Outlined an optimization plan for embeddings with FAISS to avoid recomputation.
  • Addressed text corruption issues in RAG processing by implementing a normalization function.
  • Modified query processing for OpenAI RAG to improve logging and performance tracking.
  • Resolved an AttributeError in the CRAG class and a TypeError in FAISS retrieval.

Achievements

  • Successfully integrated OpenAI into the CRAG system, improving retrieval and synthesis.
  • Enhanced code performance and reliability through optimization and error resolution.

Pending Tasks

  • Further testing and validation of the implemented changes to ensure robustness.
  • Documentation updates to reflect the new implementation and optimizations.

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