Implemented CRAG system with OpenAI enhancements

• Day: 2025-02-03
• Time: 17:15 to 18:15
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: CRAG, Openai, Python, Optimization, RAG, FAISS

Description

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.

Evidence

• source_file=2025-02-03.sessions.jsonl, line_number=2, event_count=0, session_id=f2cca1d36646a817681c6842b7b8acb8c64c25ead10bc054b4c4847beb44586b
• event_ids: []