📅 2025-02-02 — Session: Enhancing RAG AI and Document Processing Systems
🕒 00:30–22:40
🏷️ Labels: RAG AI, Document Processing, Automation, Data Parsing, Performance Optimization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session focused on enhancing both the Retrieval-Augmented Generation (RAG) AI capabilities and the document processing systems.
Key Activities
- Document Processing System: Assessed the progress in transforming a chaotic file system into a structured, automated document processing pipeline. Key components were implemented, and future optimization opportunities were identified.
- Data Parsing Workflow: Refined the data parsing workflow within the Accounting folder, addressing challenges and outlining immediate goals for processing financial documents.
- RAG AI Optimization: Developed a strategic roadmap for improving RAG AI performance by refining metadata structuring, optimizing vectorstore design, and enhancing context portability. Detailed action items were created for future work sessions.
- Performance Optimization: Explored best practices for optimizing RAG pipeline performance, focusing on practical approaches and standards for context portability and multi-domain adaptability.
- Hybrid Storage Strategy: Implemented a hybrid storage and querying strategy using Supabase, detailing architecture and best practices for efficient retrieval and metadata management.
- CRAG System Analysis: Conducted a detailed analysis of the CRAG system for integration into an existing RAG pipeline, suggesting modifications for effective integration.
- Pydantic Models Overview: Reviewed the use of Pydantic models for data validation and parsing in Python, relevant to FastAPI and AI systems.
Achievements
- Completed a comprehensive analysis of the Document Processing and Retrieval System and HierarchicalRAG System, identifying strengths, weaknesses, and integration recommendations for RAG pipelines.
Pending Tasks
- Further optimize the RAG AI’s metadata structuring and vectorstore design.
- Continue refining the data parsing workflow for accounting documents.
- Implement the recommended modifications for the CRAG system integration into the RAG pipeline.