📅 2025-02-02 — Session: Enhanced RAG AI Capabilities and Integration
🕒 21:40–22:50
🏷️ Labels: RAG AI, Metadata, Vectorstore, Integration, Performance, Supabase
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the capabilities of a Retrieval-Augmented Generation (RAG) AI by optimizing metadata management, vectorstore design, and context portability across domains. Additionally, it focused on analyzing and integrating various systems and strategies into the RAG pipeline.
Key Activities
- Developed a strategic roadmap for improving RAG AI performance through metadata structuring and vectorstore optimization.
- Discussed performance optimizations and best practices for the RAG pipeline.
- Outlined best practices for context portability and multi-domain adaptability, including vector indices separation and hierarchical embeddings.
- Designed a hybrid storage and querying strategy using Supabase for efficient document retrieval and metadata management.
- Analyzed the CRAG system for potential integration into the RAG pipeline.
- Reviewed Pydantic models for data validation in Python, applicable to AI systems.
- Conducted file analysis for RAG system integration, focusing on retrieval, processing, and metadata.
- Examined the DocumentProcessor system for integration into the RAG pipeline, highlighting strengths and weaknesses.
- Analyzed the HierarchicalRAG system for integration, suggesting the replacement of FAISS with Supabase for better efficiency.
Achievements
- A comprehensive roadmap and set of best practices were established for enhancing RAG AI capabilities.
- Identified and analyzed several systems and strategies for integration into the RAG pipeline.
Pending Tasks
- Implement the suggested optimizations and integrations into the RAG pipeline.
- Further testing and validation of the proposed strategies and systems.