📅 2025-01-30 — Session: Designed AI-Powered Storage and Retrieval System
🕒 23:25–00:00
🏷️ Labels: RAG, AI, Data Management, Openai, Embeddings, Automation
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to design a comprehensive AI-powered storage and retrieval system using Retrieval-Augmented Generation (RAG) techniques.
Key Activities
- Developed a framework for a scalable and efficient storage system for miscellaneous data files.
- Planned the integration of OpenAI embeddings with RAG for optimized information retrieval and chatbot responses.
- Outlined a detailed strategy for indexing, managing, and retrieving data within a RAG system, focusing on semantic retrieval.
- Managed VectorStore updates and persistence, ensuring efficient data handling and storage.
- Evaluated the Raptor implementation, identifying strengths and weaknesses, and suggested enhancements using LangChain and other RAG frameworks.
Achievements
- Established a structured approach for data storage and retrieval using RAG.
- Defined best practices for integrating OpenAI embeddings.
- Developed strategies for efficient data indexing and management.
Pending Tasks
- Implement the designed strategies and evaluate their effectiveness in a real-world scenario.