📅 2025-02-20 — Session: Enhanced Embedding and Retrieval Systems
🕒 20:40–22:30
🏷️ Labels: Python, FAISS, Hugging Face, Embedding, Retrieval, Caching
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the efficiency and modularity of embedding and retrieval systems using Python, FAISS, and Hugging Face models.
Key Activities
- Argument Parsing in Jupyter: Fixed command-line argument parsing for Jupyter notebooks to ensure proper execution based on specified modes.
- Dynamic Embedding Model Selection: Modified the system to allow dynamic selection of embedding models and incremental updates to FAISS indexes without full rebuilds.
- Embedding Storage Structuring: Organized embedding storage across multiple collections using FAISS and Parquet files for efficient data retrieval.
- Hugging Face Model Caching: Implemented strategies for local caching of Hugging Face models to enhance embedding process efficiency.
- Embedder Script Integration: Integrated a modular embedder script into AI workflows, improving knowledge processing and retrieval-augmented generation.
- Modular Code Structure: Developed a modular code structure for AI knowledge retrieval, focusing on embedding, storage, and retrieval modules.
- Retriever Enhancements: Enhanced the retriever system with hybrid search capabilities, transformer re-ranking, and web API integration.
Achievements
- Improved performance and modularity of embedding and retrieval systems.
- Established a structured approach for embedding storage and retrieval.
- Enhanced AI workflow integration with modular scripts and caching strategies.
Pending Tasks
- Further optimization of modular retrieval systems and exploration of additional caching strategies.
- Continued enhancements to the CLI and embedder class for better performance.