📅 2025-02-20 — Session: Enhanced AI Retrieval and Embedding Systems
🕒 20:40–22:30
🏷️ Labels: Ai Retrieval, Embedding Systems, FAISS, Hugging Face, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary objective was to enhance AI retrieval and embedding systems by implementing dynamic model selection, efficient caching, and modular code structures.
Key Activities
- Argument Parsing in Jupyter Notebooks: Improved command-line argument handling for Jupyter notebooks to ensure proper execution of embedding functions.
- Dynamic Embedding Model Selection: Implemented dynamic selection and incremental FAISS updates to optimize embedding processes.
- Structuring Embedding Storage: Organized embedding storage with FAISS and Parquet for efficient data retrieval.
- Caching Hugging Face Models: Developed strategies for efficient caching of Hugging Face models to enhance performance.
- Integration into AI Workflows: Integrated modular embedder scripts into AI workflows, enhancing retrieval-augmented generation and semantic search.
- Modular Code Structure: Refactored code for AI knowledge retrieval into modular components, enhancing organization and efficiency.
Achievements
- Successfully implemented dynamic model loading and caching strategies to improve performance.
- Enhanced the modularity and efficiency of AI retrieval systems using FAISS and Hugging Face embeddings.
- Developed a command-line interface for FAISS retrieval, enabling easier execution and testing.
Pending Tasks
- Further optimization of the modular information retrieval system, focusing on hybrid search and re-ranking capabilities.
- Continued integration of enhancements into operational retrievers, including pagination and API integration.