📅 2025-02-21 — Session: Developed and Optimized FAISS Retrieval System
🕒 01:50–06:20
🏷️ Labels: FAISS, Retrieval System, NLP, Indexing, Optimization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to develop and optimize a retrieval system using FAISS to enhance search capabilities and efficiency.
Key Activities
- Explored various AI models for text-based tasks, categorizing them into non-generative and generative models.
- Developed a hierarchical retrieval system integrating BM25 and FAISS for efficient information access.
- Reflected on the evolution of NLP technologies and the role of transformers and attention mechanisms in modern AI applications.
- Conducted a deep dive into transformers and CNNs, comparing their applications in NLP and vision tasks.
- Planned updates to the data science curriculum to align with future industry demands.
- Implemented embedding summaries for FAISS search and validated the embedding process with Python commands.
- Developed an incremental FAISS indexing script with chunk ID mapping for efficient data retrieval.
- Analyzed FAISS retrieval quality, focusing on semantic relevance and ranking consistency.
Achievements
- Successfully developed a multi-pass retrieval pipeline using FAISS and BM25.
- Improved the FAISS indexing process with chunk ID mapping and incremental indexing.
- Enhanced the retrieval quality of FAISS by addressing semantic relevance and ranking consistency.
Pending Tasks
- Further optimization of FAISS for web scraping to improve query accuracy and result quality.
Outcome
The session resulted in a robust FAISS retrieval system with improved indexing and retrieval quality, setting the foundation for future enhancements in search optimization.