📅 2025-02-20 — Session: Optimized FAISS Retrieval Techniques
🕒 14:50–15:30
🏷️ Labels: FAISS, Retrieval, Deep Learning, Experimental Design, Optimization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to analyze and enhance the performance of FAISS-based vectorstore retrieval systems by examining current matching techniques and identifying areas for improvement.
Key Activities
- Analyzed the effectiveness of vectorstore retrievers using FAISS and embeddings to match queries related to deep learning models like BERT and Wav2Vec 2.0.
- Evaluated mismatches in retrieval when queries focused on deep learning models but passages centered on statistical inference.
- Introduced key concepts in experimental design relevant to statistical analysis and data interpretation.
- Provided critical analysis and recommendations for improving FAISS retrieval setups, focusing on index type selection, query refinement, and embedding model choices.
- Addressed challenges related to length mismatches in FAISS retrieval and proposed mitigation strategies such as chunking and query expansion.
- Outlined best practices for developing quote finders and paragraph search engines, emphasizing precision and advanced retrieval techniques.
Achievements
- Identified specific issues with current FAISS retrieval setups and proposed actionable improvements.
- Enhanced understanding of experimental design principles and their application in improving retrieval accuracy.
Pending Tasks
- Implement the recommended improvements in FAISS retrieval setups and evaluate their impact on retrieval accuracy.
- Further explore the integration of experimental design principles in refining retrieval strategies.