📅 2025-02-21 — Session: Refactored FAISS pipeline and improved data retrieval
🕒 18:30–18:55
🏷️ Labels: FAISS, Data Retrieval, Embedding, Machine Learning, Query Optimization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to reset and rerun the FAISS pipeline to ensure clean data and improve data retrieval systems for both structured and unstructured data queries.
Key Activities
- Reset and Rerun FAISS Pipeline: Followed a structured workflow to purge old data, verify deletions, and restart the embedding process to maintain the integrity of the FAISS index.
- Analysis of FAISS Search Results: Conducted an analysis on search results related to ‘THE STREAM DATA MODEL’, identifying areas for improvement in ranking and relevance.
- Improvement of Data Retrieval Systems: Addressed issues in the data retrieval system by refining queries, re-ranking results, and testing different embedding models to improve accuracy.
Achievements
- Successfully reset and reran the FAISS pipeline, ensuring no duplicate embeddings and maintaining data integrity.
- Identified potential improvements in FAISS search result relevance.
- Developed actionable steps for improving data retrieval systems across different data types.
Pending Tasks
- Further testing and refinement of retrieval parameters to enhance result accuracy.
- Implementation of identified improvements in FAISS search result ranking.