Enhancing FAISS Semantic Search with Embedding Models

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📅 2025-02-21 — Session: Enhancing FAISS Semantic Search with Embedding Models

🕒 17:05–18:15
🏷️ Labels: FAISS, Embeddings, Debugging, Semantic Search, Data Integrity
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to improve the semantic search capabilities using FAISS and embedding models, focusing on debugging, optimizing search quality, and ensuring data integrity.

Key Activities

  • Evaluated the text-embedding-3-small model for retrieving semantically relevant text chunks from data science literature.
  • Assessed FAISS search results for machine learning queries, identifying issues with embeddings and suggesting debugging steps.
  • Improved search quality by addressing FAISS ranking and embedding model issues, including query specificity and chunking strategies.
  • Debugged the Embedder class to fix dimension mismatches and embedding normalization issues affecting FAISS search results.
  • Updated the Embedder class to maintain proper FAISS index tracking and fixed the store_faiss function to define faiss_idx correctly.
  • Verified FAISS index and embedding storage, ensuring no skipped indices and correct alignment with chunk IDs.
  • Analyzed FAISS search results for the ‘STREAM DATA MODEL’ query, identifying areas for improvement.
  • Implemented solutions to prevent duplicate embeddings in FAISS by modifying the store_faiss() function.

Achievements

  • Successfully debugged and optimized the FAISS search process, improving semantic relevance and data integrity.
  • Implemented effective solutions for preventing duplicate embeddings and ensuring proper index tracking.

Pending Tasks

  • Further testing and refinement of the embedding models and FAISS search strategies to enhance accuracy and performance.

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