Enhancing FAISS Semantic Search with Embedding Models

• Day: 2025-02-21
• Time: 17:05 to 18:15
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: FAISS, Embeddings, Debugging, Semantic Search, Data Integrity

Description

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.

Evidence

• source_file=2025-02-21.sessions.jsonl, line_number=2, event_count=0, session_id=1af7521a8000b9b2a1ffee83d6b688e92922a072761d4947e442f9e505c3439b
• event_ids: []