Enhanced FAISS Search and Embedding Management

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📅 2025-02-21 — Session: Enhanced FAISS Search and Embedding Management

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

Session Goal

The session aimed to evaluate and improve the semantic search quality using FAISS and embedding models, focusing on debugging and optimizing the Embedder class and FAISS index management.

Key Activities

  • Tested the text-embedding-3-small model for semantic search in data science literature.
  • Evaluated FAISS search results for machine learning queries, identifying issues with embeddings.
  • Improved search quality by refining FAISS ranking and embedding strategies.
  • Debugged the Embedder class to fix dimension mismatches and API response handling.
  • Updated the Embedder class for proper FAISS index tracking and refactored code.
  • Fixed issues with undefined faiss_idx in the store_faiss function.
  • Verified FAISS index functionality and embedding storage integrity.
  • Conducted a critical analysis of FAISS search results for the stream data model.
  • Implemented solutions to prevent duplicate embeddings in FAISS.

Achievements

  • Enhanced the semantic relevance and retrieval accuracy of FAISS search results.
  • Improved the robustness of the Embedder class and FAISS index management.
  • Successfully prevented duplicate embeddings, ensuring data integrity.

Pending Tasks

  • Further testing of the embedding models with diverse datasets to validate improvements.
  • Continuous monitoring and refinement of FAISS index management strategies.

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