A technical blog post details how to improve search functionality on a personal knowledge management (PKM) server, specifically addressing issues with ranking and embeddings. The author first tackles a ranking bug by collecting all search results, scoring them based on relevance (e.g., title matches, stem matches), sorting them, and then selecting the top five. The second, more significant problem involves implementing local embeddings using Ollama and NumPy to better understand query context, which is crucial for queries that substring matching alone cannot resolve. The process involves pulling an embedding model, chunking documents, and caching the embeddings for faster retrieval, ultimately leading to more accurate search results. AI
IMPACT Enhances search accuracy for personal knowledge bases, potentially improving developer productivity.
RANK_REASON Blog post detailing technical implementation of search improvements for a personal server.
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