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Blog post details improving PKM server search with ranking and embeddings

By PulseAugur Editorial · [1 sources] ·

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.

Read on dev.to — MCP tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Blog post details improving PKM server search with ranking and embeddings

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Blog post detailing technical implementation of search improvements for a personal server.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, infra
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High
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45 days old
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COVERAGE [1]

  1. dev.to — MCP tag TIER_1 English(EN) · Minor Keith ·

    Your MCP server's search is bad: ranking, embeddings, and what each one fixes

    <p>At the end of <a href="https://peculiarengineer.com/blog/build-your-first-mcp-server-python/" rel="noopener noreferrer">Part One</a> I left the notes server with a search I described as dumb, and promised to come back for it. Here is where it stands today, on the same query:<b…

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