{"id":1780,"date":"2026-03-10T14:46:21","date_gmt":"2026-03-10T13:46:21","guid":{"rendered":"https:\/\/cyberelk.net\/tim\/?p=1780"},"modified":"2026-03-14T13:32:32","modified_gmt":"2026-03-14T12:32:32","slug":"searching-logseq-by-concept-not-keystrokes","status":"publish","type":"post","link":"https:\/\/cyberelk.net\/tim\/2026\/03\/10\/searching-logseq-by-concept-not-keystrokes\/","title":{"rendered":"Searching Logseq by Concept, Not Keystrokes"},"content":{"rendered":"\n

Logseq<\/a> is great for dumping daily notes, but finding them again later can be a pain. If you’re looking for notes on a “connection timeout” but originally wrote “increasing the socket keepalive”, a standard keyword search will give you nothing. You end up having to guess the exact phrasing your past self used.<\/p>\n\n\n\n

I wanted a way to search my graph by concept rather than exact text matches. That’s why I put together the Logseq Semantic Search<\/a><\/strong> plugin.<\/p>\n\n\n\n\n\n\n\n

The upcoming database version of Logseq actually has semantic search built-in. But since I’m still using the standard Markdown version for my day-to-day workflow, I wanted to get that capability right now.<\/p>\n\n\n\n

Indexing the hierarchy<\/h3>\n\n\n\n

The plugin uses text embeddings to find conceptually similar blocks. But just embedding individual bullet points doesn’t work well for outliners. A block that just says “needs refactoring”<\/em> is useless on its own.<\/p>\n\n\n\n

If you’ve seen my Logsqueak<\/a> project, you’ll recognise the indexing approach here. Every block is indexed along with its complete structural lineage\u2014the page name, properties, and the full chain of parent blocks above it.<\/p>\n\n\n\n

Because it captures this nested context, the search index knows that a vague bullet point nested under billing-service<\/code> \u2192 Database Connection Pool<\/code> is actually about your Postgres setup. Searching for “optimizing billing db” will pull that specific child block right to the top of the results.<\/p>\n\n\n\n

Running it locally<\/h3>\n\n\n\n

Since a Logseq graph is essentially a private brain dump, I wanted this to run entirely locally. By default, the plugin connects to Ollama<\/a> using the lightweight nomic-embed-text<\/code> model. It’s smart enough to only re-embed blocks that have changed, so it’s relatively fast even without a GPU. (If you prefer, you can also point it at any OpenAI-compatible endpoint in the settings).<\/p>\n\n\n\n

I run Fedora Workstation and prefer to keep my host system clean, so I run Ollama via Podman. It’s incredibly straightforward to set up:<\/p>\n\n\n\n

# Start the Ollama container, exposing the default port\n# and persisting data\npodman run -d \\\n  --name ollama \\\n  -p 11434:11434 \\\n  -e \"OLLAMA_ORIGINS=*\" \\\n  -v ollama:\/root\/.ollama \\\n  docker.io\/ollama\/ollama\n\n# Pull the lightweight embedding model\npodman exec ollama ollama pull nomic-embed-text<\/code><\/pre>\n\n\n\n
Because we mapped port 11434<\/code>, the Semantic Search plugin can talk to the container seamlessly at http:\/\/localhost:11434<\/code> right out of the box. No dependency issues, just a private embedding server ready to run in the background.<\/p>\n\n\n\n
Usage<\/h3>\n\n\n\n
You can grab the plugin directly from the Logseq Marketplace. Once it’s installed, hit Alt+K<\/code> (or click the toolbar icon) to open the search modal. Try typing a natural language query\u2014like “notes about debugging pipeline failures”<\/em>\u2014and it will surface the relevant blocks even if you didn’t use the word “debugging.”<\/p>\n\n\n\n
The source code is up on GitHub<\/a> if you want to poke around or contribute.<\/p>\n","protected":false},"excerpt":{"rendered":"
Logseq is great for dumping daily notes, but finding them again later can be a pain. If you’re looking for notes on a “connection timeout” but originally wrote “increasing the socket keepalive”, a standard keyword search will give you nothing. You end up having to guess the exact phrasing your past self used. I wanted […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[3],"tags":[27,105],"class_list":["post-1780","post","type-post","status-publish","format-standard","hentry","category-software","tag-fedora","tag-logseq"],"yoast_head":"\nSearching Logseq by Concept, Not Keystrokes - PRINT HEAD<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cyberelk.net\/tim\/2026\/03\/10\/searching-logseq-by-concept-not-keystrokes\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Searching Logseq by Concept, Not Keystrokes - PRINT HEAD\" \/>\n<meta property=\"og:description\" content=\"Logseq is great for dumping daily notes, but finding them again later can be a pain. 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