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fix: linkchecker include filter port mismatch (#2242)

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* initial commit; fixed anchor links on internal docs pages

* add back in absolute paths for links in code comments

* fix: update linkchecker include filter to match server port 1314

PR #1629 changed the Hugo server to port 1314 but forgot to update
the --include filter, which still matched port 1313. This caused all
links to be excluded, making the checker a no-op (0 checked, 82277 excluded).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Fix url rewrite regex so images are not impacted

* Fix links from non-documentation pages

* Fix broken links

* more broken links

* more broken links

* broken link

* Add srcset width descriptor to .lycheeignore

* Ignore URLs that contain a % character

* Anchor regex so it matches the entire URL

---------

Co-authored-by: kanungle <neil.kanungo@gmail.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>
This commit is contained in:
Andrey Vasnetsov
2026-03-30 17:21:32 +02:00
committed by GitHub
co-authored by Claude Opus 4.6 kanungle Abdon Pijpelink
parent dc0080fffa
commit 1a40961d62
272 changed files with 872 additions and 879 deletions
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qdrant-landing/content/course/essentials/day-1/distance-metrics.md
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@@ -9,7 +9,7 @@ isLesson: true
# Distance Metrics
After vectors are stored, we can use their spatial properties to perform [nearest neighbor searches](/documentation/concepts/search/) that retrieve semantically similar items based on how close they are in this space.
After vectors are stored, we can use their spatial properties to perform [nearest neighbor searches](/documentation/search/search/) that retrieve semantically similar items based on how close they are in this space.
The position of a vector in embedding space only reflects meaning as far as the embedding model has learned to encode it. The model and its training objective tell you what "close" means.
@@ -166,4 +166,4 @@ If you are training your own model or designing custom features, use these guide
* **Dot product** accounts for magnitude and direction.
4. **Experiment:** Qdrant allows you to set distance metrics per named vector, making it easy to A/B test different metrics on your specific data.
Reference: [Distance Metrics in Qdrant Documentation](/documentation/concepts/search/#metrics)
Reference: [Distance Metrics in Qdrant Documentation](/documentation/search/search/#metrics)