fix: linkchecker include filter port mismatch (#2242)

* 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
@@ -27,7 +27,7 @@ set up your collections.
Previously, you had to send multiple requests to the Qdrant API to perform multiple non-related tasks. However, this
can cause significant network overhead and slow down the process, especially if you have a poor connection speed.
Fortunately, the [new batch search feature](/documentation/concepts/search/#batch-search-api) allows
Fortunately, the [new batch search feature](/documentation/search/search/#batch-search-api) allows
you to avoid this issue. With just one API call, Qdrant will handle multiple search requests in the most efficient way
possible. This means that you can perform multiple tasks simultaneously without having to worry about network overhead
or slow performance.
@@ -44,6 +44,6 @@ both ARM and non-ARM architectures using similar setups to understand the potent
Qdrant is a vector database that allows you to quickly search for the nearest neighbors. However, you may need to apply
additional filters on top of the semantic search. Up until version 0.10, Qdrant only supported keyword filters. With the
release of Qdrant 0.10, [you can now use full-text filters](/documentation/concepts/filtering/#full-text-match)
release of Qdrant 0.10, [you can now use full-text filters](/documentation/search/filtering/#full-text-match)
as well. This new filter type can be used on its own or in combination with other filter types to provide even more
flexibility in your searches.