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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/documentation/inference.md
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@@ -280,6 +280,6 @@ The following example demonstrates how to insert a point into a collection with
Note that, even though the request contains two inference objects, Qdrant Cloud's inference service only makes one inference request to the OpenAI API, saving one round trip and reducing costs.
A good use case for MRL is [prefetching](https://qdrant.tech/documentation/concepts/hybrid-queries/#multi-stage-queries) with smaller vectors, followed by re-scoring with the original-sized vectors, effectively balancing speed and accuracy. This example first prefetches 1000 candidates using a 64-dimensional reduced vector (`small`) and then re-scores them using the original full-size vector (`large`) to return the top 10 most relevant results:
A good use case for MRL is [prefetching](/documentation/search/hybrid-queries/#multi-stage-queries) with smaller vectors, followed by re-scoring with the original-sized vectors, effectively balancing speed and accuracy. This example first prefetches 1000 candidates using a 64-dimensional reduced vector (`small`) and then re-scores them using the original full-size vector (`large`) to return the top 10 most relevant results:
{{< code-snippet path="/documentation/headless/snippets/inference/mrl-multi-stage/" >}}