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>

qdrant-landing/content/articles/new-recommendation-api.md

@@ -17,15 +17,15 @@ does exist, and recommendation systems are a great example. Recommendations migh
 to find items close to positive and far from negative examples. This use of vector databases has many applications, including 
 recommendation systems for e-commerce, content, or even dating apps.
 
-Qdrant has provided the [Recommendation API](/documentation/concepts/search/#recommendation-api) for a while, and with the latest release, [Qdrant 1.6](https://github.com/qdrant/qdrant/releases/tag/v1.6.0), 
+Qdrant has provided the [Recommendation API](/documentation/search/search/#recommendation-api) for a while, and with the latest release, [Qdrant 1.6](https://github.com/qdrant/qdrant/releases/tag/v1.6.0), 
 we're glad to give you more flexibility and control over the Recommendation API. 
 Here, we'll discuss some internals and show how they may be used in practice.
 
 ### Recap of the old recommendations API
 
-The previous [Recommendation API](/documentation/concepts/search/#recommendation-api) in Qdrant came with some limitations. First of all, it was required to pass vector IDs for 
+The previous [Recommendation API](/documentation/search/search/#recommendation-api) in Qdrant came with some limitations. First of all, it was required to pass vector IDs for 
 both positive and negative example points. If you wanted to use vector embeddings directly, you had to either create a new point 
-in a collection or mimic the behaviour of the Recommendation API by using the [Search API](/documentation/concepts/search/#search-api).
+in a collection or mimic the behaviour of the Recommendation API by using the [Search API](/documentation/search/search/#search-api).
 Moreover, in the previous releases of Qdrant, you were always asked to provide at least one positive example. This requirement 
 was based on the algorithm used to combine multiple samples into a single query vector. It was a simple, yet effective approach. 
 However, if the only information you had was that your user dislikes some items, you couldn't use it directly.
@@ -149,7 +149,7 @@ If you want to know more about the internals of HNSW, you can check out the arti
 
 ## Food Discovery demo
 
-Our [Food Discovery demo](/articles/food-discovery-demo/) is an application built on top of the new [Recommendation API](/documentation/concepts/search/#recommendation-api). 
+Our [Food Discovery demo](/articles/food-discovery-demo/) is an application built on top of the new [Recommendation API](/documentation/search/search/#recommendation-api). 
 It allows you to find a meal based on liked and disliked photos. There are some updates, enabled by the new Qdrant release:
 
 * **Ability to include multiple textual queries in the recommendation request.** Previously, we only allowed passing a single