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/documentation/fastembed/fastembed-colbert.md

@@ -30,10 +30,10 @@ All interactions between these parts are expected to be done "later" outside the
 
 ## Using ColBERT in Qdrant
 
-Qdrant supports [multivector representations](https://qdrant.tech/documentation/concepts/vectors/#multivectors) out of the box so that you can use any late interaction model as `ColBERT` or `ColPali` in Qdrant without any additional pre/post-processing.
+Qdrant supports [multivector representations](/documentation/manage-data/vectors/#multivectors) out of the box so that you can use any late interaction model as `ColBERT` or `ColPali` in Qdrant without any additional pre/post-processing.
 
 This tutorial uses ColBERT as a first-stage retriever on a toy dataset.
-You can see how to use ColBERT as a reranker in our [multi-stage queries documentation](https://qdrant.tech/documentation/concepts/hybrid-queries/#multi-stage-queries).
+You can see how to use ColBERT as a reranker in our [multi-stage queries documentation](/documentation/search/hybrid-queries/#multi-stage-queries).
 ## Setup
 
 Install `fastembed`.
@@ -144,8 +144,8 @@ from qdrant_client import QdrantClient, models
 qdrant_client = QdrantClient(":memory:") # Qdrant is running from RAM.
 ```
 
-Now, let's create a small [collection](https://qdrant.tech/documentation/concepts/collections/) with our movie data.
-For that, we will use the [multivectors](https://qdrant.tech/documentation/concepts/vectors/#multivectors) functionality supported in Qdrant.
+Now, let's create a small [collection](/documentation/manage-data/collections/) with our movie data.
+For that, we will use the [multivectors](/documentation/manage-data/vectors/#multivectors) functionality supported in Qdrant.
 To configure multivector collection, we need to specify:
 - similarity metric between vectors;
 - the size of each vector (for ColBERT, it's **128**);

qdrant-landing/content/documentation/fastembed/fastembed-minicoil.md

@@ -77,7 +77,7 @@ documents = [
 ## Create Collection
 Let's create a collection to store and index titles.
 
-As miniCOIL was designed with Qdrant's ability to calculate the keywords Inverse Document Frequency (IDF) in mind, we need to configure miniCOIL sparse vectors with [IDF modifier](https://qdrant.tech/documentation/concepts/indexing/#idf-modifier).
+As miniCOIL was designed with Qdrant's ability to calculate the keywords Inverse Document Frequency (IDF) in mind, we need to configure miniCOIL sparse vectors with [IDF modifier](/documentation/manage-data/indexing/#idf-modifier).
 
 <aside role="status">
 Don't forget to configure the IDF modifier to use miniCOIL sparse vectors in Qdrant!

qdrant-landing/content/documentation/fastembed/fastembed-postprocessing.md

@@ -174,8 +174,8 @@ from qdrant_client import QdrantClient, models
 client = QdrantClient("http://localhost:6333")
 ```
 
-Create a [collection](/documentation/concepts/collections/) that stores both MUVERA embeddings and the original 
-multi-vector representations using [named vectors](/documentation/concepts/vectors/#named-vectors).
+Create a [collection](/documentation/manage-data/collections/) that stores both MUVERA embeddings and the original 
+multi-vector representations using [named vectors](/documentation/manage-data/vectors/#named-vectors).
 
 ```python
 client.create_collection(
@@ -220,7 +220,7 @@ client.upload_points(
 ### Hybrid Search: MUVERA Retrieval + ColBERT Reranking
 
 Now let's perform a search using the hybrid approach. Qdrant supports [multi-stage 
-queries](/documentation/concepts/hybrid-queries/#multi-stage-queries) through the `prefetch` parameter, which lets us 
+queries](/documentation/search/hybrid-queries/#multi-stage-queries) through the `prefetch` parameter, which lets us 
 combine MUVERA's fast retrieval with ColBERT's accurate rescoring in a single query.
 
 First, create query embeddings in both formats.
@@ -253,7 +253,7 @@ ColBERT's multi-vector representation. Qdrant automatically handles the MaxSim c
 
 <aside role="status">
 Qdrant's multi-stage query API handles the two-stage retrieval natively - no manual reranking code needed! Learn more 
-about <a href="/documentation/concepts/hybrid-queries/#multi-stage-queries">multi-stage queries</a>.
+about <a href="/documentation/search/hybrid-queries/#multi-stage-queries">multi-stage queries</a>.
 </aside>
 
 Display the results.

qdrant-landing/content/documentation/fastembed/fastembed-rerankers.md

@@ -145,7 +145,7 @@ from qdrant_client import QdrantClient, models
 client = QdrantClient(":memory:")  # Qdrant is running from RAM.
 ```
 
-Let's create a [collection](https://qdrant.tech/documentation/concepts/collections/) with our movie data.
+Let's create a [collection](/documentation/manage-data/collections/) with our movie data.
 
 ```python
 client.create_collection(