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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/examples/rag-contract-management-stackit-aleph-alpha.md
+2 -2
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@@ -93,7 +93,7 @@ developing business logic.
Aleph Alpha embeddings are high dimensional vectors by default, with a dimensionality of `5120`. However, a pretty
unique feature of that model is that they might be compressed to a size of `128`, with a small drop in accuracy
performance (4-6%, according to the docs). Qdrant can store even the original vectors easily, and this sounds like a
good idea to enable [Binary Quantization](/documentation/guides/quantization/#binary-quantization) to save space and
good idea to enable [Binary Quantization](/documentation/manage-data/quantization/#binary-quantization) to save space and
make the retrieval faster. Let's create a collection with such settings:
```python
@@ -234,7 +234,7 @@ llm = AlephAlpha(
Then, we can glue the components together and build the search process. `RetrievalQA` is a class that takes implements
the Question Retrieval process, with a specified retriever and Large Language Model. The instance of `Qdrant` might be
converted into a retriever, with additional filter that will be passed to the `similarity_search` method. The filter
is created as [in a regular Qdrant query](/documentation/concepts/filtering/), with the `roles` field set to the
is created as [in a regular Qdrant query](/documentation/search/filtering/), with the `roles` field set to the
user's roles.
```python