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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/tutorials-search-engineering/pdf-retrieval-at-scale.md
+2 -2
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@@ -27,7 +27,7 @@ Recent advancements in **Vision Large Language Models (VLLMs)**, such as [**ColP
VLLMs like **ColPali** and **ColQwen** generate **multivector representations** for each PDF page; the representations are stored and indexed in a vector database. During the retrieval process, models dynamically create multivector representations for (textual) user queries, and precise retrieval -- matching between PDF pages and queries -- is achieved through [late-interaction mechanism](/blog/qdrant-colpali/#how-colpali-works-under-the-hood).
<aside role="status"> Qdrant supports <a href="/documentation/concepts/vectors/#multivectors">multivector representations</a>, making it well-suited for using embedding models such as ColPali, ColQwen, or <a href="/documentation/fastembed/fastembed-colbert/">ColBERT</a></aside>
<aside role="status"> Qdrant supports <a href="/documentation/manage-data/vectors/#multivectors">multivector representations</a>, making it well-suited for using embedding models such as ColPali, ColQwen, or <a href="/documentation/fastembed/fastembed-colbert/">ColBERT</a></aside>
## Challenges of Scaling VLLMs
@@ -40,7 +40,7 @@ The heavy multivector representations produced by VLLMs make PDF retrieval at sc
To understand the impact, consider the construction of an [**HNSW index**](/articles/what-is-a-vector-database/#1-indexing-hnsw-index-and-sending-data-to-qdrant), a common indexing algorithm for vector databases. Let's roughly estimate the number of comparisons needed to insert a new PDF page into the index.
- **Vectors per page:** ~700 (ColQwen) or ~1,000 (ColPali)
- **[ef_construct](/documentation/concepts/indexing/#vector-index):** 100 (default)
- **[ef_construct](/documentation/manage-data/indexing/#vector-index):** 100 (default)
The lower bound estimation for the number of vector comparisons would be: