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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/blog/qdrant-1.15.x.md
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@@ -66,7 +66,7 @@ This approach maintains storage size and RAM usage similar to binary quantizatio
When performing nearest vector search, the query vector is compared against quantized vectors stored in the database. If the query itself remains unquantized and a scoring method exists to evaluate it directly against the compressed vectors, this allows for more accurate results without increasing memory usage.
> Quantization enables efficient storage and search of high-dimensional vectors. Learn more about this from our [**quantization**](/documentation/guides/quantization/) docs.
> Quantization enables efficient storage and search of high-dimensional vectors. Learn more about this from our [**quantization**](/documentation/manage-data/quantization/) docs.
<details>
@@ -138,7 +138,7 @@ PUT /collections/{collection_name}/index
}
```
For more information about stopwords, see the [documentation](https://qdrant.tech/documentation/concepts/indexing/#stopwords).
For more information about stopwords, see the [documentation](https://qdrant.tech/documentation/manage-data/indexing/#stopwords).
### Stemming
@@ -169,7 +169,7 @@ PUT /collections/{collection_name}/index
### Phrase Matching
With [phrase matching](/documentation/concepts/filtering/#phrase-match), you can now perform exact phrase search.
With [phrase matching](/documentation/search/filtering/#phrase-match), you can now perform exact phrase search.
It allows you to search for a specific phrase, words in exact order, within a text field.
For efficient phrase search Qdrant requires to build an additional data structure,
@@ -213,7 +213,7 @@ The above will match:
## MMR Reranking
We introduce [Maximal Marginal Relevance (MMR)](/documentation/concepts/search-relevance/#maximal-marginal-relevance-mmr) reranking to balance relevance and diversity.
We introduce [Maximal Marginal Relevance (MMR)](/documentation/search/search-relevance/#maximal-marginal-relevance-mmr) reranking to balance relevance and diversity.
MMR works by selecting the results iteratively, by picking the item with the best combination of similarity to the query and dissimilarity to the already selected items.
It prevents your top-k results from being redundant and helps surface varied but relevant answers, particularly in dense datasets with overlapping entries.
@@ -225,7 +225,7 @@ It prevents your top-k results from being redundant and helps surface varied but
Let’s say you’re building a knowledge assistant or semantic document explorer in which a single query can return multiple highly similar queries.
For instance, searching “climate change” in a scientific paper database might return several similar paragraphs.
You can diversify the results with [Maximal Marginal Relevance (MMR)](/documentation/concepts/search-relevance/#maximal-marginal-relevance-mmr).
You can diversify the results with [Maximal Marginal Relevance (MMR)](/documentation/search/search-relevance/#maximal-marginal-relevance-mmr).
Instead of returning the top-k results based on pure similarity, MMR helps select a diverse subset of high-quality results.
This gives more coverage and avoids redundant results, which is helpful in dense content domains such as academic papers, product catalogs, or search assistants.
@@ -273,7 +273,7 @@ As usual, new Qdrant release brings more performance optimization for faster and
Qdrant 1.15 introduces HNSW healing.
Instead of completely re-building HNSW index during optimization, Qdrant now tries to re-use information from the existing vector index to speed-up construction of the new one.
When points are removed from an existing [HNSW graph](https://qdrant.tech/documentation/concepts/indexing/#vector-index), new links are added to prevent isolation in the graph, and avoid decreasing search quality.
When points are removed from an existing [HNSW graph](https://qdrant.tech/documentation/manage-data/indexing/#vector-index), new links are added to prevent isolation in the graph, and avoid decreasing search quality.
{{<figure src="/blog/qdrant-1.15.x/healing.png" caption="Indexing speed with healing vs full re-index" >}}