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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/course/essentials/day-1/embedding-models.md
+4 -4
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@@ -82,7 +82,7 @@ The `indices` and `values` arrays must be the same size, and all the `indices` m
There is no need to sort the sparse representation by indices, as Qdrant will perform this internally while maintaining the correct link between each index and its value.
We will cover more about sparse vectors on day 3. If you would like to read up on the subject in advance, you can find more documentation [here](/documentation/concepts/vectors/#sparse-vectors).
We will cover more about sparse vectors on day 3. If you would like to read up on the subject in advance, you can find more documentation [here](/documentation/manage-data/vectors/#sparse-vectors).
### Multivectors
@@ -234,7 +234,7 @@ While vectors capture the essence of data, payloads hold structured metadata for
Payloads can store textual data (descriptions, tags, categories), numerical values (dates, prices, ratings), and complex structures (nested objects, arrays). When searching for dog images, for example, the vector finds visually similar images while payload filters narrow results to images taken within the last year, tagged with "vacation," or meeting specific rating criteria.
Learn more: [Payload Documentation](/documentation/concepts/payload/)
Learn more: [Payload Documentation](/documentation/manage-data/payload/)
### Payload Types
@@ -304,7 +304,7 @@ Here are some of the most common condition types:
<aside role="alert"> This list covers the most common conditions available at the time of this course. Qdrant is constantly evolving, and new filtering capabilities may have been added.
For the complete, most up-to-date list of all available filtering conditions, please refer to the **[official Filtering documentation](/documentation/concepts/filtering/#filtering-conditions)**.</aside>
For the complete, most up-to-date list of all available filtering conditions, please refer to the **[official Filtering documentation](/documentation/search/filtering/#filtering-conditions)**.</aside>
### Filtering Capabilities Reference
@@ -382,7 +382,7 @@ client.create_payload_index(
When filters are highly selective, Qdrant's query planner may bypass vector indexing entirely and use payload indexes for faster results.
For comprehensive filtering examples and advanced usage patterns, see the [Filtering Documentation](/documentation/concepts/filtering/) and [Complete Guide to Filtering in Vector Search](/articles/vector-search-filtering/).
For comprehensive filtering examples and advanced usage patterns, see the [Filtering Documentation](/documentation/search/filtering/) and [Complete Guide to Filtering in Vector Search](/articles/vector-search-filtering/).
## Key Takeaways