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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-basics/search-beginners.md
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@@ -46,11 +46,11 @@ Next, create a client connection to your Qdrant cluster using the endpoint and A
{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="client-connection" >}}
Replace `QDRANT_URL` and `QDRANT_API_KEY` with the cluster endpoint and API key you obtained in the previous step. The `cloud_inference=True` parameter enables Qdrant Cloud's [inference](/documentation/concepts/inference/) capabilities, allowing the cluster to generate vector embeddings without the need to manage your own embedding infrastructure.
Replace `QDRANT_URL` and `QDRANT_API_KEY` with the cluster endpoint and API key you obtained in the previous step. The `cloud_inference=True` parameter enables Qdrant Cloud's [inference](/documentation/inference/) capabilities, allowing the cluster to generate vector embeddings without the need to manage your own embedding infrastructure.
## 3. Create a Collection
All data in Qdrant is organized within [collections](/documentation/concepts/collections/). Since you're storing books, let's create a collection named `my_books`.
All data in Qdrant is organized within [collections](/documentation/manage-data/collections/). Since you're storing books, let's create a collection named `my_books`.
{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="create-collection" >}}
@@ -63,7 +63,7 @@ The dataset consists of a list of science fiction books. Each entry has a name,
{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="upload-data" >}}
Store each book as a [point](/documentation/concepts/points/) in the `my_books` collection, with each point consisting of a [unique ID](/documentation/concepts/points/#point-ids), a [vector](/documentation/concepts/vectors/) generated from the description, and a [payload](/documentation/concepts/payload/) containing the book's metadata:
Store each book as a [point](/documentation/manage-data/points/) in the `my_books` collection, with each point consisting of a [unique ID](/documentation/manage-data/points/#point-ids), a [vector](/documentation/manage-data/vectors/) generated from the description, and a [payload](/documentation/manage-data/payload/) containing the book's metadata:
{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="upload-points" >}}
@@ -89,9 +89,9 @@ The search engine returns the three most relevant books related to an alien inva
### Narrow down the Query
How about the most recent book from the early 2000s? Qdrant allows you to narrow down query results by applying a [filter](/documentation/concepts/filtering/). To filter for books published after the year 2000, you can filter on the `year` field in the payload.
How about the most recent book from the early 2000s? Qdrant allows you to narrow down query results by applying a [filter](/documentation/search/filtering/). To filter for books published after the year 2000, you can filter on the `year` field in the payload.
Before filtering on a payload field, create a [payload index](/documentation/concepts/indexing/#payload-index) for that field:
Before filtering on a payload field, create a [payload index](/documentation/manage-data/indexing/#payload-index) for that field:
{{< code-snippet path="/documentation/headless/snippets/tutorial-semantic-search-101/" block="create-payload-index" >}}