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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-and-examples/cloud-inference-hybrid-search.md
+3 -3
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@@ -10,8 +10,8 @@ weight: 35
In this tutorial, we'll walkthrough building a **hybrid semantic search engine** using Qdrant Cloud's built-in [inference](/documentation/cloud/inference/) capabilities. You'll learn how to:
- Automatically embed your data using [cloud Inference](/documentation/cloud/inference/) without needing to run local models,
- Combine dense semantic embeddings with [sparse BM25 keywords](https://qdrant.tech/documentation/advanced-tutorials/reranking-hybrid-search/), and
- Perform hybrid search using [Reciprocal Rank Fusion (RRF)](https://qdrant.tech/documentation/concepts/hybrid-queries/) to retrieve the most relevant results.
- Combine dense semantic embeddings with [sparse BM25 keywords](https://qdrant.tech/documentation/tutorials-search-engineering/reranking-hybrid-search/), and
- Perform hybrid search using [Reciprocal Rank Fusion (RRF)](/documentation/search/hybrid-queries/) to retrieve the most relevant results.
## Initialize the Client
Initialize the Qdrant client after creating a [Qdrant Cloud account](/documentation/cloud/) and a [dedicated paid cluster](/documentation/cloud/create-cluster/). Set `cloud_inference` to `True` to enable [cloud inference](/documentation/cloud/inference/).
@@ -41,7 +41,7 @@ Create a sample query:
{{< code-snippet path="/documentation/headless/snippets/cloud-inference/vector-search/create-sample-query/" >}}
## Run Vector Search
Here, you will ask a question that will allow you to retrieve semantically relevant results. The final results are obtained by reranking using [Reciprocal Rank Fusion](https://qdrant.tech/documentation/concepts/hybrid-queries/#hybrid-search).
Here, you will ask a question that will allow you to retrieve semantically relevant results. The final results are obtained by reranking using [Reciprocal Rank Fusion](/documentation/search/hybrid-queries/#hybrid-search).
{{< code-snippet path="/documentation/headless/snippets/cloud-inference/vector-search/run-vector-search/" >}}