fix: linkchecker include filter port mismatch (#2242)

* 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>

qdrant-landing/content/documentation/frameworks/cheshire-cat.md

@@ -25,9 +25,9 @@ CORE_PORT=1865
 
 Cheshire Cat takes great advantage of the following features of Qdrant:
 
-* [Collection Aliases](/documentation/concepts/collections/#collection-aliases) to manage the change from one embedder to another.
-* [Quantization](/documentation/guides/quantization/) to obtain a good balance between speed, memory usage and quality of the results.
-* [Snapshots](/documentation/concepts/snapshots/) to not miss any information.
+* [Collection Aliases](/documentation/manage-data/collections/#collection-aliases) to manage the change from one embedder to another.
+* [Quantization](/documentation/manage-data/quantization/) to obtain a good balance between speed, memory usage and quality of the results.
+* [Snapshots](/documentation/operations/snapshots/) to not miss any information.
 * [Community](https://discord.com/invite/tdtYvXjC4h)
 
 ![RAG Pipeline](/documentation/frameworks/cheshire-cat/stregatto.jpg)

qdrant-landing/content/documentation/frameworks/feast.md

@@ -30,7 +30,7 @@ online_store:
     port: 6333
     api_key: <your-own-key>
     vector_len: 384
-    # Reference: https://qdrant.tech/documentation/concepts/vectors/#named-vectors
+    # Reference: https://qdrant.tech/documentation/manage-data/vectors/#named-vectors
     # vector_name: text-vec
     write_batch_size: 100
 ```

qdrant-landing/content/documentation/frameworks/genkit.md

@@ -67,7 +67,7 @@ addition, there are a few optional parameters:
   dataTypePayloadKey: '_datatype';
   ```
 
-- `collectionCreateOptions`: [Additional options](/documentation/concepts/collections/#create-a-collection) when creating the Qdrant collection.
+- `collectionCreateOptions`: [Additional options](/documentation/manage-data/collections/#create-a-collection) when creating the Qdrant collection.
 
 ## Usage
 

qdrant-landing/content/documentation/frameworks/haystack.md

@@ -19,7 +19,7 @@ The new document store comes as a separate package and can be updated independen
 pip install qdrant-haystack
 ```
 
-`QdrantDocumentStore` supports [all the configuration properties](/documentation/collections/#create-collection) available in
+`QdrantDocumentStore` supports [all the configuration properties](/documentation/manage-data/collections/#create-collection) available in
 the Qdrant Python client. If you want to customize the default configuration of the collection used under the hood, you can
 provide that settings when you create an instance of the `QdrantDocumentStore`. For example, if you'd like to enable the
 Scalar Quantization, you'd make that in the following way:

qdrant-landing/content/documentation/frameworks/lakechain.md

@@ -57,7 +57,7 @@ const connector = new QdrantStorageConnector.Builder()
   .build();
 ```
 
-Since Qdrant supports [multiple vectors](/documentation/concepts/vectors/#named-vectors) per point, you can use the `withVectorName` option to specify one. The connector defaults to unnamed (default) vector.
+Since Qdrant supports [multiple vectors](/documentation/manage-data/vectors/#named-vectors) per point, you can use the `withVectorName` option to specify one. The connector defaults to unnamed (default) vector.
 
 ```typescript
 const connector = new QdrantStorageConnector.Builder()

qdrant-landing/content/documentation/frameworks/langchain.md

@@ -81,7 +81,7 @@ qdrant = Qdrant.from_documents(
 
 ### On-premise server deployment
 
-No matter if you choose to launch QdrantVectorStore locally with [a Docker container](/documentation/guides/installation/), or
+No matter if you choose to launch QdrantVectorStore locally with [a Docker container](/documentation/operations/installation/), or
 select a Kubernetes deployment with [the official Helm chart](https://github.com/qdrant/qdrant-helm), the way you're
 going to connect to such an instance will be identical. You'll need to provide a URL pointing to the service.
 

qdrant-landing/content/documentation/frameworks/langchain4j.md

@@ -24,9 +24,9 @@ Add the `langchain4j-qdrant` to your project dependencies.
 
 Before you use the following code sample, customize the following values for your configuration:
 
-- `YOUR_COLLECTION_NAME`: Use our [Collections](/documentation/concepts/collections/) guide to create or
+- `YOUR_COLLECTION_NAME`: Use our [Collections](/documentation/manage-data/collections/) guide to create or
   list collections.
-- `YOUR_HOST_URL`: Use the GRPC URL for your system. If you used the [Quick Start](/documentation/quick-start/) guide,
+- `YOUR_HOST_URL`: Use the GRPC URL for your system. If you used the [Quick Start](/documentation/quickstart/) guide,
   it may be http://localhost:6334. If you've deployed in the [Qdrant Cloud](/documentation/cloud/), you may have a
   longer URL such as `https://example.location.cloud.qdrant.io:6334`.
 - `YOUR_API_KEY`: Substitute the API key associated with your configuration.
@@ -38,7 +38,7 @@ EmbeddingStore<TextSegment> embeddingStore =
     QdrantEmbeddingStore.builder()
         // Ensure the collection is configured with the appropriate dimensions
         // of the embedding model.
-        // Reference https://qdrant.tech/documentation/concepts/collections/
+        // Reference: https://qdrant.tech/documentation/manage-data/collections/
         .collectionName("YOUR_COLLECTION_NAME")
         .host("YOUR_HOST_URL")
         // GRPC port of the Qdrant server

qdrant-landing/content/documentation/frameworks/spring-ai.md

@@ -60,7 +60,7 @@ public VectorStore vectorStore(QdrantVectorStoreConfig config, EmbeddingClient e
 
 You can now use the `VectorStore` instance backed by Qdrant as a vector store in the Spring AI APIs.
 
-<aside role="status">If the collection is not <a href="/documentation/concepts/collections/#create-a-collection">created in advance</a>, <code>QdrantVectorStore</code> will attempt to create one using cosine similarity and the dimension of the configured <code>EmbeddingClient</code>.</aside>
+<aside role="status">If the collection is not <a href="/documentation/manage-data/collections/#create-a-collection">created in advance</a>, <code>QdrantVectorStore</code> will attempt to create one using cosine similarity and the dimension of the configured <code>EmbeddingClient</code>.</aside>
 
 ## 📚 Further Reading