fix: Internal link checker, Use abs links (#1220)

* fix: links in hybrid-queries.md

* refactor: Use abs links

* fix: Check internal links

* ci: Rename job

.github/workflows/internal-dead-links.yml

@@ -27,11 +27,11 @@ jobs:
           export PATH="${CURRENT_DIR}/dart-sass:${PATH}"
           cd qdrant-landing && hugo --gc -b 'http://localhost:1313' && hugo serve &
           sleep 5 # wait for server to start
-      - name: Link Checker
+      - name: Internal Links Check
         id: lychee
         uses: lycheeverse/[email protected]
         with:
-          args: --max-redirects 0 --exclude '.*' --include 'http://localhost:1313/.*' qdrant-landing/public/
+          args: --max-redirects 0 --exclude '.*' --include 'http://localhost:1313/.*' --base http://localhost:1313/ qdrant-landing/public/
           fail: true
         env:
           GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}

qdrant-landing/content/articles/qdrant-1.7.x.md

@@ -51,7 +51,7 @@ Things have changed since then, as so many of you wanted a single tool for spars
 
 If you're coming across the topic of sparse vectors for the first time, our [Brief History of Search](/documentation/overview/vector-search/) explains the difference between sparse and dense vectors.
 
-Check out the [sparse vectors article](../sparse-vectors/) and [sparse vectors index docs](/documentation/concepts/indexing/#sparse-vector-index) for more details on what this new index means for Qdrant users.
+Check out the [sparse vectors article](/articles/sparse-vectors/) and [sparse vectors index docs](/documentation/concepts/indexing/#sparse-vector-index) for more details on what this new index means for Qdrant users.
 
 ### Discovery API
 

qdrant-landing/content/blog/azure-marketplace.md

@@ -50,7 +50,7 @@ We're incredibly excited about this collaboration with Azure Marketplace and the
 
 Ready to elevate your business with Qdrant? **Click the banner and get started today!**
 
-[![Get Started on Azure Marketplace](cta.png)](https://azuremarketplace.microsoft.com/en-en/marketplace/apps/qdrantsolutionsgmbh1698769709989.qdrant-db)
+[![Get Started on Azure Marketplace](/blog/azure-marketplace/cta.png)](https://azuremarketplace.microsoft.com/en-en/marketplace/apps/qdrantsolutionsgmbh1698769709989.qdrant-db)
 
 ### About Qdrant:
 

qdrant-landing/content/blog/qdrant-stars-announcement copy.md

@@ -130,7 +130,7 @@ I'm really excited to show the power of the Qdrant as vector database. Especiall
 
 We are happy to welcome this group of people who are deeply committed to advancing vector search technology. We look forward to supporting their vision, and helping them make a bigger impact on the community.
 
-You can find and chat with them at our [Discord Community](discord.gg/qdrant).
+You can find and chat with them at our [Discord Community](https://discord.gg/qdrant/).
 
 ### Why become a Qdrant Star?
 

qdrant-landing/content/documentation/cloud/backups.md

@@ -82,11 +82,11 @@ Here is how you can take a snapshot and recover a collection:
    - For a single node cluster, call the snapshot endpoint on the exposed URL.
    - For a multi node cluster call a snapshot on each node of the collection. 
      Specifically, prepend `node-{num}-` to your cluster URL.
-     Then call the [snapshot endpoint](../../concepts/snapshots/#create-snapshot) on the individual hosts. Start with node 0.
+     Then call the [snapshot endpoint](/documentation/concepts/snapshots/#create-snapshot) on the individual hosts. Start with node 0.
    - In the response, you'll see the name of the snapshot.
 2. Delete and recreate the collection.
 3. Recover the snapshot:
-   - Call the [recover endpoint](../../concepts/snapshots/#recover-in-cluster-deployment). Set a location which points to the snapshot file (`file:///qdrant/snapshots/{collection_name}/{snapshot_file_name}`) for each host.
+   - Call the [recover endpoint](/documentation/concepts/snapshots/#recover-in-cluster-deployment). Set a location which points to the snapshot file (`file:///qdrant/snapshots/{collection_name}/{snapshot_file_name}`) for each host.
 
 ## Backup considerations
 

qdrant-landing/content/documentation/cloud/cluster-scaling.md

@@ -27,11 +27,11 @@ Vertical scaling can be an effective way to improve the performance of a cluster
 
 In such cases, horizontal scaling may be a more effective solution. 
 
-Horizontal scaling, also known as horizontal expansion, is the process of increasing the capacity of a cluster by adding more nodes and distributing the load and data among them. The horizontal scaling at Qdrant starts on the collection level. You have to choose the number of shards you want to distribute your collection around while creating the collection.  Please refer to the [sharding documentation](../../guides/distributed_deployment/#sharding) section for details.
+Horizontal scaling, also known as horizontal expansion, is the process of increasing the capacity of a cluster by adding more nodes and distributing the load and data among them. The horizontal scaling at Qdrant starts on the collection level. You have to choose the number of shards you want to distribute your collection around while creating the collection.  Please refer to the [sharding documentation](/documentation/guides/distributed_deployment/#sharding) section for details.
 
 After that, you can configure, or change the amount of Qdrant database nodes within a cluster during cluster creation, or on the cluster detail page via "Scale" button.
 
-Important: The number of shards means the maximum amount of nodes you can add to your cluster. In the beginning, all the shards can reside on one node. With the growing amount of data you can add nodes to your cluster and move shards to the dedicated nodes using the [cluster setup API](../../guides/distributed_deployment/#cluster-scaling).
+Important: The number of shards means the maximum amount of nodes you can add to your cluster. In the beginning, all the shards can reside on one node. With the growing amount of data you can add nodes to your cluster and move shards to the dedicated nodes using the [cluster setup API](/documentation/guides/distributed_deployment/#cluster-scaling).
 
 Note, that it is currently not possible to horizontally scale down the cluster in the Qdrant Cloud UI. If you require a horizontal scale down, please open a support ticket.
 

qdrant-landing/content/documentation/concepts/collections.md

@@ -28,7 +28,7 @@ These settings can be changed at any time by a corresponding request.
 
 ## Setting up multitenancy
 
-**How many collections should you create?** In most cases, you should only use a single collection with payload-based partitioning. This approach is called [multitenancy](https://en.wikipedia.org/wiki/Multitenancy). It is efficient for most of users, but it requires additional configuration. [Learn how to set it up](../../tutorials/multiple-partitions/)
+**How many collections should you create?** In most cases, you should only use a single collection with payload-based partitioning. This approach is called [multitenancy](https://en.wikipedia.org/wiki/Multitenancy). It is efficient for most of users, but it requires additional configuration. [Learn how to set it up](/documentation/tutorials/multiple-partitions/)
 
 **When should you create multiple collections?** When you have a limited number of users and you need isolation. This approach is flexible, but it may be more costly, since creating numerous collections may result in resource overhead. Also, you need to ensure that they do not affect each other in any way, including performance-wise. 
 
@@ -139,12 +139,12 @@ client.CreateCollection(context.Background(), &qdrant.CreateCollection{
 
 In addition to the required options, you can also specify custom values for the following collection options:
 
-* `hnsw_config` - see [indexing](../indexing/#vector-index) for details.
-* `wal_config` - Write-Ahead-Log related configuration. See more details about [WAL](../storage/#versioning)
-* `optimizers_config` - see [optimizer](../optimizer/) for details.
-* `shard_number` - which defines how many shards the collection should have. See [distributed deployment](../../guides/distributed_deployment/#sharding) section for details.
+* `hnsw_config` - see [indexing](/documentation/concepts/indexing/#vector-index) for details.
+* `wal_config` - Write-Ahead-Log related configuration. See more details about [WAL](/documentation/concepts/storage/#versioning)
+* `optimizers_config` - see [optimizer](/documentation/concepts/optimizer/) for details.
+* `shard_number` - which defines how many shards the collection should have. See [distributed deployment](/documentation/guides/distributed_deployment/#sharding) section for details.
 * `on_disk_payload` - defines where to store payload data. If `true` - payload will be stored on disk only. Might be useful for limiting the RAM usage in case of large payload.
-* `quantization_config` - see [quantization](../../guides/quantization/#setting-up-quantization-in-qdrant) for details.
+* `quantization_config` - see [quantization](/documentation/guides/quantization/#setting-up-quantization-in-qdrant) for details.
 
 Default parameters for the optional collection parameters are defined in [configuration file](https://github.com/qdrant/qdrant/blob/master/config/config.yaml).
 
@@ -155,7 +155,7 @@ See [schema definitions](https://api.qdrant.tech/api-reference/collections/creat
 Vectors all live in RAM for very quick access. The `on_disk` parameter can be
 set in the vector configuration. If true, all vectors will live on disk. This
 will enable the use of
-[memmaps](../../concepts/storage/#configuring-memmap-storage),
+[memmaps](/documentation/concepts/storage/#configuring-memmap-storage),
 which is suitable for ingesting a large amount of data.
 
 ### Create collection from another collection
@@ -466,8 +466,8 @@ For rare use cases, it is possible to create a collection without any vector sto
 *Available as of v1.1.1*
 
 For each named vector you can optionally specify
-[`hnsw_config`](../indexing/#vector-index) or
-[`quantization_config`](../../guides/quantization/#setting-up-quantization-in-qdrant) to
+[`hnsw_config`](/documentation/concepts/indexing/#vector-index) or
+[`quantization_config`](/documentation/guides/quantization/#setting-up-quantization-in-qdrant) to
 deviate from the collection configuration. This can be useful to fine-tune
 search performance on a vector level.
 
@@ -476,7 +476,7 @@ search performance on a vector level.
 Vectors all live in RAM for very quick access. On a per-vector basis you can set
 `on_disk` to true to store all vectors on disk at all times. This will enable
 the use of
-[memmaps](../../concepts/storage/#configuring-memmap-storage),
+[memmaps](/documentation/concepts/storage/#configuring-memmap-storage),
 which is suitable for ingesting a large amount of data.
 
 
@@ -752,7 +752,7 @@ Outside of a unique name, there are no required configuration parameters for spa
 
 The distance function for sparse vectors is always `Dot` and does not need to be specified.
 
-However, there are optional parameters to tune the underlying [sparse vector index](../indexing/#sparse-vector-index).
+However, there are optional parameters to tune the underlying [sparse vector index](/documentation/concepts/indexing/#sparse-vector-index).
 
 ### Check collection existence
 
@@ -928,9 +928,9 @@ client.UpdateCollection(context.Background(), &qdrant.UpdateCollection{
 
 The following parameters can be updated:
 
-* `optimizers_config` - see [optimizer](../optimizer/) for details.
-* `hnsw_config` - see [indexing](../indexing/#vector-index) for details.
-* `quantization_config` - see [quantization](../../guides/quantization/#setting-up-quantization-in-qdrant) for details.
+* `optimizers_config` - see [optimizer](/documentation/concepts/optimizer/) for details.
+* `hnsw_config` - see [indexing](/documentation/concepts/indexing/#vector-index) for details.
+* `quantization_config` - see [quantization](/documentation/guides/quantization/#setting-up-quantization-in-qdrant) for details.
 * `vectors` - vector-specific configuration, including individual `hnsw_config`, `quantization_config` and `on_disk` settings.
 * `params` - other collection parameters, including `write_consistency_factor` and `on_disk_payload`. 
 
@@ -1495,14 +1495,14 @@ round of automatic optimizations has completed.
 To clarify: these numbers don't represent the exact amount of points or vectors
 you have inserted, nor does it represent the exact number of distinguishable
 points or vectors you can query. If you want to know exact counts, refer to the
-[count API](../points/#counting-points).
+[count API](/documentation/concepts/points/#counting-points).
 
 _Note: these numbers may be removed in a future version of Qdrant._
 
 ### Indexing vectors in HNSW
 
 In some cases, you might be surprised the value of `indexed_vectors_count` is lower than `vectors_count`. This is an intended behaviour and
-depends on the [optimizer configuration](../optimizer/). A new index segment is built if the size of non-indexed vectors is higher than the
+depends on the [optimizer configuration](/documentation/concepts/optimizer/). A new index segment is built if the size of non-indexed vectors is higher than the
 value of `indexing_threshold`(in kB).  If your collection is very small or the dimensionality of the vectors is low, there might be no HNSW segment
 created and `indexed_vectors_count` might be equal to `0`.
 

qdrant-landing/content/documentation/concepts/explore.md

@@ -7,7 +7,7 @@ aliases:
 
 # Explore the data
 
-After mastering the concepts in [search](../search/), you can start exploring your data in other ways. Qdrant provides a stack of APIs that allow you to find similar vectors in a different fashion, as well as to find the most dissimilar ones. These are useful tools for recommendation systems, data exploration, and data cleaning.
+After mastering the concepts in [search](/documentation/concepts/search/), you can start exploring your data in other ways. Qdrant provides a stack of APIs that allow you to find similar vectors in a different fashion, as well as to find the most dissimilar ones. These are useful tools for recommendation systems, data exploration, and data cleaning.
 
 ## Recommendation API
 

qdrant-landing/content/documentation/concepts/filtering.md

@@ -8,7 +8,7 @@ aliases:
 # Filtering
 
 With Qdrant, you can set conditions when searching or retrieving points.
-For example, you can impose conditions on both the [payload](../payload/) and the `id` of the point.
+For example, you can impose conditions on both the [payload](/documentation/concepts/payload/) and the `id` of the point.
 
 Setting additional conditions is important when it is impossible to express all the features of the object in the embedding.
 Examples include a variety of business requirements: stock availability, user location, or desired price range.
@@ -838,7 +838,7 @@ qdrant.NewMatchInt("count", 0)
 
 The simplest kind of condition is one that checks if the stored value equals the given one.
 If several values are stored, at least one of them should match the condition.
-You can apply it to [keyword](../payload/#keyword), [integer](../payload/#integer) and [bool](../payload/#bool) payloads.
+You can apply it to [keyword](/documentation/concepts/payload/#keyword), [integer](/documentation/concepts/payload/#integer) and [bool](/documentation/concepts/payload/#bool) payloads.
 
 ### Match Any
 
@@ -847,7 +847,7 @@ You can apply it to [keyword](../payload/#keyword), [integer](../payload/#intege
 In case you want to check if the stored value is one of multiple values, you can use the Match Any condition.
 Match Any works as a logical OR for the given values. It can also be described as a `IN` operator.
 
-You can apply it to [keyword](../payload/#keyword) and [integer](../payload/#integer) payloads.
+You can apply it to [keyword](/documentation/concepts/payload/#keyword) and [integer](/documentation/concepts/payload/#integer) payloads.
 
 Example:
 
@@ -909,7 +909,7 @@ In case you want to check if the stored value is not one of multiple values, you
 Match Except works as a logical NOR for the given values.
 It can also be described as a `NOT IN` operator.
 
-You can apply it to [keyword](../payload/#keyword) and [integer](../payload/#integer) payloads.
+You can apply it to [keyword](/documentation/concepts/payload/#keyword) and [integer](/documentation/concepts/payload/#integer) payloads.
 
 Example:
 
@@ -1908,7 +1908,7 @@ A special case of the `match` condition is the `text` match condition.
 It allows you to search for a specific substring, token or phrase within the text field.
 
 Exact texts that will match the condition depend on full-text index configuration.
-Configuration is defined during the index creation and describe at [full-text index](../indexing/#full-text-index).
+Configuration is defined during the index creation and describe at [full-text index](/documentation/concepts/indexing/#full-text-index).
 
 If there is no full-text index for the field, the condition will work as exact substring match.
 
@@ -2047,11 +2047,11 @@ Comparisons that can be used:
 - `lt` - less than
 - `lte` - less than or equal
 
-Can be applied to [float](../payload/#float) and [integer](../payload/#integer) payloads.
+Can be applied to [float](/documentation/concepts/payload/#float) and [integer](/documentation/concepts/payload/#integer) payloads.
 
 ### Datetime Range
 
-The datetime range is a unique range condition, used for [datetime](../payload/#datetime) payloads, which supports RFC 3339 formats. 
+The datetime range is a unique range condition, used for [datetime](/documentation/concepts/payload/#datetime) payloads, which supports RFC 3339 formats. 
 You do not need to convert dates to UNIX timestaps. During comparison, timestamps are parsed and converted to UTC.
 
 _Available as of v1.8.0_
@@ -2364,7 +2364,7 @@ qdrant.NewGeoRadius("location", 52.520711, 13.403683, 1000.0)
 It matches with `location`s inside a circle with the `center` at the center and a radius of `radius` meters.
 
 If several values are stored, at least one of them should match the condition.
-These conditions can only be applied to payloads that match the [geo-data format](../payload/#geo).
+These conditions can only be applied to payloads that match the [geo-data format](/documentation/concepts/payload/#geo).
 
 #### Geo Polygon
 Geo Polygons search is useful for when you want to find points inside an irregularly shaped area, for example a country boundary or a forest boundary. A polygon always has an exterior ring and may optionally include interior rings. A lake with an island would be an example of an interior ring. If you wanted to find points in the water but not on the island, you would make an interior ring for the island. 
@@ -2659,7 +2659,7 @@ qdrant.NewGeoPolygon("location",
 A match is considered any point location inside or on the boundaries of the given polygon's exterior but not inside any interiors.
 
 If several location values are stored for a point, then any of them matching will include that point as a candidate in the resultset. 
-These conditions can only be applied to payloads that match the [geo-data format](../payload/#geo).
+These conditions can only be applied to payloads that match the [geo-data format](/documentation/concepts/payload/#geo).
 
 ### Values count
 

qdrant-landing/content/documentation/concepts/hybrid-queries.md

@@ -10,7 +10,7 @@ hideInSidebar: false # Optional. If true, the page will not be shown in the side
 
 *Available as of v1.10.0*
 
-With the introduction of [many named vectors per point](../vectors/#named-vectors), there are use-cases when the best search is obtained by combining multiple queries, 
+With the introduction of [many named vectors per point](/documentation/concepts/vectors/#named-vectors), there are use-cases when the best search is obtained by combining multiple queries, 
 or by performing the search in more than one stage.
 
 Qdrant has a flexible and universal interface to make this possible, called `Query API` ([API reference](https://api.qdrant.tech/api-reference/search/query-points)).
@@ -793,7 +793,7 @@ Other than the introduction of `prefetch`, the `Query API` has been designed to
 
 ### Query by ID
 
-Whenever you need to use a vector as an input, you can always use a [point ID](../points/#point-ids) instead.
+Whenever you need to use a vector as an input, you can always use a [point ID](/documentation/concepts/points/#point-ids) instead.
 
 ```http
 POST /collections/{collection_name}/points/query
@@ -1397,4 +1397,4 @@ client.QueryGroups(context.Background(), &qdrant.QueryPointGroups{
 })
 ```
 
-For more information on the `grouping` capabilities refer to the reference documentation for search with [grouping](./search/#search-groups) and [lookup](./search/#lookup-in-groups).
+For more information on the `grouping` capabilities refer to the reference documentation for search with [grouping](/documentation/concepts/search/#search-groups) and [lookup](/documentation/concepts/search/#lookup-in-groups).