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

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* fix: links in hybrid-queries.md

* refactor: Use abs links

* fix: Check internal links

* ci: Rename job
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2024-10-05 00:09:41 +02:00
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qdrant-landing/content/documentation/tutorials/_index.md
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@@ -14,14 +14,14 @@ These tutorials demonstrate different ways you can build vector search into your
| Essential How-Tos | Description | Stack |
|---------------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
| [Semantic Search for Beginners](../tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
| [Simple Neural Search](../tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
| [Neural Search with FastEmbed](../tutorials/neural-search-fastembed/) | Build and deploy a neural search with our FastEmbed library. | Qdrant |
| [Multimodal Search](../tutorials/multimodal-search-fastembed/) | Create a simple multimodal search. | Qdrant |
| [Bulk Upload Vectors](../tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
| [Asynchronous API](../tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
| [Create Dataset Snapshots](../tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
| [Load HuggingFace Dataset](../tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
| [Measure Retrieval Quality](../tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality | Qdrant, Python, datasets |
| [Search Through Code](../tutorials/code-search/) | Implement semantic search application for code search tasks | Qdrant, Python, sentence-transformers, Jina |
| [Setup Collaborative Filtering](../tutorials/collaborative-filtering/) | Implement a collaborative filtering system for recommendation engines | Qdrant|
| [Semantic Search for Beginners](/documentation/tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
| [Simple Neural Search](/documentation/tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
| [Neural Search with FastEmbed](/documentation/tutorials/neural-search-fastembed/) | Build and deploy a neural search with our FastEmbed library. | Qdrant |
| [Multimodal Search](/documentation/tutorials/multimodal-search-fastembed/) | Create a simple multimodal search. | Qdrant |
| [Bulk Upload Vectors](/documentation/tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
| [Asynchronous API](/documentation/tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
| [Create Dataset Snapshots](/documentation/tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
| [Load HuggingFace Dataset](/documentation/tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
| [Measure Retrieval Quality](/documentation/tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality | Qdrant, Python, datasets |
| [Search Through Code](/documentation/tutorials/code-search/) | Implement semantic search application for code search tasks | Qdrant, Python, sentence-transformers, Jina |
| [Setup Collaborative Filtering](/documentation/tutorials/collaborative-filtering/) | Implement a collaborative filtering system for recommendation engines | Qdrant|
qdrant-landing/content/documentation/tutorials/bulk-upload.md
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@@ -101,23 +101,23 @@ client.updateCollection("{collection_name}", {
## Upload directly to disk
When the vectors you upload do not all fit in RAM, you likely want to use
[memmap](../../concepts/storage/#configuring-memmap-storage)
[memmap](/documentation/concepts/storage/#configuring-memmap-storage)
support.
During collection
[creation](../../concepts/collections/#create-collection),
[creation](/documentation/concepts/collections/#create-collection),
memmaps may be enabled on a per-vector basis using the `on_disk` parameter. This
will store vector data directly on disk at all times. It is suitable for
ingesting a large amount of data, essential for the billion scale benchmark.
Using `memmap_threshold_kb` is not recommended in this case. It would require
the [optimizer](../../concepts/optimizer/) to constantly
the [optimizer](/documentation/concepts/optimizer/) to constantly
transform in-memory segments into memmap segments on disk. This process is
slower, and the optimizer can be a bottleneck when ingesting a large amount of
data.
Read more about this in
[Configuring Memmap Storage](../../concepts/storage/#configuring-memmap-storage).
[Configuring Memmap Storage](/documentation/concepts/storage/#configuring-memmap-storage).
## Parallel upload into multiple shards
qdrant-landing/content/documentation/tutorials/search-beginners.md
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@@ -240,7 +240,7 @@ The query has been narrowed down to one result from 2008.
## Next Steps
Congratulations, you have just created your very first search engine! Trust us, the rest of Qdrant is not that complicated, either. For your next tutorial you should try building an actual [Neural Search Service with a complete API and a dataset](../../tutorials/neural-search/).
Congratulations, you have just created your very first search engine! Trust us, the rest of Qdrant is not that complicated, either. For your next tutorial you should try building an actual [Neural Search Service with a complete API and a dataset](/documentation/tutorials/neural-search/).
## Return to the bash shell