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Merge pull request #402 from Anush008/link-fixes

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fix: broken external links
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Tim Visée
2023-11-13 17:31:39 +01:00
committed by GitHub
parent 989817f4a8 e283ea6d50
commit 2e4bc7537b
3 changed files with 4 additions and 4 deletions
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qdrant-landing/content/articles/neural-search-tutorial.md
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@@ -7,7 +7,7 @@ social_preview_image: /articles_data/neural-search-tutorial/social_preview.jpg
preview_dir: /articles_data/neural-search-tutorial/preview
small_preview_image: /articles_data/neural-search-tutorial/tutorial.svg
weight: 50
author: Andrei Vasnetsov
author: Andrey Vasnetsov
author_link: https://blog.vasnetsov.com/
date: 2021-06-10T10:18:00.000Z
# aliases: [ /articles/neural-search-tutorial/ ]
@@ -222,7 +222,7 @@ qdrant_client.upload_collection(
Now we have vectors, uploaded to the vector search engine.
On the next step we will learn how to actually search for closest vectors.
The full code for this step could be found [here](https://github.com/qdrant/qdrant_demo/blob/master/qdrant_demo/init_vector_search_index.py).
The full code for this step could be found [here](https://github.com/qdrant/qdrant_demo/blob/master/qdrant_demo/init_collection_startups.py).
### Make a search API
qdrant-landing/content/articles/seed-round.md
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@@ -46,7 +46,7 @@ The current Qdrant ecosystem consists of excellent products to work with vector
{{< figure src=/articles_data/seed-round/ecosystem.png caption="Qdrant Ecosystem" alt="Qdrant Vector Database Ecosystem" >}}
Our plan for the current [open-source roadmap](https://github.com/qdrant/qdrant/blob/master/docs/roadmap/README.md) is to make billion-scale vector search affordable. Our recent release of the [Scalar Quantization](https://qdrant.tech/articles/scalar-quantization/) improves both memory usage (x4) as well as speed (x2). Upcoming [Product Quantization](https://lear.inrialpes.fr/pubs/2011/JDS11/jegou_searching_with_quantization.pdf) will introduce even another option with more memory saving. Stay tuned.
Our plan for the current [open-source roadmap](https://github.com/qdrant/qdrant/blob/master/docs/roadmap/README.md) is to make billion-scale vector search affordable. Our recent release of the [Scalar Quantization](https://qdrant.tech/articles/scalar-quantization/) improves both memory usage (x4) as well as speed (x2). Upcoming [Product Quantization](https://www.irisa.fr/texmex/people/jegou/papers/jegou_searching_with_quantization.pdf) will introduce even another option with more memory saving. Stay tuned.
Qdrant started more than two years ago with the mission of building a vector database powered by a well-thought-out tech stack. Using Rust as the system programming language and technical architecture decision during the development of the engine made Qdrant the leading and one of the most popular vector database solutions.
qdrant-landing/content/documentation/integrations/autogen.md
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@@ -99,4 +99,4 @@ ragproxyagent.initiate_chat(assistant, problem=code_problem)
## Next steps
Check out more Autogen [examples](https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat). You can find detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).
Check out more Autogen [examples](https://microsoft.github.io/autogen/docs/Examples/AgentChat). You can find detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).