adjust broken links

qdrant-landing/content/articles/faq-question-answering.md

@@ -54,7 +54,7 @@ As embeddings are vectors, one can apply a simple function to calculate the simi
 So with similarity learning, all we need to do is provide pairs of correct questions and answers.
 And then, the model will learn to distinguish proper answers by the similarity of embeddings.
 
->If you want to learn more about similarity learning and applications, check out this [article](https://blog.qdrant.tech/neural-search-tutorial-3f034ab13adc) which might be an asset.
+>If you want to learn more about similarity learning and applications, check out this [article](https://qdrant.tech/documentation/tutorials/neural-search/) which might be an asset.
 
 ## Let's build
 

qdrant-landing/content/articles/qdrant-0-10-release.md

@@ -27,7 +27,7 @@ set up your collections.
 
 Previously, you had to send multiple requests to the Qdrant API to perform multiple non-related tasks. However, this 
 can cause significant network overhead and slow down the process, especially if you have a poor connection speed. 
-Fortunately, the [new batch search feature](https://blog.qdrant.tech/batch-vector-search-with-qdrant-8c4d598179d5) allows 
+Fortunately, the [new batch search feature](https://qdrant.tech/documentation/concepts/search/#batch-search-api) allows 
 you to avoid this issue. With just one API call, Qdrant will handle multiple search requests in the most efficient way 
 possible. This means that you can perform multiple tasks simultaneously without having to worry about network overhead
 or slow performance.
@@ -37,14 +37,13 @@ or slow performance.
 To make our application accessible to ARM users, we have compiled it specifically for that platform. If it is not 
 compiled for ARM, the device will have to emulate it, which can slow down performance. To ensure the best possible 
 experience for ARM users, we have created Docker images specifically for that platform. Keep in mind that using 
-a limited set of processor instructions may affect the performance of your vector search. Therefore, [we have tested 
-both ARM and non-ARM architectures using similar setups to understand the potential impact on performance
-](https://blog.qdrant.tech/qdrant-supports-arm-architecture-363e92aa5026).
+a limited set of processor instructions may affect the performance of your vector search. Therefore, we have tested 
+both ARM and non-ARM architectures using similar setups to understand the potential impact on performance.
 
 ## Full-text filtering
 
 Qdrant is a vector database that allows you to quickly search for the nearest neighbors. However, you may need to apply 
 additional filters on top of the semantic search. Up until version 0.10, Qdrant only supported keyword filters. With the 
-release of Qdrant 0.10, [you can now use full-text filters](https://blog.qdrant.tech/qdrant-introduces-full-text-filters-and-indexes-9a032fcb5fa) 
+release of Qdrant 0.10, [you can now use full-text filters](https://qdrant.tech/documentation/concepts/filtering/#full-text-match) 
 as well. This new filter type can be used on its own or in combination with other filter types to provide even more 
 flexibility in your searches.

qdrant-landing/content/articles/what-is-rag-in-ai.md

@@ -175,6 +175,6 @@ After building your RAG chatbot, you'll be able to evaluate its performance agai
 
 ## What’s next?
 
-Have a RAG project you want to bring to life? Join our [Discord community](discord.gg/qdrant) where we’re always sharing tips and answering questions on vector search and retrieval.
+Have a RAG project you want to bring to life? Join our [Discord community](https://discord.gg/qdrant) where we’re always sharing tips and answering questions on vector search and retrieval.
 
 Learn more about how to properly evaluate your RAG responses: [Evaluating Retrieval Augmented Generation - a framework for assessment](https://superlinked.com/vectorhub/evaluating-retrieval-augmented-generation-a-framework-for-assessment).

qdrant-landing/content/documentation/frameworks/llama-index.md

@@ -36,5 +36,5 @@ index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
 
 ```
 
-The library [comes with a notebook](https://github.com/run-llama/llama_index/blob/main/docs/examples/vector_stores/QdrantIndexDemo.ipynb) 
+The library [comes with a notebook](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/QdrantIndexDemo.ipynb) 
 that shows an end-to-end example of how to use Qdrant within LlamaIndex.

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

@@ -15,7 +15,7 @@ pip install pandasai[qdrant]
 
 ## Usage
 
-You can begin a conversation by instantiating an `Agent` instance based on your Pandas data frame. The default Pandas-AI LLM requires an [API key](https://pandabi.ai.).
+You can begin a conversation by instantiating an `Agent` instance based on your Pandas data frame. The default Pandas-AI LLM requires an [API key](https://pandabi.ai).
 
 You can find the list of all supported LLMs [here](https://docs.pandas-ai.com/en/latest/LLMs/llms/)
 

qdrant-landing/content/documentation/tutorials/async-api.md

@@ -7,7 +7,7 @@ weight: 14
 
 Asynchronous programming is being broadly adopted in the Python ecosystem. Tools such as FastAPI [have embraced this new 
 paradigm](https://fastapi.tiangolo.com/async/), but it is also becoming a standard for ML models served as SaaS. For example, the Cohere SDK
-[provides an async client](https://cohere-sdk.readthedocs.io/en/latest/cohere.html#asyncclient) next to its synchronous counterpart.
+[provides an async client](https://github.com/cohere-ai/cohere-python/blob/856a4c3bd29e7a75fa66154b8ac9fcdf1e0745e0/src/cohere/client.py#L189) next to its synchronous counterpart.
 
 Databases are often launched as separate services and are accessed via a network. All the interactions with them are IO-bound and can 
 be performed asynchronously so as not to waste time actively waiting for a server response. In Python, this is achieved by 

qdrant-landing/content/use-cases/customer-support-optimization.md

@@ -7,4 +7,3 @@ sitemapExclude: True
 Current advances in NLP can reduce the retinue work of customer service by up to 80 percent.
 No more answering the same questions over and over again. A chatbot will do that, and people can focus on complex problems.
 But not only automated answering, it is also possible to control the quality of the department and automatically identify flaws in conversations.
-Read more about the "[Sentence Embeddings for Customer Support](https://blog.floydhub.com/automate-customer-support-part-one/)" case study.