Move assets to documentation/examples

qdrant-landing/content/documentation/examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md

@@ -29,7 +29,7 @@ Our application will consist of two main processes: indexing and searching. Lang
 as we will use a few components, including Cohere and Qdrant, as well as some OCI services. Here is a high-level
 overview of the architecture:
 
-![Architecture diagram of the target system](/documentation/tutorials/faq-oci-cohere-langchain/architecture-diagram.png)
+![Architecture diagram of the target system](/documentation/examples/faq-oci-cohere-langchain/architecture-diagram.png)
 
 ### Prerequisites
 
@@ -122,7 +122,7 @@ Service, so we can easily access the models.
 Our dataset will be fairly simple, as it will consist of the questions and answers from the [Oracle Cloud Free Tier
 FAQ page](https://www.oracle.com/cloud/free/faq/). 
 
-![Some examples of the Oracle Cloud FAQ](/documentation/tutorials/faq-oci-cohere-langchain/oracle-faq.png)
+![Some examples of the Oracle Cloud FAQ](/documentation/examples/faq-oci-cohere-langchain/oracle-faq.png)
 
 Questions and answers are presented in an HTML format, but we don't want to manually extract the text and adapt it for 
 each subpage. Instead, we will use the `WebBaseLoader` that just loads the HTML content from given URL and converts it 

qdrant-landing/content/documentation/examples/rag-chatbot-vultr-dspy-ollama.md

@@ -25,7 +25,7 @@ this setup as a knowledge base providing the relevant pieces of documents for a
 Hybrid Cloud mode on Vultr. The last missing piece, the DSPy application will be also running in the same environment. 
 If you work in a regulated industry, or just need to keep your data private, this tutorial is for you.
 
-![Architecture diagram](/documentation/tutorials/information-extraction-ollama-vultr/architecture-diagram.png)
+![Architecture diagram](/documentation/examples/information-extraction-ollama-vultr/architecture-diagram.png)
 
 ## Configuring the environment
 

qdrant-landing/content/documentation/examples/rag-contract-management-stackit-aleph-alpha.md

@@ -16,7 +16,7 @@ Companies want their data to be kept and processed within specific geographical
 
 [//]: # (TODO: add link to Qdrant Hybrid Cloud above)
 
-![Architecture diagram](/documentation/tutorials/contract-management-stackit-aleph-alpha/architecture-diagram.png)
+![Architecture diagram](/documentation/examples/contract-management-stackit-aleph-alpha/architecture-diagram.png)
 
 ## Components
 
@@ -157,7 +157,7 @@ documents = {
 
 This is how the documents might look like:
 
-![Example of the indexed document](/documentation/tutorials/contract-management-stackit-aleph-alpha/indexed-document.png)
+![Example of the indexed document](/documentation/examples/contract-management-stackit-aleph-alpha/indexed-document.png)
 
 Each has to be split into chunks first; there is no silver bullet. Our chunking algorithm will be simple and based on 
 recursive splitting, with the maximum chunk size of 500 characters and the overlap of 100 characters. 

qdrant-landing/content/documentation/examples/rag-customer-support-cohere-airbyte-aws.md

@@ -17,7 +17,7 @@ In this tutorial we will setup a private AI service that answers customer suppor
 
 [//]: # (TODO: add a link to the corresponding Qdrant Hybrid Cloud documentation: deployment on AWS)
 
-![Architecture diagram](/documentation/tutorials/customer-support-cohere-airbyte/architecture-diagram.png)
+![Architecture diagram](/documentation/examples/customer-support-cohere-airbyte/architecture-diagram.png)
 
 ## System design
 
@@ -116,18 +116,18 @@ use the following connectors:
 Airbyte UI will guide you through the process of setting up the source and destination and connecting them. Here is how
 the configuration of the source might look like:
 
-![Airbyte source configuration](/documentation/tutorials/customer-support-cohere-airbyte/airbyte-excel-source.png)
+![Airbyte source configuration](/documentation/examples/customer-support-cohere-airbyte/airbyte-excel-source.png)
 
 Qdrant is our target destination, so we need to set up the connection to it. We need to specify which fields should be
 included to generate the embeddings. In our case it makes complete sense to embed just the questions, as we are going
 to look for similar questions asked in the past and provide the answers. 
 
-![Airbyte destination configuration](/documentation/tutorials/customer-support-cohere-airbyte/airbyte-qdrant-destination.png)
+![Airbyte destination configuration](/documentation/examples/customer-support-cohere-airbyte/airbyte-qdrant-destination.png)
 
 Once we have the destination set up, we can finally configure a connection. The connection will define the schedule
 of the data synchronization. 
 
-![Airbyte connection configuration](/documentation/tutorials/customer-support-cohere-airbyte/airbyte-connection.png)
+![Airbyte connection configuration](/documentation/examples/customer-support-cohere-airbyte/airbyte-connection.png)
 
 Airbyte should now be ready to accept any data updates from the source and load them into Qdrant. You can monitor the
 progress of the synchronization in the UI.