Updated all concepts (except Container Apps)

docs/content/en/docs/Concepts/azure-apim.md

@@ -7,4 +7,10 @@ categories: [Azure]
 tags: [docs, apim]
 ---
 
-Azure API Management is a platform-as-a-service that provides a hybrid, multicloud management platform for APIs across all environments [1](https://learn.microsoft.com/en-us/azure/api-management/api-management-key-concepts).. It supports the complete API lifecycle and helps customers manage APIs as first-class assets throughout their lifecycle [1](https://learn.microsoft.com/en-us/azure/api-management/api-management-key-concepts).
+Azure API Management is a platform-as-a-service that provides a hybrid, multicloud management platform for APIs across all environments [1](https://learn.microsoft.com/en-us/azure/api-management/api-management-key-concepts). It supports the complete API lifecycle and helps customers manage APIs as first-class assets throughout their lifecycle [1](https://learn.microsoft.com/en-us/azure/api-management/api-management-key-concepts).
+
+![APIM imagen](https://learn.microsoft.com/en-us/azure/api-management/media/api-management-key-concepts-experiment/api-management-components.png)
+
+In **our solution** we use API Management to manage exposed endpoints, for example, Azure OpenAI, Azure Function and Azure Cognitive Search index. 
+
+More information at the offical documentation: [What is APIM?](https://learn.microsoft.com/en-us/azure/api-management/api-management-key-concepts)

docs/content/en/docs/Concepts/azure-application-insights.md

@@ -7,4 +7,25 @@ categories: [Azure]
 tags: [docs, application-insights]
 ---
 
-TODO
+Application Insights is an extension of Azure Monitor and **provides application performance monitoring (APM) features**. APM tools are useful to monitor applications from development, through test, and into production in the following ways:
+
+* Proactively understand how an application is performing.
+* Reactively review application execution data to determine the cause of an incident.
+
+Along with collecting metrics and application telemetry data, which describe application activities and health, you can use Application Insights to collect and store application trace logging data.
+
+The log trace is associated with other telemetry to give a detailed view of the activity. Adding trace logging to existing apps only requires providing a destination for the logs. You rarely need to change the logging framework.
+
+Application Insights provides **other features** including, but not limited to:
+
+* Live Metrics: Observe activity from your deployed application in real time with no effect on the host environment.
+* Availability: Also known as synthetic transaction monitoring. Probe the external endpoints of your applications to test the overall * availability and responsiveness over time.
+* GitHub or Azure DevOps integration: Create GitHub or Azure DevOps work items in the context of Application Insights data.
+* Usage: Understand which features are popular with users and how users interact and use your application.
+* Smart detection: Detect failures and anomalies automatically through proactive telemetry analysis.
+
+Application Insights supports distributed tracing, which is also known as distributed component correlation. This feature allows searching for and visualizing an end-to-end flow of a specific execution or transaction. The ability to trace activity from end to end is important for applications that were built as distributed components or microservices.
+
+In **our solution** we use Application Insights to monitor metrics and logs of the solution. 
+
+Learn more about Application Insights in the official documentation: [What is Application Insights?](https://learn.microsoft.com/en-us/azure/azure-monitor/app/app-insights-overview?tabs=net)

docs/content/en/docs/Concepts/azure-functions.md

@@ -7,4 +7,10 @@ categories: [Azure]
 tags: [docs, functions, serverless]
 ---
 
-TODO
+Azure Functions is a serverless solution that allows you to write less code, maintain less infrastructure, and save on costs. Instead of worrying about deploying and maintaining servers, the cloud infrastructure provides all the up-to-date resources needed to keep your applications running.
+
+Functions provides a comprehensive set of event-driven triggers and bindings that connect your functions to other services without having to write extra code. You focus on the code that matters most to you, in the most productive language for you, and Azure Functions handles the rest. 
+
+In **our solution*^* we use Azure Function to create chunks of the documents text and create embeddings to be added to the Azure Cognitive Search index. 
+
+Learn more about Azure Functions: [What is Azure Function?](https://learn.microsoft.com/en-us/azure/azure-functions/functions-overview?pivots=programming-language-csharp). For the best experience with the Functions documentation, choose your preferred development language from the list of native Functions languages at the top of the article.

docs/content/en/docs/Concepts/azure-openai.md

@@ -12,15 +12,15 @@ Azure OpenAI Service provides REST API access to OpenAI's powerful language mode
 Important concepts about Azure OpenAI:
 
 * [Azure OpenAI Studio](oai.azure.com)
-* Models available
+* **Models available**
   * GPT-35-Turbo series: typical "chatGPT" model, recommended for most of the Azure OpenAI projects. When we might need more capability, GPT4 can me considered (take into account it will imply more latency and cost) 
   * GPT-4 series: they are the most advanced language models, available once you fill in the [GPT4 Request Form](https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xURjE4QlhVUERGQ1NXOTlNT0w1NldTWjJCMSQlQCN0PWcu) 
   * Embeddings series: embeddings make it easier to do machine learning on large inputs representing words by capturing the semantic similarities in a vector space. Therefore, you can use embeddings to determine if two text chunks are semantically related or similar, and provide a score to assess similarity.
 
   Take into account that not all models are available in all Azure Regions, for Regional availability check the documentation: [Model summary table and region availability](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)
 
-* Deployment: once you instantiate a specific model, it will be available as deployment. You can create and delete deployments of available models as you wish. This is managed through the AOAI Studio. 
-* Quotas: the quotas available in Azure are allocated per model and per region, within a subscription. [Learn more about quotas](https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits). In the documentation you can find best practices to manage your quota.
+* **Deployment**: once you instantiate a specific model, it will be available as deployment. You can create and delete deployments of available models as you wish. This is managed through the AOAI Studio. 
+* **Quotas**: the quotas available in Azure are allocated per model and per region, within a subscription. [Learn more about quotas](https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits). In the documentation you can find best practices to manage your quota.
 
 
 In **our solution** we are using Azure OpenAI Embeddings model to vectorize the content and ChatGPT model to conversate with that content. 

docs/content/en/docs/Concepts/azure-storage.md

@@ -7,4 +7,17 @@ categories: [Azure]
 tags: [docs, storage]
 ---
 
-TODO
+Azure Blob Storage is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.
+
+Blob Storage is designed for:
+
+* Serving images or documents directly to a browser.
+* Storing files for distributed access.
+* Streaming video and audio.
+* Writing to log files.
+* Storing data for backup and restore, disaster recovery, and archiving.
+* Storing data for analysis by an on-premises or Azure-hosted service.
+
+In **our solution** we use Blob Storage to store the documents (PDFs) that will be then vectorized and indexed. 
+
+Learn more about Azure Blob Storage: [What is Azure Blob Storage?](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-overview)