fix links

Author: crpietschmann

diskann/02_Overview_Cosmos_DB/README.md

@@ -16,10 +16,10 @@ The focus for this developer guide is [Azure Cosmos DB for NoSQL](https://learn.
 
 Azure Cosmos DB offers three capacity modes: provisioned throughput, serverless and autoscale modes. creating an Azure Cosmos DB account, it's essential to evaluate the workload's characteristics in order to choose the appropriate mode to optimize both performance and cost efficiency.
 
-[**Serverless mode**](https://learn.microsoft.com/en-us/azure/cosmos-db/serverless) offers a more flexible and pay-as-you-go approach, where only the Request Units consumed are billed. This is particularly advantageous for applications with sporadic or unpredictable usage patterns, as it eliminates the need to provision resources upfront.
+[**Serverless mode**](https://learn.microsoft.com/azure/cosmos-db/serverless) offers a more flexible and pay-as-you-go approach, where only the Request Units consumed are billed. This is particularly advantageous for applications with sporadic or unpredictable usage patterns, as it eliminates the need to provision resources upfront.
 
 [**Provisioned throughput mode**](https://learn.microsoft.com/azure/cosmos-db/set-throughput) allocates a fixed amount of resources, measured in [Request Units per second (RUs/s)](https://learn.microsoft.com/azure/cosmos-db/request-units), which is ideal for applications with predictable and steady workloads. This ensures consistent performance and can be more cost-effective when there is a constant or high demand for database operations. RU/s can be set at both the database and container levels, allowing for fine-grained control over resource allocation.
 
 [**Autoscale mode**](https://learn.microsoft.com/azure/cosmos-db/provision-throughput-autoscale) builds upon the provisioned throughput mode but allows for the database or container automatically and instantly scale up or down resources based on demand, ensuring that the application can handle varying workloads efficiently. When configuring autoscale, a maximum (Tmax) value threshold is set for a predictable maximum cost. This mode is suitable for applications with fluctuating usage patterns or infrequently used applications.
 
-[**Dynamic scaling**](https://learn.microsoft.com/en-us/azure/cosmos-db/autoscale-per-partition-region) allows for the automatic and independent scaling of non-uniform workloads across regions and partitions according to usage patterns. For instance, in a disaster recovery configuration with two regions, the primary region may experience high traffic while the secondary region can scale down to idle, thereby saving costs. This approach is also highly effective for multi-regional applications, where traffic patterns fluctuate based on the time of day in each region.
+[**Dynamic scaling**](https://learn.microsoft.com/azure/cosmos-db/autoscale-per-partition-region) allows for the automatic and independent scaling of non-uniform workloads across regions and partitions according to usage patterns. For instance, in a disaster recovery configuration with two regions, the primary region may experience high traffic while the secondary region can scale down to idle, thereby saving costs. This approach is also highly effective for multi-regional applications, where traffic patterns fluctuate based on the time of day in each region.

diskann/05_Explore_OpenAI_models/README.md

@@ -2,7 +2,7 @@
 
 ## Azure OpenAI Models
 
-[Azure OpenAI is powered by a diverse set of models](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) with different capabilities.
+[Azure OpenAI is powered by a diverse set of models](https://learn.microsoft.com/azure/ai-services/openai/concepts/models) with different capabilities.
 
 | Model | Description |
 | -- | --- |

diskann/12_User_Interface/README.md

@@ -10,7 +10,7 @@ The following screenshot shows where to find the front-end application URL:
 
 Navigating to this URL in the browser accesses the front-end application. Through this front-end application User Interface, questions can be submitted to the Azure OpenAI model about the CosmicWorks company data, then it will generate responses accordingly.
 
-![Front-end Web Application User Interface](images/2024-01-17-12-42-59.png)
+![Front-end Web Application User Interface](images/2024-10-15-11-48-17.png)
 
 While the code for the SPA web application is outside the scope of this dev guide. It's worth noting that the Web App is configured with the URL for the Backend API using **App settings** with the variable named `API_ENDPOINT` under the **Settings** -> **Environment variables** pane. When the application was deployed as part of the Azure template deployment, it was automatically configured with this URL to connect the front-end SPA web application to the Backend API.
 

diskann/Labs/deploy/azuredeploy.bicep

@@ -2,7 +2,7 @@
 Azure Cosmos DB + Azure OpenAI Python developer guide lab
 ******************************************************************
 This Azure resource deployment template uses some of the following practices:
-- [Abbrevation examples for Azure resources](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ready/azure-best-practices/resource-abbreviations)
+- [Abbrevation examples for Azure resources](https://learn.microsoft.com/azure/cloud-adoption-framework/ready/azure-best-practices/resource-abbreviations)
 */
 
 /* *************************************************************** */
@@ -98,7 +98,7 @@ var appServiceSettings = {
 // Define a variable for the tag values
 var tags = {
   name: 'AzureCosmosDB-DevGuide'
-  repo: 'https://github.com/solliancenet/cosmos-db-nosql-openai-python-dev-guide'
+  repo: 'https://github.com/AzureCosmosDB/Azure-OpenAI-Python-Developer-Guide/blob/main/diskann/README.md'
 }
 
 /* *************************************************************** */
@@ -126,7 +126,7 @@ resource cosmosAccount 'Microsoft.DocumentDB/databaseAccounts@2024-05-15' = {
         name: 'EnableServerless'
       }
       {
-        /* https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search#enroll-in-the-vector-search-preview-feature */
+        /* https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search#enroll-in-the-vector-search-preview-feature */
         name: 'EnableNoSQLVectorSearch'
       }
     ]

diskann/Labs/deploy/deploy.md

@@ -12,9 +12,10 @@
 Create a folder to house the repository. Open a terminal and navigate to the folder. Clone the repository, then navigate to the `Labs/deploy` folder within the repository.
 
 ```bash
-git clone https://github.com/solliancenet/Cosmos-DB-NoSQL-OpenAI-Python-Dev-Guide.git
+git clone https://github.com/AzureCosmosDB/Azure-OpenAI-Python-Developer-Guide.git
 
 cd Cosmos-DB-NoSQL-OpenAI-Python-Dev-Guide
+cd diskann
 cd Labs
 cd deploy
 ```

diskann/Labs/lab_5_backend_api.md

@@ -6,16 +6,17 @@ The information provided in this section assumes that the dependent infrastructu
 
 ## Overview
 
-The backend api is a Python FastAPI application that will expose endpoints for the frontend application to interact with. The backend api is a containerized application that will be deployed to [Azure Container Apps](https://learn.microsoft.com/en-us/azure/container-apps/overview).
+The backend api is a Python FastAPI application that will expose endpoints for the frontend application to interact with. The backend api is a containerized application that will be deployed to [Azure Container Apps](https://learn.microsoft.com/azure/container-apps/overview).
 
 ## Clone the Backend API
 
 Create a folder to house the repository. Open a terminal and navigate to the folder. Clone the repository, then navigate to the `Backend` folder within the repository.
 
 ```bash
-git clone https://github.com/solliancenet/cosmos-db-nosql-openai-python-dev-guide.git
+git clone https://github.com/AzureCosmosDB/Azure-OpenAI-Python-Developer-Guide.git
 
 cd Azure-OpenAI-Python-Developer-Guide
+cd diskann
 cd Backend
 ```
 

vcore/Labs/deploy/azuredeploy.bicep

@@ -2,7 +2,7 @@
 Azure Cosmos DB + Azure OpenAI Python developer guide lab
 ******************************************************************
 This Azure resource deployment template uses some of the following practices:
-- [Abbrevation examples for Azure resources](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ready/azure-best-practices/resource-abbreviations)
+- [Abbrevation examples for Azure resources](https://learn.microsoft.com/azure/cloud-adoption-framework/ready/azure-best-practices/resource-abbreviations)
 */
 
 

vcore/Labs/deploy/deploy.md

@@ -15,6 +15,7 @@ Create a folder to house the repository. Open a terminal and navigate to the fol
 git clone https://github.com/AzureCosmosDB/Azure-OpenAI-Python-Developer-Guide.git
 
 cd Azure-OpenAI-Python-Developer-Guide
+cd vcore
 cd Labs
 cd deploy
 ```

vcore/Labs/lab_5_backend_api.md

@@ -6,7 +6,7 @@ The information provided in this section assumes that the dependent infrastructu
 
 ## Overview
 
-The backend api is a Python FastAPI application that will expose endpoints for the frontend application to interact with. The backend api is a containerized application that will be deployed to [Azure Container Apps](https://learn.microsoft.com/en-us/azure/container-apps/overview).
+The backend api is a Python FastAPI application that will expose endpoints for the frontend application to interact with. The backend api is a containerized application that will be deployed to [Azure Container Apps](https://learn.microsoft.com/azure/container-apps/overview).
 
 ## Clone the Backend API
 
@@ -16,6 +16,7 @@ Create a folder to house the repository. Open a terminal and navigate to the fol
 git clone https://github.com/AzureCosmosDB/Azure-OpenAI-Python-Developer-Guide.git
 
 cd Azure-OpenAI-Python-Developer-Guide
+cd vcore
 cd Backend
 ```