Initial run through grammarly

Author: codingbandit

01_Overview_Cosmos_DB/README.md

@@ -1,25 +1,23 @@
 # Overview of Azure Cosmos DB (high-level with links to docs for more information)
 
-https://learn.microsoft.com/en-us/azure/cosmos-db/introduction
-
-Azure Cosmos DB is a globally distributed, multi-model database service that enables you to query and store data using NoSQL models using one of five APIs: SQL (document database), Cassandra (column-family), MongoDB (document database), Azure Table, and Gremlin (graph database). It provides turnkey global distribution, elastic scaling of throughput and storage worldwide, single-digit millisecond latencies at the 99th percentile, and guaranteed high availability with multi-homing capabilities. Azure Cosmos DB provides comprehensive service level agreements (SLAs) for throughput, latency, availability, and consistency guarantees, something not found in any other database service.
+[Azure Cosmos DB](https://learn.microsoft.com/en-us/azure/cosmos-db/introduction) is a globally distributed, multi-model database service that enables you to query and store data using NoSQL models using one of five APIs: SQL (document database), Cassandra (column-family), MongoDB (document database), Azure Table, and Gremlin (graph database). It provides turnkey global distribution, elastic scaling of throughput and storage worldwide, single-digit millisecond latencies at the 99th percentile, and guaranteed high availability with multi-homing capabilities. Azure Cosmos DB provides comprehensive service level agreements (SLAs) for throughput, latency, availability, and consistency guarantees, something not found in any other database service.
 
 ## Azure Cosmos DB for Mongo DB
 
-Azure Cosmos DB for MongoDB simplifies utilizing Azure Cosmos DB as a MongoDB database. You can leverage your current MongoDB expertise and still use your preferred MongoDB drivers, SDKs, and tools simply by directing your application to the connection string for your account.
+[Azure Cosmos DB for MongoDB](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/introduction) simplifies utilizing Azure Cosmos DB as a MongoDB database. You can leverage your current MongoDB expertise and still use your preferred MongoDB drivers, SDKs, and tools simply by directing your application to the connection string for your account.
+
+### Azure Cosmos DB for Mongo DB API Architectures
+
+The [RU architecture](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/ru/introduction) for Azure Cosmos DB for MongoDB offers instantaneous scalability with zero warmup period, automatic and transparent sharding, and 99.999% availability. It supports active-active databases across multiple regions, cost-efficient, granular, unlimited scalability, real-time analytics, and serverless deployments where you pay only per operation.
 
-### RU Architecture
+Azure Cosmos DB for MongoDB [vCore architecture](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/introduction) integrates AI-based applications with your data, with text indexing for easy querying. Simplify your development process with high-capacity vertical scaling and free 35-day backups with a point-in-time restore (PITR).
 
-The RU architecture for Azure Cosmos DB for MongoDB offers instantaneous scalability with zero warmup period, automatic and transparent sharding, and 99.999% availability. It supports active-active databases across multiple regions, cost-efficient, granular, and unlimited scalability, real-time analytics, and serverless deployments where you pay only per operation.
+The [choice between vCore and Request Units (RU)](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/choose-model) in Azure Cosmos DB for MongoDB API depends on the workload.
 
-### vCore Architecture
+vCore provides predictable performance and cost and is ideal for running high-performance, mission-critical workloads with low latency and high throughput. With vCore, the number of vCPUs and the memory the database needs is configurable and can be scaled up or down as needed.
 
-Azure Cosmos DB for MongoDB vCore integrates AI-based applications with your data, with text indexing for easy querying. Simplify your development process with high-capacity vertical scaling and free 35 day backups with point in time restore (PITR).
+Conversely, RU is a consumption-based model that charges based on the number of operations the database performs, including reads, writes, and queries. RU is ideal for scenarios where the workload has unpredictable traffic patterns or a need to optimize cost for bursty workloads.
 
-### comparison?
-### Reference
-https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/introduction
+A steady-state workload with predictable traffic patterns is best suited for vCore since it provides more predictable performance and cost. However, RU may be a better choice if the workload has unpredictable traffic patterns or requires bursty performance since it allows you to pay only for the resources used.
 
-### Azure Cosmos DB Mongo DB vCore
-Intro to Azure Cosmos DB Mongo DB vCore, mentioning vector search, AI enablement - very high level.
-https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/introduction
+>**NOTE**: AI-supporting workloads, such as vector search, must use vCore, as vector search is not supported with RU accounts.

04_Provision_Azure_Resources/README.md

@@ -1,11 +1,11 @@
 # Provision Azure resources (Azure Cosmos DB workspace, Azure OpenAI, etc.)
 
-- Have deployment scripts
-- Walk through the deployed environment
-
-
-This should be included as a step in 05_Create_First_Cosmos_DB_Project
-
-NEEDS:
-- full solution architecture diagram
-- deployment scripts (ARM/Bicep?)
+TBD once all other modules are complete.
+
+Bicep deployment is available in the `deploy` folder of the lab repository.
+Currently deploying the following:
+    - Resource Group
+    - Azure Cosmos DB API for MongoDB vCore account
+    - Azure OpenAI resource
+      - Chat GPT-3.5 `completions` model
+      - text-embedding-ada-002 model `embeddings` model

05_Create_First_Cosmos_DB_Project/README.md

@@ -1,24 +1,24 @@
 # Create your first Cosmos DB project
 
-In this section, we'll cover how to create your first Cosmos DB project. We'll use a notebook to demonstrate the basic CRUD operations. We'll also cover how to use the Azure Cosmos DB Emulator to test your code locally.
+This section will cover how to create your first Cosmos DB project. We'll use a notebook to demonstrate the basic CRUD operations. We'll also cover using the Azure Cosmos DB Emulator to test your code locally.
 
 ## Emulator support
 
-Azure Cosmos DB has an emulator that you can use to develop your code locally. The emulator supports the API for NoSQL and API for MongoDB. Usage of the emulator does not require an Azure Subscription nor does it incur any costs so is ideal for local development and testing. The Azure Cosmos DB emulator can also be utilized with unit tests in a [GitHub Actions CI workflow](https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-develop-emulator?tabs=windows%2Cpython&pivots=api-mongodb#use-the-emulator-in-a-github-actions-ci-workflow).
+Azure Cosmos DB has an emulator that you can use to develop your code locally. The emulator supports the API for NoSQL and the API for MongoDB. The use of the emulator does not require an Azure subscription, nor does it incur any costs, so it is ideal for local development and testing. The Azure Cosmos DB emulator can also be utilized with unit tests in a [GitHub Actions CI workflow](https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-develop-emulator?tabs=windows%2Cpython&pivots=api-mongodb#use-the-emulator-in-a-github-actions-ci-workflow).
 
 There is not 100% feature parity between the emulator and the cloud service. Visit the [Azure Cosmos DB emulator](https://learn.microsoft.com/en-us/azure/cosmos-db/emulator) documentation for more details.
 
-For Windows machines, the emulator can be installed via an installer. There is a Windows container using Docker available, however it does not currently support the API for Mongo DB. A Docker image is also available for Linux that does support the API for Mongo DB.
+For Windows machines, the emulator can be installed via an installer. There is a Windows container using Docker available. However, it does not currently support the API for Mongo DB. A Docker image is also available for Linux that does support the API for Mongo DB.
 
 Learn more about the pre-requisites and installation of the emulator [here](https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-develop-emulator?tabs=windows%2Cpython&pivots=api-mongodb).
 
 >**NOTE**: When using the Azure CosmosDB emulator using the API for MongoDB it must be started with the [MongoDB endpoint options enabled](https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-develop-emulator?tabs=windows%2Cpython&pivots=api-mongodb#start-the-emulator) on the command-line.
 
-**The Azure Cosmos DB emulator does not support vector search. To complete the vector search and AI related labs, you must use an Azure Cosmos DB API for MongoDB vCore account in Azure.**
+**The Azure Cosmos DB emulator does not support vector search. To complete the vector search and AI-related labs, you must use an Azure Cosmos DB API for the MongoDB vCore account in Azure.**
 
 ## Authentication
 
-Authentication to Azure Cosmos DB API for Mongo DB is done using a connection string. The connection string is a URL that contains the authentication information for your Azure Cosmos DB account or local emulator. The username and password used when provisioning the Azure Cosmos DB API for MongoDB service are used in the connection string when authenticating to Azure.
+Authentication to Azure Cosmos DB API for Mongo DB uses a connection string. The connection string is a URL that contains the authentication information for your Azure Cosmos DB account or local emulator. The username and password used when provisioning the Azure Cosmos DB API for MongoDB service are used in the connection string when authenticating to Azure.
 
 ### Retrieving the connection string from the Cosmos DB Emulator
 
@@ -28,19 +28,19 @@ The splash screen or **Quickstart** section of the Cosmos DB Emulator will displ
 
 ### Retrieving the connection string from the Azure portal
 
-Retrieve the connection string from the Azure portal by navigating to your Azure Cosmos DB account and selecting the **Connection String** menu item on the left-hand side of the screen. The connection string contains tokens for the username an password that must be replaced with the username and password used when provisioning the Azure Cosmos DB API for MongoDB service. Additional users can be added at any time using the [Azure CLI](https://learn.microsoft.com/en-us/cli/azure/cosmosdb/mongodb/user?view=azure-cli-latest).
+Retrieve the connection string from the Azure portal by navigating to your Azure Cosmos DB account and selecting the **Connection String** menu item on the left-hand side of the screen. The connection string contains tokens for the username and password that must be replaced with the username and password used when provisioning the Azure Cosmos DB API for MongoDB service. Users can be added at any time using the [Azure CLI](https://learn.microsoft.com/en-us/cli/azure/cosmosdb/mongodb/user?view=azure-cli-latest).
 
 ![The Azure CosmosDb API for MongoDB Connection strings screen displays with the copy button next to the connection string highlighted.](media/azure_connection_string.png)
 
 ## Authorization
 
-Azure Cosmos DB API for MongoDB supports fine-grained role-based access control (RBAC) for authorization. There are a number of built-in roles available. The built-in roles are:
+Azure Cosmos DB API for MongoDB supports fine-grained role-based access control (RBAC) for authorization. There are several built-in roles available. The built-in roles are:
     1. read
     2. readWrite
     3. dbAdmin
     4. dbOwner
 
-The ability to create custom roles is also available. Custom roles can be created using the [Azure CLI](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/how-to-setup-rbac). Custom roles can include granular permissions for specific databases, collections, and operational actions. Learn more about the [Azure CLI RBAC commands to define custom roles](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/how-to-setup-rbac#azure-cli-rbac-commands).
+The ability to create custom roles is also available. Custom roles can be created using the [Azure CLI](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/how-to-setup-rbac). Customized roles can include granular permissions for specific databases, collections, and operational actions. Learn more about the [Azure CLI RBAC commands to define custom roles](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/how-to-setup-rbac#azure-cli-rbac-commands).
 
 ## Helpful tools
 
@@ -49,9 +49,9 @@ Visualization and querying tools:
     2. MongoDB Compass
     3. MongoDB Shell
 
-## Lab 1 - Create your first Cosmos DB for MongoDB application
+## Lab 1 - Create your first Cosmos DB for the MongoDB application
 
-In this lab, we'll create a Cosmos DB for MongoDB application using a notebook. You have the choice to either use the Azure Cosmos DB Emulator or an Azure Cosmos DB account in Azure. The following concepts will be covered in this lab:
+Using a notebook, we'll create a Cosmos DB for the MongoDB application in this lab. You can use the Azure Cosmos DB Emulator or an Azure Cosmos DB account in Azure. The following concepts will be covered in this lab:
     1. Create a database
     2. Create a collection
     3. Create a document

06_Load_Data/README.md

@@ -1,16 +1,16 @@
 # Load data into Azure Cosmos DB API for MongoDB
 
-Lab 1 demonstrated how to individually add data to a collection. This lab will demonstrate how to load data using bulk operations into multiple collections. This data will be used in subsequent labs to further demonstrate the capabilities of Azure Cosmos DB API for MongoDB in relation to AI.
+Lab 1 demonstrated how to add data to a collection individually. This lab will demonstrate how to load data using bulk operations into multiple collections. This data will be used in subsequent labs to explain further the capabilities of Azure Cosmos DB API for MongoDB about AI.
 
-When loading data, bulk operations are preferred over adding each document individually. Bulk operations involve performing multiple database operations together as a batch, rather than executing them one at a time. This approach is more efficient and provides several benefits:
+When loading data, bulk operations are preferred over adding each document individually. Bulk operations involve performing multiple database operations as a batch rather than executing them simultaneously. This approach is more efficient and provides several benefits:
 
 1. Performance: By issuing load operations in bulk, the lab can significantly reduce the overhead of network round-trips and database operations. This results in faster data loading and improved overall performance.
 
-2. Scalability: Bulk operations allow the lab to handle large volumes of data efficiently. They can process and load a substantial amount of customer, product, and sales data in a shorter amount of time, enabling them to scale their operations as needed.
+2. Scalability: Bulk operations allow the lab to handle large volumes of data efficiently. They can quickly process and load a substantial amount of customer, product, and sales data, enabling them to scale their operations as needed.
 
-3. Atomicity: Bulk operations ensure that all database changes within a batch are treated as a single transaction. If any individual document fails to load, the entire batch can be rolled back, maintaining data integrity and consistency.
+3. Atomicity: Bulk operations ensure that all database changes within a batch are treated as a single transaction. The entire batch can be rolled back if any document fails to load, maintaining data integrity and consistency.
 
-4. Simplified code logic: By using bulk operations, the lab can simplify their code logic and reduce the number of database queries. This results in cleaner, more manageable code and reduces the likelihood of errors or inconsistencies.
+4. Simplified code logic: By using bulk operations, the lab can simplify its code logic and reduce the number of database queries. This results in cleaner, more manageable code and reduces the likelihood of errors or inconsistencies.
 
 ## Lab 3 - Load data into Azure Cosmos DB API for MongoDB collections