renaming collections to containers in text content

Author: crpietschmann

07_Create_First_Cosmos_DB_Project/README.md

@@ -52,9 +52,9 @@ client = pymongo.MongoClient(CONNECTION_STRING)
 
 ### Creating a database
 
-When using the pymongo client, the creation of a database is automatic when referenced. No specific api calls to create a database are required, if a database already exists, a reference to the database is returned.
+When using the `pymongo` client, the creation of a database is automatic when referenced. No specific api calls to create a database are required, if a database already exists, a reference to the database is returned.
 
->**Note:**: That the creation of databases and collections are lazy, meaning they will not be created until a document is inserted into a collection.
+>**Note:**: That the creation of databases and containers are lazy, meaning they will not be created until a document is inserted into a collection.
 
 ```python
 db = client.cosmic_works

08_Load_Data/README.md

@@ -12,15 +12,15 @@ When loading data, bulk operations are preferred over adding each document indiv
 
 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 - Load data into Azure Cosmos DB for NoSQL collections
+## Lab - Load data into Azure Cosmos DB for NoSQL containers
 
-This lab will load the Cosmic Works Customer, Product, and Sales data into Azure Cosmos DB for NoSQL collections using bulk operations. Both the Azure Cosmos DB Emulator and Azure Cosmos DB account in Azure are supported for completion of this lab.
+This lab will load the Cosmic Works Customer, Product, and Sales data into Azure Cosmos DB for NoSQL containers using bulk operations. Both the Azure Cosmos DB Emulator and Azure Cosmos DB account in Azure are supported for completion of this lab.
 
 >**Note**: It is highly recommended to use a [virtual environment](https://python.land/virtual-environments/virtualenv) for all labs.
 
 Please visit the lab repository to complete [this lab](https://github.com/AzureCosmosDB/Azure-OpenAI-Python-Developer-Guide/blob/main/Labs/lab_2_load_data.ipynb).
 
-This lab demonstrates the use of bulk operations to load product, customer, and sales data into Azure Cosmos DB for NoSQL collections. As an example, the following code snippet inserts product data using the `bulk_write` method allowing for upsert functionality using the `UpdateOne` method:
+This lab demonstrates the use of bulk operations to load product, customer, and sales data into Azure Cosmos DB for NoSQL containers. As an example, the following code snippet inserts product data using the `bulk_write` method allowing for upsert functionality using the `UpdateOne` method:
 
 ```python
 # Add product data to database using bulkwrite and updateOne with upsert

09_Vector_Search_Cosmos_DB/README.md

@@ -8,7 +8,7 @@ Embedding is a way of serializing the semantic meaning of data into a vector rep
 
 Embeddings are created by sending data to an embedding model, where it is transformed into a vector, which then can be stored as a vector field within its source document in Azure Cosmos DB for NoSQL. Azure Cosmos DB for NoSQL supports the creation of vector search indexes on top of these vector fields. A vector search index is a collection of vectors in [latent space](https://idl.cs.washington.edu/papers/latent-space-cartography/) that enables a semantic similarity search across all data (vectors) contained within.
 
-![A typical embedding pipeline that demonstrates how source data is transformed into vectors using an embedding model then stored in a document in an Azure Cosmos DB vCore database and exposed via a vector search index.](media/embedding_pipeline.png)
+![A typical embedding pipeline that demonstrates how source data is transformed into vectors using an embedding model then stored in a document in an Azure Cosmos DB container and exposed via a vector search index.](media/embedding_pipeline.png)
 
 ## Why vector search?
 
@@ -38,7 +38,7 @@ On the **Settings** screen, select the **Resource** tab, then copy and record th
 
 >**NOTE**: This lab can only be completed using a deployed Azure Cosmos DB for NoSQL account due to the use of vector search. The Azure Cosmos DB Emulator does not support vector search.
 
-This lab also requires the data provided in the previous lab titled [Load data into Azure Cosmos DB API for NoSQL collections](../08_Load_Data/README.md#lab---load-data-into-azure-cosmos-db-api-for-mongodb-collections). Run all cells in this notebook to prepare the data for use in this lab.
+This lab also requires the data provided in the previous lab titled [Load data into Azure Cosmos DB API for NoSQL containers](../08_Load_Data/README.md#lab---load-data-into-azure-cosmos-db-api-for-mongodb-collections). Run all cells in this notebook to prepare the data for use in this lab.
 
 >**Note**: It is highly recommended to use a [virtual environment](https://python.land/virtual-environments/virtualenv) for all labs.
 

10_LangChain/README.md

@@ -22,7 +22,7 @@ When an incoming message is received, the retriever will vectorize the message a
 
 In this lab uses LangChain to re-implement the RAG pattern introduced in the previous lab. Take note of the readability of the code and how easy it is to compose a reusable RAG chain using LangChain that queries the products vector index in Azure Cosmos DB for NoSQL. The lab concludes with the creation of an agent with various tools for the LLM to leverage to fulfill the incoming request.
 
-This lab also requires the data provided in the previous lab titled [Load data into Azure Cosmos DB API for NoSQL collections](../08_Load_Data/README.md#lab---load-data-into-azure-cosmos-db-api-for-mongodb-collections) as well as the populated vector index created in the lab titled [Vector Search using Azure Cosmos DB for NoSQL](../09_Vector_Search_Cosmos_DB/README.md#lab---use-vector-search-on-embeddings-in-vcore-based-azure-cosmos-db-for-mongodb). Run all cells in both notebooks to prepare the data for use in this lab.
+This lab also requires the data provided in the previous lab titled [Load data into Azure Cosmos DB API for NoSQL containers](../08_Load_Data/README.md#lab---load-data-into-azure-cosmos-db-api-for-mongodb-collections) as well as the populated vector index created in the lab titled [Vector Search using Azure Cosmos DB for NoSQL](../09_Vector_Search_Cosmos_DB/README.md#lab---use-vector-search-on-embeddings-in-vcore-based-azure-cosmos-db-for-mongodb). Run all cells in both notebooks to prepare the data for use in this lab.
 
 >**Note**: It is highly recommended to use a [virtual environment](https://python.land/virtual-environments/virtualenv) for all labs.
 

11_Backend_API/README.md

@@ -4,7 +4,7 @@ In the previous lab, a LangChain agent was created armed with tools to do vector
 
 This lab implements a backend API using FastAPI that exposes the LangChain agent functionality. The provided code leverages Docker containers and includes full step-by-step instructions to run and test the API locally as well as deployed to [Azure Container Apps](https://learn.microsoft.com/azure/container-apps/overview) (leveraging the [Azure Container Registry](https://learn.microsoft.com/azure/container-registry/)).
 
-This lab also requires the data provided in the previous lab titled [Load data into Azure Cosmos DB API for NoSQL collections](../08_Load_Data/README.md#lab---load-data-into-azure-cosmos-db-api-for-mongodb-collections) as well as the populated vector index created in the lab titled [Vector Search using Azure Cosmos DB for NoSQL](../09_Vector_Search_Cosmos_DB/README.md#lab---use-vector-search-on-embeddings-in-vcore-based-azure-cosmos-db-for-mongodb). Run all cells in both notebooks to prepare the data for use in this lab.
+This lab also requires the data provided in the previous lab titled [Load data into Azure Cosmos DB API for NoSQL containers](../08_Load_Data/README.md#lab---load-data-into-azure-cosmos-db-api-for-mongodb-collections) as well as the populated vector index created in the lab titled [Vector Search using Azure Cosmos DB for NoSQL](../09_Vector_Search_Cosmos_DB/README.md#lab---use-vector-search-on-embeddings-in-vcore-based-azure-cosmos-db-for-mongodb). Run all cells in both notebooks to prepare the data for use in this lab.
 
 >**Note**: It is highly recommended to use a [virtual environment](https://python.land/virtual-environments/virtualenv) for all labs.
 

Labs/lab_1_first_application.ipynb

@@ -51,9 +51,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create a collection\n",
+    "## Create a container\n",
     "\n",
-    "Creating collections behaves similarly to the database creation. If the collection does not exist, it will be created. It's important to note that databases and collections are lazily created. This means that the database and collection will not be created until the first document is inserted."
+    "Creating containers behaves similarly to the database creation. If the collection does not exist, it will be created. It's important to note that databases and containers are lazily created. This means that the database and collection will not be created until the first document is inserted."
    ]
   },
   {

Labs/lab_2_load_data.ipynb

@@ -50,7 +50,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# empty the collections\n",
+    "# empty the containers\n",
     "db.products.bulk_write([DeleteMany({})])\n",
     "db.customers.bulk_write([DeleteMany({})])\n",
     "db.sales.bulk_write([DeleteMany({})])"

Labs/lab_4_langchain.ipynb

@@ -229,7 +229,7 @@
    "source": [
     "### Create retrievers\n",
     "\n",
-    "A separate retriever is required for each vector index. The following cell creates a VectorStoreRetriever for the products, customers, and sales collections and associated vector index."
+    "A separate retriever is required for each vector index. The following cell creates a VectorStoreRetriever for the products, customers, and sales containers and associated vector index."
    ]
   },
   {
@@ -272,7 +272,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create tools that will use vector search in Azure Cosmos DB for NoSQL collections\n",
+    "# Create tools that will use vector search in Azure Cosmos DB for NoSQL containers\n",
     "\n",
     "# create a chain on the retriever to format the documents as JSON\n",
     "products_retriever_chain = products_retriever | format_docs\n",