Naming changes

Author: codingbandit

Labs/lab_3_mongodb_vector_search.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Vector Search using Azure Cosmos DB API for MongoDB\n",
+    "# Vector Search using vCore-based Azure Cosmos DB for MongoDB\n",
     "\n",
     "This notebook demonstrates using an Azure OpenAI embedding model to vectorize documents already stored in Azure Cosmos DB API for MongoDB, storing the embedding vectors and the creation of a vector index. Lastly, the notebook will demonstrate how to query the vector index to find similar documents.\n",
     "\n",
@@ -120,7 +120,7 @@
     "    '''\n",
     "    response = ai_client.embeddings.create(input=text, model=EMBEDDINGS_DEPLOYMENT_NAME)\n",
     "    embeddings = response.data[0].embedding\n",
-    "    time.sleep(0.5) # rest period to avoid rate limiting on AOAI for free tier\n",
+    "    time.sleep(0.5) # rest period to avoid rate limiting on AOAI\n",
     "    return embeddings"
    ]
   },
@@ -270,9 +270,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Use vector search in Azure Cosmos DB API for MongoDB vCore\n",
+    "## Use vector search in vCore-based Azure Cosmos DB for MongoDB\n",
     "\n",
-    "Now that each document has its associated vector embedding and the vector indexes have been created on each collection, we can now use the vector search capabilities of Azure Cosmos DB API for MongoDB vCore."
+    "Now that each document has its associated vector embedding and the vector indexes have been created on each collection, we can now use the vector search capabilities of vCore-based Azure Cosmos DB for MongoDB."
    ]
   },
   {

Labs/lab_4_langchain.ipynb

@@ -76,9 +76,9 @@
    "source": [
     "## Vector search with LangChain\n",
     "\n",
-    "In the previous lab, the `pymongo` library was used to perform a vector search through a db command to find product documents that were most similar to the user's input. In this lab, you will use the `langchain` library to perform the same search. LangChain has a vector store class named **AzureCosmosDBVectorSearch**, a community contribution, that supports vector search in Azure CosmosDB for MongoDB API vCore.\n",
+    "In the previous lab, the `pymongo` library was used to perform a vector search through a db command to find product documents that were most similar to the user's input. In this lab, you will use the `langchain` library to perform the same search. LangChain has a vector store class named **AzureCosmosDBVectorSearch**, a community contribution, that supports vector search in vCore-based Azure Cosmos DB for MongoDB.\n",
     "\n",
-    "When establishing the connection to the vector store (MongoDB vCore), recall that in previous labs the products collection was populated and a contentVector field added that contains the vectorized embeddings of the document itself. Finally, a vector index was also created on the contentVector field to enable vector search. The vector index in each collection is named `VectorSearchIndex`.\n",
+    "When establishing the connection to the vector store (vCore-based Azure Cosmos DB for MongoDB), recall that in previous labs the products collection was populated and a contentVector field added that contains the vectorized embeddings of the document itself. Finally, a vector index was also created on the contentVector field to enable vector search. The vector index in each collection is named `VectorSearchIndex`.\n",
     "\n",
     "The return value of a vector search in LangChain is a list of `Document` objects. The LangChain `Document` class contains two properties: `page_content`, that represents the textual content that is typically used to augment the prompt, and `metadata` that contains all other attributes of the document. In the cell below, we'll use the `_id` field as the page_content, and the rest of the fields are returned as metadata.\n",
     "\n",
@@ -272,7 +272,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create tools that will use vector search in the Azure Cosmos DB for MongoDB vCore collections\n",
+    "# Create tools that will use vector search in vCore-based Azure Cosmos DB for MongoDB collections\n",
     "\n",
     "# create a chain on the retriever to format the documents as JSON\n",
     "products_retriever_chain = products_retriever | format_docs\n",

Labs/lab_5_backend_api.md

@@ -1,4 +1,4 @@
-# Lab 5 - Backend API
+# Backend API
 
 In the previous lab, a LangChain agent was created armed with tools to do vector lookups and concrete document id lookups via function calling. In this lab, the agent functionality needs to be extracted into a backend api for the frontend application that will allow users to interact with the agent.
 
@@ -231,4 +231,4 @@ The last step is to deploy the backend api container image to Azure Container Ap
 
 18. Repeat steps 8-10 from the [Run the backend api locally section](#run-the-backend-api-locally) to test the backend api running in a container on Azure Container Apps.
 
-Congratulations! You have successfully deployed the backend api to Azure Container Apps where it is ready to service the frontend application.
+Congratulations on the successful deployment of the backend api to Azure Container Apps where it is ready to service the frontend application.