fix a couple internal links

Author: crpietschmann

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 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.
+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-for-nosql-containers) 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-azure-cosmos-db-for-nosql). 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.