update directory for data, link to zip.

Author: sdgilley

articles/ai-studio/tutorials/copilot-sdk-build-rag.md

@@ -114,7 +114,7 @@ These steps deploy a model to a real-time endpoint from the AI Studio [model cat
 When you deploy the `gpt-3.5-turbo` model, find the following values in the **View Code** section, and add them to your **.env** file:
 
 ```env
-AZURE_OPENAI_ENDPOINT=<chat_model_endpoint_value>
+AZURE_OPENAI_ENDPOINT=<endpoint_value>
 AZURE_OPENAI_CHAT_DEPLOYMENT=<chat_model_deployment_name>
 AZURE_OPENAI_API_VERSION=<api_version>
 ```
@@ -155,7 +155,7 @@ The goal with this RAG-based application is to ground the model responses in you
 
 If you don't have an Azure AI Search index already created, we walk through how to create one. If you already have an index to use, you can skip to the [set the search environment variable](#set-search-index) section. The search index is created on the Azure AI Search service that was either created or referenced in the previous step.
 
-1. Use your own data or [download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/tree/main/tutorial/data) to your local machine. Unzip the file into your **rag-tutorial** folder. This data is a collection of markdown files that represent product information. The data is structured in a way that is easy to ingest into a search index. You build a search index from this data.
+1. Use your own data or [download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/blob/main/tutorial/data/product-info.zip) to your local machine. Unzip the file into your **rag-tutorial/data** folder. This data is a collection of markdown files that represent product information. The data is structured in a way that is easy to ingest into a search index. You build a search index from this data.
 
 1. The prompt flow RAG package allows you to ingest the markdown files, locally create a search index, and register it in the cloud project. Install the prompt flow RAG package: