added skill level

Author: zbyosufzai

notebooks/GenAI/azure_infra_setup/README.md

@@ -1,5 +1,7 @@
 # Setting Up Azure Environment for Azure GenAI Cloud Lab 
 
+**Skill Level: Beginner**
+
 This guide will help you set up your Azure environment to complete the activities in the [GenAI](../) directory of the NIH Cloud Lab. 
 The purpose of this guide is to walk you through an automated deployment of the resources needed to carry out these activities. 
 This automated approach utilizes a pre-built [ARM template](arm_resources.json) file, which serves as an alternative approach

notebooks/GenAI/embedding_demos/readme.md

@@ -1,5 +1,7 @@
 # Azure OpenAI Demo w/ Streamlit Frontend
 
+**Skill Level: Intermediate**
+
 The Azure OpenAI Demo w/ Streamlit Frontend is designed to host various demonstrations that showcase the capabilities of Azure OpenAI with a Streamlit frontend. Demonstrations include utilizing Azure OpenAI to create/chat over data indexes in Azure AI Search, and utilizing Azure OpenAI SDK to generate embeddings for text and query those embeddings. For documentation on building Streamlit apps, please search links below:
 - [Streamlit Documentation](https://docs.streamlit.io/get-started)
 - [Geeksforgeeks](https://www.geeksforgeeks.org/a-beginners-guide-to-streamlit/)
@@ -119,12 +121,12 @@ Streamlit's native behavior expects to run applications locally on port 8501, wh
 4. In the text editor add the following variables:
     ```bash
         AZURE_OPENAI_VERSION = "Your Azure OpenAI API version"  
-        AZURE_OPENAI_BASE = "Your Azure OpenAI API endpoint"
-        AZURE_OPENAI_KEY = "Your Azure OpenAI API key"
+        AZURE_OPENAI_ENDPOINT = "Your Azure OpenAI API endpoint"
+        AZURE_OPENAI_API_KEY = "Your Azure OpenAI API key"
         AZURE_GPT_DEPLOYMENT = "Your Azure OpenAI deployed GPT model name"
         AZURE_EMBEDDINGS_DEPLOYMENT = "Your Azure OpenAI deployed ADA model name"
-        AZURE_SEARCH_ENDPOINT = "Your Azure AI Search API endpoint"
-        AZURE_SEARCH_ADMIN_KEY = "Your Azure AI Search API key"
+        AZURE_SEARCH_SERVICE_ENDPOINT = "Your Azure AI Search API endpoint"
+        AZURE_SEARCH_API_KEY = "Your Azure AI Search API key"
         AZURE_SEARCH_INDEX = "documents-index" # The index name 'documents-index' is used as default in this demo
         BLOB_CONTAINER_NAME = "Your Azure Blob Container name hosting files from /search_documents"
         BLOB_CONNECTION_STRING = "Your Azure Blob connection string"

notebooks/GenAI/notebooks/AISearch_RAG_chatbot.ipynb

@@ -6,9 +6,11 @@
    "source": [
     "# Document Embedding and Indexing with Azure OpenAI and AI Search\n",
     "\n",
+    "**Skill Level: Beginner**\n",
+    "\n",
     "## Overview \n",
     "This tutorial provides a step-by-step guide on how to pull files from [Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), generate embeddings for these files, and store the embeddings in an [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) index. Embeddings are numerical representations of text that capture the semantic meaning of the content, facilitating advanced search and analysis. An index in AI search is a data structure that organizes these embeddings to improve the speed and efficiency of search queries. Additionally, this tutorial demonstrates how to enable users to interact with these embedding indexes through Azure AI Search and [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview), effectively allowing them to chat over the original files from Azure Blob Storage.\n",
-    "### Learning Objectives  \n",
+    "## Learning Objectives  \n",
     "1. **Vectorization**:\n",
     "    - Learn how to extract files from Azure Blob Storage.\n",
     "    - Understand how to generate embeddings using Azure OpenAI.\n",
@@ -17,8 +19,10 @@
     "    - Gain skills in interacting with Azure AI Search indexes using Azure OpenAI.\n",
     "\n",
     "Each section of this notebook will guide you through specific tasks and demonstrate how to utilize the REST APIs provided by each Azure service. By the end of this notebook, you will have a comprehensive understanding of how to integrate and utilize these Azure services to develop a robust data processing and retrieval application. \n",
-    "### Prerequisites  \n",
-    "Before proceeding with this notebook, please ensure that you have the following Azure services deployed and configured. Resources can be deployed manually in Azure portal or automated by following along with the [ARM Deployment tutorial](../azure_infra_setup/README.md):  \n",
+    "## Prerequisites  \n",
+    "Before proceeding with this notebook, please ensure that you have the following Azure services deployed and configured. Resources can be deployed manually in Azure portal or automated by following along with the [ARM Deployment tutorial](../azure_infra_setup/README.md).\n",
+    "\n",
+    "## Pricing\n",
     "  \n",
     "1. **Azure OpenAI Service**:   \n",
     "    - Ensure that you have deployed both a GPT model and an Ada model within your Azure OpenAI instance.\n",