Retail Lab 1 (#544)

* updating instructions for retail

* updates to healthcare markdown

* retail lab 1

most of screen shots are finished besides PDFS and last task

Author: uma-kumar

dev-ai-app-dev-healthcare/ai-exercise/ai-vector-search-challenge-answers.md

@@ -46,7 +46,7 @@ This lab assumes you have:
 
 ## Task 1: Challenge requirements 
 
-The data science team at SeerEquites has provided a Jupyter Notebook called `ai_vector_search_challenge.ipynb` with four key code cells. The notebook will:
+The data science team at Seer Healthcare has provided a Jupyter Notebook called `ai_vector_search_challenge.ipynb` with four key code cells. The notebook will:
 
 - **Connect to the database and import the necessary libraries and drivers.**
 - **Vectorize the `loan_type` column from the `MOCK_LOAN_DATA` table into the `LOAN_CHUNK` table.**

dev-ai-app-dev-healthcare/build/build.md

@@ -196,7 +196,7 @@ Here’s what we’ll do:
 
 ## Task 6: Create a function to create embeddings - Use Oracle Database 23ai's to create vector data 
 
-To handle follow-up questions, you will enhance the system with an AI Loan Guru powered by Oracle 23ai’s Vector Search and Retrieval-Augmented Generation (RAG). The AI Loan Guru will be able to answer questions about the loan application and provide recommendations based on the data.
+To handle follow-up questions, you will enhance the system with an AI Healthcare Guru powered by Oracle 23ai’s Vector Search and Retrieval-Augmented Generation (RAG). The AI Healthcare Guru will be able to answer questions about the return application and provide recommendations based on the data.
 
 Before answering questions, we need to prepare the data by vectoring the loan recommendations. This step:
 

dev-ai-app-dev-healthcare/introduction/introduction.md

@@ -17,7 +17,7 @@ You will:
 
   *	Combine your **enterprise data with large language models (LLMs)** using Retrieval-Augmented Generation (RAG) to answer natural language questions with context-aware accuracy.
 
-  * Automate real-world financial workflows, like loan approvals, by **embedding and querying unstructured data** in real time.
+  * Automate real-world healthcare workflows, like loan approvals, by **embedding and querying unstructured data** in real time.
 
 By the end of this workshop, you’ll understand how Oracle Database 23ai helps financial organizations:
 

dev-ai-app-dev-healthcare/user-story/user-story.md

@@ -103,7 +103,7 @@ In this first example, you will use the application to approve a patient with a
 
     ![James Smith Approved List](./images/james-smith-approved-list.png =50%x*)
 
-**Conclusion**
+**Task Summary**
 
 Once you select and save one of the 3 authorization options recommended by the AI: 
 
@@ -177,7 +177,7 @@ In this example, you will navigate the application to review a customer and deny
 
     ![James Woods Denied List](./images/james-woods-denied-list.png =50%x*)
 
-**Conclusion**
+**Task Summary**
 
 Congratulations, you have finished reviewing a patient with high fraudulent risk! Proceed to the next task.
 
@@ -207,7 +207,7 @@ Lastly, let’s explore how the system uses JSON Duality Views to handle profile
 
     ![Customer Profile Updated](./images/income-updated.png)
 
-**Conclusion**
+**Task Summary**
 
 Once the document is uploaded:
 
@@ -217,7 +217,7 @@ Once the document is uploaded:
 
 ✅ And thanks to JSON Transform and JSON Duality Views, only the relevant field is modified — leaving the rest of the profile UNTOUCHED.
 
-## Summary
+## Conlusion
 
 In conclusion our Prior Authorizations Management App was able to leverage Oracle database 23ai technologies such as **AI Vector Search, Property Graph and JSON Duality Views** to:
 

dev-ai-app-dev-retail/build/build.md

@@ -196,7 +196,7 @@ Here’s what we’ll do:
 
 ## Task 6: Create a function to create embeddings - Use Oracle Database 23ai's to create vector data 
 
-To handle follow-up questions, you will enhance the system with a virtual return officer assistant, Riley, powered by Oracle 23ai’s Vector Search and Retrieval-Augmented Generation (RAG). Riley will be able to answer questions about the return authorization and provide recommendations based on the data.
+To handle follow-up questions, you will enhance the system with a virtual return officer assistant, powered by Oracle 23ai’s Vector Search and Retrieval-Augmented Generation (RAG). The **AI Return Specialist Virtual Assistant** will be able to answer questions about the return authorization and provide recommendations based on the data.
 
 Before answering questions, we need to prepare the data by vectoring the return recommendations. This step:
 

dev-ai-app-dev-retail/codingbasics/codingbasics.md

@@ -187,7 +187,7 @@ Now, that we have established a connection, we can start creating our tables and
 
     ![query customers](./images/task3.png " ")
 
-### **Task Summary**
+**Task Summary**
 
 Congratulations! You successfully created two new tables with sample data using Python and Oracle Database.  
 
@@ -197,7 +197,9 @@ You also created a function that allows you to query your new table which we wil
 
 Next, we want to explore how we can use a **JSON Duality View** to query our new table. A JSON Duality View allows us to interact with data as JSON objects, i.e., data is stored as documents. Unlike a regular view, we can also update data in a JSON Duality View. Any updates will be reflected in our original relational tables. We will create a JSON Duality View using our newly created tables `customers` and `orders`.
 
-1. Before we create the **JSON Duality View**, we need to add some **constraints** to our new tables. Copy & paste the following code into a **new cell** and run it.
+1. Before we create the **JSON Duality View**, we need to add some **constraints** to our new tables. Constraints define specific rules and conditions that the data in a table must follow. Their main function is to maintain data integrity and ensure the accuracy and reliability of the stored information.
+
+    Copy & paste the following code into a **new cell** and run it.
 
     ```python
     <copy>
@@ -480,7 +482,7 @@ The final step in our basic coding tour with Python and the Oracle Database 23ai
 
 ## Conclusion
 
-In this lab, we learned how to use Python and Oracle's Python driver `oracledb` to interact with Oracle Database 23ai's new features. You learned how to user the `cursor` object to execute SQL queries. Using the `cursor` object, you created a **JSON Duality View** and you even used some JSON functions to query documents using SQL syntax. Then, you also learned how to connect to the database using `pymongo` and retrieve data from a table in the database using **MongoDB syntax**. You created functions to update the **JSON Duality View** and you learned how these updates are also reflected in the underlying relational database tables.
+In this lab, we learned how to use Python and Oracle's Python driver `oracledb` to interact with Oracle Database 23ai's new features. You learned how to use the `cursor` object to execute SQL queries. Using the `cursor` object, you created a **JSON Duality View** and you even used some JSON functions to query documents using SQL syntax. Then, you also learned how to connect to the database using `pymongo` and retrieve data from a table in the database using **MongoDB syntax**. You created functions to update the **JSON Duality View** and you learned how these updates are also reflected in the underlying relational database tables.
 
 In the next labs, you will see several of the coding principles learned and even more.  
 

dev-ai-app-dev-retail/connect-to-env/connect-to-env.md

@@ -28,9 +28,9 @@ To get things started we invite you to watch this video and see the lab in actio
 
 1. Review the different elements in JupyterLab:
 
-    **File browser:** The file browser organizes and manages files within the JupyterLab workspace. It supports drag-and-drop file uploads, file creation, renaming, and deletion. Users can open notebooks, terminals, and text editors directly from the browser. Navigation is fast and intuitive, with breadcrumbs and context menus that surface relevant actions. Users can right-click files to access options like duplicate, shutdown kernel, or open with a specific editor.
+    **File browser (1):** The file browser organizes and manages files within the JupyterLab workspace. It supports drag-and-drop file uploads, file creation, renaming, and deletion. Users can open notebooks, terminals, and text editors directly from the browser. Navigation is fast and intuitive, with breadcrumbs and context menus that surface relevant actions. Users can right-click files to access options like duplicate, shutdown kernel, or open with a specific editor.
 
-    **Launcher:** The launcher offers a streamlined entry point for starting new activities. Users can create Jupyter Notebooks for interactive coding with live code execution, visualizations, and rich markdown. The terminal provides direct shell access, enabling command-line operations within the JupyterLab environment. These two tools form the core of most workflows, supporting both interactive analysis and system-level tasks from a single interface.
+    **Launcher (2 and 3):** The launcher offers a streamlined entry point for starting new activities. Users can create Jupyter Notebooks for interactive coding with live code execution, visualizations, and rich markdown. The terminal provides direct shell access, enabling command-line operations within the JupyterLab environment. These two tools form the core of most workflows, supporting both interactive analysis and system-level tasks from a single interface.
 
     ![JupyterLab Launcher](./images/jupyter.png " ")
 

dev-ai-app-dev-retail/introduction/introduction.md

@@ -2,11 +2,11 @@
 
 ## About this Workshop
 
-**Data is more than an asset—it’s the engine behind every smart decision, risk calculation and growth strategy. From real-time decision-making to risk management and innovation, success hinges on how well you harness it.**
+**Data is more than an asset—it’s the engine behind every smart decision, risk calculation, and growth strategy. From real-time decision-making to risk management and innovation, success hinges on how well you harness it.**
 
 Generative AI and Oracle Database 23ai makes it possible to unlock deeper insights, faster—directly within your database.
 
-In this hands-on workshop, you’ll build a retail-focused GenAI solution using Oracle Database 23ai. 
+This hands-on lab introduces you to Generative AI and Vector Search in Oracle Database 23ai, allowing you to build real applications using multiple types of data - no setup required. You'll connect to the database using Python and the Mongo API, run queries, create JSON Duality Views, and implement AI Vector Search and retrieval-augmented generation (RAG). Explore guided use cases or experiment freely in a browser-based development environment. 
 
 To get things started we invite you to watch the introduction video to see the demo in action:
 
@@ -32,12 +32,17 @@ By the end of this workshop, you’ll understand how Oracle Database 23ai helps
 
 **Your next breakthrough is just a lab away!**
 
+#### ➡️ **Start with the demo! (Lab 1)**
 
-#### ➡️ **Start with the demo!**
+Check out the demo to see how Generative AI and Vector Search in the database, can operate against multiple types of data—empowering you to build real applications and make faster, smarter decisions. The best part? **No setup required**—just start with Lab 1 and follow the guide. You can explore guided use cases or experiment on your own using the demo application:
 
-Check out the demo to see how Generative AI and Vector Search in the database, can operate against multiple types of data—empowering you to build real applications and make faster, smarter decisions. The best part? **No setup required**—just start with Lab 1 and follow the guide. You can explore guided use cases or experiment on your own using the demo application.
+  * ✅ Login as a Return Authorization Specialist
 
-#### ➡️ **Start coding!**
+  * ✅ Review customers that have been recommended by the system to be approved or denied for a return
+
+  * ✅ Upload information to a customer's profile
+
+#### ➡️ **Start coding! (Labs 2-4)**
 
 Lab 2 describes how to get access to our browser-based development environment. Make sure to check it out before continuing to Lab 3 and 4, where you will get hands-on and start interacting with Python with Oracle Database 23ai:
 
@@ -53,15 +58,21 @@ Lab 2 describes how to get access to our browser-based development environment.
 
   * ✅ Implement AI Vector Search
 
-In lab 4, you will learn how to **implement a working example of retrieval-augmented generation (RAG) using Oracle Database 23ai.**
+  * ✅ Implement retrieval-augmented generation (RAG) using Oracle Database 23ai
+
+#### ➡️ **Start experimenting! (Lab 5)**
+
+You can also experiment with the code in your own environment. The workshop environment is flexible and allows you try out different things: Start building your own version of the prior authorization application!
+
+  * ✅ Vectorize customer data from one table into another
 
-#### ➡️ **Start experimenting!**
+  * ✅ Verify the data was vectorized
 
-You can also experiment with the code in your own environment. The workshop environment is flexible and allows you try out different things: Start building your own version of the return authorization application!
+  * ✅ Implement a similarity search
 
 ### Objectives
 
-* Learn how Oracle Database 23ai capabilities can be used to automate and enhance the return authorization process
+* Learn how Oracle Database 23ai capabilities can be used to automate and enhance the prior authorization process, justifying medical-necessity decisions
 
 * Start coding with Python and Oracle's Python driver `oracledb`