WMS ID #11777: QA Fixes (#703)

* Self-QA Updates Adding workshop changes to further align with the Self-QA checklist. * Update adb-free-container-setup.md * Post-Testing Edits V1 * Update adb-free-container-setup.md * Post-Testing Changes V2 * Updating Screenshots * WMSID# 11693: Adding the initial workshop structure. * Update adb-free-container-setup.md * Update adb-free-container-setup.md * [WMS ID #11029] DB Collective - JSON Duality Search * WMS ID #11029: Minor fix * Update manifest.json * LL ID #4004: Add JSON Duality Intro * LL ID# 4004: Minor Updates * LL ID #4004: Minor updates. * WMS ID #11693: Revising the workshop structure. * WMS ID# 11693 * HOL 46 * DB Collective - JSON Updates * LL ID #4004: DB Collective Changes * OCW & DB Collective Updates * Update new-duality-views-15.md * Update new-duality-views-15.md * Update new-duality-views-15.md * Update new-duality-views-15.md * Update new-duality-views-15.md * Update new-duality-views-15.md * LL ID #3984: Formatting Changes * HOL 46 Technical Fixes * Update inst-auth-container-setup.md * DB Collective + OCW 24 Updates * Update manifest.json * LL ID #3984: Screenshots & Minor Fixes * LL ID #3984 - Minor Updates & Fixes * Last updates. * Update similarity-search.md * Update intro-aivs-adb.md * Updates to All 23ai New Features Workshops * Reinstating the JSON Duality Views with REST section. * Update new-duality-views-updated.md * Adding screenshots and minor fixes. * Minor edit. * Adding Videos * Reformat video embeddings. * Remove videos. * WMS ID #11777: QA Fixes --------- Co-authored-by: William Masdon <[email protected]> Co-authored-by: Hope Fisher <[email protected]> Co-authored-by: Dan Wiliams <[email protected]>
oracle-livelabs · Oct 21, 2024 · c348432 · c348432
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diff --git a/ai-vector-rag/demo/demo.md b/ai-vector-rag/demo/demo.md
@@ -17,28 +17,29 @@ In this lab, you will run a RAG application interactively using a user-friendly
 This lab assumes you have:
 - All previous labs successfully completed
 
-## Task 1: Open the Notebook Environment
-This task will have you login to the Jupyter environment and run specific notebooks for this lab.
+## Task 1: Open the Jupyter Notebook Interface
 
-1. **If you have already logged into the Jupyter environment skip to step 5, otherwise** open "**View Login Info**" section of your workshop.
+1. In the upper-left corner, select **View Login Info**. 
 
    ![View login info.](images/lab1-1-view-login-info.png)
 
-2. Copy the Jupyter Notebook Password and click the Jupyter Notebook URL.
+2. You can now see all reservation details relevant to completing this workshop. Copy the Jupyter Notebook password and open the Jupyter Notebook URL.
 
-   ![Copy login details.](images/lab1-2-jupyter-notebook-info.png)
+   ![Copy the Jupyter Notebook pasword.](images/lab1-2-jupyter-notebook-info.png)
 
-3. Paste the Jupyter Notebook password you copied in the previous step into the password field.
+3. Paste the Jupyter Notebook password into the password field, as shown below.
 
    ![Enter the password.](images/lab1-3-jupyter-login.png)
 
-4. You should now see the Jupyter Notebook's landing page. 
+4. After a successfull login, you will see the Jupyter Notebook's landing page. 
    ![The Jupyter Notebook landing page.](images/lab1-4-landing-page.png)
 
-5. In the menu at the top click on **File** >> **New** >> **Terminal**.
+## Task 2: Run the RAG Application
+
+1. In the top navigation bar, open the terminal by clicking **File** >> **New** >> **Terminal**.
    ![Open the terminal.](images/lab1-5-open-terminal.png)
 
-6. In the terminal, copy and paste the code below.
+2. In the terminal, copy and paste the code below.
       ````
       <copy>
       cd /home/oracle
@@ -47,17 +48,14 @@ This task will have you login to the Jupyter environment and run specific notebo
       ````
       ![Run the code snippet in the terminal.](images/lab1-6-terminal-commands.png)
 
-7. The above commands will start a streamlit application running your Chatbot. Three URLs will appear. The last URL is the one that will allow you to connect from your browser. Click on the last URL labeled **External URL.**
-   ![Enter the password.](images/lab1-7-app-urls.png)
-
-## Task 2: Run the Application
-
-1. Once your application is running, simply follow the steps in the user interface.
+3. The above commands will start a streamlit application running your Chatbot. Three URLs will appear. Click on the last URL to interact with the application from your browser. 
+   ![Launch the app in your browser.](images/lab1-7-app-urls.png)
 
-   ![Enter the password.](images/lab1-8-app-landing-page.png)
+4. You should now see the landing page of the application you'll be building shortly. Explore the application by following the instructions on the landing page. Feel free to explore the documents with your own questions and discover how the RAG application can retrieve and provide accurate responses based on the document’s content.
 
-   Feel free to explore the documents with your own questions and discover how the RAG application can retrieve and provide accurate responses based on the document’s content.
+   Once finished you can return to these instructions.
 
+   ![View the application landing page.](images/lab1-8-app-landing-page.png)
 
 You may now **proceed to the next lab**.
 
@@ -72,5 +70,5 @@ You may now **proceed to the next lab**.
 
 ## Acknowledgements
 * **Author** - Francis Regalado, Database Product Management; David Start, Database Product Management
-* **Contributors** -
-* **Last Updated By/Date** - Francis Regalado, Database Product Manager October 2024
+* **Contributors** - Brianna Ambler, Kaylien Phan, Database Product Management
+* **Last Updated By/Date** - Brianna Ambler, October 2024
diff --git a/ai-vector-rag/demo/images/lab1-7-app-urls.png b/ai-vector-rag/demo/images/lab1-7-app-urls.png
diff --git a/ai-vector-rag/introduction/introduction.md b/ai-vector-rag/introduction/introduction.md
@@ -2,7 +2,6 @@
 
 ### **About this Workshop**
 
-
 Generative artificial intelligence (AI) excels at creating text responses based on large language models (LLMs) where the AI is trained on a massive number of data points. The good news is that the generated text is often easy to read and provides detailed responses that are broadly applicable to the questions asked of the software, often called prompts.
 
 The bad news is that the information used to generate the response is limited to the information used to train the AI, often a generalized LLM. The LLM’s data may be weeks, months, or years out of date and in a corporate AI chatbot may not include specific information about the organization’s products or services. That can lead to incorrect responses that erode confidence in the technology among customers and employees.
@@ -16,13 +15,13 @@ This lab will guide you through the process of building a Retrieval Augmented Ge
 
  ![rag image](images/rag1.png " ")
 
-Estimated Time: 30 Minutes
+**_Estimated Time: 30 Minutes_**
 
 ## About Oracle AI Vector Search
 
 Oracle AI Vector Search is a feature of Oracle Database 23ai that enables efficient searching of AI-generated vectors stored in the database. It supports fast search using various indexing strategies and can handle massive amounts of vector data. This makes it possible for Large Language Models (LLMs) to query private business data using a natural language interface, helping them provide more accurate and relevant results. Additionally, AI Vector Search allows developers to easily add semantic search capabilities to both new and existing applications.
 
-### Objectives
+### **Objectives**
 The labs in this workshop will guide you through the following tasks:
 
 - Getting familiar with the new Vector Datatype and PL/SQL packages for managing vector data and performing vector operations.
@@ -33,7 +32,6 @@ The labs in this workshop will guide you through the following tasks:
 
 By the end of the workshop, you’ll have hands-on experience working with Oracle’s AI-powered tools to build a scalable, efficient retrieval augmented generation (RAG) application.
 
-
 ## Learn More
 - [What Is Retrieval-Augmented Generation](https://www.oracle.com/artificial-intelligence/generative-ai/retrieval-augmented-generation-rag/)
 
@@ -50,5 +48,5 @@ By the end of the workshop, you’ll have hands-on experience working with Oracl
 
 ## Acknowledgements
 * **Author** - Francis Regalado, Database Product Management; David Start, Database Product Management
-* **Contributors** -
-* **Last Updated By/Date** - Francis Regalado, Database Product Manager October 2024
+* **Contributors** - Brianna Ambler, Kaylien Phan, Database Product Management
+* **Last Updated By/Date** - Brianna Ambler, October 2024
diff --git a/ai-vector-rag/rag/lab1-4-landing-page.png → ...or-rag/rag/images/lab1-4-landing-page.png b/ai-vector-rag/rag/lab1-4-landing-page.png → ...or-rag/rag/images/lab1-4-landing-page.png
diff --git a/ai-vector-rag/rag/rag.md b/ai-vector-rag/rag/rag.md
@@ -43,10 +43,9 @@ This task will have you login to the Jupyter environment and run specific notebo
 3. Paste the Jupyter Notebook password you copied in the previous step into the password field.
 
    ![Enter the password.](images/lab1-3-jupyter-login.png)
-
-4. You should be within the Jupyter Notebooks landing screen. 
-
-   ![The Jupyter Notebook landing screen.](images/lab1-4-landing-page.png)
+
+4. After a successfull login, you will see the Jupyter Notebook's landing page. 
+   ![The Jupyter Notebook landing page.](images/lab1-4-landing-page.png)
 
 5. In the left File Explorer panel, open the (**workshop**) and open(**workshop.ipynb**) notebook.
 
@@ -71,5 +70,5 @@ This task will have you login to the Jupyter environment and run specific notebo
 
 ## Acknowledgements
 * **Author** - Francis Regalado, Database Product Management; David Start, Database Product Management
-* **Contributors** - Brianna Ambler, Database Product Management
+* **Contributors** - Brianna Ambler, Kaylien Phan, Database Product Management
 * **Last Updated By/Date** - Brianna Ambler, October 2024
diff --git a/ai-vector-rag/sandbox/workshop/manifest.json b/ai-vector-rag/sandbox/workshop/manifest.json
@@ -20,7 +20,7 @@
         "filename": "../../demo/demo.md"
     },
       {
-          "title": "Lab 2: Build Your Own RAG App!",
+          "title": "Lab 2: Build Your Own RAG App",
           "description": "Exploring RAG",
           "type": "livelabs",
           "filename": "../../rag/rag.md"