Updates to WMS 11959 fsi tenancy markdown including additional copy blocks (#473)

* creating markdown for WMS 11959

* markdown for fsi

* 11959 Intro.md

* updated markdown + images for labs 1 and 2

* updates

* overview and highlights markdown

* update loan policy manuals

* Update test-the-solution-tenancy.md

* updates to images and md

* overview and highlights markdown fixes

* updated lab3&4

* lab consolidation

* sandbox markdowns

* autonomous ai db updates

* markdown updates

* markdown cosmetic updates

* updates

* updates

* updates

* adding notes to creating agent lab

* changes to Note

* changes to ai agents sandbox

* final question response screenshot added

* changing wording

* added copy blocks and updated wording

---------

Co-authored-by: Uma Kumar <[email protected]>
Co-authored-by: Daniel Hart <[email protected]>

Author: 3 people

oci-generative-ai-agents-fsi/2-setup/setup-tenancy.md

@@ -81,7 +81,20 @@ In order to facilitate those permissions, we will create a Dynamic Group which w
    ![Screenshot showing how to navigate to the dynamic groups section](./images/dynamic-group-navigation.png)
 
 1. Name the dynamic group: _oci-genai-agents-service_
+
+    ```text
+      <copy>
+      oci-genai-agents-service
+      </copy>
+      ```
 1. Provide an optional description (example: `This group represents the OCI Generative AI Agents service`)
+   
+      ```text
+      <copy>
+      This group represents the OCI Generative AI Agents service
+      </copy>
+      ```
+
 1. Select the **Match any rules defined below** option in the **Matching rules** section.
 1. Enter the following expression in the **Rule 1** textbox:
 
@@ -108,7 +121,21 @@ Next, we will create the access policies:
    ![Screenshot showing how to initiate the creation of a new policy](./images/create-new-policy-navigation.png)
 
 1. Provide a name for the policy (example: _oci-generative-ai-agents-workshop_).
+
+      ```text
+      <copy>
+      oci-generative-ai-agents-workshop
+      </copy>
+      ```
+
 1. Provide a description (example: _OCI Generative AI Agents Hands-On-Lab Policy_).
+
+      ```text
+      <copy>
+      OCI Generative AI Agents Hands-On-Lab Policy
+      </copy>
+      ```
+
 1. Make sure that the root compartment is selected.
 1. Enable the **Show manual editor** option.
 1. In the **Policy Builder** textbox, enter the following policy statements:

oci-generative-ai-agents-fsi/3-setup-the-database/setup-the-database-tenancy.md

@@ -38,7 +38,21 @@ In this task we are going to create a new ADB instance.
    ![Screenshot showing how to navigate to the create ADB page](./images/create-adb-button.png)
 
 1. For the **Display name** use: _loan-compliance_.
+   
+   ```text
+      <copy>
+      loan-compliance
+      </copy>
+      ```
+
 1. For the **Database name** use: _loancompliance_.
+
+   ```text
+      <copy>
+      loancompliance
+      </copy>
+      ```
+
 1. Under the **Compartment**, make sure that the **root** compartment is selected.
 1. Under **Workload type** make sure that **Data Warehouse** is selected.
 
@@ -302,6 +316,13 @@ In this task we are going to create a Vault and an encryption key. We are going
 
 1. Under the **Create in Compartment**, make sure that the **root** compartment is selected.
 1. For the **Name** field use: _loan-compliance-secrets_
+
+   ```text
+      <copy>
+      loan-compliance-secrets
+      </copy>
+      ```
+
 1. Click the **Create Vault** button at the bottom of the form.
 
    ![Screenshot showing how to create the vault](./images/create-vault.png)
@@ -318,6 +339,13 @@ In this task we are going to create a Vault and an encryption key. We are going
 
 1. Under the **Create in Compartment**, make sure that the **root** compartment is selected.
 1. For the **Name** field use: _loan-compliance-key_
+
+     ```text
+      <copy>
+      loan-compliance-key
+      </copy>
+      ```
+
 1. Click the **Create Key** button.
 
    ![Screenshot showing details for creating an encryption key](./images/create-key-details.png)
@@ -338,6 +366,13 @@ In this section we are going to create a connection to our database. This connec
    ![Screenshot showing how to navigate to the create vault page](./images/create-connection-button.png)
 
 1. For the **Name** field use: _loan-compliance_
+
+   ```text
+      <copy>
+      loan-compliance
+      </copy>
+      ```
+
 1. Under the **Compartment**, make sure that the **root** compartment is selected.
 1. Make sure that the **Select database** option is selected under the **Database details** section.
 1. In the **Database cloud service** drop-down, select **Oracle Autonomous Database**.
@@ -353,6 +388,13 @@ In this section we are going to create a connection to our database. This connec
    This step will create a secret which will be stored in the Vault created earlier and will contain the password for connecting to the database.
 
 1. For the **Name** field use: _loan-compliance-admin-password_
+
+   ```text
+      <copy>
+      loan-compliance-admin-password
+      </copy>
+      ```
+
 1. Select the **loan-compliance-secrets** in the **Valut in...** drop-down.
 1. Select the **loan-compliance-key** in the **Encryption key in...** drop-down.
 1. In the **User password** field, type the password you've used when you created the ADB instance.
@@ -370,6 +412,13 @@ In this section we are going to create a connection to our database. This connec
    ![Screenshot showing how to create the ](./images/create-connection-5.png)
 
 1. For the **Name** field use: _loan-compliance-wallet-secret_
+
+   ```text
+      <copy>
+      loan-compliance-wallet-secret
+      </copy>
+      ```
+
 1. Select the **loan-compliance-secrets** in the **Valut in...** drop-down.
 1. Select the **loan-compliance-key** in the **Encryption key in...** drop-down.
 1. Under the **Wallet** section, select the **Retrieve regional wallet from Autonomous Database** option.

oci-generative-ai-agents-fsi/4-create-the-agent/create-the-agent-tenancy.md

@@ -36,9 +36,30 @@ This lab assumes you have:
    ![Screenshot showing how to create a new agent](./images/create-new-agent-tenancy.png)
 
 1. For the **Name** field use: _loan compliance agent_
+
+   ```text
+      <copy>
+      loan compliance agent
+      </copy>
+      ```
+
 1. For the **Compartment** field, make sure that your compartment is selected.
 1. For the **Description** field, use: _This agent assists compliance officers in reviewing applications, workloads, and policy compliance_.
+
+   ```text
+      <copy>
+      This agent assists compliance officers in reviewing applications, workloads, and policy compliance.
+      </copy>
+      ```
+
 1. For the **Welcome message** field, use: _Hello! I’m your compliance assistant. How can I help?_
+
+   ```text
+      <copy>
+      Hello! I'm your compliance assistant. How can I help?
+      </copy>
+      ```
+
 1. Click the **Next** button.
 
    ![Screenshot showing the basic information for the agent](./images/basic-agent-info-sandbox.png =50%x*)
@@ -51,8 +72,21 @@ This lab assumes you have:
 
 1. Select the **RAG** tool option.
 1. Under the **RAG Configuration** section, use _Knowledge base loan policy articles_ in the **Name** field.
+
+   ```text
+      <copy>
+      Knowledge base loan policy articles
+      </copy>
+      ```
+
 1. For the **Description** field, use: _Retrieves lending policy manuals and underwriting rules (DTI, credit score thresholds, FHA/VA limits, manual underwriting guidance)_.
 
+   ```text
+      <copy>
+      Retrieves lending policy manuals and underwriting rules (DTI, credit score thresholds, FHA/VA limits, manual underwriting guidance).
+      </copy>
+      ```
+
    It is very important to provide a high-level description of the knowledge that this tool can retrieve. This allows the agent to make accurate decisions when choosing to invoke this tool.
 
    ![Screenshot showing the initial configuration for the RAG tool](./images/rag-tool-info-1.png)
@@ -63,6 +97,13 @@ This lab assumes you have:
    ![Screenshot showing more configuration for the RAG tool](./images/rag-tool-info-2-sandbox.jpg)
 
 1. In the **New knowledge base** form, use: _Compliance officer knowledge base loan policy articles_ for the **Name** field.
+
+   ```text
+      <copy>
+      Compliance officer knowledge base loan policy atricles
+      </copy>
+      ```
+
 1. Make sure that your compartment is selected in the **Compartment** field.
 1. In the **Data store type** field, we will select **Object storage** to be able to retrieve information from our storage bucket.
 1. Make sure that **Enable hybrid search** is checked. Enabling this option instructs the system to combine lexical and semantic search when scanning our documents.
@@ -71,6 +112,13 @@ This lab assumes you have:
    ![Screenshot showing the knowledge base configuration](./images/knowledge-base-info-1-sandbox.png)
 
 1. In the **Specify data source** form, use: _loan policy docs_ for the **Name** field.
+
+   ```text
+      <copy>
+      loan policy docs
+      </copy>
+      ```
+
 1. Make sure that the **Enable multi-modal parsing** option is **not** checked. This option enable parsing of rich content, such as charts and graphics, to allow responses based on visual elements. However, we do not have any images in our knowledge articles so right now this option is not required.
 1. Under the **Data bucket** option, select the _loan-policy-manuals_ bucket into which we've previously uploaded the knowledge articles PDF files.
 1. Check the **Select all in bucket option**. This option will automatically flag all of the file in the bucket for ingestion instead of us having to select each file individually.
@@ -84,7 +132,7 @@ This lab assumes you have:
 
    ![Screenshot showing the knowledge base configuration](./images/knowledge-base-info-2.png)
 
-1. The knowledge base will take a few minutes to create and ingest the data.
+1. The knowledge base will take a few minutes to create and ingest the data. You may proceed to the next step while the knowledge base provisions.
 1. Back at the **Add knowledge bases** panel, make sure that the checkbox next to the knowledge base name is checked.
    >💡 _If your knowledge base does not appear (“No items found”), you can still continue to the next step. The knowledge base is already selected and provisioning in the background. You may open a new tab and navigate to Agents > Knowledge Bases to confirm it is provisioning._
 
@@ -100,8 +148,21 @@ This lab assumes you have:
 
 1. Click the **SQL** option.
 1. For the **Name** field, use: _Loan Applications database_.
+
+   ```text
+      <copy>
+      Loan Applications database.
+      </copy>
+      ```
+
 1. For the **Description** field, use: _Tables contain applicants, loan applications, statuses, and officers for compliance review._.
 
+   ```text
+      <copy>
+      Tables contain applicants, loan applications, statuses, and officers for compliance review.
+      </copy>
+      ```
+
    ![Screenshot showing the initial set of the SQL tool configuration](./images/sql-tool-info-1.png)
 
 1. Under **Import database schema configuration for this tool**, selec the **Inline** option which will allow us to use the same schema text we've used when we created the database.
@@ -150,7 +211,7 @@ This lab assumes you have:
       );
       </copy>
       ```
-
+1. Under the **in-context learning examples**, leave the **None** option selected.
 1. Under the **Description of tables and columns**, select the **Inline** option.
 1. Copy and paste the following text into the **Description of tables and columns**. This verbal description contains details about each table and column. This will allow the tool to better understand the data stored in our database:
 
@@ -192,7 +253,7 @@ This lab assumes you have:
 
 1. For **Model customization**, select the **Small** option.
 1. For **Dialect**, select **Oracle SQL**.
-1. In the **Database tool connection in...** select the **connection-loancomplianceXXXX** connection we've previously created.
+1. In the **Database tool connection**, select your compartment, then choose the **connection-loancomplianceXXXX** connection we've previously created.
    >💡 _If your database tool connection does not appear (“Option not available”), select Cancel and re-add the SQL tool by repeating Task 3: Add the SQL Tool_
 
 13. Click the **Test connection** button. You should see a successful connection connection attempt.

oci-generative-ai-agents-fsi/5-test-the-solution/test-the-solution-sandbox.md

@@ -30,17 +30,17 @@ This lab assumes you have:
 ## Task 1: Overview of the chat page functionality
 
 1. If the agent is still not showing as **Active**, give it a few more minutes to complete the provisioning process.
-1. Once the agent is showing as **Active**, click the **loan compliance officer** agent in the **Agents** list.
+1. Once the agent is showing as **Active**, click the **loan compliance agent** in the **Agents** list.
 
     ![Screenshot showing the active agent in the agents list](./images/click-agent-from-table-sandbox.png)
 
 1. In the agent details page, click the **Launch chat** button.
 
     ![Screenshot showing the agent details page with the launch chat button highlighted](./images/launch-chat-button.png)
 
-1. In the chat page, on th left, make sure sure that both the **Agent compartment** and the **Agent endpoint compartment** are set to your compartment.
+1. In the chat page, on the left, make sure sure that both the **Agent compartment** and the **Agent endpoint compartment** are set to your compartment.
 
-1. On the top of the page, the **Agent** drop down should show **loan compliance officer** and the **Agent endpoint** drop down should show the newly created endpoint.
+1. On the top of the page, the **Agent** drop down should show **loan compliance agent** and the **Agent endpoint** drop down should show the newly created endpoint.
 1. In the chat window, you'll be able to see the greeting message we have configured for the agent.
 1. Other elements in the page include:
 
@@ -78,7 +78,6 @@ This lab assumes you have:
     ![Screenshot showing the SQL tool trace](./images/first-question-traces-2.png)
 
 1. The third trace shows how the agent composed the final response using the output of the previous steps.
-1. Click the **Close** button to close the traces pane.
 
     ![Screenshot showing the trace for the final response](./images/first-question-traces-3.png)
 
@@ -94,7 +93,7 @@ This lab assumes you have:
 
     ![Screenshot showing the first question for the agent](./images/send-second-question.png)
 
-1. The agent shows the correct answer: **Olivia Brown**. Using the magic of Large Language Models (LLMs) and the clues we've left in the configuration of the agent and tools, the agent was able to decipher that the loan officer with the most applications assigned to them.
+1. The agent shows the correct answer: **Olivia Brown**. Using the magic of Large Language Models (LLMs) and the clues we've left in the configuration of the agent and tools, the agent was able to decipher that the loan agent with the most applications assigned to them.
 
     ![Screenshot showing the response for the second question](./images/second-question-response.png)
 
@@ -111,7 +110,7 @@ This lab assumes you have:
 
     ![Screenshot showing the third question for the agent](./images/send-third-question.png)
 
-1. As you can see, the response included the type and amount for the two loans that have been pending for more than 7 days.
+1. The agent returned information on the one application that has been pending review for more than 7 days.
 
     ![Screenshot showing the third question for the agent](./images/third-question-response.png)
 
@@ -141,6 +140,15 @@ This lab assumes you have:
 
 1. As you can see, for this question, the agent figured out that the information required might be in the knowledge base articles. For this task it employed the RAG tool which searched for the relevant information in our loan policy docs stored in object storage. Feel free to look at the traces for this interaction which show the steps the agent took to give us the information we needed. In the response you can see that a summary of the document was provided, but, also, if you expand the **View citations** section, you'll be able to see a reference to the document(s) which were used to compose the reply with a direct link to the file(s), the page(s) from which content was extracted and more.
 
+    ![test](./images/third-question-traces-1.png)
+
+1. Next we'll ask the following: _Identify any approved applications that violate policy (DTI or credit score); cite the rule and the record._
+1. Click the Submit button.
+
+    ![test](./images/send-fourth-question.png)
+
+1. The agent successfully detected an approved applicant whose credit score was inconsistent with the requirements outlined in the DTI and Credit Policy document.
+
     ![test](./images/fourth-question-response.png)
 
 1. We invite you to try some prompts of your own to experiment with agent.
@@ -155,17 +163,17 @@ Here are a few more prompts to try with the agent:
 
 ## Summary
 
-As you've experienced, the OCI Generative AI service allows you to ask complex questions about data stored in multiple locations and get intelligent answers. By simply pointing the various tools towards your data sources and providing the right context, the agent was able to automatically determine which data source should be accessed, retrieve the data for you, compile a coherent and concise response and provide references to the original data when applicable.
+As you've experienced, the OCI AI Agents service allows you to ask complex questions about data stored in multiple locations and get intelligent answers. By simply pointing the various tools towards your data sources and providing the right context, the agent was able to automatically determine which data source should be accessed, retrieve the data for you, compile a coherent and concise response and provide references to the original data when applicable.
 
-Another interesting advantage of building solutions on top the OCI Generative AI service is that the user is no longer restricted to tasks allowed by the application user interface. With a chat interface, the user can ask questions and get answers to any question which can be answered using the data in the system even if the system engineers did not plan for that specific scenario. For example, you can ask the agent to sort the results in any way that is supported by the data even if the application was not designed to give you that option.
+Another interesting advantage of building solutions on top the OCI AI Agents service is that the user is no longer restricted to tasks allowed by the application user interface. With a chat interface, the user can ask questions and get answers to any question which can be answered using the data in the system even if the system engineers did not plan for that specific scenario. For example, you can ask the agent to sort the results in any way that is supported by the data even if the application was not designed to give you that option.
 
-Although our use-case was focused on loan compliance, the OCI Generative AI service can be used to fuel many different use-cases which require deep understanding and retrieval of information from internal data sources, reasoning over the data, summarizing it, providing insights and more.
+Although our use-case was focused on loan compliance, the OCI AI Agents service can be used to fuel many different use-cases which require deep understanding and retrieval of information from internal data sources, reasoning over the data, summarizing it, providing insights and more.
 
 ## Learn More
 
 - [Chatting with Agents in Generative AI Agents](https://docs.oracle.com/en-us/iaas/Content/generative-ai-agents/chatting.htm#chatting)
 
 ## Acknowledgements
 
-- **Author** - Uma Kumar
-- **Contributors** - Hanna Rakhsha, Daniel Hart, Deion Locklear, Anthony Marino
+- **Author** - Deion Locklear
+- **Contributors** - Hanna Rakhsha, Daniel Hart, Uma Kumar, Anthony Marino