> For the complete documentation index, see [llms.txt](https://karini-ai.gitbook.io/karini-ai-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://karini-ai.gitbook.io/karini-ai-documentation/agentic-ai-workshops/generative-bi-agentic-assistant.md).

# Generative BI Agentic Assistant

This hands-on lab guides you through building an intelligent assistant that converts natural language into Structured Query Language (SQL) queries using database schemas. You'll create a powerful agentic chatbot that provides precise data insights on the basis of autonomous reasoning.

## Use Case

Build a sophisticated Enterprise Sales Advisor chatbot that analyzes ticket sales data for various events and performances.

## Prerequisites

* Access to the Redshift catalog for enterprise sales data.
* Basic familiarity with the platform interface.

### Step 1: Catalog Creation

1. Navigate to **Catalog Schema** in menu bar on the left side.
2. Click on **Add new** in the top corner on the right side.
3. Configure the catalog:
   1. Name: "**Enterprise-Sale-Data**".
   2. Connector type: **Redshift**.
   3. Enter the required credentials provided separately.
4. Save the catalog.
5. Open the saved catalog.
6. **Browse** and select all the tables and click on **Import schema** on the right side.
7. Within **Catalog explorer**, generate business metadata for each table:
   1. Select each table individually.
   2. Enable checkboxes to generate metadata.
   3. Click Generate Metadata on the right side.
8. Publish the catalog.

Refer the video tutorial for creating catalog.

{% embed url="<https://www.loom.com/share/55028e48089d4aa3bb6a1b3581d3051a?sid=7af1d731-ea84-4f52-8516-6c5ded5a26b1>" %}

### Step 2: Create an Agent Prompt

1. Navigate to **Prompt Playground** on the left side.
2. Click on **Add new** in top corner on the right side.
3. Click on **Prompt templates** on the right side.
4. Select the **Enterprise Sales Agent-Redshift** template.
5. Increase the **Max State Updates** from **3 to 20** for complex reasoning.
6. Scroll down to **Agent input box** to add the following test query : "What is the total sales amount for each seller?"
7. Ensure that you scroll up to rename your prompt before saving.
8. You will be directed to the **Prompt Playground** and select your prompt:

<mark style="color:$warning;">\[Hint: Please Save your Changes before you navigate away while adding new tools ]</mark>

1. Click on the **Tools** tab to configure the following tools:
   1. **Catalog Tool**
      1. Click on **Add new** in tools.
      2. Enter name as “**Enterprise Sales Catalog**”.
      3. Enter the **Description** as “The catalog tool uses natural language questions to search relevant tables and columns to create a SQL query”.
      4. Select **Type**: **Catalog**.
      5. Select the catalog created in Step 1 from the dropdown menu.
      6. **Save** the tool.
   2. **Database Tool**
      1. Click **Add new** in tools.
      2. Enter name as “**Sales Performance Datawarehouse**”.
      3. Enter the **Description** as “This tool lets you run SQL queries on a specified database and table, then retrieve the results.”.
      4. Select **Type**: **Database**.
      5. Select **Connector**: **Redshift**.
      6. Enter the Redshift Datawarehouse credentials provided.
      7. **Save** the tool.

#### Test & Compare :

1. Click on Test and compare tab.&#x20;
2. Select different models \[Hint: Compare Claude Sonnet 3.7, Claude Sonnet 3.5 v2, Anthropic Claude Sonnet 4].
3. Click on **Test** right belo&#x77;**.**
4. Compare the output of the selected Models.
5. Click on **Select as best answer** to select the best-performing model as the Primary Model.
6. Optionally **Select as best answer** to assign a Fallback Model.
7. Click **Save prompt run** in the right corner.
8. **Save** and **publish** the prompt.

### Step 3: Create Agent 2.0 Recipe

1. Navigate to **Recipe** on the left side.
2. Click **Add New** in the top corner.
3. Configure recipe details:
   1. Name: "Enterprise Sales Advisor".
   2. Description: "Intelligent assistant for sales data analysis".
   3. Type: Agent 2.0
4. Set up the workflow :
   1. &#x20;Drag and drop Chat node:
      1. In the Conversation history dropdown, select **4 messages.**
      2. Enable Generate follow-up questions.
   2. Drag and drop Start node:
      1. Connect the Chat node to the Start node.
   3. Drag and drop Agent node:
      1. Select your created Agent prompt at step 2.
      2. Scroll down and Set the **Messages** dropdown to **All Messages** to maintain conversation context.
      3. Connect the Start node to the Agent node.
   4. Drag and drop End node:
      1. Connect the Agent node to the End node.
5. On the right side, set the **Number of state updates** to a higher value such as 75.&#x20;
6. Click on the canvas, then select **Save**.
7. Select your recipe from the recipe table.
8. Ensure you click on **Publish** the recipe.
9. By default, the V1 version is deployed once it is published.
10. For subsequent versions, use the Actions button and select Deploy to deploy the new version. \[Hint: Use Actions Button to select Deploy dropdown].
11. Click the **Test** button on the right side to verify functionality.
12. Test with sample questions:
    1. What is the average sales amount among top 10 sellers?
    2. What is the total sales amount for top 10 sellers?
    3. What is the total sales amount for each seller?

Refer the video to create and test recipe:

{% embed url="<https://files.gitbook.com/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F7ZrVuiAUMyuYVvrK5KaB%2Fuploads%2FXHdOgju3hkyfbIO59CSF%2FTexr-2-sql.mp4?alt=media&token=9dd54544-38a1-43c5-abf0-600dd8482fae>" %}

### Step 4: Deploy Your Copilot

1. Click the **Export** button located at the top-right corner of the recipe interface.
2. Click **Add new** to create a deployment configuration.
3. Complete the export form:
   1. Name: "Enterprise Sales Assistant"
   2. Add **Welcome message**, as preferred.
   3. Description: "The Enterprise Sales Advisor Copilot is designed to help you analyze and optimize your sales data."
4. Add **Seed Questions**:
   1. What is the average sales amount among top 10 sellers?
   2. What is the total sales amount for the top 10 sellers?
   3. What is the total sales amount for each seller?
5. Add **Error message**, as preferred.
6. Enable positive (thumbs up) and negative (thumbs down) feedback.
7. Users can also add custom feedback.
8. Select **Show traces**.
9. Click on **Save**.
10. User can access the Copilot in two ways:
    1. From the Exported Recipe.
       1. Open the saved export , click the < icon on the right panel.
       2. The Visit Copilot button will appear.
       3. Click Visit Copilot to open and interact with your Copilot.
    2. From the left menu\
       The Copilot will be displayed in the Copilots section of the vertical menu.

### Step 5: Test Your Sales Advisor

1. Navigate to the Copilot on the left side and open your chatbot.
2. Ask sample questions:
   1. How much total revenue was generated from ticket sales for events in the "Shows" category by each seller?
   2. Which events have generated the most revenue, based on sales from associated listings?
   3. What is the total sales amount by each user, including their name and email?

Refer the video to test copilot:

{% embed url="<https://files.gitbook.com/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F7ZrVuiAUMyuYVvrK5KaB%2Fuploads%2F8N9ejV0JE9XSoWHMgBIs%2FText-2-sql%20copilot.mp4?alt=media&token=f94da1fa-97fe-468d-a8c7-efacc06b82aa>" %}

All the best!


---

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