ChatGPT + Power BI = A Match Made in AI Heaven! 🤖💻🔥

Towards the end of the article, I will demonstrate how to integrate ChatGPT with Power BI using Python.

---

Hey there! Integrating a language model like ChatGPT into Power BI can be a game-changer for data analysts and business intelligence professionals.

It’s a cost-effective way to get help with your DAX queries and speed up your workflow, and it can help you build more accurate, insightful reports that drive business decisions.

So if you’re looking to take your Power BI skills to the next level, consider integrating ChatGPT into your workspace. With just a few clicks, you can start getting the answers you need faster and more efficiently than ever before.

In this article, I’ll show you how to integrate ChatGPT into Power BI to help with DAX queries. Using the AI assistant feature can help you quickly find the answers you need and speed up your workflow. So, let’s get started! 💻🚀

---

Why Integrate ChatGPT into Power BI?

As a Power BI user, you already know how powerful this tool is when it comes to data visualization and analysis. However, even the most experienced users can sometimes struggle with complex DAX queries, especially when working with large datasets.

By integrating ChatGPT to your Power BI workspace, you can quickly get answers to your questions, find relevant information, and speed up your workflow. ChatGPT can help you with a wide range of tasks, from calculating measures and building complex calculations to troubleshooting errors and optimizing your queries.

---

Integrating ChatGPT into Power BI requires some technical expertise, as you’ll need to use Power BI’s custom visual feature along with an API call to interact with ChatGPT’s API.

Here’s a step-by-step guide to help you integrate ChatGPT into Power BI:

1. Sign up for OpenAI API key: To access the ChatGPT API, you’ll first need an API key.

2. Sign up for an API key from OpenAI by visiting https://www.openai.com/api/.

3. Create a custom visual in Power BI: You can create custom visuals in Power BI using TypeScript and D3.js.

4. To set up your environment for custom visual development, follow the official guide from Microsoft: https://docs.microsoft.com/en-us/power-bi/developer/visuals/environment-setup.

5. Create a new custom visual project: Use the Power BI command-line tools to create a new custom visual project.

6. Open a terminal or command prompt and run:

pbiviz new chatgpt_visual
cd chatgpt_visual
pbiviz start

Add API call functionality: In your new custom visual project, modify the src/visual.ts file to include the necessary code for making API calls to ChatGPT.

You will need to use a library such as 'axios' to make HTTP requests. Install it by running npm install axios.

Then, modify the src/visual.ts file by adding the necessary imports and a function to call the ChatGPT API:

import * as axios from 'axios';

async function callChatGPTAPI(prompt: string, apiKey: string): Promise<string> {
    const response = await axios.default.post('https://api.openai.com/v1/engines/davinci-codex/completions', {
        prompt: prompt,
        max_tokens: 50,
        n: 1,
        stop: null,
        temperature: 1,
    }, {
        headers: {
            'Authorization': `Bearer ${apiKey}`,
            'Content-Type': 'application/json'
        }
    });
    return response.data.choices[0].text;
}

Use the API call in your visual update function: Modify the update function in the src/visual.ts file to call the ChatGPT API and display the results. For example, you can use a text element to display the response from ChatGPT.

public update(options: VisualUpdateOptions) {
    // Add your Power BI visual update code here
    // ...
// Call the ChatGPT API with a sample prompt and your API key
    callChatGPTAPI("Integrate Power BI with ChatGPT", "<your_openai_api_key>")
        .then(response => {
            // Display the response in a text element
            const textElement = this.target.append("text")
                .attr("x", 10)
                .attr("y", 50)
                .text(response);
            // Update the text element with the response from ChatGPT
            textElement.text(response);
        })
        .catch(error => {
            console.error("Error calling ChatGPT API:", error);
        });
}

Package and import the custom visual: Once you’ve completed the code, package the custom visual by running pbiviz package in the terminal.

This will generate a .pbiviz file in the dist folder.

In Power BI, import the custom visual by selecting the ellipsis (...) in the "Visualizations" pane, clicking on "Import from file," and selecting the generated .pbiviz file.

Use the custom visual in your report: Add the custom visual to your Power BI report by selecting it from the “Visualizations” pane.

Since the custom visual makes use of the ChatGPT API, you may want to provide a way for users to input prompts and display the responses within the visual.

---

Here’s an example of a DAX query you can ask ChatGPT:

What is the total sales for each product category?

And here’s the DAX expression code snippet that you can use to achieve the same result:

Total Sales = SUM('Sales'[Amount])

By integrating me into Power BI, you can quickly get answers to your questions and speed up your workflow. Here are some more examples of DAX queries you can ask ChatGPT:

What is the total sales for the current quarter?

What is the average revenue per customer for the last month?

What is the year-to-date revenue for each region?

ChatGPT can also help you troubleshoot errors in your DAX expressions.

For example, if you’re getting an error message like “A circular dependency was detected,” you can ask ChatGPT for help and guide you through the steps to fix the issue.

💡 Pro Tip: You can use the “AI Chatbot” visual to save time when building complex DAX expressions. Instead of manually typing out each calculation, you can ask ChatGPT to help you build the expression step-by-step. This can save you a lot of time and effort, especially when working with large datasets.

---

To integrate ChatGPT with Power BI using Python, you can follow the steps below:

Enabling Python in Power BI Desktop

If not already enabled, to enable Python in Power BI Desktop, follow these steps:

1. Install Python: You need to have Python installed on your computer. You can download and install Python from the official website (https://www.python.org/downloads/).

2. After installing Python, you need to install the Python integration feature in Power BI Desktop.

3. Open Power BI Desktop and go to File -> Options and settings -> Options -> Python scripting. Check the box “Python scripting” and click “OK”. This will enable Python scripting in Power BI Desktop.

4. After enabling Python scripting, you need to set the Python path in Power BI Desktop.

5. Go to File -> Options and settings -> Options -> Python scripting. Click on “Detect” to automatically detect the Python installation path or you can manually set the path by clicking on “…” and selecting the Python executable file.

6. Restart Power BI Desktop: After setting the Python path, you need to restart Power BI Desktop for the changes to take effect and you good to go.

Install the necessary Python libraries:

• openai: This is the library that provides access to the ChatGPT model.

• pandas: This library is used to create and manipulate dataframes.

• pyodbc: This library is used to connect to the Power BI data source.

You can install these libraries using pip by running the following command in your terminal or command prompt:

pip install openai pandas pyodbc

Authenticate and set up the OpenAI API:

• Create an account on the OpenAI website and obtain an API key.

• Install the OpenAI Python library by running pip install openai.

• Set the OPENAI_API_KEY environment variable to your API key.

You can authenticate and set up the OpenAI API by running the following Python code:

import openai_secret_manager  assert "openai" in openai_secret_manager.get_services() secrets = openai_secret_manager.get_secret("openai")  
# Set up the OpenAI API client 
import openai openai.api_key = secrets["api_key"]

Define a function that queries the ChatGPT model and returns the response:

def query_chatgpt(prompt):     response = openai.Completion.create(         engine="text-davinci-002",         prompt=prompt,         max_tokens=1024,         n=1,         stop=None,         temperature=0.7,     )     message = response.choices[0].text.strip()     return message

The query_chatgpt function takes a prompt as input, sends it to the ChatGPT model, and returns the response.

Connect to the Power BI data source using pyodbc:

import pyodbc  # Set up the connection string server = 'your-server-name' database = 'your-database-name' username = 'your-username' password = 'your-password' cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER='+server+';DATABASE='+database+';UID='+username+';PWD='+password)

Write a Power Query function that calls the query_chatgpt function and returns the response as a table:

Publish the Python script as a data source in Power BI:

• Open Power BI Desktop and go to the ‘Home’ tab.

• Click on ‘Transform Data’.

• Click on ‘New Source’ -> ‘Python Script’.

• Enter the Python script and click on ‘OK’.

• Click on ‘Close & Apply’.

Use the ChatGPT data source in your Power BI report:

• Go to the ‘Report’ tab.

• Click on ‘Get Data’ -> ‘More’.

• Select the ‘Python Script’ data source and click on ‘Connect’.

• Enter the prompt you want to send to the ChatGPT model.

• The response from the ChatGPT model will be displayed as a table in the Power BI report.

Note: Make sure to replace the placeholders in the code (such as your-server-name, your-database-name, your-username, and your-password) with the actual values for your environment.

Final Thoughts

Integrating ChatGPT into Power BI is a great way to speed up your workflow, improve the accuracy of your analysis, and get the answers you need quickly.

By following the steps outlined in this article, you can start using ChatGPT to help with your DAX queries in no time.

---

I hope this article helped you! Thanks for reading. 😊 Enjoyed it? Show your support with 👏, 💬, and 👤.

💰 Free E-Book 💰

I’m Gabe A, a seasoned data visualization architect and writer with a decade of experience. My goal is to offer you clear guides on diverse data science topics. With 250+ articles across 25+ Medium publications, I’ve become a trusted voice in the data science field. 📊📚

👉Break Into Tech + Get Hired