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Just ask ChatGPT, I gave it your question and this is what it said.

Your project involves combining multiple technologies: WhatsApp chatbot, N8N automation, Power BI Datasets (DAX queries), ChatGPT API, and chart generation. Let’s break it down step by step:

1. Architecture Overview

  1. WhatsApp Message Handling (N8N):

    • Use N8N to capture incoming messages from WhatsApp and route them to your backend for processing.

  2. Natural Language Understanding (ChatGPT API):

    • Use the ChatGPT API to interpret the user’s question and extract key details (e.g., product names, date ranges).

  3. Data Retrieval (DAX Queries to Power BI):

    • Use the structure of the user’s question to dynamically generate DAX queries to fetch the required data.

  4. Data Processing and Chart Generation (Microservice):

    • Use a Python-based microservice for creating visual charts (e.g., using Matplotlib or Plotly).
    • Use libraries like PDFKit or ReportLab to convert charts into PDF.

  5. Sending the Response (N8N):

    • Combine the textual response from ChatGPT and the PDF file and send it back through WhatsApp.

2. Key Technical Considerations

Micro Back-End for Processing

Yes, you should build a small backend (preferably with Python or Node.js) because:

  • N8N is not designed for complex data processing or chart generation.
  • Python has robust libraries for chart creation and PDF generation.
  • The backend can easily handle ChatGPT API requests, DAX query formation, and chart creation.

Reliability of ChatGPT API for Query Generation

  • ChatGPT can generate simple DAX queries if trained or prompted properly.
  • You might need a “query template” database that matches question patterns with DAX query templates.
  • Use ChatGPT mainly for understanding the user’s intent and generating query components.

3. Implementation Steps

Step 1: Setting Up N8N for WhatsApp

  • Integrate WhatsApp using services like Twilio or the WhatsApp Business API.
  • Set up triggers to capture incoming messages.

Step 2: Natural Language Processing with ChatGPT

  • Use ChatGPT to:

    • Parse the user’s question.
    • Identify the product, date range, or sales-related keywords.
    • Format the extracted information for a DAX query.

Step 3: Dynamic DAX Query Generation

  • Create a DAX template for common questions, like:

    EVALUATE
SUMMARIZE(
  Sales,
  Product[Name],
  "Total Quantity", SUM(Sales[Quantity]),
  "Total Sales", SUM(Sales[SalesAmount])
)

  • Use Python to replace placeholders in the DAX template based on the parsed question.

Step 4: Data Retrieval from Power BI

  • Use the Power BI REST API to execute the DAX query and retrieve the results.
  • Ensure proper authentication (OAuth) and permission handling.

Step 5: Chart and PDF Generation

  • Use Matplotlib, Seaborn, or Plotly to generate charts based on the query results.
  • Export the chart as an image (e.g., PNG) and embed it into a PDF using ReportLab or PDFKit.

Step 6: Sending the Response via WhatsApp

  • Return a structured response combining:

    • Natural language summary (from ChatGPT).
    • Chart PDF.

  • Use N8N to handle the final message sending, including the PDF as an attachment.

4. Example Workflow

  1. User: “Show me the best-selling product today.”

    • N8N captures and sends the message to your backend.

  2. Backend:

    • Uses ChatGPT to interpret the question.
    • Generates a DAX query to get the best-selling product.
    • Retrieves data using the Power BI API.
    • Generates a chart and PDF summarizing the sales.

  3. Backend sends the response to N8N, which forwards it to WhatsApp.

5. Chart Generation Example in Python

import matplotlib.pyplot as plt
from fpdf import FPDF

def generate_chart(data, title):
    products = [item['name'] for item in data]
    quantities = [item['quantity'] for item in data]
    
    plt.bar(products, quantities, color='blue')
    plt.title(title)
    plt.xlabel('Products')
    plt.ylabel('Quantity Sold')
    plt.savefig('/path/to/chart.png')

def create_pdf(chart_path, summary):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font('Arial', 'B', 16)
    pdf.cell(0, 10, summary, 0, 1, 'C')
    pdf.image(chart_path, x=10, y=30, w=180)
    pdf.output('/path/to/report.pdf')

6. Hosting the Backend

  • Deploy the microservice on the same server where N8N runs, or use a cloud service.
  • Make the API endpoint accessible to N8N for sending and receiving data.

Final Thoughts

  • ChatGPT is good for interpreting user intent and generating DAX templates.
  • A Python microservice is ideal for handling data processing, chart creation, and PDF generation.
  • N8N acts as the integration layer between WhatsApp and your backend.