Harnessing Generative AI for Automated Superset Report Generation

Introduction

Generative AI is revolutionizing how we interact with data, simplifying complex workflows such as SQL query generation and report visualization. In this article, we will explore a step-by-step approach to leveraging Generative AI for automated Superset report generation. We'll cover environment setup, AI-based query generation, execution in Superset, and automation of the reporting process.

Step 1: Setting Up the Environment

Apache Superset is an open-source data exploration and visualization tool that allows users to build dashboards and generate reports.

1. Install Required Dependencies

To begin, install the necessary dependencies:

pip install apache-superset langchain openai requests        

2. Install and Initialize Apache Superset

If you haven't already installed Apache Superset, do so with:

pip install apache-superset
superset db upgrade
superset init        

Start the Superset server:

superset run -p 8088 --with-threads --reload --debugger        

Access Superset at https://localhost:8088 and configure your database connection. Ensure your dataset is properly structured.

Step 2: Understanding Generative AI in Data Analytics

What is Generative AI?

Generative AI models, like OpenAI's GPT series and Meta’s LLaMA 3, are trained on vast datasets to generate human-like text. In our case, we will use an LLM (Large Language Model) to convert user requirements into SQL queries.

How AI-Generated Queries Improve Analytics?

1. Reduces dependency on SQL experts: Non-technical users can describe reports in plain language.
2. Improves accuracy: AI models trained with database schema can generate precise queries.
3. Speeds up workflow: Eliminates manual query writing and debugging.

Step 3: Implementing LLaMA 3 for Query Generation

1. Load the AI Model

We use LangChain to interact with the AI model:

from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool

llm = OpenAI(model_name="llama3-70b-chat")        

2. Generate SQL Queries from Business Requirements

def generate_sql(query_text):
    prompt = f"""
    Convert the following business requirement into an SQL query:
    Requirement: {query_text}
    """
    return llm(prompt)        

3. Example Input and Output

Input: "Show total sales per category for the last 6 months."

Output:

SELECT category, SUM(sales) AS total_sales 
FROM sales_data 
WHERE sales_date >= DATE_SUB(CURRENT_DATE, INTERVAL 6 MONTH)
GROUP BY category;        

Step 4: Executing the SQL Query in Superset

1. Add a New Dataset in Superset

1. Navigate to Data > Datasets.
2. Click + Dataset and select your database.
3. Paste the AI-generated SQL query and save it.

2. Create a Visualization in Superset

1. Navigate to Charts.
2. Click + Chart and select the dataset.
3. Choose a chart type (e.g., Bar Chart for sales data).
4. Configure the axes:
5. Click Run Query and save the chart.

Step 5: Automating Superset Report Generation

1. Generate an API Key for Automation

Run:

superset fab create-admin        

Get the API token from https://localhost:8088/swagger/v1.

2. Automate Report Generation using Superset API

To automate report creation and chart updates, use Superset's REST API:

2.1 Create a Dashboard Programmatically

import requests

headers = {"Authorization": "Bearer YOUR_ACCESS_TOKEN"}
data = {
    "dashboard_title": "Sales Report",
    "charts": [{"slice_id": 123}]
}
response = requests.post("https://localhost:8088/api/v1/dashboard/", json=data, headers=headers)        

This creates a new dashboard with the selected charts.

2.2 Automate Report Updates

To keep reports updated, schedule a script that refreshes queries using Superset’s API:

def refresh_dashboard(dashboard_id):
    url = f"https://localhost:8088/api/v1/dashboard/{dashboard_id}/refresh"
    response = requests.post(url, headers=headers)
    return response.json()

refresh_dashboard(1)  # Refresh dashboard with ID 1        

Step 6: Enhancing AI Query Generation with RAG (Retrieval-Augmented Generation)

1. What is RAG?

RAG enables AI models to retrieve information from external sources, improving accuracy when generating SQL queries.

2. Implementing RAG with LangChain

from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

# Load database schema into vector storage
vectorstore = FAISS.from_texts(["sales_data: category, sales, sales_date"], OpenAIEmbeddings())

# Retrieve relevant table details before generating SQL query
def retrieve_schema(query_text):
    return vectorstore.similarity_search(query_text)        

Now, when a user asks "Show sales per category," the model retrieves relevant tables before generating the query.

Conclusion

Generative AI, when combined with Superset, streamlines SQL query generation and report visualization, making analytics accessible to all. By integrating AI-driven workflows and automation, businesses can enhance decision-making with real-time insights.

Key Takeaways:

• AI models like LLaMA 3 can generate SQL queries from natural language.
• Superset allows easy execution and visualization of these queries.
• Automating reports via the Superset API reduces manual work.
• RAG enhances AI accuracy by retrieving database schema details before query generation.

By leveraging these technologies, businesses can automate and scale their data-driven decision-making processes effortlessly.