Tag Archives: #python

AI, open source, SQL, Technology

Simplifying SQL Generation with Vanna: An Open-Source Python RAG Framework


Handling databases often involves crafting complex SQL queries, which can be daunting for those who aren’t SQL experts. The need for a user-friendly solution to streamline SQL generation has led to the development of Vanna, an open-source Python framework.

The Challenge


Crafting complex SQL queries can be time-consuming and requires a deep understanding of the database structure. Existing methods might assist but often lack adaptability to various databases or compromise privacy and security.

Introducing Vanna


Vanna uses a Retrieval-Augmented Generation (RAG) model to take a unique two-step approach.

How it Works – In Two Steps (clicks)

First, users train the model on their data, and then they can pose questions to obtain SQL queries tailored to their specific database.

Key Features


Simplicity and Versatility: Vanna stands out for its simplicity and adaptability. Users can train the model using Data Definition Language (DDL) statements, documentation, or existing SQL queries, allowing for a customized and user-friendly training process.

Direct Execution:

Vanna processes user queries and returns SQL queries that are ready to be executed on the database. This eliminates the need for intricate manual query construction, providing a more accessible way to interact with databases.

High Accuracy

Vanna excels in accuracy, particularly on complex datasets. Its adaptability to different databases and portability across Language Model Models (LLMs) make it a cost-effective and future-proof solution.

Security Measures

Operating securely, Vanna ensures that database contents stay within the local environment, prioritizing privacy.

Continuous Improvement


Vanna supports a self-learning mechanism. In Jupyter Notebooks, it can be set to “auto-train” based on successfully executed queries. Other interfaces prompt users for feedback and store correct question-to-SQL pairs for continual improvement and enhanced accuracy.

Flexible Front-End Experience


Whether working in Jupyter Notebooks or extending functionality to end-users through platforms like Slackbot, web apps, or Streamlit apps, Vanna provides a flexible and user-friendly front-end experience.

Vanna addresses the common pain point of SQL query generation by offering a straightforward and adaptable solution. Its metrics underscore its accuracy and efficiency, making it a valuable tool for working with databases, regardless of SQL expertise. With Vanna, querying databases becomes more accessible and user-friendly.

As an Engineer who loves working with data, I am looking forward to trying Vanna to level up my SQL development.

GitHub

Documentation

Image Credit: Vanna AI

Technology

Refresh with Python

I started not as a developer or an engineer but as a “solution finder.” I needed to resolve an issue for a client, and Python was the code of choice. That’s how my code journey into Python began. I started learning about libraries, and my knowledge grew from there. Usually, there was an example of how to use the library and the solution. I would review the code sample and solution, then modify it to work for what I needed. However, I need to refresh whenever I step away from this type of work. Sometimes the career journey takes a detour, but that doesn’t mean you can’t continue to work and build in your area of interest.

If you want to refresh your Python skills or brush up on certain concepts, this blog post is here to help. Let’s walk you through a code sample that utilizes a famous library and demonstrates how to work with a data array. So, let’s dive in and start refreshing those Python skills!

Code Sample: Using NumPy to Manipulate a Data Array

For this example, we’ll use the NumPy library, which is widely used for numerical computing in Python. NumPy provides powerful tools for working with arrays, making it an essential data manipulation and analysis library.

This same example can be used with Azure Data Studio, my tool of choice for my coding, with the advantage of connecting directly to the SQL database in Azure, but I will save that for another blog post.

Another of my favorites is Windows Subsystem for Linux; this example would apply.

Let’s get started by installing NumPy using pip:

pip install numpy

Once installed, we can import NumPy into our Python script:

import numpy as np

Now, let’s create a simple data array and perform some operations on it:

# Create a 1-dimensional array
data = np.array([1, 2, 3, 4, 5])

# Print the array
print("Original array:", data)

# Calculate the sum of all elements in the array
sum_result = np.sum(data)
print("Sum of array elements:", sum_result)

# Calculate the average of the elements in the array
average_result = np.average(data)
print("Average of array elements:", average_result)

# Find the maximum value in the array
max_result = np.max(data)
print("Maximum value in the array:", max_result)

# Find the minimum value in the array
min_result = np.min(data)
print("Minimum value in the array:", min_result)

In this code sample, we first create a 1-dimensional array called “data” using the NumPy array() function. We then demonstrate several operations on this array:

  1. Printing the original array using the print() function.
  2. Calculating the sum of all elements in the array using np.sum().
  3. Calculating the average of the elements in the array using np.average().
  4. Finding the maximum value in the array using np.max().
  5. Finding the minimum value in the array using np.min().

By running this code, you’ll see the results of these operations on the data array.


Refreshing your Python skills is made easier with hands-on examples. In this blog post, we explored a code sample that utilized the powerful NumPy library for working with data arrays. By installing NumPy, importing it into your script, and following the walk-through, you learned how to perform various operations on an array, such as calculating the sum, average, maximum, and minimum values. Join me on my journey deeper into the world of data manipulation and analysis in Python.

Technology

Azure Data Studio – Works For Me


As a data enthusiast and professional, I am always looking for powerful tools that can simplify my data exploration and analysis tasks. I wanted to share my experience working with Azure Data Studio, a comprehensive data management and analytics tool. It has become an invaluable tool in my data and content writing journey.

  1. Intuitive User Interface:
    Azure Data Studio boasts a sleek and intuitive user interface, making navigating and performing complex data operations easy. When I launched the application, I was impressed by its clean design and well-organized layout. The intuitive interface allows me to manage connections, explore databases, write queries, and visualize data effortlessly. The well-thought-out user experience of Azure Data Studio significantly enhances my productivity and makes working with data a breeze.
  2. Multi-Platform Support:
    One of the standout features of Azure Data Studio is its multi-platform support. Azure Data Studio provides a consistent and seamless experience across different operating systems, whether you are a Windows, macOS, or Linux user. Cross-platform compatibility empowers users to work with their preferred operating system, regardless of their data management and analysis needs.
  3. Robust Querying Capabilities:
    Azure Data Studio provides robust querying capabilities, allowing me to extract valuable insights from my data. With built-in support for Transact-SQL (T-SQL), I can write complex queries, execute them against databases, and view the results in a structured manner. The IntelliSense feature provides intelligent code completion, making query writing more efficient and error-free. Additionally, the query editor supports advanced functionalities like code snippets, code formatting, and query execution plan visualization, enabling me to optimize my queries and enhance performance.
  4. Seamless Integration with Azure Services:
    Azure Data Studio seamlessly integrates with various Azure services, creating a unified data management and analytics experience. Whether I need to work with Azure SQL Database, Azure Data Lake Storage, or Azure Cosmos DB, Azure Data Studio provides built-in extensions and features that facilitate seamless integration with these services. This integration enables me to leverage the power of Azure’s cloud services directly from within the tool, simplifying data exploration, analysis, and collaboration.
  5. Coding and Development The seamless integration of Python with Azure Data Studio allows me to leverage the power of Python libraries and frameworks for data analysis, machine learning, and visualization. The intuitive interface of Azure Data Studio, combined with the flexibility of Python, enables me to write and execute Python scripts effortlessly, making complex data tasks feel accessible and manageable. Whether performing data transformations, building predictive models, or creating interactive visualizations, the combination of Azure Data Studio and Python empowers me to explore and derive insights from my data collaboratively and efficiently.
  6. Extensibility and Community Support:
    Azure Data Studio is highly extensible, allowing users to enhance its functionality through extensions and customizations. The vibrant community surrounding Azure Data Studio has developed a wide range of extensions, providing additional features, integrations, and productivity enhancements. From query optimization tools to data visualization extensions, the community-driven ecosystem of Azure Data Studio expands its capabilities and caters to diverse data needs. The availability of community support and the collaborative nature of the tool make Azure Data Studio a vibrant and constantly evolving platform.

Azure Data Studio has transformed my data exploration and analysis journey with its intuitive interface, multi-platform support, robust querying capabilities, seamless integration with Azure services, and vibrant community. Whether you are a data professional, developer, or enthusiast, Azure Data Studio offers a comprehensive and user-friendly environment to work with data efficiently and derive meaningful insights. My experience with Azure Data Studio has been exceptional, and I highly recommend it to anyone seeking a powerful tool for their data management and development endeavors.

For me, it’s all about the Data!

https://learn.microsoft.com/en-us/sql/azure-data-studio/download-azure-data-studio