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Vanna AI - Python-based AI SQL agent | Product Hunt

 1 year ago
source link: https://www.producthunt.com/posts/vanna-ai
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Vanna AI

Python-based AI SQL agent

Vanna is a Python-based AI SQL agent trained on your schema that writes complex SQL in seconds. You can use it in Jupyter, Slack, Streamlit, and anywhere else Python is used.
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We're thrilled to introduce Vanna AI – your smart SQL assistant powered by the latest advancements in large language models (LLMs). If you've ever found yourself stuck crafting the perfect SQL query for complex datasets, Vanna AI is here to revolutionize your workflow.

🌟 Key Features:

Contextual Query Generation: Just feed Vanna AI with your dataset and some relevant SQL examples, and watch it craft precise SQL queries tailored to your needs.

Iterative Learning: Our feedback loop ensures Vanna AI gets better with every interaction, refining its outputs for maximum accuracy.

Handles Real-world Mess: Dirty data? No problem. Vanna AI thrives on real-world datasets, accounting for missing values, inconsistencies, and more.

We also published a research paper on how to go from ~3% accuracy with GPT 4 to 80%+ accuracy in SQL generation - https://github.com/vanna-ai/vann...
This project is going to be amazing💯go to the moon.This could save me so much time when trying to quickly pull some data without bugging my engineering team.You've created something truly remarkable!
@zain_hoda This sounds like an incredible tool for improving efficiency in working with SQL datasets! I can see how Vanna AI can save so much time in crafting queries and dealing with real-world messy data. The iterative learning feature seems very promising. I'm interested to know, how does the feedback loop work to refine the tool's outputs for accuracy?
@jasdeep_chhabra thanks for the question! Basically we found that when the AI generates SQL that successfully returns results, most of the time, those results are correct. So when that happens, we add the correctly-generated SQL to the training set and that helps the AI “remember” for the next time you ask something similar.

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