Vanna AI – Open-sourced text-to-SQL in Python
Hey there HN! We've just open-sourced Vanna – a Python package that allows you to transform questions into SQL. We've leveraged LLMs to enable you to "ask" databases what you need, bypassing the need to "write" complex SQL. Quick Overview: - "Train" using DDL statements, documentation, or known correct SQL statements. - "Ask" questions in natural language and receive SQL, tables, and charts in return. - Open Source Flexibility: Swap storage mechanisms, customize LLMs, and choose your databases. - Local or Hosted: Operate everything locally or use our hosted version for free (including…
Vanna AI – Open-sourced text-to-SQL in Python launched on September 8, 2023 with 33 votes, #1,331 of 4,228 launches that month and more than 75% of that year's launches. The site was not reachable when checked in September 2026.
In plain words
Vanna AI is an open-source Python package that converts natural language questions into SQL queries using large language models. Users train the system with database schemas, documentation, or example queries, then ask questions in plain language to get back SQL statements, results, and charts. It supports customizable storage, different LLMs, and multiple databases, running either locally or through a hosted version. The tool integrates with Jupyter, Streamlit, Slack, and other Python environments.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey there HN! We've just open-sourced Vanna – a Python package that allows you to transform questions into SQL. We've leveraged LLMs to enable you to "ask" databases what you need, bypassing the need to "write" complex SQL. Quick Overview: - "Train" using DDL statements, documentation, or known correct SQL…
Does a similar job
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How to use LLMs to generate accurate SQL for real-world data2023 · github.com · ▲21Hey HN, We are Zain and Ashish, founders of Vanna AI. We recently embarked on an experiment to see if large language models (specifically LLMs) could help in generating SQL queries for real-world datasets. We initially started this project as a web app but realized that it was most useful and had broadest applicability as a Python package since you can then incorporate it into an existing workflow (Jupyter notebook, Slackbot, etc). We've had some good success with customer datasets but we've generally heard a lot of skepticism so we decided to write a paper about the methodology we're using…
Dataherald AI – Natural Language to SQL Engine2023 · github.com · ▲215Hi HN community. We are excited to open source Dataherald’s natural-language-to-SQL engine today (https://github.com/Dataherald/dataherald). This engine allows you to set up an API from your structured database that can answer questions in plain English. GPT-4 class LLMs have gotten remarkably good at writing SQL. However, out-of-the-box LLMs and existing frameworks would not work with our own structured data at a necessary quality level. For example, given the question “what was the average rent in Los Angeles in May 2023?” a reasonable human would either assume the…
- NLNatural Language to SQL "Text-to-SQL" API2024 · dataherald.com · ▲62
Hi HN- Today, we are releasing the hosted API for our natural language to SQL engine, which allows you to: (1) Explain Your Data: Feed in dictionaries, dbt, schemas, Confluence docs - we'll understand the business context to your data. (2) Train Your AI: Fine-tune an LLM (including GPT-4) specifically for your data, increasing accuracy and lowering latency (3) Trust the Answer: See confidence scores with each AI-generated query, stay in control. (4) Conduct complex SQL queries Problem background - Developers struggle to build NL-to-SQL into products because LLMs do not work out-of-the-box;…
Marvin – build AI functions that use an LLM as a runtime2023 · github.com · ▲127Hey HN! We're excited to share our new open-source project, Marvin. Marvin is a high-level library for building AI-powered software. We developed it to address the challenges of integrating LLMs into more traditional applications. One of the biggest issues is the fact that LLMs only deal with strings (and conversational strings at that), so using them to process structured data is especially difficult. Marvin introduces a new concept called AI Functions. These look and feel just like regular Python functions: you provide typed inputs, outputs, and docstrings. However, instead of relying on…
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Source: Show HN post ↗ · launch text quoted in part
