Alternatives
Products that do what InstantSQL does
Plain English to all types of SQL in Seconds
- 1P0
Hi everyone — thanks for your interest in PRQL — let us know any questions or feedback! We're excited to be releasing 0.2[1], the first version of PRQL you can use in your own projects. It wouldn't exist without the feedback we got from HackerNews when we originally posted the proposal. [1]: https://github.com/prql/prql/releases/tag/0.2.0
2022 · github.com
- 2DS
I played around with GPT-3 to build this demo. Select a public BigQuery dataset and describe your query in natural English, then edit the generated SQL as needed and execute it. https://app.tabbydata.com/sql-assistant-demo
2021
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- 6AS
2020 · github.com
- 7HF
2018 · github.com
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- 10NS
Would love thoughts! Here is the HF page: https://huggingface.co/chatdb/natural-sql-7b
2024 · github.com
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- 12TE
2019 · github.com
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- 15SD
SnapQL is an open-source desktop app (built with Electron) that lets you query your Postgres database using natural language. It’s schema-aware, so you don’t need to copy-paste your schema or write complex SQL by hand. Everything runs locally — your OpenAI API key, your data, and your queries — so it's secure and private. Just connect your DB, describe what you want, and SnapQL writes and runs the SQL for you.
2025 · github.com
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- 20PA
2021 · github.com
- 21DA
Hi 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…
2023 · github.com
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- 23NL
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;…
2024 · dataherald.com
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