Alternatives
Products that do what English TO SQL does
2 layer retrievals no just cosine similarity
- 1TE
2019 · github.com
- 2AA
2019 · askdata.com
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- 7DS
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
- 8ST
2015 · sqlteaching.com
- 9
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- 11PA
2021 · github.com
- 12NL
2016 · kueri.me
- 13CP
2020 · github.com
- 14SA
2021 · github.com
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- 16WM
Hey HN — We're excited to share Trellis — a snowflake for unstructured data. We've built an AI engine that turns unstructured data into structured SQL-format based on the schema you define in natural language. We spent a lot of time building ML infrastructure and realized that most data warehouses and data pipelines are not designed for unstructured data (documents, PDFs, calls). While something like a Vector database and RAG are great at search tasks, they really struggle with aggregation and SQL type queries such as 1. How many emails in the past 6 months contain complaints about the…
2024 · demo.runtrellis.com
- 17LM
2021 · os.cohere.ai
- 18TW
Built QueryWeaver, an open-source text2SQL tool that uses a graph to create a semantic layer on top of your existing databases. When you ask "show me customers who bought product X in a certain ‘REGION’ over the last Y period of time," it knows which tables to join and how. When you follow up with "just the ones from Europe," it remembers what you were talking about. Instead of feeding the model a list of tables and columns, we feed it a graph that understands what a customer is, how it connects to orders, which products belong to a campaign, and what "active user" actually means in your…
2025 · github.com
- 19SQ
Since managing the Large Language Models in production might be challenging, we've made a short demo to present how to use Cohere co.embed API and Qdrant Cloud to create a semantic QA system. This is based on bi-encoder architecture, and can be easily adopted to a different use case, like semantic search in any domain.
2022 · qdrant.tech
- 20D0
We used our platform to fine-tune a tiny text-to-SQL model using distillation from DeepSeek V3. Repo has instructions for how to replicate this. This is definitely not the best-performing model like this out there! But I found it surprising we were able to get to this much out of it: stone's throw away from a teacher 1000x the size! We also ran the same thing using the 4B Qwen and matched the teacher accuracy, though here the difference is merely 100x :) I find this pretty cool - obviously our distilled models can only do this one task and don't generalize, but that's often exactly what you…
Jan 2026 · github.com
- 21PB
2014 · pipesql.com
- 22SBSQL Basics▲26
2021 · garden.bradwoods.io
- 23PE
2013 · pgexercises.com
- 24GI
2015 · github.com
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