Dataherald AI – Natural Language to SQL Engine

In plain words

Dataherald AI is an open-source engine that converts natural language questions into SQL queries for structured databases. It enables users to set up an API that answers plain English questions by querying database tables directly. The engine addresses limitations in standard large language models by improving accuracy when handling ambiguous queries, such as distinguishing between multiple cities with the same name or understanding implicit context in questions.

written from the facts on this page · September 2026

From the sources

Interact with your SQL database, Natural Language to SQL using LLMs - Dataherald/dataherald

In the maker’s words, at launch

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…

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  We open sourced our entire text-to-SQL product2024 · github.com · ▲464

  Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent…

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  Dataherald2024 · ▲126

  Embed NL-to-SQL into your product

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  Natural 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;…

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  Would love thoughts! Here is the HF page: https://huggingface.co/chatdb/natural-sql-7b

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