Dataherald

What it does

Adaptability

The documentation describes a modular architecture whose implementations can be replaced through configuration in the .env file.checked 4 Oct 2026

API

The engine exposes endpoints for database connections and natural-language questions, and the quick start points to a Swagger UI at /docs.checked 8 Oct 2026

Components

The repository describes an engine, an enterprise API layer with authentication and organizations, an admin console for configuration and observability, and a Slackbot.checked 4 Oct 2026

Context

The context store can use business and database context, including validated question-and-SQL examples and table information, to help generate SQL.checked 4 Oct 2026

Context and learning

Users can add database context by scanning tables and columns, providing verified SQL examples, and describing tables and columns; the documentation also describes active learning from usage.checked 8 Oct 2026

Credentials

The quick start requires an OpenAI API key, an organization ID, and an encryption key before starting the engine.checked 4 Oct 2026

Database integrations

The quick start says the engine supports PostgreSQL, BigQuery, Databricks, and Snowflake connections.checked 8 Oct 2026

Database support

The quick start lists PostgreSQL, BigQuery, Databricks, and Snowflake as currently supported warehouse connections.checked 4 Oct 2026

Deployment

The quick start describes self-hosting the engine with Docker, with Mongo running in a separate container for application data.checked 4 Oct 2026

Deployment components

The repository includes an engine, an enterprise API layer with authentication and organization and user support, an admin console for configuration and observability, and a Slack bot.checked 8 Oct 2026

Evaluation

The module overview says an evaluator assigns a confidence score to generated SQL.checked 4 Oct 2026

Intended users

The documentation is intended for developers building natural-language interfaces from structured data in their own projects and contributors to the engine.checked 8 Oct 2026

License

The GitHub repository identifies the project as licensed under Apache-2.0.checked 8 Oct 2026

Notable accuracy consideration

The quick start says generated SQL may be inaccurate until business logic and data context are added to the context store.checked 8 Oct 2026

Product

Dataherald is a natural language-to-SQL engine that exposes an API for asking questions about relational data in plain English.checked 4 Oct 2026

Requirements

The engine requires an OpenAI API key, an organization ID, and an encryption key in its environment configuration before startup.checked 8 Oct 2026

Security

The quick start says database connection data is encrypted before storage in MongoDB using an ENCRYPT_KEY and recommends changing the default MongoDB username and password.checked 8 Oct 2026

Self-hosting

The quick start describes running the engine locally with Docker and Docker Compose, alongside MongoDB for application data.checked 8 Oct 2026

SQL generation

Included text-to-SQL implementations are LangChain SQL Agent, LangChain SQL Chain, LlamaIndex SQL Generator, and Dataherald SQL Agent.checked 4 Oct 2026

SQL generation options

The documentation lists LangChain SQL Agent, LangChain SQL Chain, LlamaIndex SQL Generator, and Dataherald’s in-house SQL agent as included implementations.checked 8 Oct 2026

Support and licensing

The repository is public and identifies its license as Apache-2.0; the README invites contributions to the project.checked 4 Oct 2026

Use cases

The project describes use cases including helping business users explore a data warehouse, adding Q&A to SaaS applications, and creating a ChatGPT plug-in from proprietary data.checked 8 Oct 2026

What it does

Dataherald is a natural language-to-SQL engine that lets users ask questions in plain English about structured or relational data through an API.checked 8 Oct 2026