Dataherald/dataherald
Dataherald/dataherald has 3,650 GitHub stars and 264 forks. GitHot measured 0 stars in 24 hours and -1 in 7 days. Last checked 2026-11-08 14:28 UTC. Last push 2024-07-24.
So werden diese Zahlen gemessen
Interagiere mit deiner SQL-Datenbank und wandle mithilfe von LLMs natürliche Sprache in SQL um
Gemessener Sternzuwachs
- in 24h
- 0
- in 7 days
- -1
- in 15 days
- Verlauf wird gesammelt
- in 1 month
- Verlauf wird gesammelt
Nettoänderungen in überlappenden Zeiträumen, keine Tagessummen. Fehlende Messwerte bleiben leer. Sterne zeigen Interesse, nicht Qualität.
README.md
OriginalDataherald monorepo
Query your relational data in natural language.
Dataherald is a natural language-to-SQL engine built for enterprise-level question answering over relational data. It allows you to set up an API from your database that can answer questions in plain English. You can use Dataherald to:
- Allow business users to get insights from the data warehouse without going through a data analyst
- Enable Q+A from your production DBs inside your SaaS application
- Create a ChatGPT plug-in from your proprietary data
This repository contains four components under /services which can be used together to set up an end-to-end Dataherald deployment:
- Engine: The core natural language-to-SQL engine. If you would like to use the dataherald API without users or authentication, running the engine will suffice.
- Enterprise: The application API layer which adds authentication, organizations and users, and other business logic to Dataherald.
- Admin-console: The front-end component of Dataherald which allows a GUI for configuration and observability. You will need to run both engine and enterprise for the admin-console to work.
- Slackbot: A slackbot which allows users from a slack channel to interact with dataherald. Requires both engine and enterprise to run.
For more information on each component, please take a look at their README.md files.
Running locally
Each component in the /services directory has its own docker-compose.yml file. To set up the environment, follow these steps:
- Set Environment Variables:
Each service requires specific environment variables. Refer to the
.env.examplefile in each service directory and create a.envfile with the necessary values.For the Next.js front-end app is
.env.local - Run Services: You can run all the services using a single script located in the root directory. This script creates a common Docker network and runs each service in detached mode.
Run the script to start all services:
sh docker-run.sh
Contributing
As an open-source project in a rapidly developing field, we are open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.
Ähnliche Projekte entdecken
affaan-m/ECC
System zur Leistungsoptimierung für das Ausführungs- und Steuerungsframework von Agenten. Es bietet Skills, Instinkte, Memory, Sicherheit und forschungsorientierte Entwicklung für Claude Code, Codex, Opencode, Cursor und darüber hinaus.
299,576 SterneNousResearch/hermes-agent
Der Agent, der mit dir wächst
261,696 Sternemultica-ai/andrej-karpathy-skills
Eine einzelne CLAUDE.md-Datei zur Verbesserung des Verhaltens von Claude Code, abgeleitet aus Andrej Karpathys Beobachtungen zu Fallstricken beim Programmieren mit LLMs.
230,864 Sternefirecrawl/firecrawl
Stärke deine KI-Agenten mit Daten aus dem Web und darüber hinaus. Wir bauen die Bibliothek für Superintelligenz. 🔥
199,551 Sterne